bluuebunny/crossref_metadata_2025 huggingface dataset:
Population Pharmacokinetics Modelling and Simulation of Mitotane in Patients with Adrenocortical Carcinoma: An Individualized Dose Regimen to Target All Patients at Three Months?
Authors: Cazaubon Yoann , Talineau Yohann , Feliu Catherine , Konecki Céline , Russello Jennifer , Mathieu Olivier , Djerada Zoubir
Published: October 2019

Mitotane is the most effective agent in post-operative treatment of adrenocortical carcinoma. In adults, the starting dose is 2–3 g/day and should be slightly increased to reach the therapeutic index of 14–20 mg/L. This study developed a population PK model for mitotane and to simulate recommended/high dosing regimens. We retrospectively analyzed the data files of 38 patients with 503 plasma concentrations for the pharmacokinetic analysis. Monolix version 2019R1 was used for non-linear mixed-effects modelling. Monte Carlo simulations were performed to evaluate the probability of target attainment (PTA ≥ 14 mg/L) at one month and at three months. Mitotane concentration data were best described by a linear one-compartment model. The estimated PK parameters (between-subject variability) were: 8900 L (90.4%) for central volume of distribution (V) and 70 L·h−1 (29.3%) for clearance (Cl). HDL, Triglyceride (Tg) and a latent covariate were found to influence Cl. The PTA at three months for 3, 6, 9, and 12 g per day was 10%, 55%, 76%, and 85%, respectively. For a loading dose of 15 g/day for one month then 5 g/day, the PTA in the first and third months was 57 and 69%, respectively. This is the first PKpop model of mitotane highlighting the effect of HDL and Tg covariates on the clearance as well as a subpopulation of ultrafast metabolizer. The simulations suggest that recommended dose regimens are not enough to target the therapeutic threshold in the third month.
Current Status of Soybean Anthracnose Associated with Colletotrichum truncatum in Brazil and Argentina
Authors: Dias Moab D. , Dias-Neto Justino J. , Santos Maria D.M. , Formento Angela Norma , Bizerra Lincoln V.A.S. , Fonseca Maria Esther N. , Boiteux Leonardo S. , Café-Filho Adalberto C.
Published: October 2019

Brazil and Argentina have a combined soybean area of 53.6 million hectares, which accounts for over half of the total global production. The soybean crop in South America extends from latitude 8–10° S to 32–36° S. Such a vast, almost contiguous area imposes a serious sanitary risk to the crop. Currently, the prevalence of anthracnose is increasing, with recurring reports of severe epidemics and expressive yield losses. Soybean anthracnose is mainly associated with Colletotrichum truncatum, although other Colletotrichum species have also been reported as causal agents of this disease. Knowledge about the morphological, cultural, and molecular variability of C. truncatum in South America is crucial for disease management. Here, we present data on the molecular, morphological, biological, cultural, and pathogenicity of C. truncatum isolates collected in Brazil and Argentina. Light microscopy and randomly-amplified polymorphic DNA (RAPD) analysis were used for estimating the variability of isolates. Colletotrichum truncatum displayed three types of conidiogenesis, viz. conidial formation from conidiogenous cells on hyphal extremities, in conidiomas in acervuli, and directly from fertile setae (a mechanism yet-unreported for C. truncatum). RAPD profiling was effective in revealing the genetic diversity among C. truncatum isolates. The intra-group similarity was greater among the Argentinian isolates when compared to the Brazilian group. Furthermore, the results indicated a strong correlation between geographical origin and molecular grouping, with the exclusive or semi-exclusive assembling of Brazilian and Argentinian isolates in distinct clades. Finally, a preliminary account of the reaction of soybean accessions to C. truncatum is also included.
Growth Characteristics of Adenophora triphylla var. japonica Hara Seedlings as Affected by Growing Medium
Authors: Lee Hye Ri , Kim Hyeon Min , Jeong Hyeon Woo , Kim Gam Gon , Na Chae In , Oh Myung Min , Hwang Seung Jae
Published: October 2019

