bluuebunny/crossref_metadata_2025 huggingface dataset:
Augmentation of Glucotoxicity, Oxidative Stress, Apoptosis and Mitochondrial Dysfunction in HepG2 Cells by Palmitic Acid
Authors: Alnahdi Arwa , John Annie , Raza Haider
Published: August 2019

Hyperglycemia and hyperlipidemia are the hallmarks of diabetes and obesity. Experimental and epidemiological studies have suggested that dietary management and caloric restriction are beneficial in reducing the complications of diabesity. Studies have suggested that increased availability of energy metabolites like glucose and saturated fatty acids induces metabolic, oxidative, and mitochondrial stress, accompanied by inflammation that may lead to chronic complications in diabetes. In the present study, we used human hepatoma HepG2 cells to investigate the effects of high glucose (25 mM) and high palmitic acid (up to 0.3 mM) on metabolic-, inflammatory-, and redox-stress-associated alterations in these cells. Our results showed increased lipid, protein, and DNA damage, leading to caspase-dependent apoptosis and mitochondrial dysfunction. Glucolipotoxicity increased ROS production and redox stress appeared to alter mitochondrial membrane potential and bioenergetics. Our results also demonstrate the enhanced ability of cytochrome P450s-dependent drug metabolism and antioxidant adaptation in HepG2 cells treated with palmitic acid, which was further augmented with high glucose. Altered NF-kB/AMPK/mTOR-dependent cell signaling and inflammatory (IL6/TNF-α) responses were also observed. Our results suggest that the presence of high-energy metabolites enhances apoptosis while suppressing autophagy by inducing inflammatory and oxidative stress responses that may be responsible for alterations in cell signaling and metabolism.
The Interplay between Maternal and Post-Weaning High-Fat Diet and Gut Microbiota in the Developmental Programming of Hypertension
Authors: Hsu Chien-Ning , Hou Chih-Yao , Lee Chien-Te , Chan Julie Y.H. , Tain You-Lin
Published: August 2019

Excessive intake of saturated fat has been linked to hypertension. Gut microbiota and their metabolites, short-chain fatty acids (SCFAs), are known to be involved in the development of hypertension. We examined whether maternal and post-weaning high-fat (HF) diet-induced hypertension in adult male offspring is related to alterations of gut microbiota, mediation of SCFAs and their receptors, and downregulation of nutrient-sensing signals. Female Sprague–Dawley rats received either a normal diet (ND) or HF diet (D12331, Research Diets) during pregnancy and lactation. Male offspring were put on either the ND or HF diet from weaning to 16 weeks of age, and designated to four groups (maternal diet/post-weaning diet; n = 8/group): ND/ND, HF/ND, ND/HF, and HF/HF. Rats were sacrificed at 16 weeks of age. Combined HF/HF diets induced elevated blood pressure (BP) and increased body weight and kidney damage in male adult offspring. The rise in BP is related to a downregulated AMP-activated protein kinase (AMPK)–peroxisome proliferator-activated receptor co-activator 1α (PGC-1α) pathway. Additionally, HF/HF diets decreased fecal concentrations of propionate and butyrate and decreased G protein-coupled receptor 41 (GPR41), but increased olfactory receptor 78 (Oflr78) expression. Maternal HF diet has differential programming effects on the offspring’s microbiota at 3 and 16 weeks of age. Combined HF/HF diet induced BP elevation was associated with an increased Firmicutes to Bacteroidetes ratio, increased abundance of genus Akkermansia and phylum Verrucomicrobia, and reduced abundance in genus Lactobacillus. Maternal gut microbiota-targeted dietary interventions might be reprogramming strategies to protect against programmed hypertension in children and their mothers on consumption of a fat-rich diet.
Differences in Serum Magnesium Levels in Diabetic and Non-Diabetic Patients Following One-Anastomosis Gastric Bypass
Authors: Winzer Eva , Grabovac Igor , Ludvik Bernhard , Kruschitz Renate , Schindler Karin , Prager Gerhard , Klammer Carmen , Smith Lee , Hoppichler Friedrich , Marculescu Rodrig , Wakolbinger Maria
Published: August 2019

