bluuebunny/crossref_metadata_2025 huggingface dataset:
Groundwater Flow Model and Statistical Comparisons Used in Sustainability of Aquifers in Arid Regions
Authors: Trasviña-Carrillo Javier Alexis , Wurl Jobst , Imaz-Lamadrid Miguel Angel
Published: July 2019

Groundwater provides the most important of the water resources used in the maintenance of communities in arid and semi-arid regions. In these areas, the usage of deep wells with motorized pumps in combination with the lack of effective regulatory policies and high human population growth (increase the water demand) impact the quality of the groundwater. This is especially the case for the San José del Cabo aquifer, in Baja California Sur. In the present study the groundwater flow system is analyzed in order to recognize the impact from variations in groundwater extraction and recharge on the phreatic levels and discharge values. In order to achieve this goal, a groundwater model was generated using the MODFLOW program. Different scenarios of extraction and recharge were calculated, based on different estimations of population growth. All the scenarios result in decreasing groundwater levels. As an important result, a relationship between the phreatic level and the extraction volume was found for the middle zone of the aquifer, where an average annual decrease of 0.5 m was observed from every 5 × 106 m3 additional extraction volume. This zone is up to three times more susceptible to changes in extraction values than the southern zone. As the results show, the San José del Cabo aquifer is in a fragile state where an increment in extraction is not an option without the use of remediation technics or new sources for water supply.
Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models
Authors: Skakun Sergii , Vermote Eric , Franch Belen , Roger Jean-Claude , Kussul Nataliia , Ju Junchang , Masek Jeffrey
Published: July 2019

A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.
Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging
Authors: Dayananda Supriya , Astor Thomas , Wijesingha Jayan , Chickadibburahalli Thimappa Subbarayappa , Dimba Chowdappa Hanumanthappa , Mudalagiriyappa  , Nidamanuri Rama Rao , Nautiyal Sunil , Wachendorf Michael
Published: July 2019

Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016–2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R² validation (R²val) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R²val = 0.53, rRMSEP = 13.9%), maize (R²val = 0.53, rRMSEP = 18.7%) and finger millet (R²val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546–910 nm (lablab), 750–794 nm (maize) and 686–814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.
Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network
Authors: Yi Yaning , Zhang Zhijie , Zhang Wanchang , Zhang Chuanrong , Li Weidong , Zhao Tian
Published: July 2019

Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks—FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement.
Geodetic Constraints on the Crustal Deformation along the Kunlun Fault and Its Tectonic Implications
Authors: Li Yanchuan , Shan Xinjian , Qu Chunyan
Published: July 2019

This study focuses on the crustal deformation and interseismic fault coupling along the strike-slip Kunlun fault, northern Tibet, whose western segment ruptured in the 2001 Mw 7.8 Kokoxili earthquake. We first integrated published Global Positioning System (GPS) velocity solutions and calculated strain rate fields covering the Kunlun fault. Our results show abnormally high post-earthquake strain rate values across the ruptures; furthermore, these exceed those in pre-earthquake data. Together with two tracks of interferometric synthetic aperture radar (InSAR) observations (2003–2010) and position time-series data from two continuous GPS sites, we show that the postseismic deformation of the Kokoxili earthquake may continue up to 2014; and that the postseismic transients of the earthquake affect the 2001–2014 GPS velocity solutions. We then processed the GPS data observed in 2014–2017 and obtained a dense interseismic velocity field for the northern Tibet. Using a fault dislocation model in a Bayesian framework, we estimated the slip rates and fault coupling on the Kunlun fault in 1991–2001 and 2014–2017. Results show an increase of slip rates and eastward migration of high fault coupling on the Kunlun fault after 2001. We propose the temporal variations are a result of the eastward accelerating movement, as a whole, of the Bayanhar block, whose boundaries were decoupled by several large earthquakes since 1997. Moreover, our results show the accumulated elastic strains along the Alake Lake-Tuosuo Lake segments could be balanced by an Mw 7.4–7.7 earthquake.
An Integrated Land Cover Mapping Method Suitable for Low-Accuracy Areas in Global Land Cover Maps
Authors: Zeng Tian , Wang Lei , Zhang Zengxiang , Wen Qingke , Wang Xiao , Yu Le
Published: July 2019