Adenophora triphylla var. japonica Hara is a highly valued medicinal plant that is used to treat or prevent bronchitis, cough, cancer, and obesity. However, there has been no study on the production of Adenophora triphylla var. japonica Hara seedlings in a closed-type plant production system (CPPS). This study was conducted to examine the growth characteristics of Adenophora triphylla var. japonica Hara seedlings as affected by different growing media. The seeds were sown on a 128-cell plug tray filled with urethane sponges (US), LC grow foam (LC), rockwool (RW), or terra-plugs (TP). The seedlings were cultured for a duration of 54 days under temperature 25 ± 1°C, a photoperiod of 12/12 h (light/dark), and light intensity of 180 µmol·m−2·s−1 photosynthetic photon flux density provided by RB LEDs (red:blue = 8:2) in a closed-type plant production system (CPPS). The germination rate of Adenophora triphylla var. japonica Hara was significantly highest in the TP. Also, seedling shoot growth indicators of plant height, leaf length, leaf width, number of leaves, fresh weight (FW), and dry weight (DW) of the shoot, and leaf area were markedly the greatest in the TP and the lowest in the US. The SPAD (soil-plant analysis development) value was higher in the TP and US than in the LC or RW. In addition, the seedling root growth characteristics of total root length, root surface area, root volume, and number of root tips were significantly greatest in the TP. Moreover, the maximum root diameter, FW and DW of roots were the greatest in the TP. In conclusion, the results suggest that TP are viable for the growth development of Adenophora triphylla var. japonica Hara seedlings.
Guidance as ‘Women’s Work’: A New Generation of Female Islamic Authorities in Britain
Authors: Liberatore Giulia
Published: October 2019

This article is about a new publicly visible generation of female Islamic authorities in the UK and the ways in which they make sense of what it means to be a female authority within largely male-dominated structures of knowledge production. These authorities are setting up their own institutes and emphasising the importance of drawing from within the Islamic tradition while contextualising it in the British context. On the one hand, they stress their unique ability as women to provide personal and collective guidance, based on relationships of empathy and care, that addresses the needs of Muslim women in Britain. On the other hand, they recognise the limitations of presenting guidance as ‘women’s work’, and they seek to pluralise their roles or to present gender as irrelevant in their work. By navigating between accepting, pluralising and transcending female modes of authority, they carve out legitimate spaces for themselves as female leaders while developing and imagining new understandings of Islamic knowledge and plural models of pious leadership. I argue that these multiple ways of making sense of their experiences move us away from theorising female religious leadership solely through binary tropes, such as liberal/orthodox Islam, resistance/compliance, enabling/constraining, which continue to shape research in the field.
Crux Christi Sit Mecum: Devotion to the Apotropaic Cross
Authors: Hagen Kaja Merete Haug
Published: October 2019

A late medieval paper amulet containing prayers to St. Dorothy and the Holy Cross was found in a demolished part of a medieval wooden stave church in Torpo, Norway. This article examines the content and the function of this textual amulet by placing it in a wider Scandinavian and Western European context. From the perspective of materiality and sensory-based religious practices, this article will explore the connection between the textual amulet found in Torpo and its relation to the now-lost large wooden cross in Torpo church, and to crosses believed to be wonderworking or miraculous in its proximity. By doing so, this study will shed light on the apotropaic and healing potential that the material and immaterial cross offered the pious in late medieval Norway. The last part of this article addresses the Post-Reformation theological understanding of the amulet, and its use and function in Lutheran Norwegian society.
Comparative Hagiology and/as Manuscript Studies: Method and Materiality
Authors: Zimbalist Barbara
Published: October 2019

Although the academic study of hagiography continues to flourish, the role of comparative methods within the study of sanctity and the saints remains underutilized. Similarly, while much valuable work on saints and sanctity relies on materialist methodologies, issues of critical bibliography particular to the study of hagiography have not received the theoretical attention they deserve. This essay takes up these two underattended approaches to argue for a comparative materialist approach to hagiography. Through a short case study of the Latin Vita of Lutgard of Aywières (1182–1246) written by the Dominican friar Thomas of Cantimpré (c. 1200–1270), I suggest that comparative material research into the textual history of hagiographic literature can provide us with a more comprehensive and nuanced picture of the production of any specific holy figure, as well as the evolving discourses of sanctity and holiness in general. While this suggestion emerges from my own work on medieval hagiography from the Christian Latin West, it resonates with recent arguments by Sara Ritchey and David DiValerio to call for a materially comparative approach to narratives of holy lives in any religious tradition in any time period. Furthermore, I suggest that medieval studies, and in particular medieval manuscript studies, may have much to offer to scholars of sanctity working in later periods and other settings. Offering a view of material textual scholarship as intrinsically comparative, we may expand our theoretical definitions of the comparative and its possibilities within the study of sanctity.
Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana
Authors: Jensen Daniel , Cavanaugh Kyle C. , Simard Marc , Okin Gregory S. , Castañeda-Moya Edward , McCall Annabeth , Twilley Robert R.
Published: October 2019

