bluuebunny/crossref_metadata_2025 huggingface dataset:
South West Food Community: A Place-Based Pilot Study to Understand the Food Security System
Authors: Godrich Stephanie Louise , Payet Jennifer , Brealey Deborah , Edmunds Melinda , Stoneham Melissa , Devine Amanda
Published: March 2019

The objectives of this study were to: (i) Identify initiatives supporting healthy food availability, access and utilisation in the South West region of Western Australia (WA); and (ii) understand how they were functioning as a system to enhance community-level food security (FS). This study used a novel approach; a Systemic Innovation Lab, to interview initiative leaders/stakeholders about their FS initiative. Initiative characteristics measured included those which were associated with creating the effective conditions for FS systems change. Information was uploaded to an innovative online tool, creating a ‘transition card’ (matrix) of initiatives and partnering organisations. Fifty-one participants reported on 52 initiatives. Initiatives were most likely to possess characteristics relating to reinforcing changes towards an enhanced way of working to address FS and creating disruption to the old way of working. The initiative characteristic that initiatives were least likely to possess related to identifying the different causal factors of FS, and working with other stakeholders on specific components of FS. The South West Food Community pilot project used a comprehensive yet defined approach to demonstrate the value of a place-based, co-design project. Participants and stakeholders could strengthen specific initiative characteristics to facilitate enhanced community-level FS.
Diabetes Screening Through Community Pharmacies in England: A Cost-Effectiveness Study
Authors: Wright David , Little Richard , Turner David , Thornley Tracey
Published: March 2019

Community pharmacies are commonly used to screen for patients with diabetes. The aim of this paper is to estimate the cost per test and cost per appropriately referred patient from a pharmacy perspective using a one-year decision tree model. One-way sensitivity analysis was performed to estimate the effect of geographical location and patient self-referral rate. Data was used from 164 patients screened and located in an area with average social deprivation and largely white European inhabitants and 172 patients in an area with higher social deprivation (lower than average ability to access society’s resources) and a mixed ethnicity population in England. The diabetes screening consisted of initial risk assessment via questionnaire followed by HbA1c test for those identified as high risk. The cost per person screened was estimated as £28.65. The cost per appropriately referred patient with type 2 diabetes was estimated to range from £7638 to £11,297 in deprived mixed ethnicity and non-deprived areas respectively. This increased to £12,730 and £18,828, respectively, if only 60% of patients referred chose to inform their general practitioner (GP). The cost per test and identification rates through community pharmacies was similar to that reported through medical practices. Locating services in areas of suspected greater diabetes prevalence and increasing the proportion of patients who follow pharmacist advice to attend their medical practice improves cost-effectiveness.
300 GHz Optoelectronic Transmitter Combining Integrated Photonics and Electronic Multipliers for Wireless Communication
Authors: Ali Muhsin , Pérez-Escudero Jose Manuel , Guzmán-Martínez Robinson-Cruzoe , Lo Mu-Chieh , Ederra Iñigo , Gonzalo Ramón , García-Muñoz Luis Enrique , Santamaría Gabriel , Segovia-Vargas Daniel , van Dijk Frédéric , Carpintero Guillermo
Published: March 2019

THz communications systems at carrier frequencies above 200 GHz are the key to enable next-generation mobile communication networks with 100 Gbit/s wireless data rates. One of the key questions is, which carrier frequency generation technique will be the most suitable. This is currently addressed by two separate approaches, electronics-based and photonics-based. We present in this paper a truly microwave photonic approach that benefits from the main key features of each, bandwidth, tunability, stability and fiber compatibility from photonics and power handling capability from the electronics. It is based on a Photonic Local Oscillator (PLO), generating a 100 GHz frequency, fed into an electronic frequency multiplier. A high speed uni-travelling carrier photodiode (UTC-PD) provides the 100 GHz PLO for Schottky tripler diodes, generating 300 GHz signal. To feed the UTC-PD, we present a photonic integrated mode locked laser source. According to the simulations and measurements, the developed transmitter can produce a maximum of 12 μW of THz power at 280 GHz.
Latest Achievements in Polymer Optical Fiber Gratings: Fabrication and Applications
Authors: Min Rui , Ortega Beatriz , Marques Carlos
Published: March 2019

