bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition
Authors: Valentini Emiliana , Taramelli Andrea , Cappucci Sergio , Filipponi Federico , Nguyen Xuan Alessandra
Published: April 2020

Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape.
Landslide Prediction Method Based on a Ground-Based Micro-Deformation Monitoring Radar
Authors: Qi Lin , Tan Weixian , Huang Pingping , Xu Wei , Qi Yaolong , Zhang Mingzhi
Published: April 2020

As remote sensing methods have received a lot of attention, ground-based micro- deformation monitoring radars have been widely used in recent years due to their wide range, high accuracy, and all-day monitoring capability. On the one hand, these monitoring radars break through the limitations of traditional point monitoring equipment such as the Global Navigation Satellite System (GNSS) and fissure meters in terms of monitoring scope and ease of installation. On the other hand, the data types of these monitoring radars are more varied. Therefore, it may be difficult for the data-processing method of traditional point monitoring equipment to take all advantages of this type of radar. In this paper, based on time-series monitoring data of ground-based micro-deformation monitoring radars, three parameters—extent of change (EOC), extent of stability (EOS), and extent of mutation (EOM)—are calculated according to deformation value, coherence and deformation pixels size. Then a method for landslide prediction by combining these three parameters with the inverse velocity method is proposed. The effectiveness of this method is verified by the measured data of a landslide in Yunnan Province, China. The experimental results show that the method can correctly discern deformation areas and provide more accurate monitoring results, especially when the deformation trend changes rapidly. In summary, this method can improve the response rate and prediction accuracy in extreme cases, such as rapid deformation.
Precipitation-Sensitive Dynamic Threshold: A New and Simple Method to Detect and Monitor Forest and Woody Vegetation Cover in Sub-Humid to Arid Areas
Authors: Drori Ron , Dan Harel , Sprintsin Michael , Sheffer Efrat
Published: April 2020

Remote-sensing tools and satellite data are often used to map and monitor changes in vegetation cover in forests and other perennial woody vegetation. Large-scale vegetation mapping from remote sensing is usually based on the classification of its spectral properties by means of spectral Vegetation Indices (VIs) and a set of rules that define the connection between them and vegetation cover. However, observations show that, across a gradient of precipitation, similar values of VI can be found for different levels of vegetation cover as a result of concurrent changes in the leaf density (Leaf Area Index—LAI) of plant canopies. Here we examine the three-way link between precipitation, vegetation cover, and LAI, with a focus on the dry range of precipitation in semi-arid to dry sub-humid zones, and propose a new and simple approach to delineate woody vegetation in these regions. By showing that the range of values of Normalized Difference Vegetation Index (NDVI) that represent woody vegetation changes along a gradient of precipitation, we propose a data-based dynamic lower threshold of NDVI that can be used to delineate woody vegetation from non-vegetated areas. This lower threshold changes with mean annual precipitation, ranging from less than 0.1 in semi-arid areas, to over 0.25 in mesic Mediterranean area. Validation results show that this precipitation-sensitive dynamic threshold provides a more accurate delineation of forests and other woody vegetation across the precipitation gradient, compared to the traditional constant threshold approach.
Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery
Authors: Nigon Tyler , Yang Ce , Dias Paiao Gabriel , Mulla David , Knight Joseph , Fernández Fabián
Published: April 2020

The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.
Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
Authors: Blanch Xabier , Abellan Antonio , Guinau Marta
Published: April 2020

