bluuebunny/crossref_metadata_2025 huggingface dataset:
Yield Estimates by a Two-Step Approach Using Hyperspectral Methods in Grasslands at High Latitudes
Authors: Ancin-Murguzur Francisco Javier , Taff Gregory , Davids Corine , Tømmervik Hans , Mølmann Jørgen , Jørgensen Marit
Published: February 2019

Ruminant fodder production in agricultural lands in latitudes above the Arctic Circle is constrained by short and hectic growing seasons with a 24-hour photoperiod and low growth temperatures. The use of remote sensing to measure crop production at high latitudes is hindered by intrinsic challenges, such as a low sun elevation angle and a coastal climate with high humidity, which influences the spectral signatures of the sampled vegetation. We used a portable spectrometer (ASD FieldSpec 3) to assess spectra of grass crops and found that when applying multivariate models to the hyperspectral datasets, results show significant predictability of yields (R2 > 0.55, root mean squared error (RMSE) < 180), even when captured under sub-optimal conditions. These results are consistent both in the full spectral range of the spectrometer (350–2500 nm) and in the 350–900 nm spectral range, which is a region more robust against air moisture. Sentinel-2A simulations resulted in moderately robust models that could be used in qualitative assessments of field productivity. In addition, simulation of the upcoming hyperspectral EnMap satellite bands showed its potential applicability to measure yields in northern latitudes both in the full spectral range of the satellite (420–2450 nm) with similar performance as the Sentinel-2A satellite and in the 420–900 nm range with a comparable reliability to the portable spectrometer. The combination of EnMap and Sentinel-2A to detect fields with low productivity and portable spectrometers to identify the fields or specific regions of fields with the lowest production can help optimize the management of fodder production in high latitudes.
Localization and Extraction of Road Poles in Urban Areas from Mobile Laser Scanning Data
Authors: Li You , Wang Weixi , Tang Shengjun , Li Dalin , Wang Yankun , Yuan Zhilu , Guo Renzhong , Li Xiaoming , Xiu Wenqun
Published: February 2019

Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.
Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging
Authors: Wang Wei , Xiang Deliang , Ban Yifang , Zhang Jun , Wan Jianwei
Published: February 2019

Image segmentation plays a fundamental role in image understanding and region-based applications. This paper presents a superpixel-based segmentation method for Polarimetric SAR (PolSAR) data, in which a two-stage merging strategy is proposed. First, based on the initial superpixel partition, the Wishart-merging stage (WMS) simultaneously merges the regions in homogeneous areas. The edge penalty is combined with the Wishart energy loss to ensure that the superpixels to be merged are from the same land cover. The second stage follows the iterative merging procedure, and applies the doubly flexible KummerU distribution to better characterize the resultant regions from WMS, which are usually located in heterogeneous areas. Moreover, the edge penalty and the proposed homogeneity penalty are adopted in the KummerU-merging stage (KUMS) to further improve the segmentation accuracy. The two-stage merging strategy applies the general statistical model for the superpixels without ambiguity, and more advanced model for the regions with ambiguity. Therefore, the implementing efficiency can be improved based on the WMS, and the accuracy can be increased through the KUMS. Experimental results on two real PolSAR datasets show that the proposed method can effectively improve the computation efficiency and segmentation accuracy compared with the classical merging-based methods.
Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data
Authors: Li Weijia , He Conghui , Fang Jiarui , Zheng Juepeng , Fu Haohuan , Yu Le
Published: February 2019

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.
Changes in a Giant Iceberg Created from the Collapse of the Larsen C Ice Shelf, Antarctic Peninsula, Derived from Sentinel-1 and CryoSat-2 Data
Authors: Han Hyangsun , Lee Sungjae , Kim Jae-In , Kim Seung Hee , Kim Hyun-cheol
Published: February 2019