Grating devices in polymer optical fibers (POFs) have attracted huge interest for many potential applications in recent years. This paper presents the state of the art regarding the fabrication of different types of POF gratings, such as uniform, phase-shifted, tilted, chirped, and long period gratings, and explores potential application scenarios, such as biosensing and optical communications.
Crystalline Structures and Structural Transitions of Copolyamides Derived from 1,4-Diaminobutane and Different Ratios of Glutaric and Azelaic Acids
Authors: Olmo Cristian , Casas María T. , Martínez Juan C. , Franco Lourdes , Puiggalí Jordi
Published: March 2019

Copolyamides derived from even 1,4-butanediamine and different mixtures of odd dicarboxylic acids with a great difference in the number of methylene groups (i.e., glutaric and azelaic acids with 3 and 7 groups, respectively) have been synthesized, characterized and structurally studied. Calorimetric analyses revealed a complex behavior with multiple melting peaks associated to lamellar reordering and the presence of defective crystals. Equilibrium melting temperatures were evaluated and showed a eutectic behavior with composition. Copolymers were able to crystallize even for samples with comonomer percentages close to 50%. Negative and ringed spherulites from the melt state and small lath-like lamellar crystals from dilute solution crystallizations were attained. Furthermore, calorimetric data pointed out the exclusion of the less abundant monomer from the lattice of the predominant structure. All samples at room temperature showed a similar crystalline structure (form I) defined by two predominant reflections at spacings close to 0.430 and 0.380 nm, which has been related for even-odd nylons with a two-hydrogen bonded structure. Real time synchrotron experiments showed that melt crystallized samples have two polymorphic transitions on heating, which were practically reversible and consequently were also detected during cooling from the melt state. Interestingly, a different behavior was detected among solution crystallized samples and specifically the transition to the intermediate structure (form II) was not detected during heating for samples enriched on the azelate component or more precisely when they were exclusively crystallized in the form I.
The Effect of Accelerated Aging on Polylactide Containing Plant Extracts
Authors: Moraczewski Krzysztof , Stepczyńska Magdalena , Malinowski Rafał , Karasiewicz Tomasz , Jagodziński Bartłomiej , Rytlewski Piotr
Published: March 2019

In this study, natural extracts of plant origin were used as anti-aging compounds of biodegradable polymers. Coffee (0.5–10 wt%), cocoa, or cinnamon extracts were added to the polylactide matrix. The obtained materials were subjected to an accelerated aging process (720, 1440, or 2160 h) at 45 °C and 70% relative humidity under continuous UV radiation. The effectiveness of the tested extracts was compared to a commercially available anti-aging compound, 2 wt% of butylated hydroxytoluene. Visual evaluation, scanning electron microscopy, melt flow rate, thermogravimetry, differential scanning calorimetry, tensile strength, and impact tensile tests were performed. We show that the use of smaller amounts of tested extracts is particularly advantageous, which do not adversely affect the properties of polylactide-based materials at low contents. At the same time, their effectiveness in stabilizing tested properties during the accelerated aging process is mostly comparable to or greater than the reference compound.
Macro- and Microphase Separated Protein-Polyelectrolyte Complexes: Design Parameters and Current Progress
Authors: Horn Justin M. , Kapelner Rachel A. , Obermeyer Allie C.
Published: March 2019

Protein-containing polyelectrolyte complexes (PECs) are a diverse class of materials, composed of two or more oppositely charged polyelectrolytes that condense and phase separate near overall charge neutrality. Such phase-separation can take on a variety of morphologies from macrophase separated liquid condensates, to solid precipitates, to monodispersed spherical micelles. In this review, we present an overview of recent advances in protein-containing PECs, with an overall goal of defining relevant design parameters for macro- and microphase separated PECs. For both classes of PECs, the influence of protein characteristics, such as surface charge and patchiness, co-polyelectrolyte characteristics, such as charge density and structure, and overall solution characteristics, such as salt concentration and pH, are considered. After overall design features are established, potential applications in food processing, biosensing, drug delivery, and protein purification are discussed and recent characterization techniques for protein-containing PECs are highlighted.
Cyclical Fevers in a 4-year-old Boy with IgA Deficiency
Authors: Yang Joshua Y.C.
Published: March 2019