In land cover mapping, an area with complex topography or heterogeneous land covers is usually poorly classified and therefore defined as a low-accuracy area. The low-accuracy areas are important because they restrict the overall accuracy (OA) of global land cover classification (LCC) data generated. In this paper, low-accuracy areas in China (extracted from the MODIS global LCC maps) were taken as examples, identified as the regions having lower accuracy than the average OA of China. An integrated land cover mapping method targeting low-accuracy regions was developed and tested in eight representative low-accuracy regions of China. The method optimized procedures of image choosing and sample selection based on an existent visually-interpreted regional LCC dataset with high accuracies. Five algorithms and 16 groups of classification features were compared to achieve the highest OA. The support vector machine (SVM) achieved the highest mean OA (81.5%) when only spectral bands were classified. Aspect tended to attenuate OA as a classification feature. The optimal classification features for different regions largely depends on the topographic feature of vegetation. The mean OA for eight low-accuracy regions was 84.4% by the proposed method in this study, which exceeded the mean OA of most precedent global land cover datasets. The new method can be applied worldwide to improve land cover mapping of low-accuracy areas in global land cover maps.
Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew
Authors: Lin Yunung Nina , Yun Sang-Ho , Bhardwaj Alok , Hill Emma M.
Published: July 2019

In this study we explored the application of synthetic aperture radar (SAR) intensity time series for urban flood detection. Our test case was the flood in Lumberton, North Carolina, USA, caused by the landfall of Hurricane Matthew on 8 October 2016, for which airborne imagery—taken on the same day as the SAR overpass—is available for validation of our technique. To map the flood, we first carried out normalization of the SAR intensity observations, based on the statistics from the time series, and then construct a Bayesian probability function for intensity decrease (due to specular reflection of the signal) and intensity increase (due to double bounce) cases separately. We then formed a flood probability map, which we used to create our preferred flood extent map using a global cutoff probability of 0.5. Our flood map in the urban area showed a complicated mosaicking pattern of pixels showing SAR intensity decrease, pixels showing intensity increase, and pixels without significant intensity changes. Our approach shows improved performance when compared with global thresholding on log intensity ratios, as the time series-based normalization has accounted for a certain level of spatial variation by considering the different history for each pixel. This resulted in improved performance for urban and vegetated regions. We identified smooth surfaces, like asphalt roads, and SAR shadows as the major sources of underprediction, and aquatic plants and soil moisture changes were the major sources of overprediction.
Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes
Authors: Masood Farah , Abdullah Hussein A. , Seth Nitin , Simmons Heather , Brunner Kevin , Sejdic Ervin , Schalk Dane R. , Graham William A. , Hoggatt Amber F. , Rosene Douglas L. , Sledge John B. , Nesathurai Shanker
Published: July 2019

This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).
Sensor Fault Detection and Signal Restoration in Intelligent Vehicles
Authors: Byun Yeun-Sub , Kim Baek-Hyun , Jeong Rag-Gyo
Published: July 2019

This paper presents fault diagnosis logic and signal restoration algorithms for vehicle motion sensors. Because various sensors are equipped to realize automatic operation of the vehicle, defects in these sensors lead to severe safety issues. Therefore, an effective and reliable fault detection and recovery system should be developed. The primary idea of the proposed fault detection system is the conversion of measured wheel speeds into vehicle central axis information and the selection of a reference central axis speed based on this information. Thus, the obtained results are employed to estimate the speed for all wheel sides, which are compared with measured values to identify fault and recover the fault signal. For fault diagnosis logic, a conditional expression is derived with only two variables to distinguish between normal and fault; further, an analytical redundancy structure and a simple diagnostic logic structure are presented. Finally, an off-line test is conducted using test vehicle information to validate the proposed method; it demonstrates that the proposed fault detection and signal restoration algorithm can satisfy the control performance required for each sensor failure.
Measurement and Forecasting of High-Speed Rail Track Slab Deformation under Uncertain SHM Data Using Variational Heteroscedastic Gaussian Process
Authors: Wang Qi-Ang , Ni Yi-Qing
Published: July 2019