The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points (GCPs) are not available or the camera system cannot be properly calibrated. This paper presents a new workflow called Point Cloud Stacking (PCStacking) that overcomes these restrictions by making the most of the iterative solutions in both camera position estimation and internal calibration parameters that are obtained during bundle adjustment. The basic principle of the stacking algorithm is straightforward: it computes the median of the Z coordinates of each point for multiple photogrammetric models to give a resulting PC with a greater precision than any of the individual PC. The different models are reconstructed from images taken simultaneously from, at least, five points of view, reducing the systematic errors associated with the photogrammetric reconstruction workflow. The algorithm was tested using both a synthetic point cloud and a real 3D dataset from a rock cliff. The synthetic data were created using mathematical functions that attempt to emulate the photogrammetric models. Real data were obtained by very low-cost photogrammetric systems specially developed for this experiment. Resulting point clouds were improved when applying the algorithm in synthetic and real experiments, e.g., 25th and 75th error percentiles were reduced from 3.2 cm to 1.4 cm in synthetic tests and from 1.5 cm to 0.5 cm in real conditions.
Gait Characteristics under Imposed Challenge Speed Conditions in Patients with Parkinson’s Disease During Overground Walking
Authors: Lee Myeounggon , Youm Changhong , Noh Byungjoo , Park Hwayoung , Cheon Sang-Myung
Published: April 2020

Evaluating gait stability at slower or faster speeds and self-preferred speeds based on continuous steps may assist in determining the severity of motor symptoms in Parkinson’s disease (PD) patients. This study aimed to investigate the gait ability at imposed speed conditions in PD patients during overground walking. Overall, 74 PD patients and 52 age-matched healthy controls were recruited. Levodopa was administered to patients in the PD group, and all participants completed imposed slower, preferred, and faster speed walking tests along a straight 15-m walkway wearing shoe-type inertial measurement units. Reliability of the slower and faster conditions between the estimated and measured speeds indicated excellent agreement for PD patients and controls. PD patients demonstrated higher gait asymmetry (GA) and coefficient of variance (CV) for stride length and stance phase than the controls at slower speeds and higher CVs for phases for single support, double support, and stance. CV of the double support phase could distinguish between PD patients and controls at faster speeds. The GA and CVs of stride length and phase-related variables were associated with motor symptoms in PD patients. Speed conditions should be considered during gait analysis. Gait variability could evaluate the severity of motor symptoms in PD patients.
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3
Authors: Liu Guoxu , Nouaze Joseph Christian , Touko Mbouembe Philippe Lyonel , Kim Jae Ho
Published: April 2020

Automatic fruit detection is a very important benefit of harvesting robots. However, complicated environment conditions, such as illumination variation, branch, and leaf occlusion as well as tomato overlap, have made fruit detection very challenging. In this study, an improved tomato detection model called YOLO-Tomato is proposed for dealing with these problems, based on YOLOv3. A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model. Moreover, the model replaces the traditional rectangular bounding box (R-Bbox) with a circular bounding box (C-Bbox) for tomato localization. The new bounding boxes can then match the tomatoes more precisely, and thus improve the Intersection-over-Union (IoU) calculation for the Non-Maximum Suppression (NMS). They also reduce prediction coordinates. An ablation study demonstrated the efficacy of these modifications. The YOLO-Tomato was compared to several state-of-the-art detection methods and it had the best detection performance.
Monitoring of Interfacial Debonding of Concrete Filled Pultrusion-GFRP Tubular Column Based on Piezoelectric Smart Aggregate and Wavelet Analysis
Authors: Yang Wenwei , Yang Xia , Li Shuntao
Published: April 2020

The concrete filled pultrusion-GFRP (Glass Fiber Reinforced Polymer) tubular column (CFGC) is popular in hydraulic structures or regions with poor environmental conditions due to its excellent corrosion resistance. Considering the influence of concrete hydration heat, shrinkage, and creep, debonding may occur in the interface between the GFRP tube and the concrete, which will greatly reduce the cooperation of the GFRP tube and concrete, and will weaken the mechanical property of CFGC. This paper introduces an active monitoring method based on the piezoelectric transducer. In the active sensing approach, the smart aggregate (SA) embedded in the concrete acted as a driver to transmit a modulated stress wave, and the PZT (Lead Zirconate Titanate) patches attached on the outer surface of CFGC serve as sensors to receive signals and transfer them to the computer for saving. Two groups of experiments were designed with the different debonding areas and thicknesses. The artificial damage of CFGC was identified and located by comparing the value of the delay under pulse excitation and the difference of wavelet-based energy under sweep excitation, and the damage indexes were defined based on the wavelet packet energy to quantify the level of the interface damage. The results showed that the debonding damage area of CFGC can be identified effectively through the active monitoring method, and the damage index can accurately reflect the damage level of the interface of GFRP tube and concrete. Therefore, this method can be used to identify and evaluate the interface debonding of CFGC in real time. In addition, if the method can be combined with remote sensing technology, it can be used as a real-time remote sensing monitoring technology to provide a solution for interface health monitoring of CFGC.
Specific Loss Power of Co/Li/Zn-Mixed Ferrite Powders for Magnetic Hyperthermia
Authors: Barrera Gabriele , Coisson Marco , Celegato Federica , Martino Luca , Tiwari Priyanka , Verma Roshni , Kane Shashank N. , Mazaleyrat Frédéric , Tiberto Paola
Published: April 2020