The giant tabular iceberg A68 broke away from the Larsen C Ice Shelf, Antarctic Peninsula, in July 2017. The evolution of A68 would have been affected by both the Larsen C Ice Shelf, the surrounding sea ice, and the nearby shallow seafloor. In this study, we analyze the initial evolution of iceberg A68A—the largest originating from A68—in terms of changes in its area, drift speed, rotation, and freeboard using Sentinel-1 synthetic aperture radar (SAR) images and CryoSat-2 SAR/Interferometric Radar Altimeter observations. The area of iceberg A68A sharply decreased in mid-August 2017 and mid-May 2018 via large calving events. In September 2018, its surface area increased, possibly due to its longitudinal stretching by melting of surrounding sea ice. The decrease in the area of A68A was only 2% over 1.5 years. A68A was relatively stationary until mid-July 2018, while it was surrounded by the Larsen C Ice Shelf front and a high concentration of sea ice, and when its movement was interrupted by the shallow seabed. The iceberg passed through a bay-shaped region in front of the Larsen C Ice Shelf after July 2018, showing a nearly circular motion with higher speed and greater rotation. Drift was mainly inherited from its rotation, because it was still located near the Bawden Ice Rise and could not pass through by the shallow seabed. The freeboard of iceberg A68A decreased at an average rate of −0.80 ± 0.29 m/year during February–November 2018, which could have been due to basal melting by warm seawater in the Antarctic summer and increasing relative velocity of iceberg and ocean currents in the winter of that year. The freeboard of the iceberg measured using CryoSat-2 could represent the returned signal from the snow surface on the iceberg. Based on this, the average rate of thickness change was estimated at −12.89 ± 3.34 m/year during the study period considering an average rate of snow accumulation of 0.82 ± 0.06 m/year predicted by reanalysis data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The results of this study reveal the initial evolution mechanism of iceberg A68A, which cannot yet drift freely due to the surrounding terrain and sea ice.
Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar
Authors: Feng Xuan , Zhou Haoqiu , Liu Cai , Zhang Yan , Liang Wenjing , Nilot Enhedelihai , Zhang Minghe , Dong Zejun
Published: February 2019

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.
Influence of Land Use and Land Cover Change on the Formation of Local Lightning
Authors: Kar Sanjib Kumar , Liou Yuei-An
Published: February 2019

Land use and land cover (LULC) play a crucial role in the interaction between the land and atmosphere, influencing climate at local, regional, and global scales. LULC change due to urbanization has significant impacts on local weather and climate. Land-cover changes associated with urbanization create higher air temperatures compared to the surrounding rural area, known as the “urban heat island (UHI)” effect. Urban landscapes also affect formation of convective storms. In recent years, the effect of urbanization on local convections and lightning has been studied very extensively. In this paper a long-term study has been carried out taking cloud-to-ground (CG) lightning data (1998–2012) from Tai-Power Company, and particulate matter (PM10), sulfur dioxide (SO2) data (2003–2012) from the Environmental Protection Administration (EPA) of Taiwan, in order to investigate the influence of LULC change through urbanization on CG lightning activity over Taipei taking into account in situ data of population growth, land use change and mean surface temperature (1965–2010). The thermal band of the Land-Sat 7 satellite was used to generate the apparent surface temperature of New Taipei City. It was observed that an enhancement of 60–70% in the flash density over the urban areas compared to their surroundings. The spatial distribution of the CG lightning flashes follows closely the shape of the Taipei city heat island, thereby supporting the thermal hypothesis. The PM10 and SO2 concentrations showed a positive linear correlation with the number of cloud-to-ground flashes, supporting the aerosol hypothesis. These results indicate that both hypotheses should be considered to explain the CG lightning enhancements over the urban areas. The results obtained are significant and interesting and have been explained from the thermodynamic point of view.
Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
Authors: Li Xin , Li Xingxing , Ma Fujian , Yuan Yongqiang , Zhang Keke , Zhou Feng , Zhang Xiaohong
Published: February 2019

