bluuebunny/crossref_metadata_2025 huggingface dataset:
Randomized Trial of Marine n-3 Polyunsaturated Fatty Acids for the Prevention of Cerebral Small Vessel Disease and Inflammation in Aging (PUFA Trial): Rationale, Design and Baseline Results
Authors: Bowman Gene L. , Silbert Lisa C. , Dodge Hiroko H. , Lahna David , Hagen Kirsten , Murchison Charles F. , Howieson Diane , Kaye Jeffrey , Quinn Joseph F. , Shinto Lynne
Published: March 2019

Vascular risk factors for age-related cognitive decline are significant, and their management may ultimately prove the most successful strategy for reducing risk and sustaining cognitive health. This randomized, double-blinded, placebo-controlled trial with parallel group allocation to either marine n-3 polyunsaturated fatty acids (n-3 PUFA) or soybean oil placebo assesses the effects on the total volume of accumulation in cerebral white matter hyperintensities (WMH), a potentially modifiable neurovascular component of age-related cognitive decline. Total WMH accumulation over 3 years is the primary endpoint. The safety and efficacy of n-3 PUFA is evaluated in older adults with significant WMH and suboptimum plasma n-3 PUFA as inclusion criteria. One hundred and two non-demented older adults were enrolled with a mean age of 81.1 (±4.4) years, WMH of 19.4 (±16.1) cm3, and a plasma n-3 PUFA of 86.64 (±29.21) µg/mL. 61% were female, 28% were apolipoprotein E epsilon 4 carriers, and the mean mini-mental state exam (MMSE) was 27.9 (±1.7). This trial provides an initial evaluation of n-3 PUFA effects on WMH, a reproducible and valid risk biomarker for cognitive decline, as well as on inflammatory biomarkers thought to play a role in WMH accumulation. We present the baseline results and operational experience of enriching a study population on advanced age, blood n-3 PUFA, and magnetic resonance imaging (MRI) derived WMH with biomarker outcomes (WMH, inflammation markers) in a dementia prevention paradigm.
Exploring Common Culinary Herbs and Spices as Potential Anti-Quorum Sensing Agents
Authors: Cosa Sekelwa , Chaudhary Sushil Kumar , Chen Weiyang , Combrinck Sandra , Viljoen Alvaro
Published: March 2019

Quorum sensing controls bacterial pathogenesis and virulence; hence, interrupting this system renders pathogenic bacteria non-virulent, and presents a novel treatment for various bacterial infections. In the search for novel anti-quorum sensing (AQS) compounds, 14 common culinary herbs and spices were screened for potential antipathogenicity activity against Chromobacterium violaceum ATCC 12472. Extracts of Glycyrrhiza glabra (liquorice), Apium graveolens (celery), Capsicum annuum (cayenne pepper) and Syzygium anisatum (aniseed) demonstrated good AQS potential, yielding opaque halo zones ranging from 12–19 mm diameter at sub-minimum inhibitory concentrations (0.350–4.00 mg/mL). For the same species, the percentage reduction in violacein production ranged from 56.4 to 97.3%. Zones with violacein inhibitory effects were evident in a celery extract analysed using high performance thin layer chromatography-bio-autography. The major active compound was isolated from celery using preparative-high performance liquid chromatography-mass spectrometry and identified using gas chromatography-mass spectrometry (GC-MS) as 3-n-butyl-4,5-dihydrophthalide (sedanenolide). Potent opaque zones of inhibition observed on the HPTLC-bio-autography plate seeded with C. violaceum confirmed that sedanenolide was probably largely responsible for the AQS activity of celery. The bacteriocidal properties of many herbs and spices are reported. This study, however, was focussed on AQS activity, and may serve as initial scientific validation for the anti-infective properties ascribed to several culinary herbs and spices.
Perceived Correspondence of Health Effects as a New Determinant Influencing Purchase Intention for Functional Food
Authors: Temesi Ágoston , Bacsó Ágnes , Grunert Klaus G. , Lakner Zoltán
Published: March 2019