An important research effort on the design of the magnetic particles is increasingly required to optimize the heat generation in biomedical applications, such as magnetic hyperthermia and heat-assisted drug release, considering the severe restrictions for the human body’s exposure to an alternating magnetic field. Magnetic nanoparticles, considered in a broad sense as passive sensors, show the ability to detect an alternating magnetic field and to transduce it into a localized increase of temperature. In this context, the high biocompatibility, easy synthesis procedure and easily tunable magnetic properties of ferrite powders make them ideal candidates. In particular, the tailoring of their chemical composition and cation distribution allows the control of their magnetic properties, tuning them towards the strict demands of these heat-assisted biomedical applications. In this work, Co0.76Zn0.24Fe2O4, Li0.375Zn0.25Fe2.375O4 and ZnFe2O4 mixed-structure ferrite powders were synthesized in a ‘dry gel’ form by a sol-gel auto-combustion method. Their microstructural properties and cation distribution were obtained by X-ray diffraction characterization. Static and dynamic magnetic measurements were performed revealing the connection between the cation distribution and magnetic behavior. Particular attention was focused on the effect of Co2+ and Li+ ions on the magnetic properties at a magnetic field amplitude and the frequency values according to the practical demands of heat-assisted biomedical applications. In this context, the specific loss power (SLP) values were evaluated by ac-hysteresis losses and thermometric measurements at selected values of the dynamic magnetic fields.
Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care
Authors: R Latha , P Vetrivelan
Published: April 2020

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.
Inlet Effect Caused by Multichannel Structure for Molecular Electronic Transducer Based on a Turbulent-Laminar Flow Model
Authors: Zhou Qiuzhan , He Qi , Chen Yuzhu , Bao Xue
Published: April 2020

The actual fluid form of an electrolyte in a molecular electronic converter is an important factor that causes a decrease in the accuracy of a molecular electronic transducer (MET) liquid motion sensor. To study the actual fluid morphology of an inertial electrolyte in molecular electron transducers, an inlet effect is defined according to the fluid morphology of turbulent-laminar flow, and a numerical simulation model of turbulent-laminar flow is proposed. Based on the turbulent-laminar flow model, this paper studies the variation of the inlet effect intensity when the thickness of the outermost insulating layer is 50 µm and 100 µm, respectively. Meanwhile, the changes of the inlet effect intensity and the error rate of central axial velocity field are also analyzed when the input signal intensity is different. Through the numerical experiment, it verifies that the thickness of the outermost insulating layer and the amplitude of the input signal are two important factors which can affect the inlet effect intensity and also the accuracy of the MET. Therefore, this study can provide a theoretical basis for the quantitative study on the performance optimization of a MET liquid sensor.
Inspection of Trivalent Chromium Conversion Coatings Using Laser Light: The Unexpected Role of Interference on Cold-Rolled Aluminium
Authors: Rischmueller Joerg , Toschke Yannic , Imlau Mirco , Schlag Mareike , Brüning Hauke , Brune Kai
Published: April 2020

