bluuebunny/crossref_metadata_2025 huggingface dataset:
Recycling of Carbon Fibers from CFRP Waste by Microwave Thermolysis
Authors: Deng Jianying , Xu Lei , Zhang Libo , Peng Jinhui , Guo Shenghui , Liu Jianhua , Koppala Sivasankar
Published: April 2019

With the growth of the use of carbon fiber-reinforced polymer (CFRP) in various fields, the recovery of carbon fibers from CFRP waste is becoming a significant research direction. In the present work, degrading epoxy resin and recycling carbon fibers from CFRP waste by microwave thermolysis and traditional thermolysis were studied. The carbon fibers were successfully recovered by thermolysis under an oxygen atmosphere in this study. The properties of the recovered carbon fibers were characterized by field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and Raman spectroscopy. The result shows that using microwave thermolysis to recover carbon fibers from CFRP waste is an attractive prospect. Compared to the traditional method, the reaction time was reduced by 56.67%, and the recovery ratio was increased by 15%. Microwave thermolysis is faster, more efficient, requires less energy, and obtains cleaner recovered carbon fibers than those recovered using traditional thermolysis.
Benzoic Acid Derivatives of Ifloga spicata (Forssk.) Sch.Bip. as Potential Anti-Leishmanial against Leishmania tropica
Authors: Shah Syed , Ullah Farhat , Ayaz Muhammad , Sadiq Abdul , Hussain Sajid , Ali Shah Azhar-ul-Haq , Shah Syed , Ullah Nazif , Ullah Farman , Ullah Ikram , Nadhman Akhtar
Published: April 2019

This study aimed to appraise the anti-leishmanial potentials of benzoic acid derivatives, including methyl 3,4-dihydroxybenzoate (compound 1) and octadecyl benzoate (compound 2), isolated from the ethnomedicinally important plant Ifloga spicata (I. spicata). Chemical structures were elucidated via FT-IR, mass spectrometry, and multinuclear (1H and 13C) NMR spectroscopy. Anti-leishmanial potentials of the compounds were assessed using Leishmania tropica promastigotes. Moreover, acridine orange fluorescent staining was performed to visualize the apoptosis-associated changes in promastigotes under a fluorescent microscope. A SYTOX assay was used to check rupturing of Leishmania promastigote cell membranes using 0.1% Triton X-100 as positive control. A DNA interaction assay was carried out to assess DNA attachment potential. AutoDock software was used to check the binding affinity of compounds with surface enzyme leishmanolysin gp63 (1LML). Both compounds exhibited considerable anti-leishmanial potential, with LD50 values of 10.40 ± 0.09 and 14.11 ± 0.11 μg/mL for compound 1 and compound 2, respectively. Both compounds showed higher binding affinity with the leishmanolysin (gp63) receptor/protease of Leishmania, as assessed using computational analysis. The binding scores of compounds 1 and 2 with target gp63 were −5.3 and −5.6, respectively. The attachment of compounds with this receptor resulted in their entry into the cell where they bound with Leishmania DNA, causing apoptosis. The results confirmed that the investigated compounds have anti-leishmanial potential and are potential substitutes as natural anti-leishmanial agents against L. tropica.
Metabolic Engineering and Fermentation Process Strategies for L-Tryptophan Production by Escherichia coli
Authors: Liu Lina , Bilal Muhammad , Luo Hongzhen , Zhao Yuping , Iqbal Hafiz M. N.
Published: April 2019

L-tryptophan is an essential aromatic amino acid that has been widely used in medicine, food, and animal feed. Microbial biosynthesis of L-tryptophan through metabolic engineering approaches represents a sustainable, cost-effective, and environmentally friendly route compared to chemical synthesis. In particular, metabolic pathway engineering allows enhanced product titers by inactivating/blocking the competing pathways, increasing the intracellular level of essential precursors, and overexpressing rate-limiting enzymatic steps. Based on the route of the L-tryptophan biosynthesis pathway, this review presents a systematic and detailed summary of the contemporary metabolic engineering approaches employed for L-tryptophan production. In addition to the engineering of the L-tryptophan biosynthesis pathway, the metabolic engineering modification of carbon source uptake, by-product formation, key regulatory factors, and the polyhydroxybutyrate biosynthesis pathway in L-tryptophan biosynthesis are discussed. Moreover, fermentation bioprocess optimization strategies used for L-tryptophan overproduction are also delineated. Towards the end, the review is wrapped up with the concluding remarks, and future strategies are outlined for the development of a high L-tryptophan production strain.
Imbalance Market Real Options and the Valuation of Storage in Future Energy Systems
Authors: Moriarty John , Palczewski Jan
Published: April 2019

