bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring the Vulnerability of the Dam and Dikes in Germano Iron Mining Area after the Collapse of the Tailings Dam of Fundão (Mariana-MG, Brazil) Using DInSAR Techniques with TerraSAR-X Data
Authors: Mura José C. , Gama Fábio F. , Paradella Waldir R. , Negrão Priscila , Carneiro Samuel , De Oliveira Cleber G. , Brandão Waldiza S.
Published: September 2018

The Fundão tailings dam in the Germano iron mining complex (Mariana, Brazil) collapsed on the afternoon of 5 November 2015, and around 32.6 million cubic meters of mining waste spilled from the dam, causing polluion with mining waste along a trajectory of 668 km, extending to the Atlantic Ocean. The Sela & Tulipa and Selinha dikes, and the main Germano tailings dam, were directly or indirectly affected by the accident. This work presents an investigation using Advanced-Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques for risk assessment in these critical structures during 18 months after the catastrophic event. The approach was based on the integration of SBAS (Small Baseline Subset) and PSI (Persistent Scatterer Interferometry) techniques, aiming at detecting linear and nonlinear ground displacements in these mining structures. It used a set of 48 TerraSAR-X images acquired on ascending mode from 11 November 2015 to 15 May 2017. The results provided by the A-DInSAR analysis indicated an overall stability in the dikes and in the main wall of Germano tailings dam, which is in agreement with in situ topographic monitoring. In addition, it was possible to detect areas within the reservoir showing accumulated values of up to −125 mm of subsidence, probably caused by settlements of the waste dry material due to the interruption of the mining waste deposition, and values up to −80 mm on auxiliary dikes, probably caused by continuous traffic of heavy equipment. The spatiotemporal information of surface displacement of this large mining structure can be used for future operational planning and risk control.
Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations
Authors: Zeng Yelu , Xu Baodong , Yin Gaofei , Wu Shengbiao , Hu Guoqing , Yan Kai , Yang Bin , Song Wanjuan , Li Jing
Published: September 2018

This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.
Google Earth Engine Applications Since Inception: Usage, Trends, and Potential
Authors: Kumar Lalit , Mutanga Onisimo
Published: September 2018

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.
Fusion of Multi-Baseline and Multi-Orbit InSAR DEMs with Terrain Feature-Guided Filter
Authors: Dong Yuting , Liu Baobao , Zhang Lu , Liao Mingsheng , Zhao Ji
Published: September 2018

Interferometric synthetic aperture radar (InSAR) is an effective technology for generating high-precision digital elevation models (DEMs). However, the vertical accuracy of InSAR DEMs is limited by the contradiction between height measurement sensitivity and phase unwrapping reliability in terms of normal baseline length as well as data voids caused by layover or shadow effects. In order to alleviate these two unfavorable factors, in this study, a novel InSAR DEM fusion method with guided filter is developed and assessed with multiple bistatic TanDEM-X InSAR data pairs of different normal baselines acquired from different orbits. Unlike the widely used fusion method based on pixel-by-pixel weighted average, the guided-filter-based method incorporates local spatial context information into the fusion and can thus effectively alleviate the noise effect and automatically fill in data voids. As a result of the local edge-preserving capability of the guided filter, the proposed fusion method can preserve terrain details by maintaining gradient consistency and introducing terrain features as guidance image. Furthermore, the proposed fusion method is computationally efficient owing to the linear time complexity of guided filter. The experimental results show that the fused DEM with guided filter can depict terrain details well and smooth the “salt-and-pepper” noise and fill in almost all of the data voids. The root mean square error (RMSE) of the fused InSAR DEM with guided filter is lower than those of the weighted average fused InSAR DEM and the TanDEM-X DEM released by the German Aerospace Center (DLR), thus validating the effectiveness of the fusion method proposed in this study.
An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection
Authors: Awrangjeb Mohammad , Gilani Syed Ali Naqi , Siddiqui Fasahat Ullah
Published: September 2018

Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints, and missing roof planes attract human interaction and often produces high reconstruction errors. This research introduces a new 3-D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. It identifies any missing planes through an analysis using the 3-D plane intersection lines between adjacent planes. Then, it generates new planes to fill gaps of missing planes. Finally, it obtains complete building models through insertion of approximate wall planes and building floor. The reported research in this paper then uses the generated building models to detect 3-D changes in buildings. Plane connections between neighbouring planes are first defined to establish relationships between neighbouring planes. Then, each building in the reference and test model sets is represented using a graph data structure. Finally, the height intensity images, and if required the graph representations, of the reference and test models are directly compared to find and categorise 3-D changes into five groups: new, unchanged, demolished, modified and partially-modified planes. Experimental results on two Australian datasets show high object- and pixel-based accuracy in terms of completeness, correctness, and quality for both 3-D roof reconstruction and change detection techniques. The proposed change detection technique is robust to various changes including addition of a new veranda to or removal of an existing veranda from a building and increase of the height of a building.
Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms
Authors: Duarte-Carvajalino Julio M. , Alzate Diego F. , Ramirez Andrés A. , Santa-Sepulveda Juan D. , Fajardo-Rojas Alexandra E. , Soto-Suárez Mauricio
Published: September 2018

This work presents quantitative prediction of severity of the disease caused by Phytophthora infestans in potato crops using machine learning algorithms such as multilayer perceptron, deep learning convolutional neural networks, support vector regression, and random forests. The machine learning algorithms are trained using datasets extracted from multispectral data captured at the canopy level with an unmanned aerial vehicle, carrying an inexpensive digital camera. The results indicate that deep learning convolutional neural networks, random forests and multilayer perceptron using band differences can predict the level of Phytophthora infestans affectation on potato crops with acceptable accuracy.
Mitigation of Tropospheric Delay in SAR and InSAR Using NWP Data: Its Validation and Application Examples
Authors: Cong Xiaoying , Balss Ulrich , Rodriguez Gonzalez Fernando , Eineder Michael
Published: September 2018

The neutral atmospheric delay has a great impact on synthetic aperture radar (SAR) absolute ranging and on differential interferometry. In this paper, we demonstrate its effective mitigation by means of the direction integration method using two products from the European Centre for Medium-Range Weather Forecast: ERA-Interim and operational data. Firstly, we shortly review the modeling of the neutral atmospheric delay for the direct integration method, focusing on the different refractivity models and constant coefficients available. Secondly, a thorough validation of the method is performed using two approaches. In the first approach, numerical weather prediction (NWP) derived zenith path delay (ZPD) is validated against ZPD from permanent GNSS (global navigation satellite system) stations on a global scale, demonstrating a mean accuracy of 14.5 mm for ERA-Interim. Local analysis shows a 1 mm improvement using operational data. In the second approach, NWP derived slant path delay (SPD) is validated against SAR SPD measured on corner reflectors in more than 300 TerraSAR-X High Resolution SpotLight acquisitions, demonstrating an accuracy in the centimeter range for both ERA-Interim and operational data. Finally, the application of this accurate delay estimate for the mitigation of the impact of the neutral atmosphere on SAR absolute ranging and on differential interferometry, both for individual interferograms and multi-temporal processing, is demonstrated.
End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks
Authors: Xu Yuelei , Zhu Mingming , Li Shuai , Feng Hongxiao , Ma Shiping , Che Jun
Published: September 2018

Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of “divide and conquer” and “integral loss’’ are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.
Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face
Authors: Boubanga-Tombet Stephane , Huot Alexandrine , Vitins Iwan , Heuberger Stefan , Veuve Christophe , Eisele Andreas , Hewson Rob , Guyot Eric , Marcotte Frédérick , Chamberland Martin
Published: September 2018

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.
Segmentation-Based Color Channel Registration for Disparity Estimation of Dual Color-Filtered Aperture Camera
Authors: Song Shuxiang , Park Sangwoo , Paik Joonki
Published: September 2018