Aboveground biomass (AGB) plays a critical functional role in coastal wetland ecosystem stability, with high biomass vegetation contributing to organic matter production, sediment accretion potential, and the surface elevation’s ability to keep pace with relative sea level rise. Many remote sensing studies have employed either imaging spectrometer or synthetic aperture radar (SAR) for AGB estimation in various environments for assessing ecosystem health and carbon storage. This study leverages airborne data from NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to assess their unique capabilities in combination to estimate AGB in coastal deltaic wetlands. Here we develop AGB models for emergent herbaceous and forested wetland vegetation in coastal Louisiana. In addition to horizontally emitted, vertically received (HV) backscatter, SAR parameters are expressed by the Freeman–Durden polarimetric decomposition components representing volume and double-bounce scattering. The imaging spectrometer parameters include normalized difference vegetation index (NDVI), reflectance from 290 visible-shortwave infrared (VSWIR) bands, the first derivatives from those bands, or partial least squares (PLS) x-scores derived from those data. Model metrics and cross-validation indicate that the integrated models using the Freeman-Durden components and PLS x-scores improve AGB estimates for both wetland vegetation types. In our study domain over Louisiana’s Wax Lake Delta (WLD), we estimated a mean herbaceous wetland AGB of 3.58 Megagrams/hectare (Mg/ha) and a total of 3551.31 Mg over 9.92 km2, and a mean forested wetland AGB of 294.78 Mg/ha and a total of 27,499.14 Mg over 0.93 km2. While the addition of SAR-derived values to imaging spectrometer data provides a nominal error decrease for herbaceous wetland AGB, this combination significantly improves forested wetland AGB prediction. This integrative approach is particularly effective in forested wetlands as canopy-level biochemical characteristics are captured by the imaging spectrometer in addition to the variable structural information measured by the SAR.
Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa
Authors: Buma Willibroad Gabila , Lee Sang-Il
Published: October 2019

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.
Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade
Authors: Ma Dandan , Yuan Yuan , Wang Qi
Published: October 2019

A hyperspectral image usually covers a large scale of ground scene, which contains various materials with different spectral properties. When directly exploring the background information using all the image pixels, complex spectral interactions and inter-/intra-difference of different samples will significantly reduce the accuracy of background evaluation and further affect the detection performance. To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model. Through identifying separability of hyperspectral pixels, background samples are sifted out layer-by-layer according to their separable degrees from anomalies, which can ensure the accuracy and distinctiveness of background representation. First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering. Second, depending on different separability computed by sparse representation, samples are separated into different sets which can effectively and completely reflect various characteristics of background across all the cascade layers. Meanwhile, some potential abnormal targets are removed at each selection step to avoid their effects on subsequent layers. Finally, comprehensively taking different good properties of all the separability-aware layers into consideration, a simple multilayer anomaly detection strategy is adopted to obtain the final detection map. Extensive experimental results on five real-world hyperspectral images demonstrate our method’s superior performance. Compared with seven representative anomaly detection methods, our method improves the average detection accuracy with great advantages.
A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning
Authors: Wang Siheng , Yang Dong , Li Zhen , Liu Liangyun , Huang Changping , Zhang Lifu
Published: October 2019

Remote sensing (RS) provides operational monitoring of terrestrial vegetation. For optical RS, vegetation information is generally derived from surface reflectance (ρ). More generally, vegetation indices (VIs) are built on the basis of ρ as proxies for vegetation traits. At canopy level, ρ can be affected by a variety of factors, including leaf constituents, canopy structure, background reflectivity, and sun-sensor geometry. Consequently, VIs are mixtures of different information. In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales. The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF) learning. Specifically, combined factors such as canopy chlorophyll content (CCC) and canopy water content (CWC) are introduced in the RF-based GSA. We find that for most VIs, the leaf area index is the most influential factor, while the broad-band sensor-derived enhanced VI (EVI) exhibits a strong sensitivity to CCC, and the universal normalized VI (UNVI) is sensitive to CWC. The potential and uncertainty for the application of all the considered VIs are analyzed according to the GSA results. The results can help to improve the use of VIs in different contexts, and the RF-based GSA method can be further applied in more sophisticated situations.
First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
Authors: Carreno-Luengo Hugo , Luzi Guido , Crosetto Michele
Published: October 2019