The fusion of low earth orbit (LEO) constellation and Global Navigation Satellite Systems (GNSS) can increase the number of visible satellites and optimize spatial geometry, which is expected to improve the performance of precise point positioning (PPP) ambiguity resolution (AR). In addition, the multi-frequency signals of LEO satellites can bring a variety of observation combinations, which is potential to further improve the efficiency of PPP AR. In this contribution, multi-frequency PPP AR was achieved with the augmentation of different LEO constellations. Three types of LEO constellations were designed with 60, 192, and 288 satellites. Moreover, the corresponding observation data were simulated with the GNSS observations over the ground stations. The LEO constellations were designed to transmit navigation signals on three frequencies: L1, L2, and L5 at 1575.42, 1227.6, and 1176.45 MHz, respectively, which are consistent with the GPS signals. For PPP AR, the uncalibrated phase delay (UPD) products of GNSS and LEO were estimated first. Furthermore, the quality of UPD products was also analyzed. The research findings show that the performance of estimated LEO UPD is comparable to that of GNSS UPD. Based on the UPD products, LEO-augmented multi-GNSS PPP AR can be achieved. Numerous results show that the performance of single-system and multi-GNSS PPP AR can be significantly improved by introducing the LEO constellations. The augmentation performance is more remarkable in the case of increasing LEO satellites. The time to first fix (TTFF) of the GREC fixed solution can be shortened from 7.1 to 4.8, 1.1, and 0.7 min, by introducing observations of 60-, 192-, and 288-LEO constellations, respectively. The positioning accuracy of multi-GNSS fixed solutions is also improved by about 60%, 80%, and 90% with the augmentation of 60-, 192-, and 288-LEO constellations, respectively. Compared to the dual-frequency solutions, the triple-frequency LEO-augmented PPP fixed solution presents a better performance. The TTFF of GREC fixed solutions is shortened to 33 s with the augmentation of 288-LEO constellation under the triple-frequency environment. It is worth indicating that the 288-satellite LEO-only PPP AR was conducted in dual-frequency and triple-frequency modes, respectively. The averaged TTFFs of both modes are 71.8 s and 55.2 s, respectively. It indicates that LEO constellation with 288 satellites is capable of achieving high-precision positioning independently and shows an even better performance than GNSS-only solutions.
Generalized-Capon Method for Diff-Tomo SAR Analyses of Decorrelating Scatterers
Authors: Lombardini Fabrizio , Cai Francesco
Published: February 2019

In synthetic aperture radar (SAR) remote sensing, Differential Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography. Diff-Tomo produces advanced 4D (3D+Time) SAR imaging capabilities, extensively applied to urban deformation monitoring. More recently, it has been shown that, through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating scatterers, the important decorrelation-robust forest Tomography functionality is possible. To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an adaptive, just semi-parametric, generalized-Capon Diff-Tomo method, first conceived at University of Pisa in 2013, for joint extraction of height and dynamical information of natural distributed (volumetric) scatterers, with its formalization and a series of insights. Particular reference is given to the important functionality of the separation of different decorrelation mechanisms in forest layers. Representative simulated and P-band forest data sample results are also shown. The new Diff-Tomo method is getting a flexible and rich decorrelation-robust Tomography functionality, and is able to profile height-varying temporal decorrelation, for significantly distributed scatterers.
Optical Fiber Sensor Performance Evaluation in Soft Polyimide Film with Different Thickness Ratios
Authors: He Yanlin , Zhang Xu , Zhu Lianqing , Sun Guangkai , Lou Xiaoping , Dong Mingli
Published: February 2019

To meet the application requirements of curvature measurement for soft biomedical robotics and flexible morphing wings of aircraft, the optical fiber Bragg grating (FBG) shape sensor for soft robots and flexible morphing wing was implemented. This optical FBG is embedded in polyimide film and then fixed in the body of a soft robot and morphing wing. However, a lack of analysis on the embedded depth of FBG sensors in polyimide film and its sensitivity greatly limits their application potential. Herein, the relationship between the embedded depth of the FBG sensor in polyimide film and its sensitivity and stability are investigated. The sensing principle and structural design of the FBG sensor embedded in polyimide film are introduced; the bending curvatures of the FBG sensor and its wavelength shift in polyimide film are studied; and the relationship between the sensitivity, stability, and embedded depth of these sensors are verified experimentally. The results showed that wavelength shift and curvature have a linear relationship. With the sensor’s curvature ranging from 0 m−1 to 30 m−1, their maximum sensitivity is 50.65 pm/m−1, and their minimum sensitivity is 1.96 pm/m−1. The designed FBG sensor embedded in polyimide films shows good consistency in repeated experiments for soft actuator and morphing wing measurement; the FBG sensing method therefore has potential for real applications in shape monitoring in the fields of soft robotics and the flexible morphing wings of aircraft.
An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
Authors: Ismael Waleed , Gao Mingsheng , Al-Shargabi Asma , Zahary Ammar
Published: February 2019

Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for paid volume, and I/O throughput as well as handling huge amount of stored data in the cloud. These call for data pre-processing at the network edge before data transmission over the network takes place. Data reduction is a method for mitigating such problems. Most state-of-the-art data reduction approaches employ a single tier, such as gateways, or two tiers, such gateways and the cloud data center or sensor nodes and base station. In this paper, an approach for IoT data reduction is proposed using in-networking data filtering and fusion. The proposed approach consists of two layers that can be adapted at either a single tier or two tiers. The first layer of the proposed approach is the data filtering layer that is based on two techniques, namely data change detection and the deviation of real observations from their estimated values. The second layer is the data fusion layer. It is based on a minimum square error criterion and fuses the data of the same time domain for specific sensors deployed in a specific area. The proposed approach was implemented using Python and the evaluation of the approach was conducted based on a real-world dataset. The obtained results demonstrate that the proposed approach is efficient in terms of data reduction in comparison with Least Mean Squares filter and Papageorgiou’s (CLONE) method.
Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France
Authors: Bazzi Hassan , Baghdadi Nicolas , El Hajj Mohammad , Zribi Mehrez
Published: February 2019

The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France as a function of precipitation data, in order to investigate the potential of S1-SSM maps for detecting heavy rainfalls. First, the correlation between S1-SSM maps and rainfall maps provided by the Global Precipitation Mission (GPM) was investigated. Then, we analyzed the effect of the S1-SSM temporal resolution on detecting heavy rainfall events and the impact of these events on S1-SSM values as a function of the number of days that separated the heavy rainfall and the S1 acquisition date (cumulative rainfall more than 60 mm in 24 hours or 80 mm in 48 hours). The results showed that the six-day temporal resolution of the S1-SSM map doesn’t always permit the detection of an extreme rainfall event, because confusion will appear between high S1-SSM values due to extreme rainfall events occurring six days before the acquisition of S1-SSM, and high S1-SSM values due to light rain a few hours before the acquisition of Sentinel-1 images. Moreover, the monitoring of extreme rain events using only soil moisture maps remains difficult, since many environmental parameters could affect the value of SSM, and synthetic aperture radar (SAR) doesn’t allow the estimation of very high soil moistures (higher than 35 vol.%).
UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status
Authors: Yao Lili , Wang Qing , Yang Jinbo , Zhang Yu , Zhu Yan , Cao Weixing , Ni Jun
Published: February 2019

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.
Emerging Point-of-care Technologies for Food Safety Analysis
Authors: Choi Jane Ru , Yong Kar Wey , Choi Jean Yu , Cowie Alistair C.
Published: February 2019

Food safety issues have recently attracted public concern. The deleterious effects of compromised food safety on health have rendered food safety analysis an approach of paramount importance. While conventional techniques such as high-performance liquid chromatography and mass spectrometry have traditionally been utilized for the detection of food contaminants, they are relatively expensive, time-consuming and labor intensive, impeding their use for point-of-care (POC) applications. In addition, accessibility of these tests is limited in developing countries where food-related illnesses are prevalent. There is, therefore, an urgent need to develop simple and robust diagnostic POC devices. POC devices, including paper- and chip-based devices, are typically rapid, cost-effective and user-friendly, offering a tremendous potential for rapid food safety analysis at POC settings. Herein, we discuss the most recent advances in the development of emerging POC devices for food safety analysis. We first provide an overview of common food safety issues and the existing techniques for detecting food contaminants such as foodborne pathogens, chemicals, allergens, and toxins. The importance of rapid food safety analysis along with the beneficial use of miniaturized POC devices are subsequently reviewed. Finally, the existing challenges and future perspectives of developing the miniaturized POC devices for food safety monitoring are briefly discussed.
A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine
Authors: Zheng Yilai , Wang Tianzhen , Xin Bin , Xie Tao , Wang Yide
Published: February 2019