Laser-based inspection of trivalent chromium conversion coatings on rough, cold-rolled aluminium substrates is studied from a basic physics perspective by means of angle and wavelength dependent measurements. As a result, we show that the correlation between the scattered laser light and the coating weight of the conversion layer is dominated by the phenomenon of interference. The combined experimental and numerical approach of our study is based on an appropriate layer model which was developed from a set of reference measurements of confocal microscopy, electron microscopy and X-ray photoelectron spectroscopy. The aluminium alloy AA3003 with a trivalent chromium conversion coating serves as an example. Our derived model is capable to reconstruct the reflectance of a laser beam at grazing incidence even for a pronounced surface roughness of R q ≈ 300   n m , for different coating thicknesses less than 70 n m corresponding to coating weights between zero and 0.5 g / m 2 and for laser wavelengths from 405 n m to 785 n m . In our discussion we outline the possibility to transfer the results to other aluminium alloys and/or other metallic substrates, as well as their potential for industrial applications such as 100% inline-capability, costs, velocity and ruggedness.
Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map
Authors: Kang Jeong Min , Yoon Tae Sung , Kim Euntai , Park Jin Bae
Published: April 2020

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.
Renovation of Automation System Based on Industrial Internet of Things: A Case Study of a Sewage Treatment Plant
Authors: Zhu Wanhao , Wang Zhidong , Zhang Zifan
Published: April 2020

The Industrial Internet of Things (IIoT) is of great significance to the improvement of industrial efficiency and quality, and to reduce industrial costs and resources. However, there are few openly-reported practical project applications based on the IIoT up to now. For legacy automation devices in traditional industry, it is especially challenging to realize the upgrading of industrial automation adopting the IIoT technology with less investment. Based on the practical engineering experience, this paper introduces the automation renovation of a sewage treatment plant. The legacy automation devices are upgraded by the central controller of a STM32 processor (Produced by STMicroelectronics company, located in Geneva, Switzerland), and the WeChatApplet (Developed by Tencent company, located in Shenzhen, China) is used as the extended host computer. A set of remote monitoring and control systems of sewage treatment based on the IIoT is built to realize the wide-area monitoring and control of sewage treatment. The paper describes the field hardware system, wide-area monitoring and control application program, management cloud platform and security technologies in detail. The actual operation results show that the monitoring system has the requirements of high accuracy, good real-time performance, reliable operation and low cost.
Coherent Integration Method Based on Radon-NUFFT for Moving Target Detection Using Frequency Agile Radar
Authors: Pan Jiameng , Zhu Qian , Bao Qinglong , Chen Zengping
Published: April 2020

This paper considers the coherent integration problem for moving target detection using frequency agile (FA) radar, involving range cell migration (RCM) and the nonuniform phase fluctuations among different pulses caused by range-agile frequency (R-AF) coupling and velocity-time-agile frequency (V-T-AF) coupling. After the analysis of the term corresponding to the phase fluctuation caused by V-T-AF coupling, the term can be regarded as related to an equivalent non-uniform slow time, and nonuniform fast Fourier transform (NUFFT) could be the solution. So a fast coherent integration method combining Radon Fourier transform (RFT) and NUFFT based on low-rank approximation, i.e., Radon-NUFFT, is proposed. In this method, the RCM is solved by Radon algorithm via target trajectory searching, the non-uniform phase fluctuation caused by R-AF coupling is compensated by constructing a compensation item corresponding to the range and agile frequency. In addition, the compensation of the non-uniform phase fluctuation caused by V-T-AF coupling is converted into a problem of spectral analysis of non-uniform sampling complex-valued signal, which is solved by the NUFFT based on low rank approximation. Compared with the existing methods, the proposed method can realize the coherent integration for FA radar accurately and quickly. The effectiveness of the proposed method is verified by simulation experiments.
Estimation of the Number of Passengers in a Bus Using Deep Learning
Authors: Hsu Ya-Wen , Chen Yen-Wei , Perng Jau-Woei
Published: April 2020