Uncertainty in sensor data complicates the construction of baseline models for the measurement and forecasting (M&F) of high-speed rail (HSR) track slab deformation. Standard Gaussian process (GP) assumes a uniform noise throughout the input space. However, in the application to modelling of HSR structural health monitoring (SHM) data, this assumption can be unrealistic, because of its unique heteroscedastic uncertainty that is induced by dynamic train loading, electromagnetic interference, large temperature variation, and daily maintenance actions of railway track infrastructure. Therefore, this study firstly develops a novel online SHM system enabled by fiber Bragg grating (FBG) technology to eliminate electromagnetic interference on SHM data for continuous and long-term monitoring of track slab deformation, with the capacity of temperature self-compensation. To deal with different sources of uncertainty, the study explores Variational Heteroscedastic Gaussian Process (VHGP) approach while using variational Bayesian and Gaussian approximation for data modelling, estimation of the monitoring data uncertainty, and further data forecasting. The results demonstrate that the VHGP framework yields more robust regression results and the estimated confidence level can better depict the heteroscedastic variances of the noise in HSR data. Higher accuracy for both regression and forecasting is gained through VHGP and the position with maximum noise can be more accurately forecasted with a smooth varying confidence interval. Based on in-situ measurement data, the uncertainty levels for all sensors are estimated together with corresponding deformation profiles for the instrumented segment and three typical types of uncertainty are summarized during the M&F process of HSR track slab deformation.
X-ray Imaging Analysis of Silo Flow Parameters Based on Trace Particles Using Targeted Crowdsourcing
Authors: Romanowski Andrzej , Łuczak Piotr , Grudzień Krzysztof
Published: July 2019

This paper presents a novel method for tomographic measurement and data analysis based on crowdsourcing. X-ray radiography imaging was initially applied to determine silo flow parameters. We used traced particles immersed in the bulk to investigate gravitational silo flow. The reconstructed images were not perfect, due to inhomogeneous silo filling and nonlinear attenuation of the X-rays on the way to the detector. Automatic processing of such data is not feasible. Therefore, we used crowdsourcing for human-driven annotation of the trace particles. As we aimed to extract meaningful flow parameters, we developed a modified crowdsourcing annotation method, focusing on selected important areas of the silo pictures only. We call this method “targeted crowdsourcing”, and it enables more efficient crowd work, as it is focused on the most important areas of the image that allow determination of the flow parameters. The results show that it is possible to analyze volumetric material structure movement based on 2D radiography data showing the location and movement of tiny metal trace particles. A quantitative description of the flow obtained from the horizontal and vertical velocity components was derived for different parts of the model silo volume. Targeting the attention of crowd workers towards either a specific zone or a particular particle speeds up the pre-processing stage while preserving the same quality of the output, quantified by important flow parameters.
Autonomous RFID Sensor Node Using a Single ISM Band for Both Wireless Power Transfer and Data Communication
Authors: Okba Abderrahim , Henry Dominique , Takacs Alexandru , Aubert Hervé
Published: July 2019

This paper addresses the implementation of autonomous radiofrequency identification sensor nodes based on wireless power transfer. For size reduction, a switching method is proposed in order to use the same frequency band for both supplying power to the nodes and wirelessly transmitting the nodes’ data. A rectenna harvests the electromagnetic energy delivered by the dedicated radiofrequency source for charging a few-mF supercapacitor. For supercapacitors of 7 mF, it is shown that the proposed autonomous sensor nodes were able to wirelessly communicate with the reader at 868 MHz for 10 min without interruption for a tag-to-reader separation distance of 1 meter. This result was obtained from effective radiated powers of 2 W during the supercapacitor charging and of 100 mW during the wireless data communication.
Reliable and Fast Localization in Ambiguous Environments Using Ambiguity Grid Map
Authors: Li Gen , Meng Jie , Xie Yuanlong , Zhang Xiaolong , Huang Yu , Jiang Liquan , Liu Chao
Published: July 2019

In real-world robotic navigation, some ambiguous environments contain symmetrical or featureless areas that may cause the perceptual aliasing of external sensors. As a result of that, the uncorrected localization errors will accumulate during the localization process, which imposes difficulties to locate a robot in such a situation. Using the ambiguity grid map (AGM), we address this problem by proposing a novel probabilistic localization method, referred to as AGM-based adaptive Monte Carlo localization. AGM has the capacity of evaluating the environmental ambiguity with average ambiguity error and estimating the possible localization error at a given pose. Benefiting from the constructed AGM, our localization method is derived from an improved Dynamic Bayes network to reason about the robot’s pose as well as the accumulated localization error. Moreover, a portal motion model is presented to achieve more reliable pose prediction without time-consuming implementation, and thus the accumulated localization error can be corrected immediately when the robot moving through an ambiguous area. Simulation and real-world experiments demonstrate that the proposed method improves localization reliability while maintains efficiency in ambiguous environments.
Low-Slow-Small (LSS) Target Detection Based on Micro Doppler Analysis in Forward Scattering Radar Geometry
Authors: Alhaji Musa Surajo , Raja Abdullah Raja Syamsul Azmir , Sali Aduwati , Ismail Alyani , Abdul Rashid Nur Emileen
Published: July 2019