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.
The Good Food Revolution: Building Community Resiliency in the Mississippi Delta
Authors: Hossfeld Leslie , Kerr Laura , Belue Judy
Published: February 2019

The Mississippi Delta represents one of the greatest concentrations of rural persistent poverty in the United States. High unemployment, high food insecurity, higher rates of obesity and diabetes, and low access to healthy, affordable food characterize much of the 18 counties in the region. In the face of this, The Good Food Revolution, a community-based program to address food related health and thereby employment, developed in response to significant need in three small communities in North Bolivar County, Mississippi, bringing together community members, public and private sector organizations, researchers and students. This paper examines the process of community-engaged scholarship from the theoretical lens on building community capacity and resiliency developed by Chaskin. Increasing community capacity for all participants in the Good Food Revolution project through community-engaged scholarship has built resilient communities that are engaging more communities.
Physical Fitness Characteristics of High-level Youth Football Players: Influence of Playing Position
Authors: Bujnovsky David , Maly Tomas , Ford Kevin , Sugimoto Dai , Kunzmann Egon , Hank Mikulas , Zahalka Frantisek
Published: February 2019

The aim of this study was to determine whether the speed, agility, aerobic and anaerobic capacities of football players varied by playing positions. Elite youth football players (n = 123, age = 15.7 ± 0.5 years) who played in six different positions, as goalkeepers (GK), full backs (FB), central defenders (CD), wide midfielders (WM), central midfielders (CM), and attackers (AT), were assessed. Multivariate analysis of variances was used to compare the following variables: Linear running sprint for 5 m (S5) and 10 m (S10), flying sprint for 20 m (F20), agility 505 test with turn on the dominant (A505D) and non-dominant leg (A505N), agility K-test, Yo-Yo intermittent recovery (YYIR1) test and repeat sprint ability (RSA) test. The results showed significant influence of playing positions on linear-running sprint performance (F1,123 = 6.19, p < 0.01, ηp2 = 0.23). Midfielders reached significantly higher performance levels (CM = 2.44 ± 0.08 s, WM = 2.47 ± 0.13 s) in the A505N test compared to GK (2.61 ± 0.23 s). Outfield players had significantly higher performance in both YYIR1 and RSA tests compared to GK (p < 0.01). The results of this study may provide insightful strategies for coaches and clinical practitioners for developing position-specific conditioning programs.
Mine Tailings Geopolymers as a Waste Management Solution for A More Sustainable Habitat
Authors: Paiva Helena , Yliniemi Juho , Illikainen Mirja , Rocha Fernando , Ferreira Victor M.
Published: February 2019

The demand for low environmental impact of materials in our habitat is one of the current societal challenges. Along with other solutions of waste valorisation, alkali activation as geopolymers can be one possible solution of waste valorisation because they may allow, for instance, an alternative solution for cement-based materials in some applications and it is one contribution for circular economy. This work has focused on the development and processing of geopolymers that incorporates as a fine aggregate a high-sulfidic mining waste (mine tailing), a difficult waste to process. Rheology analysis was applied as an important step to understand not only the geopolymers behaviour but also its transition from the fresh to the hardened state. The effect of precursor binder type (metakaolin or blast furnace slag), of mine tailing content and also the effect of temperature and curing conditions of different formulations were studied in this solution. It was possible to conclude that although this particular mine tailing is not a geopolymer binder precursor, it may be incorporated as an alternative fine aggregate in construction products. Furthermore, rheology could be used to follow up the geopolymer alkali-activation process and even to setup proper curing conditions and components contents in order to optimize the final mechanical strength of this material as a waste management solution. The final properties of these geopolymers compositions were adequate and after 28 days of curing, these geopolymers exhibit significant chemical resistance under severe test conditions.
Evaluation of the Use of a City Center through the Use of Bluetooth Sensors Network
Authors: Martínez Plumé Javier , Marténez Durá Juan José , Cirilo Gimeno Ramón Vicente , Soriano García Francisco Ramón , García Celda Antonio
Published: February 2019