For the development of intelligent transportation systems, if real-time information on the number of people on buses can be obtained, it will not only help transport operators to schedule buses but also improve the convenience for passengers to schedule their travel times accordingly. This study proposes a method for estimating the number of passengers on a bus. The method is based on deep learning to estimate passenger occupancy in different scenarios. Two deep learning methods are used to accomplish this: the first is a convolutional autoencoder, mainly used to extract features from crowds of passengers and to determine the number of people in a crowd; the second is the you only look once version 3 architecture, mainly for detecting the area in which head features are clearer on a bus. The results obtained by the two methods are summed to calculate the current passenger occupancy rate of the bus. To demonstrate the algorithmic performance, experiments for estimating the number of passengers at different bus times and bus stops were performed. The results indicate that the proposed system performs better than some existing methods.
Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks
Authors: Martín-Barrio Andrés , Roldán-Gómez Juan Jesús , Rodríguez Iván , del Cerro Jaime , Barrientos Antonio
Published: April 2020

Hyper-redundant robots are highly articulated devices that present numerous technical challenges such as their design, control or remote operation. However, they offer superior kinematic skills than traditional robots for multiple applications. This work proposes an original and custom-made design for a discrete and hyper-redundant manipulator. It is comprised of 7 sections actuated by cables and 14 degrees of freedom. It has been optimized to be very robust, accurate and capable of moving payloads with high dexterity. Furthermore, it has been efficiently controlled from the actuators to high-level strategies based on the management of its shape. However, these highly articulated systems often exhibit complex shapes that frustrate their spatial understanding. Immersive technologies emerge as a good solution to remotely and safely teleoperate the presented robot for an inspection task in a hazardous environment. Experimental results validate the proposed robot design and control strategies. As a result, it is concluded that hyper-redundant robots and immersive technologies should play an important role in the near future of automated and remote applications.
Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review
Authors: Jacob Rodrigues Mariana , Postolache Octavian , Cercas Francisco
Published: April 2020

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.
Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office
Authors: Kapuscinski Tomasz , Wysocki Marian
Published: April 2020

The paper addresses the recognition of dynamic Polish Sign Language expressions in an experimental system supporting deaf people in an office when applying for an ID card. A method of processing a continuous stream of RGB-D data and a feature vector are proposed. The classification is carried out using the k-nearest neighbors algorithm with dynamic time warping, hidden Markov models, and bidirectional long short-term memory. The leave-one-subject-out protocol is used for the dataset containing 121 Polish Sign Language sentences performed five times by four deaf people. A data augmentation method is also proposed and tested. Preliminary observations and conclusions from the use of the system in a laboratory, as well as in real conditions with an experimental installation in the Office of Civil Affairs are given.
Performance Limits of GNSS Code-Based Precise Positioning: GPS, Galileo & Meta-Signals
Authors: Das Priyanka , Ortega Lorenzo , Vilà-Valls Jordi , Vincent François , Chaumette Eric , Davain Loïc
Published: April 2020

This contribution analyzes the fundamental performance limits of traditional two-step Global Navigation Satellite System (GNSS) receiver architectures, which are directly linked to the achievable time-delay estimation performance. In turn, this is related to the GNSS baseband signal resolution, i.e., bandwidth, modulation, autocorrelation function, and the receiver sampling rate. To provide a comprehensive analysis of standard point positioning techniques, we consider the different GPS and Galileo signals available, as well as the signal combinations arising in the so-called GNSS meta-signal paradigm. The goal is to determine: (i) the ultimate achievable performance of GNSS code-based positioning systems; and (ii) whether we can obtain a GNSS code-only precise positioning solution and under which conditions. In this article, we provide clear answers to such fundamental questions, leveraging on the analysis of the Cramér–Rao bound (CRB) and the corresponding Maximum Likelihood Estimator (MLE). To determine such performance limits, we assume no external ionospheric, tropospheric, orbital, clock, or multipath-induced errors. The time-delay CRB and the corresponding MLE are obtained for the GPS L1 C/A, L1C, and L5 signals; the Galileo E1 OS, E6B, E5b-I, and E5 signals; and the Galileo E5b-E6 and E5a-E6 meta-signals. The results show that AltBOC-type signals (Galileo E5 and meta-signals) can be used for code-based precise positioning, being a promising real-time alternative to carrier phase-based techniques.
Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube
Authors: Park MinJi , Ko Byoung Chul
Published: April 2020