This study has revealed the role of a new factor, perceived correspondence of health effects, in consumer acceptance of functional foods. Using a web survey of 1016 people, we hypothesized and verified the following: when an ingredient does not occur naturally in the carrier but the consumer assigns the same health effect to it as to the carrier, the product’s acceptance will be more positive than it would be if an identical health effect was not associated with the carrier and the functional ingredient. Factors influencing consumer acceptance were examined via binary logistic regression models. According to the results, if a functional food developer fortifies the carrier with an ingredient that does not occur naturally in the carrier, the product can expect higher acceptance if the health effects perceived by consumers are properly matched. In general, it has been found that expected taste and awareness of the product were decisive in all demographic and income groups, whereas perceived correspondence of health effects had a lesser, but still positive influence on acceptance.
Epidemiology of Tick-Borne Encephalitis (TBE) in Germany, 2001–2018
Authors: Hellenbrand Wiebke , Kreusch Teresa , Böhmer Merle , Wagner-Wiening Christiane , Dobler Gerhard , Wichmann Ole , Altmann Doris
Published: March 2019

We reviewed tick-borne encephalitis (TBE) surveillance and epidemiology in Germany, as these underlie public health recommendations, foremost vaccination. We performed descriptive analyses of notification data (2001–2018, n = 6063) according to region, demographics and clinical manifestations and calculated incidence trends using negative binomial regression. Risk areas were defined based on incidence in administrative districts. Most cases (89%) occurred in the federal states of Baden-Wurttemberg and Bavaria, where annual TBE incidence fluctuated markedly between 0.7–2.0 cases/100,000 inhabitants. A slight but significantly increasing temporal trend was observed from 2001–2018 (age-adjusted incidence rate ratio (IRR) 1.02 (95% confidence interval (CI): 1.01–1.04)), primarily driven by high case numbers in 2017–2018. Mean incidence was highest in 40–69-year-olds and in males. More males (23.7%) than females (18.0%, p = 0.02) had severe disease (encephalitis or myelitis), which increased with age, as did case-fatality (0.4% overall; 2.1% among ≥70-year-olds). Risk areas increased from 129 districts in 2007 to 161 in 2019. Expansion occurred mainly within existent southern endemic areas, with slower contiguous north-eastern and patchy north-western spread. Median vaccination coverage at school entry in risk areas in 2016–2017 ranged from 20%–41% in 4 states. Increasing TBE vaccine uptake is an urgent priority, particularly in high-incidence risk areas.
Chain-Branched Polyhydroxylated Octahydro-1H-Indoles as Potential Leads against Lysosomal Storage Diseases
Authors: Estévez Juan C. , González Marcos A. , Villaverde M. Carmen , Hirokami Yuki , Kato Atsushi , Sussman Fredy , Reza David , Estévez Ramón J.
Published: March 2019

Here, the synthesis and glycosidase inhibition properties of the two first known 3-ethyloctahydro-1H-indole-4,5,6-triols are reported. This study shows the transformation of d-glucose into polyhydroxylated 1-(2-nitrocyclohexane) acetaldehydes, followed by a protocol involving the formation of the azacyclopentane ring. Results of inhibitory potency assays and docking calculations show that at least one of them could be a lead for optimization in the search for compounds that behave like folding chaperones in lysosomal storage diseases.
Smart Nanovesicles for Drug Targeting and Delivery
Authors: Marianecci Carlotta , Carafa Maria
Published: March 2019

Nanovesicles are highly-promising and versatile systems for the delivery and/or targeting of drugs, biomolecules and contrast agents [...]
3D Printed Pellets (Miniprintlets): A Novel, Multi-Drug, Controlled Release Platform Technology
Authors: Awad Atheer , Fina Fabrizio , Trenfield Sarah J. , Patel Pavanesh , Goyanes Alvaro , Gaisford Simon , Basit Abdul W.
Published: March 2019