Patients with obesity and type 2 diabetes mellitus (T2DM) are regarded to have reduced serum magnesium (Mg) concentrations. We aimed to assess the changes in serum Mg concentrations at 12-month follow-up in patients, with and without T2DM, who underwent one anastomosis gastric bypass surgery. Overall, 50 patients (80% female, age 42.2 (12.5) years) with morbid obesity (mean baseline BMI 43.8 (4.3) kg/m2) were included in the analysis. Half of the included patients had T2DM diagnosed at baseline, and these patients showed lower serum Mg concentration (0.78 (0.07)) vs. 0.83 (0.05) mmol/L; p = 0.006), higher blood glucose levels (129.9 (41.3) vs. 87.6 (8.1) mg/dL; p < 0.001) as well as HbA1c concentrations (6.7 (1.4) vs. 5.3 (0.5)%; p < 0.001). During follow-up, BMI and glucose levels showed a decrease; however, serum Mg levels remained stable. At baseline 42% of patients were found to be Mg deficient, which was reduced to 33% at six months and to 30% at 12 months follow-up. Moreover, patients with T2DM had an odds ratio of 9.5 (95% CI = 3.0–29.7; p < 0.001) for magnesium deficiency when compared to patients without T2DM. Further research into the role of Mg and its role in T2DM and other obesity-related comorbidities are needed.
Inflammation, Appetite and Food Intake in Older Hospitalized Patients
Authors: Sieske Lars , Janssen Gregor , Babel Nina , Westhoff Timm Henning , Wirth Rainer , Pourhassan Maryam
Published: August 2019

The effect of inflammation on appetite and food intake has been rarely studied in humans. In this study, we examined the association of C-reactive protein (CRP), as an inflammatory marker, with appetite and food intake among older hospitalized patients. A total of 200 older individuals, who were consecutively admitted to a geriatric acute care ward, participated in this prospective observational study. Appetite was evaluated using the Edmonton Symptom Assessment System (ESAS) and the Simplified Nutritional Appetite Questionnaire (SNAQ), respectively. Food intake was measured according to plate diagram method and participants were categorized as having food intake <75% and ≥75% of meals served. Nutritional status was evaluated using the Mini Nutritional Assessment Short Form (MNA-SF). In addition, serum CRP was analyzed and the levels >3.0 (mg/dL) were considered as moderate to severe inflammation. Of total population with mean age 81.4 ± 6.6 years (62.5% females), 51 (25.5%) had no inflammation and 88 (44.0%) and 61 (30.5%) had mild and moderate to severe inflammation, respectively. According to MNA-SF, 9.0% and 60.0% had normal nutritional status or a risk of malnutrition, respectively, whereas 31.0% were malnourished. Based on the SNAQ-appetite-question, 32.5% of the patients demonstrated poor and very poor appetite whereas 23.5% reported severe loss of appetite according to ESAS. Ninety-five (48.0%) of the participants had food intake <75% of the meals offered. Significant associations between SNAQ-appetite (p = 0.003) and ESAS-appetite (p = 0.013) scores and CRP levels were observed. In addition, significant differences were observed in CRP levels between intake ≥75% and <75% of meals served (p < 0.001). Furthermore, there were significant associations between appetite and nutritional status whereas malnourished older patients demonstrated a decreased appetite compared to those with normal nutritional status (p = 0.011). In a regression analysis, inflammation was the major independent risk factor for patients’ appetite (p = 0.003) and food intake (p = 0.011) whereas other variables such as infection (p = 0.960), chronic inflammatory diseases (p = 0.371), age (p = 0.679) and gender (p = 0.447) do not show any impact on appetite. Our findings confirm that poor appetite and low food intake are associated with inflammation in older hospitalized patients, suggesting that inflammation may contribute an important aspect to the development of malnutrition in these patients.
HFE Related Hemochromatosis: Uncovering the Inextricable Link between Iron Homeostasis and the Immunological System
Authors: Porto Graça , Cruz Eugénia , Teles Maria José , de Sousa Maria
Published: August 2019

The HFE gene (OMIM 235200), most commonly associated with the genetic iron overload disorder Hemochromatosis, was identified by Feder et al. in 1996, as a major histocompatibilty complex (MHC) class I like gene, first designated human leukocyte antigen-H (HLA-H). This discovery was thus accomplished 20 years after the realization of the first link between the then “idiopathic” hemochromatosis and the human leukocyte antigens (HLA). The availability of a good genetic marker in subjects homozygous for the C282Y variant in HFE (hereditary Fe), the reliability in serum markers such as transferrin saturation and serum ferritin, plus the establishment of noninvasive methods for the estimation of hepatic iron overload, all transformed hemochromatosis into a unique age related disease where prevention became the major goal. We were challenged by the finding of iron overload in a 9-year-old boy homozygous for the C282Y HFE variant, with two brothers aged 11 and 5 also homozygous for the mutation. We report a 20 year follow-up during which the three boys were seen yearly with serial determinations of iron parameters and lymphocyte counts. This paper is divided in three sections: Learning, applying, and questioning. The result is the illustration of hemochromatosis as an age related disease in the transition from childhood to adult life and the confirmation of the inextricable link between iron overload and the cells of the immune system.
Isolation of the Flavonoid from Bamboo Residues and Its Application as Metal Ion Sensor in Vitro
Authors: Su Yan , Dong Huiling , Li Min , Lai Chenhuan , Huang Caoxing , Yong Qiang
Published: August 2019