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.
Effects of the Use of Good Agricultural Practices on Aflatoxin Levels in Maize Grown in Nandi County, Kenya
Authors: Marete Grace Nkirote , Kanja Laetitia Wakonyu , Mbaria James Mucunu , Okumu Mitchel Otieno , Ateku Penina Afwande , Korhonen Hannu , Joutsjoki Vesa
Published: April 2020

Aflatoxin contaminated maize is of public health concern in Kenya. Training farmers on good agricultural practice (GAP) has been touted as a mitigative measure. Little is known of the effect of such training on aflatoxin levels in maize grown in Kenya. This study evaluated what effect training farmers on GAP has on aflatoxin levels in maize grown in in maize grown in Kaptumo, Kilibwoni, and Kipkaren divisions in Nandi County. Ninety farmers were recruited for the study and interviewed on GAP. Maize samples were additionally collected from the participating farmers and analyzed for aflatoxins using competitive enzyme-linked immunosorbent assay (c-ELISA). All farmers prepared the land before planting, did correct spacing between the planted crops, carried out weeding, cleaned their stores before use, checked the condition of the maize after harvesting, sorted maize after shelling, and knew aflatoxins. A majority of the farmers (90%) used fertilizers, dried maize after harvesting, knew that aflatoxins were harmful to humans, and used clean transport in transporting the harvested maize. About 98% of farmers did stooking after harvesting and 97% used wooden pallets in the maize stores. The percentage of farmers who practiced early planting, top dressing, crop rotation, raising stores above the ground, applying insecticide after shelling and feeding damaged/rotten seeds to their animals was 84–96%, 62–80%, 67–85%, 86–98%, 63–81%, and 7–21% respectively. About 18/90 (20%) of all farmers reported that they had a relative who had died from liver cancer and the mean aflatoxin levels in season 1 were significantly different from season 2 (1.92 ± 1.07 ppb; 1.30 ± 1.50 ppb). Our findings suggest that although training farmers to adopt good agricultural practices was observed to be efficient in mitigating the problem of aflatoxins, the receptiveness of farmers to different aspects of the training may have differed. Therefore, in designing an optimized regional aflatoxin contamination strategy, local applicability should be considered.
New Insights into Advanced Drug Delivery and Absorption Systems
Authors: Vecsernyés Miklós
Published: April 2020

The therapeutic effect of a drug depends on the method of effective delivery of the active ingredient to the site(s) of the pharmacological target area [...]
Improved Solubility and Dissolution Rates in Novel Multicomponent Crystals of Piperine with Succinic Acid
Authors: Zaini Erizal , Afriyani  , Fitriani Lili , Ismed Friardi , Horikawa Ayano , Uekusa Hidehiro
Published: April 2020

The objectives of this study were to prepare and characterize a novel piperine–succinic acid multicomponent crystal phase and to evaluate the improvement in the solubility and dissolution rate of piperine when prepared in the multicomponent crystal formation. The solid-state characterization of the novel multicomponent crystal was performed by powder X-ray diffraction (XRD), differential scanning calorimetry (DSC), and Fourier transform-infrared (FT-IR) spectroscopy. Solubility and dissolution rate profiles were evaluated in distilled water. The physical stability was evaluated under high relative humidity (75% and 100% RH). The determination of the single crystal X-ray diffraction structure revealed that this novel multicomponent crystal was a cocrystalline phase of piperine–succinic acid (2:1 molar ratio). The differential scanning calorimetry thermogram of the cocrystal showed a single and sharp endothermic peak at 110.49 °C. The cocrystal resulted in greater solubility and a faster dissolution rate of piperine than intact piperine. This improvement was a result of the formation of a channel structure in the cocrystal. In addition, the cocrystal was stable under a humid condition.
Phosphate Sorption onto Structured Soil
Authors: Saki Hermin , Liu Haojie , Lennartz Bernd
Published: April 2020