As decarbonisation progresses and conventional thermal generation gradually gives way to other technologies including intermittent renewables, there is an increasing requirement for system balancing from new and also fast-acting sources such as battery storage. In the deregulated context, this raises questions of market design and operational optimisation. In this paper, we assess the real option value of an arrangement under which an autonomous energy-limited storage unit sells incremental balancing reserve. The arrangement is akin to a perpetual American swing put option with random refraction times, where a single incremental balancing reserve action is sold at each exercise. The power used is bought in an energy imbalance market (EIM), whose price we take as a general regular one-dimensional diffusion. The storage operator’s strategy and its real option value are derived in this framework by solving the twin timing problems of when to buy power and when to sell reserve. Our results are illustrated with an operational and economic analysis using data from the German Amprion EIM.
Effects of Voltage Dips on Robotic Grasping
Authors: Carbone Giuseppe , Ceccarelli Marco , Fabrizi Christopher , Varilone Pietro , Verde Paola
Published: April 2019

This paper addresses the effects of electric power quality on robotic operations. A general overview is reported to highlight the main characteristics of electric power quality and it’s effects on a powered system by considering an end-user’s viewpoint. Then, the authors outline the influence of voltage dip effects by focusing on robotic grasping applications. A specific case study is reported, namely that of LARM Hand IV, a three-fingered robotic hand which has been designed and built at LARM in Cassino, Italy. A dedicated test rig has been developed and set up to generate predefined voltage dips. Experimental tests are carried out to evaluate the effects of different types of voltage dip on the grasping of objects.
Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
Authors: Fernández-Martínez Marcos , Yu Rong , Gamon John , Hmimina Gabriel , Filella Iolanda , Balzarolo Manuela , Stocker Benjamin , Peñuelas Josep
Published: April 2019

Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIRV, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.
Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (EPICS) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors
Authors: Shrestha Mahesh , Leigh Larry , Helder Dennis
Published: April 2019

Pseudo invariant calibration sites (PICS) have been extensively used for the radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types was used; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. This potential increase in temporal resolution could result in increased sensitivity for the quicker identification of changes in sensor response.
Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area
Authors: Zhang Keqi , Gann Daniel , Ross Michael , Biswas Himadri , Li Yuepeng , Rhome Jamie
Published: April 2019

The TanDEM-X (TDX) mission launched by the German Aerospace Center delivers unprecedented global coverage of a high-quality digital elevation model (DEM) with a pixel spacing of 12 m. To examine the relationships of terrain, vegetation, and building elevations with hydrologic, geologic, geomorphologic, or ecologic factors, quantification of TDX DEM errors at a local scale is necessary. We estimated the errors of TDX data for open ground, forested, and built areas in a coastal urban environment by comparing the TDX DEM with LiDAR data for the same areas, using a series of error measures including root mean square error (RMSE) and absolute deviation at the 90% quantile (LE90). RMSE and LE90 values were 0.49 m and 0.79 m, respectively, for open ground. These values, which are much lower than the 10 m LE90 specified for the TDX DEM, highlight the promise of TDX DEM data for mapping hydrologic and geomorphic features in coastal areas. The RMSE/LE90 values for mangrove forest, tropical hardwood hammock forest, pine forest, dense residential, sparse residential, and downtown areas were 1.15/1.75, 2.28/3.37, 3.16/5.00, 1.89/2.90, 2.62/4.29 and 35.70/51.67 m, respectively. Regression analysis indicated that variation in canopy height of densely forested mangrove and hardwood hammock was well represented by the TDX DEM. Thus, TDX DEM data can be used to estimate tree height in densely vegetated forest on nearly flat topography next to the shoreline. TDX DEM errors for pine forest and residential areas were larger because of multiple reflection and shadow effects. Furthermore, the TDX DEM failed to capture the many high-rise buildings in downtown, resulting in the lowest accuracy among the different land cover types. Therefore, caution should be exercised in using TDX DEM data to reconstruct building models in a highly developed metropolitan area with many tall buildings separated by narrow open spaces.
Inundation Extent Mapping by Synthetic Aperture Radar: A Review
Authors: Shen Xinyi , Wang Dacheng , Mao Kebiao , Anagnostou Emmanouil , Hong Yang
Published: April 2019