Selective laser sintering (SLS) is a single-step three-dimensional printing (3DP) process that can be leveraged to engineer a wide array of drug delivery systems. The aim of this work was to utilise SLS 3DP, for the first time, to produce small oral dosage forms with modified release properties. As such, paracetamol-loaded 3D printed multiparticulates, termed miniprintlets, were fabricated in 1 mm and 2 mm diameters. Despite their large surface area compared with a conventional monolithic tablet, the ethyl cellulose-based miniprintlets exhibited prolonged drug release patterns. The possibility of producing miniprintlets combining two drugs, namely paracetamol and ibuprofen, was also investigated. By varying the polymer, the dual miniprintlets were programmed to achieve customised drug release patterns, whereby one drug was released immediately from a Kollicoat Instant Release matrix, whilst the effect of the second drug was sustained over an extended time span using ethyl cellulose. Herein, this work has highlighted the versatility of SLS 3DP to fabricate small and intricate formulations containing multiple active pharmaceutical ingredients with distinct release properties.
A Novel Approach of Promoting Adhesion of Reinforcing Cord to Elastomers by Plasma Polymerization
Authors: Dierkes Wilma , Louis André , Noordermeer Jacques , Blume Anke
Published: March 2019

Adhesion of cords to elastomers is crucial for many elastomeric products, such as tires and V-belts. The best adhesion system so far is based on a combination of resorcinol, formaldehyde, and a latex (RFL). However, this cord treatment has serious disadvantages in terms of processing and toxicity. A promising alternative is a plasma treatment of the cords prior to be embedded in the elastomer. For rayon cords, a plasma polymerization of sulfur-containing precursors results in adhesion levels close to RFL treatment. However, for polyethylene terephthalate (PET) cords, this treatment is not satisfactory. For this type of cords, a water-plasma activation followed by a silane dip is more promising, as 72% of the adhesion level of RFL treatment could be achieved. For rayon, an even higher adhesion level was realized.
Jainism, Yoga, and Ecology: A Course in Contemplative Practice for a World in Pain
Authors: Miller Christopher Patrick
Published: March 2019

This article proposes an introductory course to Jainism vis-à-vis the categories of yoga and ecology. Following a short introduction, the main section of this paper introduces the contents of the syllabus for this upper division undergraduate theological studies course. Students will learn not only the history and philosophy of Jainism, but will also undertake basic Jain contemplative practices. Contemplative practice is used not merely as a technique of self-care, but rather, following some of Jainism’s foundational textual sources, first and foremost as a method for helping students to form a sense of ethical relationship and empathy with the world around them. Using such a pedagogical approach, which I situate as a specific form of “high-impact” learning, I suggest that at the completion of the course students will be better equipped to respond to our shared social and environmental crises. This article serves as both an introduction of this course to the academic community, as well as an invitation to scholars and professors of South Asian religious traditions to adopt the pedagogical approach proposed herein.
Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison
Authors: Ou Guanglong , Li Chao , Lv Yanyu , Wei Anchao , Xiong Hexian , Xu Hui , Wang Guangxing
Published: March 2019

Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg/ha and the underestimations for the plots with AGB values larger than 180 Mg/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB.
Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness
Authors: Carrasco Luis , Giam Xingli , Papeş Monica , Sheldon Kimberly
Published: March 2019