Single-lens-based optical range finding systems were developed as an efficient, compact alternative for conventional stereo camera systems. Among various single-lens-based approaches, a multiple color-filtered aperture (MCA) system can generate disparity information among color channels, as well as normal color information. In this paper, we consider a dual color-filtered aperture (DCA) system as the most minimal version of the MCA system and present a novel inter-color image registration algorithm for disparity estimation. This proposed registration algorithm consists of three steps: (i) color channel independent feature extraction; (ii) feature-based adaptive weight disparity estimation; and (iii) color mapping matrix (CMM)-based cross-channel image registration. Experimental results show that the proposed method can not only generate an accurate disparity map, but also realize high quality cross-channel registration with a disparity prior for DCA-based range finding and color image enhancement.
Lab-on-Chip for Exosomes and Microvesicles Detection and Characterization
Authors: Chiriacò Maria Serena , Bianco Monica , Nigro Annamaria , Primiceri Elisabetta , Ferrara Francesco , Romano Alessandro , Quattrini Angelo , Furlan Roberto , Arima Valentina , Maruccio Giuseppe
Published: September 2018

Interest in extracellular vesicles and in particular microvesicles and exosomes, which are constitutively produced by cells, is on the rise for their huge potential as biomarkers in a high number of disorders and pathologies as they are considered as carriers of information among cells, as well as being responsible for the spreading of diseases. Current methods of analysis of microvesicles and exosomes do not fulfill the requirements for their in-depth investigation and the complete exploitation of their diagnostic and prognostic value. Lab-on-chip methods have the potential and capabilities to bridge this gap and the technology is mature enough to provide all the necessary steps for a completely automated analysis of extracellular vesicles in body fluids. In this paper we provide an overview of the biological role of extracellular vesicles, standard biochemical methods of analysis and their limits, and a survey of lab-on-chip methods that are able to meet the needs of a deeper exploitation of these biological entities to drive their use in common clinical practice.
Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises
Authors: Fan Ying , Zhang Yonggang , Wang Guoqing , Wang Xiaoyu , Li Ning
Published: September 2018

In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.
Miniature Uncooled and Unchopped Fiber Optic Infrared Thermometer for Application to Cutting Tool Temperature Measurement
Authors: Heeley Andrew D. , Hobbs Matthew J. , Laalej Hatim , Willmott Jon R.
Published: September 2018

A new infrared thermometer, sensitive to wavelengths between 3 μm and 3.5 μm, has been developed. It is based on an Indium Arsenide Antimony (InAsSb) photodiode, a transimpedance amplifier, and a sapphire fiber optic cable. The thermometer used an uncooled photodiode sensor and received infrared radiation that did not undergo any form of optical chopping, thereby, minimizing the physical size of the device and affording its attachment to a milling machine tool holder. The thermometer is intended for applications requiring that the electronics are located remotely from high-temperature conditions incurred during machining but also affording the potential for use in other harsh conditions. Other example applications include: processes involving chemical reactions and abrasion or fluids that would otherwise present problems for invasive contact sensors to achieve reliable and accurate measurements. The prototype thermometer was capable of measuring temperatures between 200 °C and 1000 °C with sapphire fiber optic cable coupling to high temperature conditions. Future versions of the device will afford temperature measurements on a milling machine cutting tool and could substitute for the standard method of embedding thermocouple wires into the cutting tool inserts. Similarly, other objects within harsh conditions could be measured using these techniques and accelerate developments of the thermometer to suit particular applications.
Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks
Authors: Sheng Xueli , Chen Yang , Guo Longxiang , Yin Jingwei , Han Xiao
Published: September 2018

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.
Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
Authors: Naqvi Aqeel Hussain , Lim Sungjoon
Published: September 2018

Owing to the rapid growth in wireless data traffic, millimeter-wave (mm-wave) communications have shown tremendous promise and are considered an attractive technique in fifth-generation (5G) wireless communication systems. However, to design robust communication systems, it is important to understand the channel dynamics with respect to space and time at these frequencies. Millimeter-wave signals are highly susceptible to blocking, and they have communication limitations owing to their poor signal attenuation compared with microwave signals. Therefore, by employing highly directional antennas, co-channel interference to or from other systems can be alleviated using line-of-sight (LOS) propagation. Because of the ability to shape, switch, or scan the propagating beam, phased arrays play an important role in advanced wireless communication systems. Beam-switching, beam-scanning, and multibeam arrays can be realized at mm-wave frequencies using analog or digital system architectures. This review article presents state-of-the-art phased arrays for mm-wave mobile terminals (MSs) and base stations (BSs), with an emphasis on beamforming arrays. We also discuss challenges and strategies used to address unfavorable path loss and blockage issues related to mm-wave applications, which sets future directions.
Comparison of Inspecting Non-Ferromagnetic and Ferromagnetic Metals Using Velocity Induced Eddy Current Probe
Authors: Feng Bo , Ribeiro Artur L. , Rocha Tiago J. , Ramos Helena G.
Published: September 2018