The increase in drone misuse by civilian apart from military applications is alarming and need to be addressed. This drone is characterized as a low altitude, slow speed, and small radar cross-section (RCS) (LSS) target and is considered difficult to be detected and classified among other biological targets, such as insects and birds existing in the same surveillance volume. Although several attempts reported the successful drone detection on radio frequency-based (RF), thermal, acoustic, video imaging, and other non-technical methods, however, there are also many limitations. Thus, this paper investigated a micro-Doppler analysis from drone rotating blades for detection in a special Forward Scattering Radar (FSR) geometry. The paper leveraged the identified benefits of FSR mode over conventional radars, such as improved radar cross-section (RCS) value irrespective of radar absorbing material (RAM), direct signal perturbation, and high resolutions. To prove the concept, a received signal model for micro-Doppler analysis, a simulation work, and experimental validation are elaborated and explained in the paper. Two rotating blades aspect angle scenarios were considered, which are (i) when drone makes a turn, the blade cross-sectional area faces the receiver and (ii) when drone maneuvers normally, the cross-sectional blade faces up. The FSR system successfully detected a commercial drone and extracted the micro features of a rotating blade. It further verified the feasibility of using a parabolic dish antenna as a receiver in FSR geometry; this marked an appreciable achievement towards the FSR system performance, which in future could be implemented as either active or passive FSR system.
Exploring Flood-Related Unintentional Fatal Drowning of Children and Adolescents Aged 0–19 Years in Australia
Authors: Peden Amy E. , Franklin Richard C.
Published: July 2019

Disasters, such as flooding, are predicted to increase. Drowning is one of the leading causes of death during times of flood. This study examined the little explored topic of child drowning during floods, with the aim of identifying risk factors to inform prevention strategies. A retrospective, total population examination of cases of children and adolescents aged 0–19 years who died from unintentional flood-related drowning in Australia for the 16-year period 1 July 2002 to 30 June 2018 was undertaken. Univariate and chi-square analysis was conducted, with Fisher’s exact test used for cell counts <5. Across the study period, 44 flood-related drowning deaths occurred among children and adolescents (63.6% male; 34.1% aged 10–14 years). Almost all (84.1%) occurred in rivers, creeks, or streams in flood, with the remaining incidents occurring in storm water drains (n = 7). Leading activities immediately prior to drowning were non-aquatic transport (40.9%), swimming in floodwaters (25.0%), and falls into floodwaters (15.9%). Flood-related fatal drowning among children and adolescents is rare (0.05 per 100,000 population), however flood-drowning risk increases as remoteness increases, with children and adolescents drowning in floodwaters in very remote areas at a rate 57 times that of major cities. All drownings are preventable, and this study has identified key causal factors that must be considered in advocacy and prevention efforts. These include: the importance of adult supervision, avoiding flooded waterways when driving or for recreational purposes, and the increased risks for those residing in geographically isolated and socially disadvantaged areas. Findings must be considered when developing interventions and advocacy for the purposes of the reduction of child and adolescent drowning during times of flood.
Upstream Social Marketing for Implementing Mobile Government
Authors: Burksiene Valentina , Dvorak Jaroslav , Duda Mantas
Published: July 2019

This article analyses the main aspects of upstream social marketing for the implementation of mobile government (MGov). The methodology of current research is based on the systematic literature review in the fields of MGov and social marketing. According to our findings, most researchers investigated MGov from the side of citizens (consumers) and emphasised the benefits to them while changing their attitudes and behaviours in employing mobile applications. However, as there is a lack of research from the side of governmental bodies, in this paper we were looking for new meanings, attitudes and values from their perspective. Limitations of employment of MGov occur due knowledge gap among decision makers and public policy formers (upstream audience). Therefore, we argue that upstream social marketing for the upstream audience would bring success in faster MGov implementation. Specific social marketing would be mostly valuable on the municipal level that is the closest substance to the society. Thus, in our paper, we emphasise the benefit of the MGov for the local upstream audience and propose possible external marketers as well as the motivating theses based on the 7P of marketing mix (consisting of seven P elements: Product, Price, Place or physical evidence, Promotion, Participants or people, Processes, Political power) for the successful MGov on municipal level.
The Role of Culture in Urban Travel Patterns: Quantitative Analyses of Urban Areas Based on Hofstede’s Culture Dimensions
Authors: Dingil Ali Enes , Rupi Federico , Schweizer Joerg , Stasiskiene Zaneta , Aalipour Kasra
Published: July 2019