While immunodeficiencies, such as (Immunoglobulin A) IgA deficiency, may predispose pediatric patients to respiratory illnesses, they are also associated with autoinflammatory conditions. Distinguishing between these possible complications requires an awareness of these uncommon conditions. We report a case of a four-year-old boy with a history of IgA deficiency presenting to his primary care provider with fever of one week’s duration accompanied by abdominal, ear, and throat pain. The patient’s mother reported that he had these symptoms twice a month for the prior ten months during which he occasionally developed oral ulcers. Between episodes, the patient fully recovered without complications. Considering the patient’s history of IgA deficiency, it was originally assumed that the patient had strep throat and coincidental resolution of his symptoms after initiation of antibiotic therapy supported this diagnosis. However, due to the recurrent nature of his symptoms, a preliminary diagnosis of periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) was made. A referral to an otolaryngologist was made, and findings of adenopathy and tonsillitis suggestive of PFAPA were confirmed. The patient was scheduled for adenoidectomy and tonsillectomy, which were performed without complication, leading to the cessation of these episodes.
Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images
Authors: Hu Jie , Peng Jie , Zhou Yin , Xu Dongyun , Zhao Ruiying , Jiang Qingsong , Fu Tingting , Wang Fei , Shi Zhou
Published: March 2019

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.
A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images
Authors: Ma Wenping , Guo Qiongqiong , Wu Yue , Zhao Wei , Zhang Xiangrong , Jiao Licheng
Published: March 2019

Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection.
Examining the Multi-Seasonal Consistency of Individual Tree Segmentation on Deciduous Stands Using Digital Aerial Photogrammetry (DAP) and Unmanned Aerial Systems (UAS)
Authors: Nuijten Rik J.G. , Coops Nicholas C. , Goodbody Tristan R.H. , Pelletier Gaetan
Published: March 2019

Digital aerial photogrammetric (DAP) techniques applied to unmanned aerial system (UAS) acquired imagery have the potential to offer timely and affordable data for monitoring and updating forest inventories. Development of methods for individual tree crown detection (ITCD) and delineation enables the development of individual tree-based, rather than stand based inventories, which are important for harvesting operations, biomass and carbon stock estimations, forest damage assessment, and forest monitoring in mixed species stands. To achieve these inventory goals, consistent and robust DAP estimates are required over time. Currently, the influence of seasonal changes in deciduous tree structure on the consistency of DAP point clouds, from which tree-based inventories can be derived, is unknown. In this study, we investigate the influence of the timing of DAP acquisition on ITCD accuracies and estimation of tree attributes for a deciduous-dominated forest stand in New Brunswick, Canada. UAS imagery was acquired five times between June and September 2017 over the same stand and consistently processed into DAP point clouds. Airborne laser scanning (ALS) data, acquired the same year, was used to reconstruct a digital terrain model (DTM) and served as a reference for UAS-DAP-based ITCD. Marker-controlled watershed segmentation (MCWS) was used to delineate individual tree crowns. Accuracy index percentages between 55% (July 25) and 77.1% (September 22) were achieved. Omission errors were found to be relatively high for the first three DAP acquisitions (June 7, July 5, and July 25) and decreased gradually thereafter. The commission error was relatively high on July 25. Point cloud metrics were found to be predominantly consistent over the 4-month period, however, estimated tree heights gradually decreased over time, suggesting a trade-off between ITCD accuracies and measured tree heights. Our findings provide insight into the potential influence of seasonality on DAP-ITCD approaches to derive individual tree inventories.
Why Not a Single Image? Combining Visualizations to Facilitate Fieldwork and On-Screen Mapping
Authors: Kokalj Žiga , Somrak Maja
Published: March 2019