Fluorescence sensors prepared from natural polymers have received increasing attention based on their luminescence characteristics for bioimaging, cell imaging, and intracellular detection of inorganic metabolites. In this work, flavonoids isolated from bamboo residues (BRF) were applied as fluorescence sensors for different metal cations’ detection in vitro. Results showed the optimal flavonoids extraction condition of solid to liquid ratio, ethanol concentration, extraction time and temperature were determined at 1:25, 50%, 240 min and 90 °C, respectively, resulting in an extraction yield with 104.7 mg/100 g bamboo residues. The BRF is mainly composed of isoorientin, isovitexin, pinosylvin, tricin and isorhamnetin by liquid chromatography–mass spectrometry (LC-MS) analysis. It is found that the BRF displayed strong blue-green emission as well as notable excitation, which can selectively and sensitively detect Fe3+ with the limit of detection (LOD) as low as 38.0 nM. In the Fe3+ detection was no obvious interference by other cations except for Al3+. In addition, the BRF displayed excellent biocompatibility that can be applied to bioimages of the intracellular detection of Fe3+ in L02 cells. Finally, it is found that the BRF possessed significant antioxidant properties in scavenging H2O2-induced endogenous reactive oxygen species (ROS) in a zebrafish module (in vivo) and L02 cells (in vitro). These results showed that the flavonoid products sustainably isolated from an abundant lignocellulosic waste appear to be effective fluorescent sensors for Fe3+ detection in biological systems with excellent biocompatibility and antioxidant activity.
The Effect of Bulky Substituents on Two π-Conjugated Mesogenic Fluorophores. Their Organic Polymers and Zinc-Bridged Luminescent Networks
Authors: Diana Rosita , Panunzi Barbara , Concilio Simona , Marrafino Francesco , Shikler Rafi , Caruso Tonino , Caruso Ugo
Published: August 2019

From a dicyano-phenylenevinylene (PV) and an azobenzene (AB) skeleton, two new symmetrical salen dyes were obtained. Terminal bulky substituents able to reduce intermolecular interactions and flexible tails to guarantee solubility were added to the fluorogenic cores. Photochemical performances were investigated on the small molecules in solution, as neat crystals and as dopants in polymeric matrixes. High fluorescence quantum yield in the orange-red region was observed for the brightest emissive films (88% yield). The spectra of absorption and fluorescence were predicted by Density Functional Theory (DFT) calculations. The predicted energy levels of the frontier orbitals are in good agreement with voltammetry and molecular spectroscopy measures. Employing the two dyes as dopants of a nematic polymer led to remarkable orange or yellow luminescence, which dramatically decreases in on-off switch mode after liquid crystal (LC) order was lost. The fluorogenic cores were also embedded in organic polymers and self-assembly zinc coordination networks to transfer the emission properties to a macro-system. The final polymers emit from red to yellow both in solution and in the solid state and their photoluminescence (PL) performance are, in some cases, enhanced when compared to the fluorogenic cores.
Study on Three-Dimensional Stress Field of Gob-Side Entry Retaining by Roof Cutting without Pillar under Near-Group Coal Seam Mining
Authors: Sun Xiaoming , Liu Yangyang , Wang Junwei , Li Jiangbing , Sun Shijie , Cui Xuebin
Published: August 2019

In order to explore the distribution law of stress field under the mining mode of gob-side entry retaining by roof cutting without pillar (GERRCP) under goaf, based on the engineering background of 8102 and 9101 working faces in Xiashanmao coal mine, the stress field distribution of GERRCP and traditional remaining pillar was studied by means of theoretical analysis and numerical simulation. The simulation results showed that: (1) in the front of the working face, the vertical peak stress of non-pillar mining was smaller than that of the remaining pillar mining, and it could effectively control stress concentration in surrounding rock of the mining roadway; the trend of horizontal stress distribution of the two was the same, and the area, span and peak stress of stress the rise zone were the largest in large pillar mining and the minimum in non-pillar mining. (2) On the left side of the working face, the vertical stress presented increasing-decreasing characteristics under non-pillar mining mode and saddle-shaped distribution characteristics under the remaining pillar mining mode respectively. Among them, the peak stress was the smallest under non-pillar mining, and compared with the mining of the large pillar and small pillar, non-pillar mining decreased by 12–21% and 3–10% respectively. The position of peak stress of the former was closer to the mining roadway, indicating that the width of the plastic zone of the surrounding rock of the non-pillar mining was smaller and bearing capacity was higher. In the mining of the large and small pillar, the horizontal stress formed a high stress concentration in the pillar and 9102 working face respectively. In non-pillar mining, the horizontal stress concentration appeared in solid coal, but the concentration area was small.
Cooling Performance Assessment of a Slinky Closed Loop Lake Water Heat Pump System under the Climate Conditions of Pakistan
Authors: Shahzad Muhammad Kashif , Rehan Mirza Abdullah , Ali Muzaffar , Mustafa Azeem , Abbas Zafar , Mujtaba Muhammad , Akram Muhammad Imran , Yousaf Muhammad Rabeet
Published: August 2019