A velocity induced eddy current probe has been used to detect cracks in both non-ferromagnetic and ferromagnetic metals. The simulation and experimental results show that this probe can successfully detect cracks in both cases, but further investigation shows that the underlying principles for inspecting non-ferromagnetic and ferromagnetic metals are actually different. For an aluminum plate, the induced eddy current density and the signal amplitude both increase with probe speed, which means the signal is caused by velocity induced eddy currents. For a steel plate, probe speed changes the baselines of the testing signals; however, it has little influence on signal amplitudes. Simulation results show that the signal for cracks in a steel plate is mainly caused by direct magnetic field perturbation rather than velocity induced eddy currents.
Sensoring a Generative System to Create User-Controlled Melodies
Authors: Navarro-Cáceres María , Hashimoto Wataru , Rodríguez-González Sara , Pérez-Lancho Belén , Corchado Juan Manuel
Published: September 2018

The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music.
Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model
Authors: Khan Muhammad Hassan , Schneider Manuel , Farid Muhammad Shahid , Grzegorzek Marcin
Published: September 2018

Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.
Antifibrotic Activity and In Ovo Toxicity Study of Liver-Targeted Curcumin-Gold Nanoparticle
Authors: Adlia Amirah , Tomagola Ilham , Damayanti Sophi , Mulya Ardyanto , Rachmawati Heni
Published: September 2018

Conjugation of curcumin and gold with green chemistry is an approach to improve the effectiveness of curcumin as anti-fibrosis. In this work, curcumin and gold were conjugated to deliver curcumin to the liver. Curcumin-gold nanoparticles (cAuNPs) were prepared by varying curcumin pH and concentration. The successful of cAuNPs formation were identified by using UV-visible and FTIR spectrophotometers. The particle size and morphology were analyzed using particle size analyzer and cryo-TEM respectively. In vitro antioxidant assay was performed to determine the curcumin activity after conjugation. Physical and chemical stabilities of cAuNPs were studied for one month at 5 °C, 25 °C, and 40 °C. Furthermore, the cAuNPs activity to modulate early marker of fibrosis was tested on NIH/3T3 cells. The optimum condition for cAuNPs synthesis was by using 1.5 mM curcumin at pH 9.3. As compared to free curcumin, cAuNPs showed higher antioxidant activity and maintained the nanosize after stored for one month. In line with the antioxidant activity, cAuNPs 0.25–1 μg/mL reduced the collagen production by NIH/3T3 cells. More importantly, cAuNPs did not demonstrate any effect on the development of chicken embryo. Taken together, the attachment of gold to curcumin in the form of cAuNPs is promising for curcumin targeting to treat hepatic fibrosis.
The Impact of Single-Family Rental REITs on Regional Housing Markets: A Case Study of Nashville, TN
Authors: Chilton Ken , Silverman Robert Mark , Chaudhry Rabia , Wang Chihaungji
Published: September 2018