Visualization products computed from a raster elevation model still form the basis of most archaeological and geomorphological enquiries of lidar data. We believe there is a need to improve the existing visualizations and create meaningful image combinations that preserve positive characteristics of individual techniques. In this paper, we list the criteria a good visualization should meet, present five different blend modes (normal, screen, multiply, overlay, luminosity), which combine various images into one, discuss their characteristics, and examine how they can be used to improve the visibility (recognition) of small topographical features. Blending different relief visualization techniques allows for a simultaneous display of distinct topographical features in a single (enhanced) image. We provide a “recipe” and a tool for a mix of visualization techniques and blend modes, including all the settings, to compute a visualization for archaeological topography that meets all of the criteria of a good visualization.
Ship Profile Imaging Using Multipath Backscattering
Authors: Le Caillec Jean-Marc , Habonneau Jérôme , Khenchaf Ali
Published: March 2019

This paper addresses the estimation of the height of a point scatterer over a sea surface via multipath exploitation for a High Range Resolution radar that is using pulse range compression, such as Synthetic Aperture Radars. We first focus our attention on the physical model, in particular on the specular/diffuse reflection coefficients, this coefficients being derived from the empirical Miller Brown and Vegh model. The gravity waves are also simulated since they modify the acquisition geometry such as the local grazing angle. Secondly, the signal model is derived, thus allowing an easy derivation of the time delays (direct echo and replicas), these time delays being converted into a height estimation for possible automatic ship recognition applications. Our algorithm is a non-conventional radar signal processing, in other words it uses the backscattered pulse over before range compression and demodulation. The aim of the paper is to understand for which radar and sea parameters, as well as acquisition scenes, it is possible to extract the scatterer height information using the multipath of the backscattered electromagnetic wave.
Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters
Authors: Dinnat Emmanuel P. , Le Vine David M. , Boutin Jacqueline , Meissner Thomas , Lagerloef Gary
Published: March 2019

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.
Analysis of Ku- and Ka-Band Sea Surface Backscattering Characteristics at Low-Incidence Angles Based on the GPM Dual-Frequency Precipitation Radar Measurements
Authors: Yan Qiushuang , Zhang Jie , Fan Chenqing , Meng Junmin
Published: March 2019

The co-located normalized radar backscatter cross section measurements from the Global Precipitation Measurement (GPM) Ku/Ka-band dual-frequency precipitation radar (DPR) and sea surface wind; wave and temperature observations from the National Data Buoy Center (NDBC) moored buoys are used to analyze the dependence and sensitivity of Ku- and Ka-band backscatter on surface conditions at low-incidence angles. Then the potential for inverting wind and wave parameters directly from low-incidence σ0 measurements is discussed. The results show that the KaPR σ0 is more sensitive to surface conditions than the KuPR σ0 overall. Nevertheless; both the KuPR σ0 and KaPR σ0 are strongly correlated with wind speed (U10) and average wave steepness (δa) with the exception of specific transitional incidence angles. Moreover, U10 and δa could be retrieved from pointwise σ0 near nadir and near 18°. Near 18°; wind direction information is needed as the effect of wind direction on σ0 becomes increasingly significant with incidence angle. To improve the performance of U10 retrieval; especially for low U10; auxiliary δa information would be most helpful; and sea surface temperature is better taken into account. Other wave parameters; such as significant wave height; wave period and wave age; are partly correlated with σ0. It is generally more difficult to retrieve those parameters directly from pointwise σ0. For the retrieval of those wave parameters; various auxiliary information is needed. Wind direction and wave direction cannot be retrieved from pointwise σ0.
Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network
Authors: Zhang Xiaodong , Zhu Kun , Chen Guanzhou , Tan Xiaoliang , Zhang Lifei , Dai Fan , Liao Puyun , Gong Yuanfu
Published: March 2019

Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset.
Translating Fire Impacts in Southwestern Amazonia into Economic Costs
Authors: Campanharo Wesley A. , Lopes Aline P. , Anderson Liana O. , da Silva Thiago F. M. R. , Aragão Luiz E. O. C.
Published: March 2019