Introduction—culture is an interpretation code of societies, which may explain common preferences in a place. Prediction of alternative transport systems, which could be adopted in a city at peace can help urban transport planners and policy makers adjust urban environments in a more sustainable manner. This paper attempts to investigate the role of Hofstede’s culture dimensions (HCD) on urban travel patterns in 87 urban areas and 41 countries. Analysis—this is the first, systematic analysis investigating the effect of culture on urban travel patterns with open source data from different urban areas around the world. The relationship between HCD and some urban travel patterns such as mode choices (individual transportation and public transportation), car ownership, and infrastructure accessibility (road infrastructure per capita) was demonstrated. In addition, the relationship between culture and some demographic indicators (population density and GDP per capita) closely associated with travel choices are checked. The relations between indicators were identified through correlations and regression models, and calibrated to quantify the relation between indicators. Results and Conclusions—good correlation values between Hofstede’s fundamental culture dimension: individualism/collectivism (IND/COL) and urban travel patterns were demonstrated with a reasonably good fit. The analysis showed that countries with higher individualism build more individualistic transport-related environments, which in turn result in more driving. On the other hand, collective nations tend to use more public transportation. There is significant evidence that, in the case of nations, an increase in tree culture dimensions: collectivism, uncertainty, and masculinity, results in greater usage of public transport.
Getting to the Root of Selenium Hyperaccumulation—Localization and Speciation of Root Selenium and Its Effects on Nematodes
Authors: Prins Christine N. , Hantzis Laura J. , Valdez-Barillas Jose R. , Cappa Jennifer J. , Fakra Sirine C. , Milano de Tomasel Cecilia , Wall Diana H. , Pilon-Smits Elizabeth A. H.
Published: July 2019

Elemental hyperaccumulation protects plants from many aboveground herbivores. Little is known about effects of hyperaccumulation on belowground herbivores or their ecological interactions. To examine effects of plant selenium (Se) hyperaccumulation on nematode root herbivory, we investigated spatial distribution and speciation of Se in hyperaccumulator roots using X-ray microprobe analysis, and effects of root Se concentration on root-associated nematode communities. Perennial hyperaccumulators Stanleya pinnata and Astragalus bisulcatus, collected from a natural seleniferous grassland contained 100–1500 mg Se kg−1 root dry weight (DW). Selenium was concentrated in the cortex and epidermis of hyperaccumulator roots, with lower levels in the stele. The accumulated Se consisted of organic (C-Se-C) compounds, indistinguishable from methyl-selenocysteine. The field-collected roots yielded 5–400 nematodes g−1 DW in Baermann funnel extraction, with no correlation between root Se concentration and nematode densities. Even roots containing > 1000 mg Se kg−1 DW yielded herbivorous nematodes. However, greenhouse-grown S. pinnata plants treated with Se had fewer total nematodes than those without Se. Thus, while root Se hyperaccumulation may protect plants from non-specialist herbivorous nematodes, Se-resistant nematode taxa appear to associate with hyperaccumulators in seleniferous habitats, and may utilize high-Se hyperaccumulator roots as food source. These findings give new insight into the ecological implications of plant Se (hyper)accumulation.
Using A Soft Conformable Foot Sensor to Measure Changes in Foot Strike Angle During Running
Authors: van Werkhoven Herman , Farina Kathryn A. , Langley Mark H.
Published: July 2019