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.
Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals
Authors: Kivimäki Ella , Lindqvist Hannakaisa , Hakkarainen Janne , Laine Marko , Sussmann Ralf , Tsuruta Aki , Detmers Rob , Deutscher Nicholas M. , Dlugokencky Edward J. , Hase Frank , Hasekamp Otto , Kivi Rigel , Morino Isamu , Notholt Justus , Pollard David F. , Roehl Coleen , Schneider Matthias , Sha Mahesh Kumar , Velazco Voltaire A. , Warneke Thorsten , Wunch Debra , Yoshida Yukio , Tamminen Johanna
Published: April 2019

Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (XCH 4). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4. These differences are linked to the regional CH 4 sources and sinks, and call for further research.
Regional Variations of Land-Use Development and Land-Use/Cover Change Dynamics: A Case Study of Turkey
Authors: Ustaoglu Eda , Aydınoglu Arif Cagdaş
Published: April 2019

Population growth, economic development and rural-urban migration have caused rapid expansion of urban areas and metropolitan regions in Turkey. The structure of urban administration and planning has faced different socio-economic and political challenges, which have hindered the structured and planned development of cities and regions, resulting in an irregular and uneven development of these regions. We conducted detailed comparative analysis on spatio-temporal changes of the identified seven land-use/cover classes across different regions in Turkey with the use of Corine Land Cover (CLC) data of circa 1990, 2000, 2006 and 2012, integrated with Geographic Information System (GIS) techniques. Here we compared spatio-temporal changes of urban and non-urban land uses, which differ across regions and across different hierarchical levels of urban areas. Our findings have shown that peri-urban areas are growing more than rural areas, and even growing more than urban areas in some regions. A deeper look at regions located in different geographical zones pointed to substantial development disparities across western and eastern regions of Turkey. We also employed multiple regression models to explain any possible drivers of land-use change, regarding both urban and non-urban land uses. The results reveal that the three influencing factors-socio-economic characteristics, regional characteristics and location, and development constraints, facilitate land-use change. However, their impacts differ in different geographical locations, as well as with different hierarchical levels.
Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia
Authors: Adriano Bruno , Xia Junshi , Baier Gerald , Yokoya Naoto , Koshimura Shunichi
Published: April 2019

This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.
Uncertainties in Evapotranspiration Estimates over West Africa
Authors: Jung Hahn Chul , Getirana Augusto , Arsenault Kristi R. , Holmes Thomas R.H. , McNally Amy
Published: April 2019

An evapotranspiration (ET) ensemble composed of 36 land surface model (LSM) experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET) is used to investigate uncertainties in ET estimate over five climate regions in West Africa. Diagnostic ET datasets show lower uncertainty estimates and smaller seasonal variations than the LSM-based ET values, particularly in the humid climate regions. Overall, the impact of the choice of LSMs and meteorological forcing datasets on the modeled ET rates increases from north to south. The LSM formulations and parameters have the largest impact on ET in humid regions, contributing to 90% of the ET uncertainty estimates. Precipitation contributes to the ET uncertainty primarily in arid regions. The LSM-based ET estimates are sensitive to the uncertainty of net radiation in arid region and precipitation in humid region. This study serves as support for better determining water availability for agriculture and livelihoods in Africa with earth observations and land surface models.
Application of a Waveguide-Mode Sensor to Blood Testing for Hepatitis B Virus, Hepatitis C Virus, Human Immunodeficiency Virus and Treponema pallidum Infection
Authors: Uno Shigeyuki , Shimizu Takenori , Tanaka Torahiko , Ashiba Hiroki , Fujimaki Makoto , Tanaka Mutsuo , Awazu Koichi , Makishima Makoto
Published: April 2019