The structural heterogeneity of vegetation is a key factor for explaining animal diversity patterns at a local scale. Improvements in airborne light detection and ranging (lidar) technologies have enabled researchers to study forest 3D structure with increasing accuracy. Most structure–animal diversity work has focused on structural metrics derived from lidar returns from canopy and terrain features. Here, we built new lidar structural metrics based on the Leaf Area Density (LAD) at each vegetation height layer, and used these metrics to study how different aspects of forest structural heterogeneity explain variation in bird species richness. Our goals were to test: (1) whether LAD-based metrics better explained bird species richness compared to metrics based on the top of the canopy; and (2) if different aspects of structural heterogeneity had diverse effects on bird richness. We used discrete lidar data together with 61 breeding landbird points provided by the National Ecological Observatory Network at five forest sites of the eastern US. We used the lidar metrics as predictors of bird species richness and analyzed the shape of the response curves against each predictor. Metrics based on LAD measurements had better explanatory power (43% of variance explained) than those based on the variation of canopy heights (32% of variance explained). Dividing the forest plots into smaller grids allowed us to study the within-plot horizontal variation of the vertical heterogeneity, as well as to analyze how the vegetation density is horizontally distributed at each height layer. Bird species richness increased with horizontal heterogeneity, while vertical heterogeneity had negative effects, contrary to previous research. The increasing capabilities of lidar will allow researchers to characterize forest structure with higher detail. Our findings highlight the need for structure–animal diversity studies to incorporate metrics that are able to capture different aspects of forest 3D heterogeneity.
Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
Authors: Ilieva Maya , Polanin Piotr , Borkowski Andrzej , Gruchlik Piotr , Smolak Kamil , Kowalski Andrzej , Rohm Witold
Published: March 2019

The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.
A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing
Authors: Xu Feng , Diner David J. , Dubovik Oleg , Schechner Yoav
Published: March 2019

Aerosol retrieval algorithms used in conjunction with remote sensing are subject to ill-posedness. To mitigate non-uniqueness, extra constraints (in addition to observations) are valuable for stabilizing the inversion process. This paper focuses on the imposition of an empirical correlation constraint on the retrieved aerosol parameters. This constraint reflects the empirical dependency between different aerosol parameters, thereby reducing the number of degrees of freedom and enabling accelerated computation of the radiation fields associated with neighboring pixels. A cross-pixel constraint that capitalizes on the smooth spatial variations of aerosol properties was built into the original multi-pixel inversion approach. Here, the spatial smoothness condition is imposed on principal components (PCs) of the aerosol model, and on the corresponding PC weights, where the PCs are used to characterize departures from the mean. Mutual orthogonality and unit length of the PC vectors, as well as zero sum of the PC weights also impose stabilizing constraints on the retrieval. Capitalizing on the dependencies among aerosol parameters and the mutual orthogonality of PCs, a perturbation-based radiative transfer computation scheme is developed. It uses a few dominant PCs to capture the difference in the radiation fields across an imaged area. The approach is tested using 27 observations acquired by the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) during multiple NASA field campaigns and validated using collocated AERONET observations. In particular, aerosol optical depth, single scattering albedo, aerosol size, and refractive index are compared with AERONET aerosol reference data. Retrieval uncertainty is formulated by accounting for both instrumental errors and the effects of multiple types of constraints.
Automatic Wheat Ear Counting Using Thermal Imagery
Authors: Fernandez-Gallego Jose A. , Buchaillot Ma. Luisa , Aparicio Gutiérrez Nieves , Nieto-Taladriz María Teresa , Araus José Luis , Kefauver Shawn C.
Published: March 2019

Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from a moderately high resolution handheld thermal camera. Three experimental sites under different growing conditions in Spain were used on a set of 24 varieties of durum wheat for this study. The automatic pipeline system developed uses contrast enhancement and filter techniques to segment image regions detected as ears. The approach is based on the temperature differential between the ears and the rest of the canopy, given that ears usually have higher temperatures due to their lower transpiration rates. Thermal images were acquired, together with RGB images and in situ (i.e., directly in the plot) visual ear counting from the same plot segment for validation purposes. The relationship between the thermal counting values and the in situ visual counting was fairly weak (R2 = 0.40), which highlights the difficulties in estimating ear density from one single image-perspective. However, the results show that the automatic thermal ear counting system performed quite well in counting the ears that do appear in the thermal images, exhibiting high correlations with the manual image-based counts from both thermal and RGB images in the sub-plot validation ring (R2 = 0.75–0.84). Automatic ear counting also exhibited high correlation with the manual counting from thermal images when considering the complete image (R2 = 0.80). The results also show a high correlation between the thermal and the RGB manual counting using the validation ring (R2 = 0.83). Methodological requirements and potential limitations of the technique are discussed.
Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm
Authors: Zhang Guodong , Zhou Hongmin , Wang Changjing , Xue Huazhu , Wang Jindi , Wan Huawei
Published: March 2019