The potential association between running foot strike analysis and performance and injury metrics has created the need for reliable methods to quantify foot strike pattern outside the laboratory. Small, wireless inertial measurement units (IMUs) allow for unrestricted movement of the participants. Current IMU methods to measure foot strike pattern places small, rigid accelerometers and/or gyroscopes on the heel cap or on the instep of the shoe. The purpose of this study was to validate a thin, conformable IMU sensor placed directly on the dorsal foot surface to determine foot strike angles and pattern. Participants (n = 12) ran on a treadmill with different foot strike patterns while videography and sensor data were captured. Sensor measures were compared against traditional 2D video analysis techniques and the results showed that the sensor was able to accurately (92.2% success) distinguish between rearfoot and non-rearfoot foot strikes using an angular velocity cut-off value of 0°/s. There was also a strong and significant correlation between sensor determined foot strike angle and foot strike angle determined from videography analysis (r = 0.868, p < 0.001), although linear regression analysis showed that the sensor underestimated the foot strike angle. Conformable sensors with the ability to attach directly to the human skin could improve the tracking of human dynamics and should be further explored.
e-Commerce Sustainability: The Case of Pinduoduo in China
Authors: Chang Younghoon , Wong Siew , Libaque-Saenz Christian , Lee Hwansoo
Published: July 2019

Internet-based platforms are competing fiercely in the Chinese market by fostering an omni-channel shopping ecosystem. Companies have grouped under two giants, Alibaba and Tencent, leading to one-to-many competition dynamics in the Chinese market. In the midst of this competition, the three-year-old Pinduoduo has gained attention with its fast growth, positioning itself as the third biggest e-commerce platform in China with 19.4% of the market share. Pinduoduo has targeted a niche market made up of cheap products, low-income workers, and small business owners in small cities. Recently, however, many Pinduoduo users have returned to their former platforms, due to the proliferation of fake and low-quality products on the platform. Our study aims to investigate the reasons that prompted users to return to other platforms after their Pinduoduo experience. We conducted in-depth interviews to understand this phenomenon and built a research model based on risk theory and the customer resistance to change (CRC) framework. Results suggest that switching costs and relative attractiveness influence CRC, while risk perceptions form negative attitudes toward CRC, which finally result in negative word of mouth. Finally, theoretical and practical implications are discussed.
Grassroots Roma Women Organizing for Social Change: A Study of the Impact of ‘Roma Women Student Gatherings’
Authors: Aiello Emilia , Amador-López Jelen , Munté-Pascual Ariadna , Sordé-Martí Teresa
Published: July 2019

Scientific literature has focused on the constraints that Roma women have faced to overcome the racism and inequalities that they and the Roma people as a whole have suffered. However, less attention has been paid to how Roma women organize to challenge this reality. Drawing on a qualitative case study about the Roma Association of Women Drom Kotar Mestipen (Barcelona) and specifically on the analysis of one of its activities, the ‘Roma women student gatherings’ (known as ‘Trobades’ in Catalan), this article contributes evidence to show how Roma women are fighting to improve their own living conditions and those of their people by organizing at the grassroots level. The communicative analysis reveals the impacts that these gatherings have on the individual and societal levels. First, the gatherings have impacts on the individual level, as many of the women who participate in them are exposed to and embrace new educational projects, thus acquiring more skills to be better prepared to later access the labor market. Second, their impact is also evidenced on the societal level, as the gatherings enhance Roma women’s associational life, resulting in new mobilizations and often making women who were once in the shadows become community leaders.
Changes of Microbial Diversity in Rhizosphere Soils of New Quality Varieties of Winter Wheat Cultivation in Organic Farming
Authors: Gałązka Anna , Grzęda Emilia , Jończyk Krzysztof
Published: July 2019

The aim of this paper was to evaluation functional diversity in rhizosphere soils of new quality varieties of winter wheat cultivation in organic farming. Field experiments were carried out in 2017 and 2018. Twelve commercial winter wheat varieties were selected for testing: Arktis, Bellisa, Estivus, Fidelius, Hondia, Jantarka, KWS Ozon, Linus, Markiza, Ostka Strzelecka, Pokusa, and Rokosz. Winter wheat cultivars were chosen for their high yielding potential and good tolerance to fungal diseases. In the plant production conducted in accordance with the principles of organic farming, the selection of the best quality varieties is a key element of agrotechnics. The samples of rhizosphere soils were collected each year in two seasons: spring and summer. The basic parameters of soil biological activities and microbial biodiversity indicators were determined. The high variability of biological activity and functional diversity of rhizosphere soils in the growing season between particular varieties of winter wheat was observed. The rhizosphere soils from varieties such as Bellisa, Arktis, Jantarka, Fidelius, Ostka Strzelecka, Pokusa, Rokosz and KWS Ozon were characterized by high biological activity and functional biodiversity. On the other hand, the soils collected from the varieties Estivus, Fidelius, Jantarkaand Hondia were characterized by medium and low biological activity and biodiversity indices. The highest yield was found in winter wheat varieties such as Bellisa, Fidelius and Jantarka. The results of these analyses allows for a more complete characterization of the yield potential of the tested varieties and their suitability for cultivation in the conditions of organic farming, taking into account the biological activity of soils.
Updated Models of Passenger Transport Related Energy Consumption of Urban Areas
Authors: Dingil Ali Enes , Schweizer Joerg , Rupi Federico , Stasiskiene Zaneta
Published: July 2019