Testing for blood-transmitted infectious agents is an important aspect of safe medical treatment. During emergencies, such as significant earthquakes, many patients need surgical treatment and/or blood transfusion. Because a waveguide mode (WM) sensor can be used as a portable, on-site blood testing device in emergency settings, we have previously developed WM sensors for detection of antibodies against hepatitis B virus and hepatitis C virus and for forward ABO and Rh(D) and reverse ABO blood typing. In this study, we compared signal enhancement methods using secondary antibodies conjugated with peroxidase, a fluorescent dye, and gold nanoparticles, and found that the peroxidase reaction method offers superior sensitivity while gold nanoparticles provide the most rapid detection of anti-HBs antibody. Next, we examined whether we could apply a WM sensor with signal enhancement with peroxidase or gold nanoparticles to detection of antibodies against hepatitis C virus, human immunodeficiency virus and Treponema pallidum, and HBs antigen in plasma. We showed that a WM sensor can detect significant signals of these infectious agents within 30 min. Therefore, a portable device utilizing a WM sensor can be used for on-site blood testing of infectious agents in emergency settings.
A Printed Wearable Dual-Band Antenna for Wireless Power Transfer
Authors: Haerinia Mohammad , Noghanian Sima
Published: April 2019

In this work, a dual-band printed planar antenna, operating at two ultra-high frequency bands (2.5 GHz/4.5 GHz), is proposed for wireless power transfer for wearable applications. The receiving antenna is printed on a Kapton polyimide-based flexible substrate, and the transmitting antenna is on FR-4 substrate. The receiver antenna occupies 2.1 cm 2 area. Antennas were simulated using ANSYS HFSS software and the simulation results are compared with the measurement results.
Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion
Authors: Su Yu , Zhang Ke , Wang Jingyu , Madani Kurosh
Published: April 2019

With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models use auditory features like log-mel spectrogram (LM) and mel frequency cepstral coefficient (MFCC), or raw waveform to train deep neural networks for environment sound classification (ESC) are unsatisfactory. In this paper, we first propose two combined features to give a more comprehensive representation of environment sounds Then, a fourfour-layer convolutional neural network (CNN) is presented to improve the performance of ESC with the proposed aggregated features. Finally, the CNN trained with different features are fused using the Dempster–Shafer evidence theory to compose TSCNN-DS model. The experiment results indicate that our combined features with the four-layer CNN are appropriate for environment sound taxonomic problems and dramatically outperform other conventional methods. The proposed TSCNN-DS model achieves a classification accuracy of 97.2%, which is the highest taxonomic accuracy on UrbanSound8K datasets compared to existing models.
Vibration-Based In-Situ Detection and Quantification of Delamination in Composite Plates
Authors: Mei Hanfei , Migot Asaad , Haider Mohammad Faisal , Joseph Roshan , Bhuiyan Md Yeasin , Giurgiutiu Victor
Published: April 2019

This paper presents a new methodology for detecting and quantifying delamination in composite plates based on the high-frequency local vibration under the excitation of piezoelectric wafer active sensors. Finite-element-method-based numerical simulations and experimental measurements were performed to quantify the size, shape, and depth of the delaminations. Two composite plates with purpose-built delaminations of different sizes and depths were analyzed. In the experiments, ultrasonic C-scan was applied to visualize the simulated delaminations. In this methodology, piezoelectric wafer active sensors were used for the high-frequency excitation with a linear sine wave chirp from 1 to 500 kHz and a scanning laser Doppler vibrometer was used to measure the local vibration response of the composite plates. The local defect resonance frequencies of delaminations were determined from scanning laser Doppler vibrometer measurements and the corresponding operational vibration shapes were measured and utilized to quantify the delaminations. Harmonic analysis of local finite element model at the local defect resonance frequencies demonstrated that the strong vibrations only occurred in the delamination region. It is shown that the effect of delamination depth on the detectability of the delamination was more significant than the size of the delamination. The experimental and finite element modeling results demonstrate a good capability for the assessment of delamination with different sizes and depths in composite structures.
Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods
Authors: Majidov Ikhtiyor , Whangbo Taegkeun
Published: April 2019

Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.
High Spatial Resolution Simulation of Sunshine Duration over the Complex Terrain of Ghana
Authors: Adamu Mustapha , Qiu Xinfa , Shi Guoping , Nooni Isaac Kwesi , Wang Dandan , Zhu Xiaochen , Hagan Daniel Fiifi T. , Lim Kam Sian Kenny T.C.
Published: April 2019