Soil–phosphorus interactions are frequently studied employing the slurry technique, in which soil samples are intensively mixed with phosphate solutions of various concentrations. The result of such experiments is a “phosphate sorption potential” because the thorough mixing of soil and phosphate solution as obtained by overhead or horizontal shaking of the slurry would probably not occur under natural conditions, especially if the soil is structured. Here, we wanted to test the impact of soil structure on phosphorus (P) removal from aqueous solution. Soil aggregates of a defined size class were prepared by carefully sieving the soil. The soil aggregates were individually wrapped in an inert fabric and placed on a sieve, which was lowered into a basin containing a phosphate solution of a given concentration. The decrease of the phosphate solution concentration with time was registered at fixed intervals, and adsorbed amounts were quantified by differences between initial concentrations and concentrations at the time of sampling. Pre-tests on fine earth revealed that sorption was more pronounced in the classical slurry batch experiment than in the approach used in this study. Differences between methods were more pronounced at lower initial phosphate concentrations. The increase in P sorption in the classical batch experiment continued over 24 h to 140 mg kg−1, while the adsorbed P amount remained constant (64 mg kg−1) after 6 h in the diffusion experiment. Interestingly, it was observed that the sorption onto soil aggregates was elevated as compared to unstructured fine earth. The sorption capacity of aggregates was approximately one third higher than that of the fine earth samples according to optimized Freundlich adsorption coefficients. This was unexpected since it was assumed that the soil surface area available for sorption processes is greater or at least far more accessible if the unstructured fine earth is exposed to the phosphate solution. We conclude that if the inner pore space of soil aggregates is readily accessible and diffusion is not hindered, the overall retention capacity of intact aggregates might be higher than that of the disturbed soil because the intra-aggregate pore space can accommodate a certain fraction of phosphate in addition to the adsorbed amount at particle surfaces. The presented experimental approach allows for studying sorption processes in well-structured and fine earth in conditions that perform better compared to the natural situation. Additional testing of the method for different soil types is advisable.
Safety or Travel: Which Is More Important? The Impact of Disaster Events on Tourism
Authors: Ma Haiyan , Chiu Yung-ho , Tian Xiaocong , Zhang Juanjuan , Guo Quan
Published: April 2020

Tourism is making an increasingly considerable contribution to the sustainable development of world economy, but its development is susceptible to a series of disaster events. The impact of disaster events on tourists’ travel decisions is receiving ever-growing attention. In this study, disasters are classified into two categories: namely, natural disasters and man-made disasters. Among these disasters, earthquakes and terrorist attacks—as the most representative two types—are taken as research examples. By virtue of a difference-in-difference research method and online review data from TripAdvisor, multiple incidents that have occurred in different countries are systematically and comparatively analyzed for verifying the effects of catastrophic events with varying natures, frequencies, and intensities on tourism. The main findings are as follows: (1) both natural disasters and man-made disasters have a negative effect on the number of tourists and the tourist experience; (2) higher frequency and intensity of terrorist attacks may not correspond to tourism, and terrorist attacks exert a more influential impact on the safety image of tourist destinations; (3) compared with the scale and intensity of earthquakes, the frequency of earthquakes has a greater effect on tourism; (4) compared with terrorist attacks, earthquakes have a greater effect on the number of tourists.
Higher Education Institutions as Knowledge Brokers in Smart Specialisation
Authors: Kangas Riikka , Aarrevaara Timo
Published: April 2020