Introduction: As the global warming threat has become more concrete in recent years, there is a need to update transport energy consumptions of cities and to understand how they relate to population density and transport infrastructure. Transportation is one of the major sources of global warming and this update is an important warning for urban planners and policy makers to take action in a more consistent way. Analysis: This paper estimates and analyzes the passenger transport energy per person per year with a large and diverse sample set based on comparable, directly observable open-source data of 57 cities, distributed over 33 countries. The freight transport energy consumption, which accounts for a large portion of urban transport energy, is not considered. The main focus of the analysis is to establish a quantitative relation between population density, transport infrastructure and transport energy consumption. Results: In a first step, significant linear relations have been found between road length per inhabitant, the road infrastructure accessibility (RIA) and private car mode share as well as between RIA and public transport mode share. Results show further relation between travel distance, population density and RIA. In a second step, a simplified model has been developed that explains the non-linear relation between the population density and RIA. Finally, based on this relation and the above findings, a hyperbolic function between population density and transport energy has been calibrated, which explains the rapid increase of transport energy consumption of cities with low population density. Conclusions: The result of the this study has clearly identified the high private car mode share as main cause for the high transport energy usage of such cities, while the longer average commute distance in low-population density cities has a more modest influence on their transport energy consumption.
A Responsive Pricing Grid Operator Sourcing from Competing Generators under Uncertain Supply and Demand
Authors: Xie Jiaping , Wei Lihong , Zhang Weisi , Xia Yu , Li Jing
Published: July 2019

Electricity supply chains involve more sources of uncertainty than typical production and manufacturing supply chains, owing to the intermittent nature of renewable energy generation. Therefore, it is critical but challenging to mitigate supply disruption risks by improving management methods. The extant literature has mainly investigated the sourcing strategies of manufacturers with price-taking suppliers. Where there is an option to source from multiple generators, including regular but unreliable generators and reliable backup generators, a flexible sourcing strategy is usually regarded as the best tactic for the grid operator. Our objective is to evaluate the costs and benefits of flexible sourcing and sole sourcing when generators are strategic price-setters. In this paper, we develop a Stackelberg game with wholesale prices contingent on the dominant grid operator’s sourcing strategy. We describe and analyze the resulting equilibriums under different scenarios. The results show that the grid operator does not necessarily benefit from a backup generator and that the flexible sourcing mode is not in fact optimal, except when the disruption ratio of the unreliable generator is medium and the penalty-sharing ratio of the unreliable generator is low. The model is applied to a numerical case study of a real-word electricity supply chain to illustrate the validity and effectiveness of the proposed conclusions.
The Alfredo Namitete Agroecology Credit System: A New Business Model That Supports Small-Scale Lending
Authors: Hilmi Angela
Published: July 2019

A major obstruction in the development of sustainable agriculture is the weakness of the financial and banking sectors in supporting smallholder farming. While farmers need to invest in their farms, they struggle to find credit schemes adapted to their specific needs. This study explores the literature on a range of credit systems applied in different geographical and historical contexts to analyse the underlying drivers of their successes or otherwise. In light of this review, the study investigates a farmers’ association, Alfredo Namitete (AN), in Mozambique, offering a range of agroecology credit modalities. It is then assessed as to whether a new business model initiated with seed funding could be self-managed by the association itself and lead to greater autonomy. The AN pilot tested three schemes between 2015 and 2019. Based on the findings, i.e., better production, increased revenue and greater self-determination, the study combines elements for a new business model for small-scale lending. It concludes that to be effective, a credit scheme needs to meet several conditions simultaneously: believe in the genuine will to repay, abolish the lender–borrower distance, ensure a role for women in decision making, add a savings mechanism, combine individual and collective investments and, finally, reserve funds for solidarity and climate issues.
Modelling Environmental Burdens of Indoor-Grown Vegetables and Herbs as Affected by Red and Blue LED Lighting
Authors: Pennisi Giuseppina , Sanyé-Mengual Esther , Orsini Francesco , Crepaldi Andrea , Nicola Silvana , Ochoa Jesús , Fernandez Juan , Gianquinto Giorgio
Published: July 2019