Understanding the effects of Earth’s surface topography on Global Navigation Satellite Systems Reflectometry (GNSS-R) space-borne data is important to calibrate experimental measurements, so as to provide accurate soil moisture content (SMC) retrievals. In this study, several scientific observables obtained from delay-Doppler maps (DDMs) ⟨ | Y r , t o p o ( τ , f ) | 2 ⟩ generated on board the Cyclone Global Navigation Satellite System (CyGNSS) mission were evaluated as a function of several topographic parameters derived from a digital elevation model (DEM). This assessment was performed as a function of Soil Moisture Active Passive (SMAP)-derived SMC at grazing angles θ e ~ [20,30] ° and in a nadir-looking configuration θ e ~ [80,90] °. Global scale results showed that the width of the trailing edge (TE) was small T E ~ [100, 250] m and the reflectivity was high Γ ~ [–10, –3] dB over flat areas with low topographic heterogeneity, because of an increasing coherence of Earth-reflected Global Positioning System (GPS) signals. However, the strong impact of several topographic features over areas with rough topography provided motivation to perform a parametric analysis. A specific target area with little vegetation, low small-scale surface roughness, and a wide variety of terrains in South Asia was selected. A significant influence of several topographic parameters i.e., surface slopes and curvatures was observed. This triggered our study of the sensitivity of T E and Γ to SMC and topographic wetness index ( T W I ). Regional scale results showed that T E and Γ are strongly correlated with the T W I , while the sensitivity to SMC was almost negligible. The Pearson correlation coefficients of T E and Γ with T W I are r Γ ~ 0.59 and r T E ~−0.63 at θ e ~ [20, 30] ° and r Γ ~ 0.48 and r T E ~ −0.50 at θ e ~ [80, 90] °, respectively.
Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting
Authors: Myers Emily , Kerekes John , Daughtry Craig , Russ Andrew
Published: October 2019

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.
Soil Moisture Estimation by GNSS Multipath Signal
Authors: Chang Xin , Jin Taoyong , Yu Kegen , Li Yunwei , Li Jiancheng , Zhang Qiang
Published: October 2019

Global navigation satellite system (GNSS) multipath signals received by a geodetic-quality GNSS receiver can be used to estimate the water content of soil around the antenna. The direct signals from satellite to GNSS antenna are the most valuable signals in geodetic measurement, such as positioning, navigation, GNSS control network, deformation monitoring, and so on. However, the GNSS antenna also captures the reflected signals from the ground, which contain information of surrounding environment, so that useful information about the reflection surface can be inferred by analyzing the reflected signal. This technique is termed as GNSS-interferometric reflectometry. The signal-to-noise ratio (SNR) data recorded by a receiver contains SNR component of reflected signals, which is related to the soil moisture of the ground. The changes of soil moisture content will cause the change of soil permittivity and reflectivity which are the key factors that make further change of the SNR of reflected signals. We used the measured data to evaluate the correlation between amplitude of multipath induced SNR time series and real soil moisture. An improved soil moisture estimation algorithm based on multipath induced SNR amplitude data is proposed in this paper. The performance of the proposed soil moisture estimation method is evaluated using the 15-month data recorded by PBO H2O GNSS station and a 14-day experiment in Wuhan, China. The experimental results show that the estimated soil moisture has a strong correlation with the real soil moisture and the estimation accuracy in terms of root-mean-square error (RMSE) is 0.0345 cm3cm−3 and 0.0339 cm3cm−3, respectively. Meanwhile, the application scope of the method is given.
An Empirical Assessment of Angular Dependency for RedEdge-M in Sloped Terrain Viticulture
Authors: Gong Chizhang , Buddenbaum Henning , Retzlaff Rebecca , Udelhoven Thomas
Published: October 2019