The effectiveness of societal interaction has become a key aspect in evaluating the success of higher education institutions (HEIs) in performing their duties. These factors have been built into institutional funding models, and the funding of research follows a similar approach. External stakeholders are now having to share in undertaking some of the functions that will define higher education institutions’ external activities, societal interaction and impact on society. The European Union’s smart specialisation strategy is such a factor. This initiative allows higher education institutions to implement policies by building regional clusters. The counterparts of higher education institutions in these clusters of smart specialisation are knowledge-intensive enterprises, high-tech service providers, educational institutions, the Arctic Smartness Specialisation Platform and other centers of expertise for smart specialisation. In this paper, we have analysed the role of higher education institutions as knowledge brokers in smart specialisation though a qualitative analysis of 20 interviews conducted during the implementation of the smart specialisation project. Our findings show that the knowledge broker role can be promoted from four perspectives: the social dimension of networks; decision-making and control; cluster building; and exchange elements. The clarification and legitimation of the role of higher education institutions as knowledge brokers in these areas would give smart specialisation more impetus to reach its goals.
Student Concepts after a Didactic Experiment in Heritage Education
Authors: Trabajo-Rite Mónica , Cuenca-López Jose María
Published: April 2020

This work highlights the importance of heritage education as a vehicle for citizen education. We present an analysis of concepts of heritage, citizenship, and territory held by third-year social science students. The results are obtained from statistical analysis of a questionnaire given before and after a didactic intervention and the application of a table of categories drawn up based on heritage education in terms of the parameters we consider desirable, from a holistic, socio-critical, motivating, and participatory perspective. The outcomes show the success of the intervention, with the students presenting better reflective and critical knowledge of their environment, showing appreciation, respect, protection, and dissemination of heritage as a symbol of identity and favoring the commitment to sustainable actions.
Urban Crime Occurrences in Association with Built Environment Characteristics: An African Case with Implications for Urban Design
Authors: Silva Patrik , Li Lin
Published: April 2020

Empirically, the physical spatial arrangement of places provides us with a clue about the likelihood for crime opportunities based on the principles of crime prevention through environmental design (CPTED). Although we know that the quality of the urban built environment influences people’s behavior, its measurement as a variable is not an easy task. In this study, we present and develop a set of urban built environment indicators (UBEIs) based on two datasets: building footprints and road networks at the neighborhood level in the city of Praia, Cape Verde. We selected the four most relevant UBEIs to create a single urban built environment indicator (CUBEI), and then, explored their relationships with five types of crime (i.e., burglary, robbery, mugging, residential robbery, and crimes involving weapons) using correlation and regression analysis. Our results showed a consistent and statistically significant relationship between different types of crimes with both the UBEIs and CUBEI, suggesting that a poor urban built environment is associated with an increase of all types of crimes investigated in this study. Thus, to minimize crime incidents, urban planners should rehabilitate or design neighborhoods from the earlier stage, considering the principles of CPTED and broken window theory (BWT).
Integrating Environmental, Geographical and Social Data to Assess Sustainability in Hydrographic Basins: The ESI Approach
Authors: Couto Edivando Vitor do , Oliveira Pablo B. , Vieira Luciane Maria , Schmitz Marcelo H. , Ferreira José Hilário D.
Published: April 2020

The elaboration of environmental sustainability indexes (ESI) aims to describe the complexity between social, environmental and ecological health. These indexes play a crucial role by helping stakeholders during the decision-making process and by identifying possible sites that require practical sustainable actions. In this study, we aim to elaborate an ESI for hydrographic basins. We use three factors to build the index: land use; vulnerability to soil degradation and topographic land attributes; and human development. The human development factor includes the dimensions of education, sanitation, longevity and income. These three factors are recommended by the Organization for Economic Cooperation and Development and reflect the pressure–state–impact assessment model. To verify the effectiveness of the proposed ESI, we applied it in a hydrographic basin located in southern Brazil. The sustainability level variation in the basin was classified into five groups: very low (0%), low (17%), intermediate (72%), high (11%) and very high (0%). The predominance of the intermediate sustainability level was found to mainly owe to the extent of agricultural land and the high degree of susceptibility to soil erosion in areas with low environmental conservation. In areas with native vegetation, environmental sustainability was found to be higher than in areas with other land use types. The resulting ESI will help facilitate future studies in environmental, economic or social dimensions in any hydrographic basin.