The U.S. Congress authorized the creation of real estate investment trusts (REITs) in 1960 so companies could develop publically traded real estate investment portfolios. REITs focus on commercial property, retail property, and rental property. During the last decade, REITs became more active in regional housing markets across the U.S. Single-family rental (SFR) REITs have grown tremendously, buying up residential properties across the country. In some regional housing markets, SFR REITs own noticeable shares of single-family homes. In those settings, SFR REITs take large numbers of housing units off of real estate markets where homeownership transactions occur and manage these properties as part of commercial rental inventories. This has resulted in a new category of multiple property owners, composed of institutional investors as opposed to individual investors, which further exacerbates property wealth concentration and polarization. This study examines the socio–spatial distribution of properties in SFR REIT portfolios to determine if SFR REIT properties tend to cluster in distinct areas. This study will focus on the regional housing market in Nashville, TN. Nashville has one of the most active SFR REIT sectors in the country. County tax assessor records were used to identify SFR REIT properties. These data were joined with U.S. Census data to create a profile of communities. The data were analyzed using SPSS statistical software and GIS software. Our analysis suggests that neighborhoods with clusters of SFR REITs fit the SFR REIT business model. Clusters occur in communities with newer homes, residents with higher levels of educational attainment, and middle to upper-middle incomes. The paper concludes with several recommendations for future research on SFR REITs.
Preparing Versus Persuading: Inequalities between Scottish State schools in University Application Guidance Practices
Authors: Ferguson Jennifer M. , Griffiths Dave
Published: September 2018

A university education is often regarded as a means for increasing social mobility, with attendance at a leading university seen as a pathway to an advantaged socio-economic status. However, inequalities are observable in attendance levels at leading UK universities, with children from less advantaged backgrounds less likely to attend the top universities (generally known as the Russell Group institutions). In this paper, we explore the different levels of assistance provided to state school children in preparing for their university applications. Guidance teachers and pupils at a range of Scottish state schools were interviewed. We find that inequalities exist in the cultivation of guidance provided by state schools, with high attainment schools focusing on preparing applicants to be desirable to leading universities, whilst low attainment schools focus on persuading their students that university is desirable.
Concussion in University Level Sport: Knowledge and Awareness of Athletes and Coaches
Authors: Kirk Ben , Pugh Jamie N. , Cousins Rosanna , Phillips Shaun M.
Published: September 2018

Using a cross-sectional survey concussion knowledge was evaluated among forty university-level athletes (n = 20, rugby union players; n = 20, Gaelic football players) and eight experienced team coaches (n = 2, rugby union; n = 2, Gaelic football; n = 1, soccer; n = 1, hockey; n = 1, netball; n = 1, basketball). Levels of knowledge of concussion were high across all participants. Coaches had higher knowledge scores for almost all areas; however, there was evidence of important gaps even in this group. Knowledge was not sufficient in identifying concussion, and when it is safe to return to play following a concussion. Impaired knowledge of how to recognise a concussion, and misunderstanding the need for rest and rehabilitation before return to play presents a hazard to health from second impact and more catastrophic brain injury. We discuss reasons for these guideline misconceptions, and suggest that attitude issues on the significance of concussion may underlie a willingness to want to play with a concussion. This suggests the current education on sport-related concussion needs to be expanded for the appropriate management of university-level contact sports.
Co-Activation, Estimated Anterior and Posterior Cruciate Ligament Forces, and Motor Unit Activation Strategies during the Time Course of Fatigue
Authors: Smith Cory M. , Housh Terry J. , Hill Ethan C. , Keller Joshua L. , Johnson Glen O. , Schmidt Richard J.
Published: September 2018

This study aimed to combine co-activation as well as anterior and posterior cruciate ligament force estimations with the motor unit activation strategies employed by the primary muscles that are involved in the movement at the knee joint. Fourteen male subject performed 25 maximal concentric isokinetic leg extension muscle actions at 120 s−1. Electromyographic and mechanomyographic signals from the vastus lateralis and bicep femoris, as well as force, were used to measure co-activation, and estimated anterior and posterior ligament forces during the time course of fatigue. There were decreases in quadriceps force and increases in hamstring force during the 25 leg extensions. The posterior cruciate ligament force was greater than the anterior cruciate ligament force during each leg extension. Both the posterior and anterior cruciate ligament forces decreased during the 25 leg extensions. Each muscle indicated unique neuromuscular responses, which may explain the decreases in quadriceps force and increases in the hamstring force. The combination of anterior and posterior cruciate ligament force estimation and motor unit activation strategies helped to provide a better understanding of the fatigue-related mechanism that was utilized to avoid injury and increase or maintain joint stability during the time course of fatigue.
Environmental Restoration in Hydropower Development—Lessons from Norway
Authors: Auestad Inger , Nilsen Yngve , Rydgren Knut
Published: September 2018