For grape canopy pixels captured by an unmanned aerial vehicle (UAV) tilt-mounted RedEdge-M multispectral sensor in a sloped vineyard, an in situ Walthall model can be established with purely image-based methods. This was derived from RedEdge-M directional reflectance and a vineyard 3D surface model generated from the same imagery. The model was used to correct the angular effects in the reflectance images to form normalized difference vegetation index (NDVI) orthomosaics of different view angles. The results showed that the effect could be corrected to a certain scope, but not completely. There are three drawbacks that might restrict a successful angular model construction and correction: (1) the observable micro shadow variation on the canopy enabled by the high resolution; (2) the complexity of vine canopies that causes an inconsistency between reflectance and canopy geometry, including effects such as micro shadows and near-infrared (NIR) additive effects; and (3) the resolution limit of a 3D model to represent the accurate real-world optical geometry. The conclusion is that grape canopies might be too inhomogeneous for the tested method to perform the angular correction in high quality.
A Fog Computing Enabled Virtual Power Plant Model for Delivery of Frequency Restoration Reserve Services
Authors: Pop Claudia , Antal Marcel , Cioara Tudor , Anghel Ionut , Salomie Ioan , Bertoncini Massimo
Published: October 2019

Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services act as a barrier for small scale prosumers participation forcing the implementation of new cooperative business models at the local level. This paper is proposing a fog computing infrastructure for the local management of energy systems and the creation of coalitions of prosumers able to provide ancillary services to the grid. It features an edge devices layer for energy monitoring of individual prosumers, a fog layer providing Information and Communication Technologies (ICT) techniques for managing local energy systems by implementing cooperative models, and a cloud layer where the service specific technical requirements are defined. On top, a model has been defined allowing the dynamical construction of coalitions of prosumers as Virtual Power Plants at the fog layer for the provisioning of frequency restoration reserve services while considering both the prosumers’ local constraints and the service ones as well as the constituents’ profit maximization. Simulation results show our solution effectiveness in selecting the optimal coalition of prosumers to reliably deliver the service meeting the technical constraints while featuring a low time and computation overhead being feasible to be run closer to the edge.
Remote Sensing Greenness and Urbanization in Ecohydrological Model Analysis: Asia and Australasia (1982–2015)
Authors: Cai Danlu , Fraedrich Klaus , Guan Yanning , Guo Shan , Zhang Chunyan , Sun Rui , Wu Zhixiang
Published: October 2019

Linking remote sensing information and ecohydrological models to improve understanding of terrestrial biosphere responses to climate and land use change has become the subject of increased interest due to the impacts of current global changes and the effect on the sustainability of human lifestyles. An application to Asia and Australasia (1982–2015) is presented, revealing the following results: (i) The broad distribution of regions with the enhanced vegetation greenness only follows the general pattern as for the whole, without obvious dependence on regional or climate fluxes ratios. That indicates a prevailing increasing greenness over land due to both the impacts of current global changes and the sustainability of human lifestyles; (ii) regions with vegetation greenness reduction reveal a unique distribution, concentrating in the water-limited domain due to the impacts of external (climatically “dry gets drier and wet gets wetter”) and internal (anthropogenically increased evaporation) changes; (iii) the external changes of dryness diverge at the boundary separating energy from water-limited regimes, and the internal changes indicate large-scale afforestation and deforestation) that occur mainly in China and Russia due to a conservation program and illegal logging, respectively, and a massive conversion of tropical forest to industrial tree plantations in Southeast Asia, leading to an increased evaporation.
A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
Authors: Sousa Daniela , Hernandez Diego , Oliveira Francisco , Luís Miguel , Sargento Susana
Published: October 2019

The Internet of Things (IoT) is a rapidly evolving technology that is changing almost every business, and aquaculture is no exception. In this work we present an integrated IoT platform for the acquisition of environmental data and the monitoring of aquaculture environments, supported by a real-time communication and processing network. The complete monitoring platform consists of environmental sensors equipped in a swarm of mobile Unmanned Surface Vehicles (USVs) and Buoys, capable of collecting aquatic and outside information, and sending it to a central station where it will be stored and processed. The sensing platform, formed by the USVs and Buoys, are equipped with multi-communication technology: IEEE 802.11n (Wi-Fi) and Bluetooth for short range communication, for mission delegation and the transmission of data collection, and LoRa for periodic report. On the back-end side, supported by FIWARE technology, an interactive web-based platform can be used to define sensing missions and for data visualization. Results on the sensing platform lifetime, mission control and delay processing time are presented to assess the performance of the aquatic monitoring system.
Piezoelectric Micromachined Ultrasonic Transducers with a Cost-Effective Bottom-Up Fabrication Scheme for Millimeter-Scale Range Finding
Authors: Feng Guo-Hua , Liu Hua-Jin
Published: October 2019