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo” approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.
Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding
Authors: Liu Peng , Li Ying , Liu Bingxin , Chen Peng , Xu Jin
Published: March 2019

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.
Determination of Crop Water Stress Index by Infrared Thermometry in Grapefruit Trees Irrigated with Saline Reclaimed Water Combined with Deficit Irrigation
Authors: Romero-Trigueros Cristina , Bayona Gambín José María , Nortes Tortosa Pedro Antonio , Alarcón Cabañero Juan José , Nicolás Nicolás Emilio
Published: March 2019

Water is not always accessible for agriculture due to its scarcity. In order to successfully develop irrigation strategies that optimize water productivity characterization of the plant, the water status is necessary. We assessed the suitability of thermal indicators by infrared thermometry (IRT) to determine the water status of grapefruit in a commercial orchard with long term irrigation using saline reclaimed water (RW) and regulated deficit irrigation (RDI) in Southeastern Spain. The results showed that Tc-Ta differences were positive in a wide range of vapor pressure deficits (VPD), and the major Tc-Ta were found at 10.00 GMT, before and after the highest daily values of VPD and solar radiation, respectively, were reached. In addition, we evaluated the relationships between Tc-Ta and VPD to establish the Non-Water Stressed Baselines (NWSBs), which are necessary to accurately calculate the crop water stress index (CWSI). Two important findings were found, which include i) the best significant correlations (p < 0.005) found at 10.00 GMT and their slopes were positive, and ii) NWSBs showed a marked hourly and seasonal variation. The hourly shift was mainly explained by the variation in solar radiation since both the NWSB-slope and the NWSB-intercept were significantly correlated with a zenith solar angle (θZ) (p < 0.005). The intercept was greater when θZ was close to 0 (at midday) and the slope displayed a marked hysteresis throughout the day, increasing in the morning and decreasing in the afternoon. The NWSBs determination, according to the season improved most of their correlation coefficients. In addition, the relationship significance of Tc-Ta versus VPD was higher in the period where the intercept and Tc-Ta were low. CWSI was the thermal indicator that showed the highest level of agreement with the stem water potential of the different treatments even though Tc and Tc-Ta were also significantly correlated. We highlight the suitability of thermal indicators measured by IRT to determine the water status of grapefruits under saline (RW) and water stress (RDI) conditions.
UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring
Authors: Krause Stuart , Sanders Tanja G.M. , Mund Jan-Peter , Greve Klaus
Published: March 2019

The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.
Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection
Authors: Mondini Alessandro C. , Santangelo Michele , Rocchetti Margherita , Rossetto Enrica , Manconi Andrea , Monserrat Oriol
Published: March 2019