Hydropower is expanding globally and is regarded a key measure for mitigating climate change, but it also results in major environmental degradation, both at local scale and more widely. We can learn lessons about how restoration can be used to alleviate these problems from failures and successes in countries with a long history of hydropower development, such as Norway. Here, hydropower projects grew larger over time, and in the 1960s, the emerging environmentalist movement started to challenge hydropower developments because of their negative impacts on the environment. The Norwegian Water Resources and Energy Directorate then appointed a landscape architect who became very influential, particularly due to his skills in aesthetics and photo documentation. He developed principles for designing self-sustaining environments which he called “living nature”, and in particular proposed methods of restoring barren, unattractive, alpine spoil heaps. Later, restoration methods and goals have changed in response to new insights and the changing goals of ecological restoration. Here, we present current best practice for the alpine biome and sum up general lessons in three points: restoration can represent a sustainable, ‘third way’ in the conflict between conservation and development; including a wider group of professionals may improve restoration goals and methods, and effective use of visual communication can be a good way of gaining support for new restoration principles.
Traditional Farming and Sustainable Development of an Indigenous Community in the Mountain Area—A Case Study of Wutai Village in Taiwan
Authors: Ba Qing-Xiong , Lu Dau-Jye , Kuo Warren , Lai Po-Hsin
Published: September 2018

The present study aimed to explore traditional farming and its role in sustainable development of the mountainous area based on the indigenous community of Wutai in Taiwan as a case study. It adopted qualitative methods with an ethnographic orientation, to conduct in-depth interviews, participant observation, and focus groups as an integral component of public participation geographic information system (PPGIS), and aerial photo analysis to collect and analyze field data, mainly in 2013 and 2017. The results revealed the continuation of traditional farming practices guided by the traditional farming calendar and characterized by mixed cropping, inter cropping, and rotation, which optimized the use of limited arable lands in the area. These practices also contributed to maximizing and securing local food supply, and maintaining endemic crop varieties. The results suggested that traditional farming offered a way to overcome the limitation of modern agriculture and support ecotourism as a sustainable alternative to mass tourism, by preserving crop diversity, social institutions and cultural traditions, and stabilizing the local environment. Furthermore, our findings showed that traditional farming, in keeping with local capacity, was adaptable to the impacts of climate change. In the last two decades, a returning tide of young residents and retired people involved in traditional farming might play a key role in the slowing down of the loss of agricultural lands in Wutai, influenced by the fashion of healthy foods and environments, as well as development of local ecotourism industry. Learned from this study, while there would be some opportunity for traditional farming to be recognized as one of the key components to promote the sustainable development of indigenous villages in mountain areas, more policy incentives might be considered.
The Effects of Environmental Value Orientations and Experience-Use History on the Conservation Value of a National Park
Authors: Han Ju Hyoung , Choi Andy S. , Oh Chi-Ok
Published: September 2018

National parks are protected areas where special efforts are taken to conserve wildlife species and their habitats. However, the management of parks presents many diverse challenges. One effective mitigation strategy is understanding and improving the connections between environmental awareness and responsible behavior. This study examined this relationship by integrating the value orientations and environmental attitudes of individuals into the developmental process to understand individual conservation behavioral intentions measured as willingness to pay (WTP). The conceptual framework was constructed on the premise that the WTP is hierarchically developed: first, from value orientations and experience-use history, and, second, from attitudes toward national park management. Mt. Seorak National Park has faced diverse problems, including land use conflict from the development of cable cars and high-speed railroad tunnels through the park, and was chosen as a study site. The data were collected from 800 respondents through an online survey that considered the geographical area of residence. This study employed a confirmatory factor analysis and structural equation modeling along with a contingent valuation method to measure WTP. The findings from the study indicated that WTP was connected to specific dimensions of value orientations, attitudes, and experience-use history. The theoretical and practical implications are also discussed.
Fabless Semiconductor Firms’ Financial Performance Determinant Factors: Product Platform Efficiency and Technological Capability
Authors: Park Jun , Kook Sang , Im Hyeonu , Eum Soomin , Lee Chulung
Published: September 2018