This study proposes a novel piezoelectric micromachined ultrasonic transducer (PMUT), fabricated on a metal foil. Using a bottom-up, cost-effective micromachining technique, the PMUTs made of electrodes, a piezoelectric film, or electrode-sandwiched structures with versatile patterns were implemented on a large-area foil thinner rather than regular paper. The proposed microfabrication facilitated the PMUT to be able to generate ultrasonic waves with fundamental and harmonic resonances. The fourth-order resonances of the fabricated PMUT functionally operated at an ultrasonic spectrum of approximately 30 kHz as an ultrasonic emitter. The developed PMUT was paired with a microelectromechanical system (MEMS) microphone module for range-finding applications in the range of several tens of millimeters. A signal-processing scheme was developed to extract the representative pattern from the acquired signals that were emitted and received. The pattern enabled finding the distance between the PMUT and the microphone using time-of-flight and strength-variation technology. The developed PMUT-microphone pair demonstrated its range-finding performance, displaying an error of less than 0.7% using the time-of-flight method.
An Onboard Vision-Based System for Autonomous Landing of a Low-Cost Quadrotor on a Novel Landing Pad
Authors: Liu Xuancen , Zhang Shifeng , Tian Jiayi , Liu Longbin
Published: October 2019

In this paper, an onboard vision-based system for the autonomous landing of a low-cost quadrotor is presented. A novel landing pad with different optical markers sizes is carefully designed to be robustly recognized at different distances. To provide reliable pose information in a GPS (Global Positioning System)-denied environment, a vision algorithm for real-time landing pad recognition and pose estimation is implemented. The dynamic model of the quadrotor is established and a system scheme for autonomous landing control is presented. A series of autonomous flights have been successfully performed, and a video of the experiment is available online. The efficiency and accuracy of the presented vision-based system is demonstrated by using its position and attitude estimates as control inputs for the autonomous landing of a self-customized quadrotor.
Statistical Evaluation of Barkhausen Noise Testing (BNT) for Ground Samples
Authors: Tomkowski Robert , Sorsa Aki , Santa-aho Suvi , Lundin Per , Vippola Minnamari
Published: October 2019

Barkhausen noise testing (BNT) is a nondestructive method for investigating many properties of ferromagnetic materials. The most common application is the monitoring of grinding burns caused by introducing locally high temperatures while grinding. Other features, such as microstructure, residual stress changes, hardening depth, and so forth, can be monitored as well. Nevertheless, because BNT is a method based on a complex magnetoelectric phenomenon, it is not yet standardized. Therefore, there is a need to study the traceability and stability of the measurement method. This study aimed to carry out a statistical analysis of ferromagnetic samples after grinding processes by the use of BNT. The first part of the experiment was to grind samples in different facilities (Sweden and Finland) with similar grinding parameters, different grinding wheels, and different hardness values. The second part was to evaluate measured BNT parameters to determine significant factors affecting BNT signal value. The measurement data from the samples were divided into two different batches according to where they were manufactured. Both grinding batches contained measurement data from three different participants. The main feature for calculation was the root-mean-square (RMS) value. The first processing step was to normalize the RMS values for all the measurements. A standard analysis of variance (ANOVA) was applied for the normalized dataset. The ANOVA showed that the grinding parameters had a significant impact on the BNT signal value, while the other investigated factors (e.g., participant) were negligible. The reasons for this are discussed at the end of the paper.
Investigation of a Magnetic Tunnel Junction Based Sensor for the Detection of Defects in Reinforced Concrete at High Lift-Off
Authors: Mohd Noor Sam Muhamad Arif Ihsan , Jin Zhenhu , Oogane Mikihiko , Ando Yasuo
Published: October 2019