In order to achieve the objectives of Smart Cities, public administrations need to take measures to regulate mobility, which undoubtedly requires a high level of information and sensorization. Until the implementation of the connected vehicle takes place, it is still necessary to install sensors to obtain information about mobility. Bluetooth sensors are becoming a useful tool due to the low cost of equipment and installation. The use of Bluetooth sensors in cities, with short distances between sensors, makes it necessary to propose new classification algorithms that allow the trips of pedestrians and vehicles to be differentiated. This article presents the study carried out in the city of Valencia to determine the use of motor vehicles in the historic center and propose a new classification algorithm to distinguish between an onboard Bluetooth device and the same device carried by a pedestrian when it is not possible to use the travel time for the classification due to the short distance between sensors. This causes very similar or even indistinguishable travel times for vehicles and for pedestrians. We also propose an algorithm that allows vehicles to be classified according to what type of trip is made always through the historical center of Valencia, whether it is to make a shorter itinerary through the city or to access the center for any type of business. This algorithm would enable the Origin-Destination matrix of an urban network with short distances between sensors if they are available in all entries and exits. Likewise, the results obtained have allowed to positively evaluate the algorithm defined to distinguish between trips made by a pedestrian or a vehicle in a city, using the MAC address of their mobile devices with very short distances among sensors. The results of this study show that it is possible to use Bluetooth technology, with low cost installations, to evaluate the use of the city by motor vehicles.
Restoration of Long-Term Monoculture Degraded Tea Orchard by Green and Goat Manures Applications System
Authors: Jiang Yuhang , Arafat Yasir , Letuma Puleng , Ali Liaqat , Tayyab Muhammad , Waqas Muhammad , Li Yanchun , Lin Weiwei , Lin Sheng , Lin Wenxiong
Published: February 2019

Tea is an economic shrubby plant in tropical and subtropical regions of the world. To obtain high yield in tea cultivation, chemical fertilizer application rates have generally been used. However, a large quantity of chemical fertilizer application in a long-term continuously ratooned and monoculture tea orchard can inevitably lead to soil acidification and a decline in fertility. Therefore, the restoration of soil fertility and the sustainable development of tea planting by organic ways are critical for the tea industry. In this study, field trials were conducted in the tea orchard that was continuously ratooned and mono-cultured for 20 years. Nitrogen fertilizer (NF), Laredo soybeans green manure (LF), and goat manure (GM) treatments were applied to restore optimum acidity, soil fertility, microbial activity, and the community structure of a long-term continuously monoculture tea orchard. This paper investigated that the pH value was increased from 4.23 to 4.32 in GM and LF, respectively. Similarly, the content of exchangeable acidity (EA) was decreased by 1.21 and 1.46 cmol·kg−1 in GM and LF, respectively. Available nutrient results indicated that the content of NH4+-N was increased by 3.96, 4.38, NO3−-N by 1.07, 2.16, AP by 3.46, 6.86, AK by 0.26, 0.3 mg kg−1 in GM and LF treatments, respectively. Enzyme analysis revealed that the activity of urease and sucrase was promoted by 7.98 mg·g−1·24 h−1 and 6.77 mg·g−1·24 h−1, respectively, in LF treatment. Likewise, the activity of acid phosphatase and polyphenol oxidase was sharply increased by 2.3 mg·g−1 h−1 and 63.07 mg·g−1 h−1 in LF treatments. Additionally, the activity of urease, sucrase, acidic phosphatase, polyphenol oxidase, and peroxidase were also significantly increased by applying GM treatments. Meanwhile, LF and GM treatments significantly improved soil microbial biomass as well as low weight organic acid content in degraded tea rhizosphere. Furthermore, high throughput sequence results illustrated that the relative abundance of Rhizobiaceae and Bradyrhizobiaceae families increased in LF and GM treatments, respectively, which are mostly a kind of nitrogen fixer and plant growth promoting bacteria. Taken together, the physiological traits of the new sprouts and the biochemical components of new tea leaves were also significantly improved by GM and LF treatments. From this study, it is concluded that LF and GM are good agriculture management practices, which promote plant growth, yield, and nutrient availability by maintaining and improving pH, enhancing available nutrients status, improving the secretion of low molecular weight organic acids, and balancing the microbial community structure in the long-term mono-cultured tea orchard.
Towards Sustainable Urban Spatial Structure: Does Decentralization Reduce Commuting Times?
Authors: Kim Chunil , Choi Choongik
Published: February 2019