This paper presents an experimental evaluation of a closed loop lake water heat pump (LWHPs) system based on the slinky coiled configuration. Initially, a mathematical model is developed in the Engineering Equation Solver (EES) for the heat pump system and the submerged coils in a lake. System performance is determined for the submerged slinky copper coils under the various operating conditions. Afterwards, parametric analysis is performed considering different influencing parameters, such as the lake water temperature, ambient temperature, and mass flow rate of the circulating fluid at constant lake depth of 4 ft. The experimental setup is developed for 3.51 kW cooling capacity after cooling load calculation for a small room. In the current study, slinky copper coils are used to exchange heat with lake water. The experimental setup is installed in Taxila, Pakistan, and the system’s performance is analyzed during selected days. After experimentation based on hourly and daily operation characteristics, it is observed that the lake water temperature has significant influence on the heat transfer rate between slinky coil and lake water. While the lake water temperature in summer decreases and increases in winter with the depth. The resulted daily average coefficient of performance (COP) of the system is within the range of 3.24–3.46 during the selected days of cooling season. Based on these results, it can be concluded that the LWHP systems can be considered a viable solution for Pakistan having a well-established canal system.
An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes
Authors: Jia Linlin , Xiang Laisheng , Liu Xiyu
Published: August 2019

The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.
Why Young Adult Believers Are Turning Away from Religions: With a Focus on Religious Congregations in Downtown Seoul
Authors: Song Francis Jae-ryong
Published: August 2019

The sociological study of youth religion is a growing field of research. This essay focuses on the rising number of young Korean adult believers from five downtown religious congregations in Seoul who are leaving their congregations due to increased cynicism and as they re-examine their religious faith. Based on a series of in-depth interviews (Focus Group Interviews (FGIs)) conducted with both young adult believers and priests, this paper examines several key factors including why young adult believers decide to leave their congregations, how their congregations react to the changing environment, and whether they can develop innovative approaches to deal with the emerging situation. Although each and every one of the congregations considers this phenomenon to be a serious issue, the solutions they have applied to retain and attract young adult believers in and to their faith have so far been ineffective. Nonetheless, it is likely that the reason behind the failure to keep more young adults in their faith and congregation may correlate to how competitive and innovative solutions provided by the congregational leadership are as they attempt to tackle the ongoing issue of retaining and attracting young adult congregational members.
Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products
Authors: Bhagwat Tejas , Klein Igor , Huth Juliane , Leinenkugel Patrick
Published: August 2019

Globally, the number of dams increased dramatically during the 20th century. As a result, monitoring water levels and storage volume of dam-reservoirs has become essential in order to understand water resource availability amid changing climate and drought patterns. Recent advancements in remote sensing data show great potential for studies pertaining to long-term monitoring of reservoir water volume variations. In this study, we used freely available remote sensing products to assess volume variations for Lake Mead, Lake Powell and reservoirs in California between 1984 and 2015. Additionally, we provided insights on reservoir water volume fluctuations and hydrological drought patterns in the region. We based our volumetric estimations on the area–elevation hypsometry relationship, by combining water areas from the Global Surface Water (GSW) monthly water history (MWH) product with corresponding water surface median elevation values from three different digital elevation models (DEM) into a regression analysis. Using Lake Mead and Lake Powell as our validation reservoirs, we calculated a volumetric time series for the GSWMWH–DEMmedian elevation combinations that showed a strong linear ‘area (WA) – elevation (WH)’ (R2 > 0.75) hypsometry. Based on ‘WA-WH’ linearity and correlation analysis between the estimated and in situ volumetric time series, the methodology was expanded to reservoirs in California. Our volumetric results detected four distinct periods of water volume declines: 1987–1992, 2000–2004, 2007–2009 and 2012–2015 for Lake Mead, Lake Powell and in 40 reservoirs in California. We also used multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) for San Joaquin drainage in California to assess regional links between the drought indicators and reservoir volume fluctuations. We found highest correlations between reservoir volume variations and the SPEI at medium time scales (12–18–24–36 months). Our work demonstrates the potential of processed, open source remote sensing products for reservoir water volume variations and provides insights on usability of these variations in hydrological drought monitoring. Furthermore, the spatial coverage and long-term temporal availability of our data presents an opportunity to transfer these methods for volumetric analyses on a global scale.
Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau
Authors: Pan Yuanjin , Chen Ruizhi , Ding Hao , Xu Xinyu , Zheng Gang , Shen Wenbin , Xiao YiXin , Li Shuya
Published: August 2019