In this paper, we propose a remote sensing model based on a 1 × 1 km spatial resolution to estimate the spatio-temporal distribution of sunshine percentage (SSP) and sunshine duration (SD), taking into account terrain features and atmospheric factors. To account for the influence of topography and atmospheric conditions in the model, a digital elevation model (DEM) and cloud products from the moderate-resolution imaging spectroradiometer (MODIS) for 2010 were incorporated into the model and subsequently validated against in situ observation data. The annual and monthly average daily total SSP and SD have been estimated based on the proposed model. The error analysis results indicate that the proposed modelled SD is in good agreement with ground-based observations. The model performance is evaluated against two classical interpolation techniques (kriging and inverse distance weighting (IDW)) based on the mean absolute error (MAE), the mean relative error (MRE) and the root-mean-square error (RMSE). The results reveal that the SD obtained from the proposed model performs better than those obtained from the two classical interpolators. This results indicate that the proposed model can reliably reflect the contribution of terrain and cloud cover in SD estimation in Ghana, and the model performance is expected to perform well in similar environmental conditions.
Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation
Authors: Hernandez-Suarez Aldo , Sanchez-Perez Gabriel , Toscano-Medina Karina , Perez-Meana Hector , Portillo-Portillo Jose , Sanchez Victor , García Villalba Luis
Published: April 2019

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.
Online Monitoring of Moisture Diffusion in Carbon Fiber Composites Using Miniaturized Flexible Material Integrated Sensors
Authors: Hübner Martina , Lepke Dennis , Hardi Elisabeth , Koerdt Michael , Herrmann Axel S. , Lang Walter
Published: April 2019

Moisture diffusion in carbon fiber composites changes the mechanical properties of the composite. Therefore, a monitoring method of the actual content of moisture in the composite is important. However, at the moment there are no online methods established. A common method is the measurement of the mass changes due to water uptake. This method is not suitable for online monitoring of a real composite part in service. We demonstrate that miniaturized flexible interdigital sensors are suitable for moisture measurement inside the carbon fiber composite. These sensors are directly integrated inside the composite. It was already demonstrated that these can be successfully used for resin-curing monitoring as a primary application. Here we demonstrate that the same sensors are also suitable for moisture measurement inside the material. In order to do so, we expose samples with and without integrated sensors to hot-wet conditions and measure the dielectric changes with the sensors and the mass gain. The moisture concentration and the measured admittance can be directly correlated to each other. This demonstrates that the sensors can be used for moisture measurement as a secondary application. In addition, it is shown that the sensors have the potential to measure the moisture locally inside the material.
Bandwidth Modeling of Silicon Retinas for Next Generation Visual Sensor Networks
Authors: Khan Nabeel , Martini Maria G.
Published: April 2019

Silicon retinas, also known as Dynamic Vision Sensors (DVS) or event-based visual sensors, have shown great advantages in terms of low power consumption, low bandwidth, wide dynamic range and very high temporal resolution. Owing to such advantages as compared to conventional vision sensors, DVS devices are gaining more and more attention in various applications such as drone surveillance, robotics, high-speed motion photography, etc. The output of such sensors is a sequence of events rather than a series of frames as for classical cameras. Estimating the data rate of the stream of events associated with such sensors is needed for the appropriate design of transmission systems involving such sensors. In this work, we propose to consider information about the scene content and sensor speed to support such estimation, and we identify suitable metrics to quantify the complexity of the scene for this purpose. According to the results of this study, the event rate shows an exponential relationship with the metric associated with the complexity of the scene and linear relationships with the speed of the sensor. Based on these results, we propose a two-parameter model for the dependency of the event rate on scene complexity and sensor speed. The model achieves a prediction accuracy of approximately 88.4% for the outdoor environment along with the overall prediction performance of approximately 84%.
Enhancing Sensor Network Security with Improved Internal Hardware Design
Authors: Wang Weizheng , Deng Zhuo , Wang Jin
Published: April 2019