This paper contributes to the existing debate on the co-location hypothesis, by devising a proximity measure and controlling for a set of other urban form measures. Utilizing the LEHD (Longitudinal Employer–Household Dynamics) Origin-Destination Employment Statistics (LODES) data that provide the number of jobs by a finer geography, this paper measured the degree of centralization, proximity, and job–housing mismatch. Multiple regression analysis revealed that the job–worker proximity leads to a shorter commuting time. In addition, the results focusing on suburban areas revealed that the impact of the job–worker imbalance and the impact of job–worker mismatch on the commuting time are greater in the suburb in comparison with the city center.
Green Governance and International Business Strategies of Emerging Economies’ Multinational Enterprises: A Multiple-Case Study of Chinese Firms in Pollution-Intensive Industries
Authors: Lin Runhui , Gui Yuan , Xie Zaiyang , Liu Lu
Published: February 2019

With the global consensus on the need for sustainability practices, green governance has attracted increasing attention from international business (IB) scholars and multinational enterprise (MNE) managers. In this study, we propose a more fine-grained framework of the green governance context along two dimensions: foreign direct investment (FDI) policy and environmental regulation. Then, we examine the framework using cluster analysis. On the basis of a multiple-case study comprising 11 Chinese MNEs in pollution-intensive industries operating in four different green governance contexts, we conclude that (1) the green governance context is a significant factor in MNEs’ global location choices and is an important driving force behind MNEs’ response patterns; (2) environmental capabilities enable MNEs to surmount a host country’s environmental entry barrier and facilitate wider global business deployment; (3) technological capabilities increase MNEs’ competitive edge and allow them to better harness a host country’s growth opportunities; (4) there are four types of green governance response patterns, and the details of the proposed classification structure and its validation are presented; and (5) both strict environmental regulation and friendly FDI policy can positively influence MNEs’ adoption of more active response patterns, and greater availability of environmental and technological capabilities does not affect MNEs’ environmental commitment. This study contributes to the international strategy-capability-environment alignment of emerging economies’ multinational enterprises (EMNEs) in different green governance contexts.
Legal Technologies in Action: The Future of the Legal Market in Light of Disruptive Innovations
Authors: Hongdao Qian , Bibi Sughra , Khan Asif , Ardito Lorenzo , Khaskheli Muhammad Bilawal
Published: February 2019

Legal technologies (digital solutions to providing legal services) have turned into the essential element of competition among legal-market stakeholders. This study highlights the competitive superiority of legal services based on the concept of innovative disruptive technologies in comparison to traditional firms. For this purpose, the present study identified several trends in the evolution of the legal tech market (i.e., changing landscapes of legal business, delivery models, legal-market segments, and disruptive technologies); moreover, qualitative content analysis has been conducted to provide sound grounds for cost-benefit analysis. Furthermore, cost-benefit analysis makes a comparison of the monetary, social and psychological costs, benefits of legal technologies and traditional firms from clients’ and lawyers’ viewpoints. This has been, in fact, neglected by extant research. Based on these analyses, a new model for legal business has developed to highlight how potential advancements can take place in legal practices. Our findings reveal that legal technologies make legal services more affordable and easily accessible, while helping lawyers to develop innovative solutions and a relaxed working environment. In addition, this study contributes to the literature on service innovation with a focus on how new services and business models have been developed in the legal market as a result of digital technologies. Eventually, this study adds theoretical and practical implications to the research discussing the advent of legal technologies.
Hofstede’s Collectivistic Values and Sustainable Growth of Online Group Buying
Authors: Han Bangwool , Kim Minho
Published: February 2019