Surface and deep potential geophysical signals respond to the spatial redistribution of global mass variations, which may be monitored by geodetic observations. In this study, we analyze dense Global Positioning System (GPS) time series in the Eastern Tibetan Plateau using principal component analysis (PCA) and wavelet time-frequency spectra. The oscillations of interannual and residual signals are clearly identified in the common mode component (CMC) decomposed from the dense GPS time series from 2000 to 2018. The newly developed spherical harmonic coefficients of the Gravity Recovery and Climate Experiment Release-06 (GRACE RL06) are adopted to estimate the seasonal and interannual patterns in this region, revealing hydrologic and atmospheric/nontidal ocean loads. We stack the averaged elastic GRACE-derived loading displacements to identify the potential physical significance of the CMC in the GPS time series. Interannual nonlinear signals with a period of ~3 to ~4 years in the CMC (the scaled principal components from PC1 to PC3) are found to be predominantly related to hydrologic loading displacements, which respond to signals (El Niño/La Niña) of global climate change. We find an obvious signal with a period of ~6 yr on the vertical component that could be caused by mantle-inner core gravity coupling. Moreover, we evaluate the CMC’s effect on the GPS-derived velocities and confirm that removing the CMC can improve the recognition of nontectonic crustal deformation, especially on the vertical component. Furthermore, the effects of the CMC on the three-dimensional velocity and uncertainty are presented to reveal the significant crustal deformation and dynamic processes of the Eastern Tibetan Plateau.
Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data
Authors: Hamdi Zayd Mahmoud , Brandmeier Melanie , Straub Christoph
Published: August 2019

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Network (CNN) architectures. In this study, we tested and implemented an algorithm based on CNNs in an ArcGIS environment for automatic detection and mapping of damaged areas. The algorithm was trained and tested on a forest area in Bavaria, Germany. . It is a based on a modified U-Net architecture that was optimized for the pixelwise classification of multispectral aerial remote sensing data. The neural network was trained on labeled damaged areas from after-storm aerial orthophotos of a ca. 109 k m 2 forest area with RGB and NIR bands and 0.2-m spatial resolution. Around 10 7 pixels of labeled data were used in the process. Once the network is trained, predictions on further datasets can be computed within seconds, depending on the size of the input raster and the computational power used. The overall accuracy on our test dataset was 92 % . During visual validation, labeling errors were found in the reference data that somewhat biased the results because the algorithm in some instance performed better than the human labeling procedure, while missing areas affected by shadows. Our results are very good in terms of precision, and the methods introduced in this paper have several additional advantages compared to traditional methods: CNNs automatically detect high- and low-level features in the data, leading to high classification accuracies, while only one after-storm image is needed in comparison to two images for approaches based on change detection. Furthermore, flight parameters do not affect the results in the same way as for approaches that require DSMs and DTMs as the classification is only based on the image data themselves, and errors occurring in the computation of DSMs and DTMs do not affect the results with respect to the z component. The integration into the ArcGIS Platform allows a streamlined workflow for forest management, as the results can be accessed by mobile devices in the field to allow for high-accuracy ground-truthing and additional mapping that can be synchronized back into the database. Our results and the provided automatic workflow highlight the potential of deep learning on high-resolution imagery and GIS for fast and efficient post-disaster damage assessment as a first step of disaster management.
Radar Measurements of Morphological Parameters and Species Identification Analysis of Migratory Insects
Authors: Hu Cheng , Kong Shaoyang , Wang Rui , Zhang Fan
Published: August 2019

Migratory insect identification has been concerning entomology and pest managers for a long time. Their nocturnal behavior, as well as very small radar cross-section (RCS), makes individual detection challenging for any radar network. Typical entomological radars work at the X-band (9.4 GHz) with a vertical pencil beam. The measured RCS can be used to estimate insect mass and wingbeat frequency, and then migratory insects can be categorized into broad taxon classes using the estimated parameters. However, current entomological radars cannot achieve species identification with any higher precision or confidence. The limited frequency range of current insect radars have precluded the acquisition of more information useful for the identification of individual insects. In this paper, we report an improved measurement method of insect mass and body length using a radar with many more measurement frequencies than current entomological radars. The insect mass and body length can be extracted from the multi-frequency RCSs with uncertainties of 16.31% and 10.74%, respectively. The estimation of the thorax width and aspect ratio can also be achieved with uncertainties of 13.37% and 7.99%, respectively. Furthermore, by analyzing the statistical data of 5532 insects representing 23 species in East China, we found that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species under the achievable measurement precision of the proposed technique. These findings provide promising improvements of individual parameter measurement for entomological radars and imply a possibility of species identification with higher precision.
Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images
Authors: Baup Frédéric , Ameline Maël , Fieuzal Rémy , Frappart Frédéric , Corgne Samuel , Berthoumieu Jean-François
Published: August 2019