With the rapid development of the Internet-of-Things (IoT), sensors are being widely applied in industry and human life. Sensor networks based on IoT have strong Information transmission and processing capabilities. The security of sensor networks is progressively crucial. Cryptographic algorithms are widely used in sensor networks to guarantee security. Hardware implementations are preferred, since software implementations offer lower throughout and require more computational resources. Cryptographic chips should be tested in a manufacturing process and in the field to ensure their quality. As a widely used design-for-testability (DFT) technique, scan design can enhance the testability of the chips by improving the controllability and observability of the internal flip-flops. However, it may become a backdoor to leaking sensitive information related to the cipher key, and thus, threaten the security of a cryptographic chip. In this paper, a secure scan test architecture was proposed to resist scan-based noninvasive attacks on cryptographic chips with boundary scan design. Firstly, the proposed DFT architecture provides the scan chain reset mechanism by gating a mode-switching detection signal into reset input of scan cells. The contents of scan chains will be erased when the working mode is switched between test mode and functional mode, and thus, it can deter mode-switching based noninvasive attacks. Secondly, loading the secret key into scan chains of cryptographic chips is prohibited in the test mode. As a result, the test-mode-only scan attack can also be thwarted. On the other hand, shift operation under functional mode is disabled to overcome scan attack in the functional mode. The proposed secure scheme ensures the security of cryptographic chips for sensor networks with extremely low area penalty.
Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles
Authors: Wu Qing , Shen Xudong , Jin Yuanzhe , Chen Zeyu , Li Shuai , Khan Ameer Hamza , Chen Dechao
Published: April 2019

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.
Vibration Monitoring of the Mechanical Harvesting of Citrus to Improve Fruit Detachment Efficiency
Authors: Castro-Garcia Sergio , Aragon-Rodriguez Fernando , Sola-Guirado Rafael R. , Serrano Antonio J. , Soria-Olivas Emilio , Gil-Ribes Jesús A.
Published: April 2019

The introduction of a mechanical harvesting process for oranges can contribute to enhancing farm profitability and reducing labour dependency. The objective of this work is to determine the spread of the vibration in citrus tree canopies to establish recommendations to reach high values of fruit detachment efficiency and eliminate the need for subsequent hand-harvesting processes. Field tests were carried out with a lateral tractor-drawn canopy shaker on four commercial plots of sweet oranges. Canopy vibration during the harvesting process was measured with a set of triaxial accelerometer sensors with a datalogger placed on 90 bearing branches. Monitoring of the vibration process, fruit production, and branch properties were analysed. The improvement of fruit detachment efficiency was possible if both the hedge tree and the machinery were mutually adjusted. The hedge should be trained to facilitate access of the rods and to encourage external fructification since the internal canopy branches showed 43% of the acceleration vibration level of the external branches. The machine should be adjusted to vibrate the branches at a vibration time of at least 5.8 s, after the interaction of the rod with the branch, together with a root mean square acceleration value of 23.9 m/s2 to a complete process of fruit detachment.
On-Line Monitoring of Pipe Wall Thinning by a High Temperature Ultrasonic Waveguide System at the Flow Accelerated Corrosion Proof Facility
Authors: Oh Se-beom , Cheong Yong-moo , Kim Dong-jin , Kim Kyung-mo
Published: April 2019

Pipe wall thinning and leakage due to flow accelerated corrosion (FAC) are important safety concerns for nuclear power plants. A shear horizontal ultrasonic pitch/catch technique was developed for the accurate monitoring of the pipe wall-thickness. A solid couplant should be used to ensure high quality ultrasonic signals for a long operation time at an elevated temperature. We developed a high temperature ultrasonic thickness monitoring method using a pair of shear horizontal transducers and waveguide strips. A computer program for on-line monitoring of the pipe thickness at high temperature was also developed. Both a conventional buffer rod pulse-echo type and a developed shear horizontal ultrasonic waveguide type for a high temperature thickness monitoring system were successfully installed to test a section of the FAC proof test facility. The overall measurement error was estimated as ±15 μm during a cycle ranging from room temperature to 150 °C. The developed waveguide system was stable for about 3300 h and sensitive to changes in the internal flow velocity. This system can be used for high temperature thickness monitoring in all industries as well as nuclear power plants.
PlantTalk: A Smartphone-Based Intelligent Hydroponic Plant Box
Authors: Van Lan-Da , Lin Yi-Bing , Wu Tsung-Han , Lin Yun-Wei , Peng Syuan-Ru , Kao Lin-Hang , Chang Chun-Hao
Published: April 2019