The semiconductor industry is experiencing a rapid change since new markets and new technologies have emerged to give insights to product innovation. The semiconductor industry is now specializing into the integrated device manufacturer (IDM), fabless, and foundry sectors. We investigated the determinant factors that affect the financial performance of firms in the fabless sector, which is the most technology-intensive and product-oriented sector among the three sectors. The correlation among technological capability, product platform, and financial performance is analyzed by structural equation modeling. The data includes 17,256 patents from 2005 to 2014 and financial data from 2012 to 2016 from 57 firms that run businesses in the fabless sector. Specifically, technological capability includes technological assets, technology breadth, and technology depth. New product development occurs by applying product platform efficiency. Financial performance includes growth and profitability. The results show that advancing product platform efficiency brings positive effects to financial performance. Also, increasing technological depth and technological assets not only improve product platform efficiency, but also bring positive effects to financial performance. In addition, technological depth affected growth positively, and technological breadth affected profitability positively. The results show the direction that new product development strategy needs to take.
Preparing Public Pedagogies with ICT: The Case of Pesticides and Popular Education in Brazil
Authors: Paredes-Labra Joaquín , Siri Ivana-Mariel , Oliveira Alessandro
Published: September 2018

This article analyzes the conditions through which it is possible to launch a project of public pedagogy with information and communications technologies (ICT) on environmental education and pesticides. This is a public pedagogy that is agreed between diverse actors (universities, environmental agencies, local authorities, schools, and farmers) and adapted to their needs and demands, based on the study of an ongoing project. The methodology is qualitative, with interviews with key informants and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis developed with the contestants. In the development of the project, the conditions in which the theme of the project is lived and worked by the different actors who were included have been highlighted. The discussions that were held offered different points of view about the priorities of public health, environmental problems, and social and economic outputs. Among the conclusions, some difficulties came up about how to start public pedagogies that are managed by citizenship. The idea came from the community.
Fit between Organizational Culture and Innovation Strategy: Implications for Innovation Performance
Authors: Chen Zhi , Huang Shenglan , Liu Chong , Min Min , Zhou Liying
Published: September 2018

Although prior studies have indicated the interrelationships between specific types of innovation strategy and specific elements of organizational culture, few studies simultaneously evaluate the relationship between the two multi-dimensional constructs in holistic perspective. Based on configuration theory, we conceptualize fit as ‘profile deviation’, and investigate the fit between an organization’s culture and its innovation strategy. Data were collected from 183 Chinese organizations. We examine the hypothesis that greater fit between organizational culture and innovation strategy encourages superior innovation speed and innovation quality. Our results provide evidence that in the group of organizations exhibiting either exploratory or exploitative innovation strategy, the more similar the organizational culture configurations are to those of the top performers, the higher their innovation speed and innovation quality are. In the group of organizations exhibiting ambidextrous innovation strategy, the fit between organizational culture and innovation strategy is insignificantly associated with innovation speed and innovation quality. Implications for applying the culture–strategy fit in innovation management are discussed.
Developing Knowledge-Based Resources: The Role of Entrepreneurs’ Social Network Size and Trust
Authors: Yin Miaomiao , Jahanshahi Asghar Afshar
Published: September 2018

Entrepreneurs’ social networks play a crucial role in developing knowledge-based resources for their new ventures. Although most studies in an entrepreneurship context find that trust is very important when entrepreneurs develop social networks, limited research examines how trust can explain the variation in the relationship between an entrepreneur’s social networks and a firm’s knowledge-based resources. Therefore, the major objective of the paper is to understand the effects of the size of an entrepreneur’s social network on his or her firm’s knowledge-based resources with high and low levels of trust. Our data were collected from surveys administered to 476 entrepreneurs in China in 2018. Our multiple regression analysis indicates that social networks reinforce knowledge-based resources in a situation where entrepreneurs highly trust their major networks partners in their business environment (e.g., family, close friends, consultants, suppliers, peers, etc.). However, with a low level of trust, the relationship between social network and knowledge-based resources is curvilinear (inverse U-shaped). Our empirical validations showed that the relationship between social network and a firm’s knowledge-based resources is highly contingent to the level of trust among network members.