This study investigates the moderating effects of consumer personality traits and the mediating effects of social influence susceptibility in consumers on the relationship between individuals’ cultural orientations and intentions toward online group buying (OGB). The Big Five personality dimensions, normative social influence, and Hofstede’s cultural dimensions are considered. Because understanding what motivates consumer intentions toward OGB is a major focus of firms and OGB website operators, this study contributes to the topic by identifying consumers’ personality traits and levels of susceptibility to social influences, which impact OGB intentions. Findings in Study 1 reveal that consumer personality traits (e.g., agreeableness) moderate the impact of individual cultural orientations (i.e., collectivism) on intentions toward OGB. Study 2 describes the mediation process of susceptibility to normative social influences on OGB intentions. The findings from this study bridge a literature gap in the context of OGB by demonstrating how OGB site operators can improve marketing strategies, leading to increase sustainable purchase intention. The article concludes with managerial implications by providing insights for online group buying sites and firms, particularly those that target collectivistic cultural markets.
The Concept of Risk and Possibilities of Application of Mathematical Methods in Supporting Decision Making for Sustainable Energy Development
Authors: Rabe Marcin , Streimikiene Dalia , Bilan Yuriy
Published: February 2019

This study is devoted to presentation of the concept of risk, and the possibility of applying mathematical methods in supporting decision making in the energy sector to promote sustainable energy development. The problem with risk assessment in the energy sector arises mainly due to the difficulty of expressing risk in numerical terms. To avoid risk, it is necessary to set the criteria and objectives of measurement before making decisions in the energy sector. The aim of this study is to try to fill in this gap by means of comparing decisions under risk conditions within models supporting energy decisions. The authors’ focus is on the problem of risk in supporting decision making towards sustainable energy sector development, which is the main target of the European Union (EU) energy policies. Without the ability to determine the probability of occurrence of certain phenomena and their inclusion into the model, it is not possible to determine how well the solutions resulting from the models are accurate, and what is the probability of their implementation under specific conditions linked to renewable energy development.
Handling Tensions between Natural and Utility Purpose of Farm Animals and Crop Plants
Authors: Struik Paul , Bos Arie , van Mansvelt Jan , Sprangers Diederick , Zoeteman Kees
Published: February 2019

We analyze the extent to which humans respect farm animals and crop plants and whether humans provide them adequate care. Concerns are justified, since many farm animals and crop plants have become completely dependent on humans for their survival. We explore whether the concept of ‘telos’ can be useful. From a biocentric and ecocentric custodian position, an animal or a plant possesses a telos, a nature of its own and a natural, species-specific purpose, but also a telos through its destination imposed by humanity because of its usefulness and service to human beings. Based on both forms of telos farm animals and crop plants deserve respect and have a moral value. Both forms of telos seem to oppose each other: the more humans domesticate the animal or plant, the more it must sacrifice its ‘natural telos’ to serve its ‘utility telos’. Humans should enable the animal or the plant to realize its natural telos in harmony with its utility telos. Both forms of telos could be reconciled if humans fulfil their moral duty to support farm animals and crops in completing their production cycle in a respectful manner. We formulate some guidelines to operationalize, and give meaning to, the concept of telos.
Deliberation and the Promise of a Deeply Democratic Sustainability Transition
Authors: Wironen Michael B. , Bartlett Robert V. , Erickson Jon D.
Published: February 2019

Ecological economics arose as a normative transdiscipline aiming to generate knowledge and tools to help transition the economy toward a scale which is sustainable within the bounds of the earth system. Yet it remains unclear in practice how to legitimize its explicitly normative agenda. One potential means for legitimation can be found in deliberative social and political theory. We review how deliberative theory has informed ecological economics, pointing to three uses: first, to support valuation of non-market goods and services; second, to inform environmental decision-making more broadly; third, to ground alternative theories of development and wellbeing. We argue that deliberation has been used as problem-solving theory, but that its more radical implications have rarely been embraced. Embracing a deliberative foundation for ecological economics raises questions about the compatibility of deeply democratic practice and the normative discourses arguing for a sustainability transition. We highlight three potential mechanisms by which deliberation may contribute to a sustainability transition: preference formation; normative evaluation; and legitimation. We explore each in turn, demonstrating the theoretical possibility that deliberation may be conducive in and of itself to a sustainability transition. We point to a series of challenges facing the “scaling up” of deliberative systems that demand further empirical and theoretical work. These challenges constitute a research agenda for a deeply democratic sustainability transition and can inform the future development of ecological economics and other normative, critical transdisciplines.