This paper proposes an IoT-based intelligent hydroponic plant factory solution called PlantTalk. The novelty of our approach is that the PlantTalk intelligence can be built through an arbitrary smartphone. We show that PlantTalk can flexibly configure the connections of various plant sensors and actuators through a smartphone. One can also conveniently write Python programs for plant-care intelligence through the smart phone. The developed plant-care intelligence includes automatic LED lighting, water spray, water pump and so on. As an example, we show that the PlantTalk intelligence effectively lowers the CO2 concentration, and the reduction speed is 53% faster than a traditional plant system. PlantTalk has been extended for a plant factory called AgriTalk.
Performance and Participation in the ‘Vasaloppet’ Cross-Country Skiing Race during a Century
Authors: Romancuk Nastja , Nikolaidis Pantelis T. , Villiger Elias , Chtourou Hamdi , Rosemann Thomas , Knechtle Beat
Published: April 2019

This study investigated gender differences in performance and participation and the role of nationality during one century in one of the largest cross-country (XC) skiing events in the world, the ‘Vasaloppet‘ in Sweden. The total number of female and male athletes who finished (n = 562,413) this race between 1922 and 2017 was considered. Most of the finishers were Swedish (81.03% of women and 88.39% of men), followed by Norwegians and Finnish. The overall men-to-women ratio was 17.5. A gender × nationality association was observed for participation (χ2 = 1,823.44, p < 0.001, φ = 0.057), with the men-to-women ratio ranging from 6.7 (USA) to 19.1 (Sweden). For both genders, the participation (%) of Swedish decreased, and that of all other nationalities (except Swiss) increased across years. Regarding the mean race time, men were faster than women by 14.5% (7 h 52 min 17 s versus 9 h 00 min 55 s, respectively). A trivial gender×nationality interaction regarding the race time was observed (p < 0.001, η2 < 0.001), with gender differences ranging from 4.4% (USA) to 22.0% (Iceland). The race time increased across calendar years for both women (r = 0.45, p = 0.006, moderate magnitude) and men (r = 0.25, p = 0.015, small magnitude). On the basis of these findings, we concluded that a relatively small number of women pariticipates in XC skiing. Therefore, the development of public health policies targeting the participation of women in XC skiing should be a concern in the countries with a tradition of this sport.
Profiling the Instantaneous Power Consumption of Electric Machinery in Agricultural Environments: An Algebraic Approach
Authors: Romero Schmidt Javier , Eguren Javier , Auat Cheein Fernando
Published: April 2019

One of the upcoming challenges in precision agriculture is the development of electric machinery able to replace traditional combustion engines. This step toward green agriculture practices still has to face the lifetime of the batteries. Despite their technological advancement, batteries’ charges do not last as long as fueled engines. The route planning problem (RPP), for example, has to be re-thought according to the available energy resources since the machinery might exhaust its power without finishing the route. This work focuses in part on such a vast problem by proposing and testing an algebraic, yet simple technique to obtain instantaneous power consumption (IPC) profiles to be used by the RPP. The technique presented herein uses the knowledge of the terrain, the kinematic and dynamic constraints of the vehicle, and its electric model. The methodology followed is later validated in a real grove—i.e., trees cultivated in rows—showing that our power profiling technique reaches errors smaller than 10% when estimating the IPC and the associated energy required. This result can lead to better decisions by the farmer.
Modeling American Household Fluid Milk Consumption and their Resulting Greenhouse Gas Emissions
Authors: Stankiewicz Sebastian K. , Auras Rafael , Selke Susan
Published: April 2019

U.S. consumers are the largest contributors to food waste generation (FWG), but few models have explained how households waste food. This study examines how discrete-event simulation (DES) can identify areas for reducing FWG through packaging and consumer milk consumption behavioral changes. Household model parameters included: amount and type of consumption, type and number of containers bought, buying behavior, and shelf life of milk. Simulations comparing the purchase of quart, half gallon, and gallon milk containers were run for 10,000 days to identify which package type reduced waste for 50 1, 2 and 4-person households. Based on consumption averages from the U.S. National Dairy Council, results suggest that if 1 and 4-person households change their purchasing behavior from 1 half-gallon to 1 quart and 2 gallons to 3 half-gallons, they can reduce their greenhouse gas (GHG) emissions from milk consumption by 33% and 12%, respectively, without reducing their total milk consumption. Purchasing enough smaller containers to be equivalent to a larger size decreased spoilage, but not enough to reduce a consumer’s total milk consumption GHG emissions. Results showed that packaging accounts for 5% of the total milk consumption GHG emissions; most of a consumer’s impact comes from milk spoilage and consumption.