This work aims to provide daily estimates of the evolution of popcorn dry masses at the field scale using an agro-meteorological model, named the simple algorithm for yield model combined with a water balance model (SAFY-WB), controlled by the Green Area Index (GAI), derived from satellite images acquired in the microwave and optical domains. Synthetic aperture radar (SAR) satellite information (σ°VH/VV) was provided by the Sentinel-1A (S1-A) mission through two orbits (30 and 132), with a repetitiveness of six days. The optical data were obtained from the Landsat-8 mission. SAR and optical data were acquired over one complete agricultural season, in 2016, over a test site located in the southwest of France. The results show that the total dry masses of corn can be estimated accurately (R² = 0.92) at daily time steps due to a combination of satellite and model data. The SAR data are more suitable for characterizing the first period of crop development (until the end of flowering), whereas the optical data can be used throughout the crop cycle. Moreover, the model offers good performances in plant (R2 = 0.90) and ear (R2 = 0.93) mass retrieval, irrespective of the phenological stage. The results also reveal that four phenological stages (four to five leaves, flowering, ripening, and harvest) can be accurately predicted by the proposed approach (R2 = 0.98; root-mean-square error (RMSE) = seven days). Nevertheless, some important points must be taken into account before assimilation, namely the SAR signal must be corrected with respect to thermal noise before being assimilated, and the relationship estimated between the GAI and SAR signal must be performed over fields cultivated without intercrops. These results are unique in the literature and provide a new way to better monitor corn production over time.
Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images
Authors: Lu Bing , He Yuhong
Published: August 2019

Different types of methods have been developed to retrieve vegetation attributes from remote sensing data, including conventional empirical regressions (i.e., linear regression (LR)), advanced empirical regressions (e.g., multivariable linear regression (MLR), partial least square regression (PLSR)), machine learning (e.g., random forest regression (RFR), decision tree regression (DTR)), and radiative transfer modelling (RTM, e.g., PROSAIL). Given that each algorithm has its own strengths and weaknesses, it is essential to compare them and evaluate their effectiveness. Previous studies have mainly used single-date multispectral imagery or ground-based hyperspectral reflectance data for evaluating the models, while multi-seasonal hyperspectral images have been rarely used. Extensive spectral and spatial information in hyperspectral images, as well as temporal variations of landscapes, potentially influence the model performance. In this research, LR, PLSR, RFR, and PROSAIL, representing different types of methods, were evaluated for estimating vegetation chlorophyll content from bi-seasonal hyperspectral images (i.e., a middle- and a late-growing season image, respectively). Results show that the PLSR and RFR generally performed better than LR and PROSAIL. RFR achieved the highest accuracy for both images. This research provides insights on the effectiveness of different models for estimating vegetation chlorophyll content using hyperspectral images, aiming to support future vegetation monitoring research.
Environmental Differences between Migratory and Resident Ungulates—Predicting Movement Strategies in Rocky Mountain Mule Deer (Odocoileus hemionus) with Remotely Sensed Plant Phenology, Snow, and Land Cover
Authors: Robb Benjamin , Huang Qiongyu , Sexton Joseph , Stoner David , Leimgruber Peter
Published: August 2019

Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result from anthropogenic changes to landscape dynamics. Efforts to conserve and restore migrations require an understanding of the ecological characteristics driving the behavioral tradeoff between migration and residence. We identified migratory and resident behaviors of 42 mule deer (Odocoileus hemionus) based on GPS locations and correlated their locations to remotely sensed indicators of forage quality, land cover, snow cover, and human land use. The model classified mule deer seasonal migratory and resident niches with an overall accuracy of 97.8% and cross-validated accuracy of 81.2%. The distance to development was the most important variable in discriminating in which environments these behaviors occur, with resident niche space most often closer to developed areas than migratory niches. Additionally, snow cover in December was important for discriminating summer migratory niches. This approach demonstrates the utility of niche analysis based on remotely sensed environmental datasets and provides empirical evidence of human land use impacts on large-scale wildlife migrations.
A Tree Species Mapping Method from UAV Images over Urban Area Using Similarity in Tree-Crown Object Histograms
Authors: Feng Xiaoxue , Li Peijun
Published: August 2019