Between 1998 and 2017, climate-related disasters represented 91% of all occurrences worldwide, causing approximately US$ 2.245 billion of direct economic losses. In the Amazon region, fire is used as a widely spread technique for land clearing, agricultural management, hunting, and religious rituals. However, over the past 20 years, severe droughts caused a major amplification of fire occurrences, leading to several socioeconomic and environmental impacts. Particularly in Acre state, located in the southwestern Brazilian Amazon, the occurrence of extensive fires, associated with extreme climatic events, has been reported since 2005. However, fire dynamics, land tenure relationships, and associated impacts are poorly quantified. In this study, we aim to investigate the following: (1) The spatiotemporal variability of fire dynamics during anomalously dry and regular climate conditions; (2) the attribution of fire occurrence and land tenure relationship, and (3) the environmental, social, and economic impacts caused by fires and its consequences for Acre’s economy. We analyzed information on the spatial patterns of fire, its direct impacts on land use and land cover, carbon stocks, CO2 emissions, the indirect impact on human illness, and finally the costs of these impacts from 2008 to 2012. During the studied period, burned areas were concentrated around the major cities and roads, forming polygons up to 0.6 km2. However, in 2010, an extremely dry year, fires spread to remote areas, impacting protected private areas and sustainable-use conservation areas. In 2010, the total area affected by forest fires was approximately 16 times greater than in meteorologically normal years. The total economic loss estimated in 2010 was around US$ 243.36 ± 85.05 million and for the entire period, US$ 307.46 ± 85.41 million. These values represent 7.03 ± 2.45% and 9.07 ± 2.46% of Acre’s gross domestic product (GDP), respectively.
Chimera: A Multi-Task Recurrent Convolutional Neural Network for Forest Classification and Structural Estimation
Authors: Chang Tony , Rasmussen Brandon P. , Dickson Brett G. , Zachmann Luke J.
Published: March 2019

More consistent and current estimates of forest land cover type and forest structural metrics are needed to guide national policies on forest management, carbon sequestration, and ecosystem health. In recent years, the increased availability of high-resolution (<30 m) imagery and advancements in machine learning algorithms have opened up a new opportunity to fuse multiple datasets of varying spatial, spectral, and temporal resolutions. Here, we present a new model, based on a deep learning architecture, that performs both classification and regression concurrently, thereby consolidating what was previously several independent tasks and models into one stream. The model, a multi-task recurrent convolutional neural network that we call the Chimera, integrates varying resolution, freely available aerial and satellite imagery, as well as relevant environmental factors (e.g., climate, terrain) to simultaneously classify five forest cover types (‘conifer’, ‘deciduous’, ‘mixed’, ‘dead’, ‘none’ (non-forest)) and to estimate four continuous forest structure metrics (above ground biomass, quadratic mean diameter, basal area, canopy cover). We demonstrate the performance of our approach by training an ensemble of Chimera models on 9967 georeferenced (true locations) Forest Inventory and Analysis field plots from the USDA Forest Service within California and Nevada. Classification diagnostics for the Chimera ensemble on an independent test set produces an overall average precision, recall, and F1-score of 0.92, 0.92, and 0.92. Class-wise F1-scores were high for ‘none’ (0.99) and ‘conifer’ (0.85) cover classes, and moderate for the ‘mixed’ (0.74) class samples. This demonstrates a strong ability to discriminate locations with and without trees. Regression diagnostics on the test set indicate very high accuracy for ensembled estimates of above ground biomass ( R 2 = 0.84 , RMSE = 37.28 Mg/ha), quadratic mean diameter ( R 2 = 0.81 , RMSE = 3.74 inches), basal area ( R 2 = 0.87 , RMSE = 25.88 ft 2 /ac), and canopy cover ( R 2 = 0.89 , RMSE = 8.01 percent). Comparative analysis of the Chimera ensemble versus support vector machine and random forest approaches demonstrates increased performance over both methods. Future implementations of the Chimera ensemble on a distributed computing platform could provide continuous, annual estimates of forest structure for other forested landscapes at regional or national scales.
Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning
Authors: Lin Huiping , Chen Hang , Wang Hongmiao , Yin Junjun , Yang Jian
Published: March 2019

Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.
Photovoltaic Array Fault Diagnosis Based on Gaussian Kernel Fuzzy C-Means Clustering Algorithm
Authors: Liu Shengyang , Dong Lei , Liao Xiaozhong , Cao Xiaodong , Wang Xiaoxiao
Published: March 2019

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays.
Photo-Induced Force Microscopy by Using Quartz Tuning-Fork Sensor
Authors: Jahng Junghoon , Kwon Hyuksang , Lee Eun Seong
Published: March 2019

We present the photo-induced force microscopy (PiFM) studies of various nano-materials by implementing a quartz tuning fork (QTF), a self-sensing sensor that does not require complex optics to detect the motion of a force probe and thus helps to compactly configure the nanoscale optical mapping tool. The bimodal atomic force microscopy technique combined with a sideband coupling scheme is exploited for the high-sensitivity imaging of the QTF-PiFM. We measured the photo-induced force images of nano-clusters of Silicon 2,3-naphthalocyanine bis dye and thin graphene film and found that the QTF-PiFM is capable of high-spatial-resolution nano-optical imaging with a good signal-to-noise ratio. Applying the QTF-PiFM to various experimental conditions will open new opportunities for the spectroscopic visualization and substructure characterization of a vast variety of nano-materials from semiconducting devices to polymer thin films to sensitive measurements of single molecules.
Correlation between the Fluctuations in Worldwide Seismicity and Atmospheric Carbon Pollution
Authors: Carpinteri Alberto , Niccolini Gianni
Published: March 2019

The crucial stages in the geochemical evolution of the Earth’s crust, ocean, and atmosphere could be explained by the assumed low-energy nuclear reactions (LENR) that are triggered by seismic activity. LENR result in the fission of medium-weight elements accompanied by neutron emissions, involving Fe and Ni as starting elements, and C, N, O as resultants. Geochemical data and experimental evidences support the LENR hypothesis. A spectral analysis of the period 1955-2013 shows common cycles between interannual changes in atmospheric CO2 growth rate and global seismic-moment release, whereas the trending behavior of the atmospheric CO2 was in response to the anthropogenic emissions. Assuming a correlation between such seismic and atmospheric fluctuations, the latter could be explained by cycles of worldwide seismicity, which would trigger massively LENR in the Earth’s Crust. In this framework, LENR from active faults could be considered as a relevant cause of carbon formation and degassing of freshly-formed CO2 during seismic activity. However, further studies are necessary to validate the present hypothesis which, at the present time, mainly aims to stimulate debate on the models which regulates atmospheric CO2.
Fungicide and Bactericide Effects on Carbon and Nitrogen Cycling in Soils: A Meta-Analysis
Authors: Ullah Mohammad Rahmat , Dijkstra Feike A.
Published: March 2019

Fungi and bacteria play a central role in the cycling of carbon (C) and nitrogen (N), which has been frequently assessed by manipulating their abundance in soil with the application of fungicides and bactericides. We conducted a meta-analysis using 61 publications to investigate whether fungicides and bactericides have distinct effects on soil C- and N- cycling, and how they vary with land type and soil properties. Most fungicides and bactericides had significant negative effects on microbial biomass C and N. However, they had mixed effects on soil respiration, N pools, and transformation processes, varying strongly with the type of fungicide and bactericide. Available NO3− was lightly affected, while N2O emission was reduced by most biocides. The application of fungicides had neutral effects on respiration, NH4+, and ammonification in agro-ecosystems, but positive effects in forests. Effect sizes of available NO3− and nitrification in response to bactericides were sensitive to soil pH and C content. Our results suggest that most fungicides and bactericides inhibit microbial growth, but that they have mixed effects on respiration and N cycling. Biocides need to be carefully evaluated for unintentional side effects before they are used in assessing the role of fungi and bacteria for C- and N- cycling.
Is the Bike Segment of Modern Olympic Triathlon More a Transition towards Running in Males than It Is in Females?
Authors: Piacentini Maria , Bianchini Luca , Minganti Carlo , Sias Marco , Di Castro Andrea , Vleck Veronica
Published: March 2019