Notwithstanding that indoor farming is claimed to reduce the environmental pressures of food systems, electricity needs are elevated and mainly associated with lighting. To date, however, no studies have quantified the environmental and economic profile of Light Emitting Diodes (LED) lighting in indoor farming systems. The goal of this study is to quantify the effect of varying the red (R) and blue (B) LED spectral components (RB ratios of 0.5, 1, 2, 3 and 4) on the eco-efficiency of indoor production of lettuce, chicory, rocket and sweet basil from a life cycle perspective. The functional unit of the assessment was 1 kg of harvested fresh plant edible product, and the International Reference Life Cycle Data System (ILCD) method was employed for impact assessment. Even though most of the materials of the LED lamp and electronic elements were imported from long distances (14,400 km), electricity consumption was the largest contributor to the environmental impacts (with the LED lamps being the main electricity consumers, approximately 70%), apart from the resources use indicator, where the materials of the lamps and the mineral nutrients were also relevant. RB0.5 was the most energy-efficient light treatment but had the lowest eco-efficiency scores due to the lower crop yields.
Shared Short-Term Rentals for Sustainable Tourism in the Social-Network Age: The Impact of Online Reviews on Users’ Purchase Decisions
Authors: Zhao Jie , Peng Zhixiang
Published: July 2019

With the development of social networks and the Internet-based sharing economy, shared short-term rentals are emerging as a new kind of service that provides a convenient way for people to buy short-term rental services in cities through social-network-enabled platforms. However, like other social-network-based services, shared short-term rental is also likely to be impacted by online reviews. This paper aims to investigate the impact of online reviews on users’ purchase decisions toward shared short-term rentals, and further to provide optimization suggestions for the future advance of shared short-term rentals. The contributions of this paper are many-fold. First, we introduce the Stimuli-Organism-Response (SOR) model into the study and propose new variables for the model, including stimulus variables, organism variables, response variable, and moderating variables. Second, we propose eight hypotheses to evaluate the impact of online reviews on users’ purchase decisions toward shared short-term rentals. Finally, we collect data through a questionnaire survey and present comprehensive results on many aspects. Based on the data analysis, we find out that the quality of online reviews impacts users’ perceived value and perceived risk, which in turn impacts users’ purchase decisions toward shared short-term rentals. In addition, the cognitive needs of users can adjust the impact of online reviews on the perceived risk of users but have no explicit adjusting effect for users’ perceived value. Further, we present some research implications as well as suggestions for rental platforms to advance shared short-term rentals in the Internet age.
A Hybrid MCDM Model for Evaluating Strategic Alliance Partners in the Green Biopharmaceutical Industry
Authors: Chang Mu-Hsin , Liou James J. H. , Lo Huai-Wei
Published: July 2019

Since the rise of strategic alliances which play such an important role in industry today, the biopharmaceutical industry worldwide has entered an era of rapid change and collaborative thinking. The strategic alliance is one of the most important strategies for the green biopharmaceutical industry. Member organizations in these alliances work together to create more advantageous biotechnologies based on environmental protection to achieve mutual benefits. In the past, there have been only a few studies discussing partner evaluations and the selection process for the green biopharmaceutical industry, so the criteria or indicators are still not complete. Therefore, this study proposes a novel multi-criteria decision-making (MCDM) framework for strategic alliance partner evaluation that combines the best-worst method (BWM) and the fuzzy TOPSIS technique based on the concept of aspiration level (called fuzzy TOPSIS-AL) to evaluate the performance and priority rankings of strategic alliance partners. The BWM overcomes the shortcomings of small sample sizes and streamlines the number of conventional pairwise comparisons needed. The fuzzy TOPSIS-AL technique introduces the concept of the aspiration level, thereby leading to more reasonable suggestions for improvement. In addition, data from a multinational green biopharmaceutical company survey are utilized to demonstrate the validity and applicability of the proposed model.