Magnetic flux leakage (MFL) testing is a method of non-destructive testing (NDT), whereby the material is magnetized, and when a defect is present, the magnetic flux lines break out of the material. The magnitude of the leaked magnetic flux decreases as the lift-off (distance from the material) increases. Therefore, for detection at high lift-off, a sensitive magnetic sensor is required. To increase the output sensitivity, this paper proposes the application of magnetic tunnel junction (MTJ) sensors in a bridge circuit for the NDT of reinforced concrete at high lift-off. MTJ sensors were connected to a full-bridge circuit, where one side of the arm has two MTJ sensors connected in series, and the other contains a resistor and a variable resistor. Their responses towards a bias magnetic field were measured, and, based on the results, the sensor circuit sensitivity was 0.135 mV/mT. Finally, a reinforced concrete specimen with a 1 cm gap in the center was detected. The sensor module (with an amplifier and low pass filter circuits) could determine the gap even at 50 cm, suggesting that MTJ sensors have the potential to detect defects at high lift-off values and have a promising future in the field of NDT.
K-Means Spreading Factor Allocation for Large-Scale LoRa Networks
Authors: Asad Ullah Muhammad , Iqbal Junnaid , Hoeller Arliones , Souza Richard Demo , Alves Hirley
Published: October 2019

Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by 1.53 times.
Energy-Efficient Massive Data Dissemination through Vehicle Mobility in Smart Cities
Authors: Naseer Salman , Liu William , Sarkar Nurul I
Published: October 2019

One of the main challenges of operating a smart city (SC) is collecting the massive data generated from multiple data sources (DSs) and transmitting them to the control units (CUs) for further data processing and analysis. These ever-increasing data demands require not only more and more capacity of the transmission channels but also results in resource over-provision to meet the resilience requirements, thus the unavoidable waste as a result of the data fluctuations throughout the day. In addition, the high energy consumption (EC) and carbon discharge from these data transmissions posing serious issues to the environment we live in. Therefore, to overcome the issues of intensive EC and carbon emission (CE) of massive data dissemination in SCs, we propose an energy-efficient and carbon reduction approach by using the daily mobility of the existing vehicles as an alternative communications channel to accommodate the data dissemination in SCs. To illustrate the effectiveness and efficiency of our approach, we take the Auckland City in New Zealand as an example, assuming massive data generated by various sources geographically scattered throughout the Auckland region, to the control centres located in the city. Results obtained show that our proposed approach can provide up to four times faster transferring the large volume of data by using the existing daily vehicles’ mobility, than the conventional transmission network. Moreover, our proposed approach offers about 32% less EC and CE than that of conventional network transmission approach.
An Adaptive Exposure Fusion Method Using Fuzzy Logic and Multivariate Normal Conditional Random Fields
Authors: Lin Yu-Hsiu , Hua Kai-Lung , Lu Hsin-Han , Sun Wei-Lun , Chen Yung-Yao
Published: October 2019

High dynamic range (HDR) has wide applications involving intelligent vision sensing which includes enhanced electronic imaging, smart surveillance, self-driving cars, intelligent medical diagnosis, etc. Exposure fusion is an essential HDR technique which fuses different exposures of the same scene into an HDR-like image. However, determining the appropriate fusion weights is difficult because each differently exposed image only contains a subset of the scene’s details. When blending, the problem of local color inconsistency is more challenging; thus, it often requires manual tuning to avoid image artifacts. To address this problem, we present an adaptive coarse-to-fine searching approach to find the optimal fusion weights. In the coarse-tuning stage, fuzzy logic is used to efficiently decide the initial weights. In the fine-tuning stage, the multivariate normal conditional random field model is used to adjust the fuzzy-based initial weights which allows us to consider both intra- and inter-image information in the data. Moreover, a multiscale enhanced fusion scheme is proposed to blend input images when maintaining the details in each scale-level. The proposed fuzzy-based MNCRF (Multivariate Normal Conditional Random Fields) fusion method provided a smoother blending result and a more natural look. Meanwhile, the details in the highlighted and dark regions were preserved simultaneously. The experimental results demonstrated that our work outperformed the state-of-the-art methods not only in several objective quality measures but also in a user study analysis.
High Rate Dynamic Monitoring with Fabry–Perot Interferometric Sensors: An Alternative Interrogation Technique Targeting Biomedical Applications
Authors: Domingues M. Fátima , Tavares Cátia , Alberto Nélia , Radwan Ayman , André Paulo , Antunes Paulo
Published: October 2019