In 2009, the International Triathlon Union created a new triathlon race format: The World Triathlon Series (WTS), for which only athletes with a top 100 world ranking are eligible. Therefore, the purpose of this study was to analyze the influence of the three disciplines on performance within all the WTS Olympic distance races within two Olympic cycles, and to determine whether their relative contribution changed over the years. Methods: For each of a total of 44 races, final race time and position as well as split times (and positions), and summed time (and position) at each point of the race were collected and included in the analysis. Athletes were divided into 4 groups according to their final race placing (G1: 1st–3rd place; G2: 4–8th place; G3: 8–16th place and G4: ≥17th place). Two-way multivariate ANOVAs were conducted to compare the main effects of years and rank groups. For females, there were significant differences in the swim and bike segment only between G4 and the other groups (p range from 0.001–0.029), whilst for the run segment each group differed significantly from each other (p < 0.001). For males, there were significant differences in swim only between G4 and the other groups (p range from 0.001–0.039), whilst for the running segment each group differed significantly from the others (p < 0.001). Although we found running to be the segment where there were significant differences between performance groups, it is apparently important for overall success that a good runner be positioned with the first cycling pack. However, bike splits were not different between either of the four male groups or between the first 3 groups of the females. At this very high level of performance, at least in the males, the bike leg seems to be a smooth transition towards running.
Effect of Social Security System on Consumption through Income and Uncertainty: Evidence from China
Authors: Deng Xiaoyu , Tian Jing , Chen Rong
Published: March 2019

In our paper, we test how the social security system affects citizens’ consumption behavior through narrowing the income gap between residents with different social status and decreasing the uncertainty of income and cost. In the theoretical part, we firstly extend the model of Keynes to test the income distribution hypothesis; then, we use the coefficient of absolute risk aversion (CARA) model and a two-period model to test the uncertainty hypothesis. Empirically, (1) we use China’s data (2003–2012) to test the theoretical results, and find that the social security system has a positive effect on consumption by reducing the income gap; (2) according to a 10-year panel data (2003–2012), we find that reducing income uncertainty can increase consumption. Besides, medical insurance and endowment insurance have a positive impact on consumption by reducing citizens’ cost uncertainty, but to different extents.
Storing E-waste in Green Infrastructure to Reduce Perceived Value Loss through Landfill Siting and Landscaping: A Case Study in Nanjing, China
Authors: Chen Fu , Li Xiaoxiao , Yang Yongjun , Hou Huping , Liu Gang-Jun , Zhang Shaoliang
Published: March 2019

Electronic waste (e-waste) represents a severe global environmental issue due to the fast upgrading and updating of electronic products and the high environmental risk. Current low recycling technology, high economic cost, and weak disposal capability make it difficult for e-waste to be rendered 100% harmless. E-waste disposal requires new site-selection methods and site-saving technology to take into account the loss of public perceived value. This study attempts to improve e-waste disposal through siting and landscaping to reduce perceived value loss. The first step is to determine the minimum distance for landfill siting by surveying the minimum loss of perceived value and to use the geographic information system (GIS) to sketch the suitable landfill site thereafter. To optimize the landfill landscape, a landscape infrastructure and its filling process have been designed to reduce the environmental risk and ensure future reuse potential. The application case showed that the minimum distance is 521 m, which was sensitive to the educational level and occupation of residents. The key to landfill landscaping is the construction of isolation layers and the integration of the landfill and urban landscape. The method described in this paper is characterized by minimizing the perceived loss of value to the public, reducing environmental risks, and preserving the resource value of e-waste. This design could provide an alternative to current electronic waste processing methods.
Analyzing the Determinants of Individual Action on Climate Change by Specifying the Roles of Six Values in South Korea
Authors: Kwon Seol-A , Kim Seoyong , Lee Jae Eun
Published: March 2019