Timely and accurate information about spatial distribution of tree species in urban areas provides crucial data for sustainable urban development, management and planning. Very high spatial resolution data collected by sensors onboard Unmanned Aerial Vehicles (UAV) systems provide rich data sources for mapping tree species. This paper proposes a method of tree species mapping from UAV images over urban areas using similarity in tree-crown object histograms and a simple thresholding method. Tree-crown objects are first extracted and used as processing units in subsequent steps. Tree-crown object histograms of multiple features, i.e., spectral and height related features, are generated to quantify within-object variability. A specific tree species is extracted by comparing similarity in histogram between a target tree-crown object and reference objects. The proposed method is evaluated in mapping four different tree species using UAV multispectral ortho-images and derived Digital Surface Model (DSM) data collected in Shanghai urban area, by comparing with an existing method. The results demonstrate that the proposed method outperforms the comparative method for all four tree species, with improvements of 0.61–5.81% in overall accuracy. The proposed method provides a simple and effective way of mapping tree species over urban area.
Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China
Authors: Yang Liwei , Gao Xiaoqing , Li Zhenchao , Jia Dongyu , Jiang Junxia
Published: August 2019

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.
24-Gaze-Point Calibration Method for Improving the Precision of AC-EOG Gaze Estimation
Authors: bin Suhaimi Muhammad Syaiful Amri , Matsushita Kojiro , Sasaki Minoru , Njeri Waweru
Published: August 2019

This paper sought to improve the precision of the Alternating Current Electro-Occulo-Graphy (AC-EOG) gaze estimation method. The method consisted of two core techniques: To estimate eyeball movement from EOG signals and to convert signals from the eyeball movement to the gaze position. In conventional research, the estimations are computed with two EOG signals corresponding to vertical and horizontal movements. The conversion is based on the affine transformation and those parameters are computed with 24-point gazing data at the calibration. However, the transformation is not applied to all the 24-point gazing data, but to four spatially separated data (Quadrant method), and each result has different characteristics. Thus, we proposed the conversion method for 24-point gazing data at the same time: To assume an imaginary center (i.e., 25th point) on gaze coordinates with 24-point gazing data and apply an affine transformation to 24-point gazing data. Then, we conducted a comparative investigation between the conventional method and the proposed method. From the results, the average eye angle error for the cross-shaped electrode attachment is x = 2.27 ° ± 0.46 ° and y = 1.83 ° ± 0.34 ° . In contrast, for the plus-shaped electrode attachment, the average eye angle error is is x = 0.94 ° ± 0.19 ° and y = 1.48 ° ± 0.27 ° . We concluded that the proposed method offers a simpler and more precise EOG gaze estimation than the conventional method.
Development of a Novel Piezoelectric Harvester Excited by Raindrops
Authors: Doria Alberto , Fanti Giulio , Filipi Gino , Moro Federico
Published: August 2019

The impact of raindrops on a dry surface leads to a splashing phenomenon that dissipates a lot of energy. To improve energy collection, a novel piezoelectric raindrop energy harvester equipped with a spoonful of water was developed. The advantages and the drawbacks of this solution were analyzed with the aid of numerical simulations. A series of experimental tests were carried out in a laboratory with simulated raindrops. Experimental results showed that the negative effect of the added water mass was exceeded by the positive effects related to the impact of the raindrop on a liquid surface. Tests carried out connecting the harvester to a resistive load showed that the prototype was able to collect more energy than a simple cantilever harvester.
Toward Cost-Effective Mobile Video Streaming through Environment-Aware Watching State Prediction
Authors: Wang Xuanyu , Zhang Weizhan , Gao Xiang , Wang Jingyi , Du Haipeng , Zheng Qinghua
Published: August 2019

Mobile video applications are becoming increasingly prevalent and enriching the way people learn and are entertained. However, on mobile terminals with inherently limited resources, mobile video streaming services consume too much energy and bandwidth, which is an urgent problem to solve. At present, research on cost-effective mobile video streaming typically focuses on the management of data transmission. Among such studies, some new approaches consider the user’s behavior to further optimize data transmission. However, these studies have not adequately discussed the specific impact of the physical environment on user behavior. Therefore, this paper takes into account the environment-aware watching state and proposes a cost-effective mobile video streaming scheme to reduce power consumption and mobile data usage. First, the watching state is predicted by machine learning based on user behavior and the physical environment during a given time window. Second, based on the resulting prediction, a downloading algorithm is introduced based on the user equipment (UE) running mode in the LTE system and the VLC player. Finally, according to the corresponding experimental results obtained in a real-world environment, the proposed approach, compared to its benchmarks, effectively reduces the data usage (14.4% lower than that of energy-aware, on average) and power consumption (about 19% when there are screen touches) of mobile devices.
Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions
Authors: Seno Aldyandra Hami , Aliabadi M.H. Ferri
Published: August 2019