Despite landslides impact the society worldwide every day, landslide information is inhomogeneous and lacking. When landslides occur in remote areas or where the availability of optical images is rare due to cloud persistence, they might remain unknown, or unnoticed for long time, preventing studies and hampering civil protection operations. The unprecedented availability of SAR C-band images provided by the Sentinel-1 constellation offers the opportunity to propose new solutions to detect landslides events. In this work, we perform a systematic assessment of Sentinel-1 SAR C-band images acquired before and after known events. We present the results of a pilot study on 32 worldwide cases of rapid landslides entailing different types, sizes, slope expositions, as well as pre-existing land cover, triggering factors and climatic regimes. Results show that in about eighty-four percent of the cases, changes caused by landslides on SAR amplitudes are unambiguous, whereas only in about thirteen percent of the cases there is no evidence. On the other hand, the signal does not allow for a systematic use to produce inventories because only in 8 cases, a delineation of the landslide borders (i.e., mapping) can be manually attempted. In a few cases, cascade multi-hazard (e.g., floods caused by landslides) and evidences of extreme triggering factors (e.g., strong earthquakes or very rapid snow melting) were detected. The method promises to increase the availability of information on landslides at different spatial and temporal scales with benefits for event magnitude assessment during weather-related emergencies, model tuning, and landslide forecast model validation, in particular when accurate mapping is not required.
An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping
Authors: Wang Tong , Tang Ronglin , Li Zhao-Liang , Jiang Yazhen , Liu Meng , Niu Lu
Published: March 2019

Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.
Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery
Authors: Wen Dawei , Huang Xin , Zhang Anlu , Ke Xinli
Published: March 2019

Inner-city redevelopment is regarded as an effective way to promote land-use efficiency and optimize land-use structure, especially with the establishment of urban growth boundaries in Chinese cities. However, inner-city redevelopment as compared to urban sprawl has been rarely monitored in 2D space, let alone in 3D space. Therefore, in this paper, a novel approach to generate time-series 3D building maps (i.e., building footprint and height) based on high-resolution (2 m) multi-view ZY-3 satellite imagery was proposed. In the proposed method, the building footprint was updated by an object-based image-to-map change detection method, which employed spectral (i.e., HSV and NDVI) and structural features (i.e., morphological building index) to extract non-building and building objects, respectively; building height was estimated automatically through semi-global matching of multi-view images. We applied the proposed method to four representative Chinese megacities, i.e., Beijing, Xi’an, Shanghai, and Wuhan, for the period 2012–2017, and detected building footprints with overall accuracies ranging from 84.84% to 97.60%. The building height estimation was also relatively accurate, with the bias, slope, and root-mean-square error being −0.49–2.30 m, 0.93–1.10 m, and 4.94–7.31 m, respectively. Our results show that the total building coverage decreased over the study period, accompanied by an increase in both area-weighted building height and floor area ratio. In addition, compact low-rise buildings have been replaced by open high-rise buildings in the urban redevelopment process. Moreover, due to the scattered spatial distribution of the redevelopment sites, the local spatial aggregation patterns of building density are unlikely to shift between hotspots (i.e., spatial aggregation of high values) and coldspots (i.e., spatial aggregation of low values).
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things
Authors: Nespoli Pantaleone , Useche Pelaez David , Díaz López Daniel , Gómez Mármol Félix
Published: March 2019

The Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objects are able to communicate autonomously through already existing network infrastructures, thus generating a more concrete integration between real world and computer-based systems. On the downside, the great benefit carried by the IoT paradigm in our life brings simultaneously severe security issues, since the information exchanged among the objects frequently remains unprotected from malicious attackers. The paper at hand proposes COSMOS (Collaborative, Seamless and Adaptive Sentinel for the Internet of Things), a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using multiple defensive rings, resulting in a more accurate and robust protection. Additionally, we discuss the current deployment of the sentinel on a commodity device (i.e., Raspberry Pi). Exhaustive experiments are conducted on the sentinel, demonstrating that it performs meticulously even in heavily stressing conditions. Each defensive layer is tested, reaching a remarkable performance, thus proving the applicability of COSMOS in a distributed and dynamic scenario such as IoT. With the aim of easing the enjoyment of the proposed sentinel, we further developed a friendly and ease-to-use COSMOS App, so that end-users can manage sentinel(s) directly using their own devices (e.g., smartphone).
Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors
Authors: Ali Muhammad Usman , Hur Soojung , Park Yongwan
Published: March 2019

Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.
An Optical MEMS Acoustic Sensor Based on Grating Interferometer
Authors: Zhang Mengying , Wu Gaomi , Ren Dipeng , Gao Ran , Qi Zhi-Mei , Liang Xingdong
Published: March 2019

Acoustic detection is of great significance because of its wide applications. This paper reports a Micro-Electro-Mechanical System (MEMS) acoustic sensor based on grating interferometer. In the MEMS structure, a diaphragm and a micro-grating made up the interference cavity. A short-cavity structure was designed and fabricated to reduce the impact of temperature on the cavity length in order to improve its stability against environment temperature variations. Besides this, through holes were designed in the substrate of the grating to reduce the air damping of the short-cavity structure. A silicon diaphragm with a 16.919 µm deep cavity and 2.4 µm period grating were fabricated by an improved MEMS process. The fabricated sensor chip was packaged on a conditioning circuit with a laser diode and a photodetector for acoustic detection. The output voltage signal in response to an acoustic wave is of high quality. The sensitivity of the acoustic sensor is up to −15.14 dB re 1 V/Pa @ 1 kHz. The output signal of the high-stability acoustic sensor almost unchanged as the environment temperature ranged from 5 °C to 55 °C.
Biocompatible Cantilevers for Mechanical Characterization of Zebrafish Embryos using Image Analysis
Authors: Tomizawa Yuji , Dixit Krishna , Daggett David , Hoshino Kazunori
Published: March 2019

We have developed a force sensing system to continuously evaluate the mechanical elasticity of micrometer-scale (a few hundred micrometers to a millimeter) live tissues. The sensing is achieved by measuring the deflection of force sensitive cantilevers through microscopic image analysis, which does not require electrical strain gauges. Cantilevers made of biocompatible polydimethylsiloxane (PDMS) were actuated by a piezoelectric actuator and functioned as a pair of chopsticks to measure the stiffness of the specimen. The dimensions of the cantilevers were easily adjusted to match the size, range, and stiffness of the zebrafish samples. In this paper, we demonstrated the versatility of this technique by measuring the mechanical elasticity of zebrafish embryos at different stages of development. The stiffness of zebrafish embryos was measured once per hour for 9 h. From the experimental results, we successfully quantified the stiffness change of zebrafish embryos during embryonic development.
Stress Monitoring on GFRP Anchors Based on Fiber Bragg Grating Sensors
Authors: Kou Hai-Lei , Li Wang , Zhang Wang-Chun , Zhou Yuan , Zhou Xiao-Long
Published: March 2019

Glass fiber-reinforced polymer (GFRP) bolts have been widely used in some applications of grouted anchors because of the advantages of better resistance to corrosion, high strength-to-weight ratio, low electromagnetic properties, and so on. This study presents a field test to assess the feasibility of fiber Bragg grating (FBG) sensors in monitoring the stress profile of GFRP anchors during pulling test. Two GFRP anchors were fully instrumented with FBG sensors and then installed into the ground using a drilling and grouting method. To measure the stress profile along test anchors, seven bare FBG sensors were arranged in a single optical fiber and then embedded in the middle of GFRP bolts in the process of extrusion molding. The procedure for embedding bare FBG sensors into GFRP bolts is introduced first. Then, the axial forces and shear stresses that were calculated from the measurements of the FBG sensors are discussed. The field test results indicate that the embedded FBG technology was feasible to monitor the stress state of GFRP anchors during pulling.
Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters
Authors: Hou Jing , Yang Yan , Gao Tian
Published: March 2019

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.
Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision
Authors: Troncoso-Pastoriza Francisco , Eguía-Oller Pablo , Díaz-Redondo Rebeca P. , Granada-Álvarez Enrique , Erkoreka Aitor
Published: March 2019

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.
Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps
Authors: Zhu Pingping , Ferrari Silvia , Morelli Julian , Linares Richard , Doerr Bryce
Published: March 2019

This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time.