The serious problems stemming from climate change require an active response it. This study focuses on the role of value factors in action on climate change. Individuals’ values systematically influence the fundamental orientation of their attitudes and behaviors. Therefore, this study analyzes whether six values, namely: ideology, environmental justice, religiosity, personal norms, scientific optimism, and environmentalism, influence action on climate change directly or indirectly, and compares their effects with perception factors’ impact. The results indicate that religiosity decreased action on climate change, whereas personal norms, science and technology (S&T) optimism, and environmentalism increased such action. Among the perception factors, perceived risks and benefits, trust, and knowledge increased action on climate change. Furthermore, perception factors explained action on climate change more than value factors did. Moreover, value factors (i.e., S&T optimism and environmentalism) moderated the impacts of perceived risks, perceived benefits, and negative emotions on action against climate change.
Spare Parts Cost Management for Long-Term Economic Sustainability: Using Fuzzy Activity Based LCC
Authors: Durán Orlando , Afonso Paulo Sergio , Durán Paulo Andrés
Published: March 2019

The concept of life cycle sustainability assessment provides an interdisciplinary space to discuss the main challenges in addressing sustainability from a long-term perspective. However, uncertainty related to cost parameters constitutes one of the main challenges in LCC. This uncertainty prevents managers from precisely anticipating the exact values of important variables. These variables include the costs of spare parts inventories, which in some cases can constitute high percentages of the total cost of a product or service. We propose an activity-based costing model that handles uncertainty using triangular numbers, along with a matrix representation, and Weibull functions to incorporate the projected equipment reliability into the model. A case study based on a spare parts distribution center, which serves the mining industry in Northern Chile, is presented. The results obtained are compared with a traditional costing method. The advantage of using the proposed approach over the traditional one is evident. Future research is directed towards the implementation of a larger number of inventory policies, along with experimentation with other types of fuzzy numbers.
USLE K-Factor Method Selection for a Tropical Catchment
Authors: Marques Valter S. , Ceddia Marcos B. , Antunes Mauro A. H. , Carvalho Daniel F. , Anache Jamil A. A. , Rodrigues Dulce B. B. , Oliveira Paulo Tarso S.
Published: March 2019

The use of the Universal Soil Loss Equation (USLE) and the Sediment Delivery Ratio (SDR) facilitates sediment yield (SY) estimates in watersheds. However, the soil loss predictions are frequently unrealistic because of the methods used to estimate the USLE’s factors. Here, we evaluated the performance of methods to estimate the soil erodibility (K-factor) and the influence of its estimation in the SY predictions. K-factor values were obtained from three widely used equations and using a portable rainfall simulator. These values were used to compute annual average soil loss and SY in a tropical watershed. We compared SY estimates with a 15-month observed sediment discharge dataset sampled in the catchment outlet. The most reliable method for the K-factor estimating was the USLE nomograph. Furthermore, our results indicate that the use of a portable rainfall simulator to estimate the K-factor tends to underestimate soil loss and sediment delivery.
Comprehensive Research on Remote Sensing Monitoring of Grassland Degradation: A Case Study in the Three-River Source Region, China
Authors: Zhang Ying , Zhang Chaobin , Wang Zhaoqi , An Ru , Li Jianlong
Published: March 2019

In this study, we proposed climate use efficiency (CUE), a new index in monitoring grassland ecosystem function, to mitigate the disturbance of climate fluctuation. A comprehensive evaluation index (EI), combining with actual vegetation net primary productivity (NPP), CUE, vegetation coverage, and surface bareness, was constructed for the dynamic remote sensing monitoring of grassland degradation/restoration on a regional scale. By using this index, the grassland degradation/restoration in the Three-River Source Region (TRSR) was quantitatively evaluated during 2001–2016, which has been an important ecological barrier area in China. Results showed the following: During the study period, the grassland of Yellow River source (SRYe) had high vegetation coverage, NPP, CUE, and low bareness, whereas Yangtze River source (SRYa) had low vegetation coverage, NPP, CUE, and high bareness. The vegetation coverage and CUE of the grassland showed upward trends, with annual change rates of 0.75% and 0.45% year −1. The surface bareness and NPP showed downward trends, with annual change rates of −0.37% year−1 and −0.24 g C m−2 yr−2, respectively. Assessment of EI revealed that 67.18% of the grassland of TRSR showed a recovery trend during the study period. The overall restoration of the SRYe was the best, followed by SRYa. However, the status of Lancang River source (SRLa) was poor.