A parametric investigation of the effect of impactor stiffness as well as environmental and operational conditions on impact contact behaviour and the subsequently generated lamb waves in composite structures is presented. It is shown that differing impactor stiffness generates the most significant changes in contact area and lamb wave characteristics (waveform, frequency, and amplitude). A novel impact localisation method was developed based on the above observations that allows for variations due to differences in impactor stiffness based on modifications of the reference database method and the Akaike Information Criterion (AIC) time of arrival (ToA) picker. The proposed method was compared against a benchmark method based on artificial neural networks (ANNS) and the normalised smoothed envelope threshold (NSET) ToA extraction method. The results indicate that the proposed method had comparable accuracy to the benchmark method for hard impacts under various environmental and operational conditions when trained only using a single hard impact case. However, when tested with soft impacts, the benchmark method had very low accuracy, whilst the proposed method was able to maintain its accuracy at an acceptable level. Thus, the proposed method is capable of detecting the location of impacts of varying stiffness under various environmental and operational conditions using data from only a single impact case, which brings it closer to the application of data driven impact detection systems in real life structures.
Millennial Perceptions of Fast Fashion and Second-Hand Clothing: An Exploration of Clothing Preferences Using Q Methodology
Authors: Sorensen Katelyn , Johnson Jorgensen Jennifer
Published: August 2019

Millennials are becoming more conscientious of the products they buy, as well as the social and environmental implications behind them. However, Millennials with low discretionary incomes have limited choices in today’s marketplace, and it is unclear as to how these consumers perceive low-cost apparel options, including fast fashion apparel and second-hand apparel. The purpose of this study is to explore and compare Millennials’ perceptions of inexpensive fast fashion and second-hand apparel. The Q methodology was employed to determine patterns among perceptions. Participants sorted 14 statements describing ideal clothing items into a Q sort grid. The Q sort grid forced participants to rank statements by the degree to which they agree (or disagree). Once the Q sort grid was completed, participants shared their thoughts (qualitatively) as to why they ranked each statement the way that they did. The findings indicate that there are varied perceptions across the Millennial generation, which resulted in the emergence of four distinct factors for both fast fashion and second-hand apparel. The findings of this study are extensive. Fast fashion retailers and consignment shops will need to update their future strategies in order to target the Millennial generation.
The Effects of Energy Drink Consumption on Cognitive and Physical Performance in Elite League of Legends Players
Authors: Thomas Casey J. , Rothschild Jeffrey , Earnest Conrad P. , Blaisdell Aaron
Published: August 2019

To examine the cognitive and physical changes associated with consuming an energy drink concurrent to video gaming, we examined a convenience sample of nine elite League of Legends (LoL) e-sport players (21 ± 2 y, BMI 25.6 ± 3.4 kg/m2) consuming an energy drink (ReloadTM) or placebo (Placebo) in a randomized, double-blind, placebo-controlled cross-over trial. Participants completed the same test battery prior to treatment consumption and after playing each of three competitive LoL games. Primary outcomes included measures of attention (Erikson Flanker Test), reaction time (Go/No-Go test) and working memory (n-back test). Secondary outcomes examined fatigue (hand grip strength and finger tap speed). Statistical analysis was performed by repeated-measures analysis of variance (RM-ANOVA) and reported as the mean (standard deviation [SD]) or mean change (95% confidence interval [CI]). Participants reported sleeping 8.1 (1.2) h/night, playing LoL 10.3 (2.1) h/d, playing other video games 1.8 (2.8) h/d, and exercising 4.2 (1.7) times per week. Overall, we observed no significant time, group, or group-by-time interactions for any measured performance index with the exception of a significant improvement for the n-back test, where the Reload group demonstrated a significant within-group improvement: Reload [−171 ms (95% CI, −327.91, −14.09), p < 0.004], Placebo [−92 ms (95% CI, −213.63, 29.63)]. However, no between-group differences were noted (38.50 ms, 95% CI, −141.89, 64.89, p = 0.803). Our findings suggest that elite eSport athletes do not demonstrate a mental or physical improvement in performance relative to the treatment supplement or indices measured in this study.
Merging Science Education into Communication: Developing and Validating a Scale for Science Edu-Communication Utilizing Awareness, Enjoyment, Interest, Opinion formation, and Understanding Dimensions (SEC-AEIOU)
Authors: Wu Leon Yufeng , Wu Shannah Pinhsuan , Chang Chun-Yen
Published: August 2019

For better understanding of how the public perceive the information in science communication; this study sought to develop the scale of Science Edu-Communication (SEC), an instrument to measure AEIOU: Awareness; Enjoyment; Interest; Opinion formation; and Understanding. The AEIOU framework was adopted for use as the major component of SEC to depict participants’ general perceptions of science communication from their daily life experiences. Responses from 121 participants were analysed using exploratory factor analysis; item discrimination; and qualitative coding analysis. Results support SEC-AEIOU as a valid and reliable instrument to measure the effectiveness of science communication experiences. Additionally; SEC-AEIOU can serve as a framework for research and practice to bridge science communication and science education. In particular; science communicators; educators; and institutions that engage in science communication and educational activities may benefit from such a metric. This scale seeks to assist in building a robust framework to facilitate the trend of bridging science communication and science education: Science Edu-Communication.