Fabry–Perot interferometric (FPI) sensors are an accurate and well-established sensing technology that are used to monitor a wide range of parameters such as strain, temperature, and refractive index, among many others. Nevertheless, due to the limited number and high cost of existing interrogation techniques for FPIs, its use is often restricted to discrete measurements, not being so explored for dynamic applications. The development of an alternative interrogation technique for a high rate of acquisition may propel this type of sensor into less explored fields such as dynamic biomedical applications. In this work, we present the theoretical and experimental analyses of an FPI sensing architecture by using an alternative high rate dynamic acquisition methodology, based on frequency to amplitude conversion, where the FPI spectral shift is detuned by the convolution of the optical light source with the FPI interference pattern. The good agreement between the theoretical and experimental results verified the reliability of the proposed methodology. Moreover, preliminary results show that the developed sensing architecture can be a suitable solution to monitor biomedical parameters such as the carotid pulse wave.
Accident Rate as a Measure of Safety Assessment in Polish Civil Engineering
Authors: Cieslewicz Wieslawa , Araszkiewicz Krystyna , Sikora Pawel
Published: October 2019

Civil engineering is characterised by high volatility with respect to working conditions, which are the source of many threats to worker life and health and contribute to high accident rates. The purpose of this paper is to analyse and evaluate the phenomenon of accidents in Polish civil engineering and define the direction of changes that should occur in the safety area. The studies included in this research covered the years 2004–2018. The following indicators were used to assess the accident rates: (1) The total number of persons injured in workplace accidents and this total divided into fatal, severe, and minor accidents; (2) indicators of frequency of accidents in total and this total divided into minor, severe, and fatal accidents; (3) and an indicator of the severity of accidents. From the analysis of statistical data for the years 200–2018, the changes in accident rate parameters demonstrate the continuous improvement of workplace safety in the Polish civil engineering sector. From the analysed data from the 15 years, a clear decrease in the value of the applied indicators is apparent, including both the number of people involved in accidents and the frequency and severity of accidents.
Massification, Marketisation and Loss of Differentiation in Pre-Entry Marketing Materials in UK Higher Education
Authors: Knight Elizabeth
Published: October 2019

Since the mid-1970s, the higher education system in the UK has massified. Over this period, the government policy drivers for higher education have shifted towards a homogenised rationale, linking higher education to the economic well-being of the country. The massification of higher education has involved a widening of participation from traditional students to new and diverse student cohorts with differing information needs. The increased positioning of students as consumers by higher education means the student choice process has become complex. Drawing on a recently conferred doctorate, this article asks whether the messages sent by institutions about the motivation for undertaking a degree have changed during the recent period of massification of UK higher education. It asks how such changes are reflected, overtly or in coded form, in the institutional pre-entry ‘prospectus’ documents aimed at students. Taking a discourse-historical approach, the work identifies six periods of discourse change between 1976 and 2013, analysing prospectuses from four case-study institutions of different perceived status. The research finds that the materials homogenise gradually over the period and there is a concordant concealment of the differential status, purpose and offer of the institutions, alongside an increase in the functional importance of the coded signalling power of the differential prestige of undergraduate degrees within the UK. This research’s finding that the documents produced by institutions have become increasingly difficult to differentiate highlights equity issues in provision of marketing in terms of widening participation and fair access aims.
Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone
Authors: Mahmood Bawan , Kianfar Jalil
Published: October 2019

Traffic impact assessment is a key step in the process of work zone planning and scheduling for transportation agencies. Microscopic traffic simulation models enable transportation agencies to conduct detailed analyses of work zone mobility performance measures during the planning and scheduling process. However, traffic simulation results are valid only when the simulation model is calibrated to replicate driver behavior that is observed in the field. Few studies have provided guidance on the calibration of traffic simulation models at work zones and have offered driver behavior parameters that reproduce capacity values that are observed in the field. This paper contributes to existing knowledge of work zone simulation by providing separate driver behavior model parameters for heavy vehicles and passenger vehicles. The driver behavior parameters replicate the flow and speed at the work zone taper and at roadway segments upstream of the work zone. A particle swarm optimization framework is proposed to improve the efficiency of the calibration process. The desired time headway was found to be 2.31 seconds for heavy vehicles and 1.53 seconds for passenger cars. The longitudinal following threshold was found to be 17.64 meters for heavy vehicles and 11.70 meters for passenger cars. The proposed parameters were tested against field data that had not previously been used in the calibration of driver behavior models. The average absolute relative error for flow rate at the taper was 10% and the mean absolute error was 54 veh/h/ln. The GEH statistic for the validation dataset was 1.48.