bluuebunny/crossref_metadata_2025 huggingface dataset:
Satellite Sea Surface Temperature Product Comparison for the Southern African Marine Region
Authors: Carr Matthew , Lamont Tarron , Krug Marjolaine
Published: March 2021

Several satellite-derived Sea Surface Temperature (SST) products were compared to determine their potential for research and monitoring applications around the southern African marine region. This study provides the first detailed comparison for the region, demonstrating good overall agreement (variance < 0.4 °C2) between merged SST products for most of the South African marine region. However, strong disagreement in absolute SST values (variance of 0.4–1.2 °C2 and differences of up to 6 °C) was observed at well-known oceanographic features characterized by complex temperature structures and strong SST gradients. Strong seasonal bias in the discrepancy between SST was observed and shown to follow seasonal increases in cloud cover or local oceanographic dynamics. Disagreement across the L4 products showed little dependence on their spatial resolutions. The periods of disagreement were characterized by large deviations among all products, which resulted mainly from the lack of input observations and reliance on interpolation schemes. This study demonstrates that additional methods such as the ingestion of additional in situ observations or daytime satellite acquisitions, especially along the west coast of southern Africa, might be required in regions of strong SST gradient, to improve their representations in merged SST products. The use of ensemble means may be more appropriate when conducting research and monitoring in these regions of high SST variance.
Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine
Authors: Lin Jinhuang , Jin Xiaobin , Ren Jie , Liu Jingping , Liang Xinyuan , Zhou Yinkang
Published: March 2021

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.
Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification
Authors: Xu Hao , Yao Wei , Cheng Li , Li Bo
Published: March 2021

In recent years, benefiting from the rapid development of deep learning technology in the field of computer vision, the study of hyperspectral image (HSI) classification has also made great progress. However, compared with ordinary RGB images, HSIs are more like 3D cubes; therefore, it is necessary and beneficial to explore classification methods suitable for the very special data structure of HSIs. In this paper, we propose Multiple Spectral Resolution 3D Convolutional Neural Network (MSR-3DCNN) for HSI classification tasks. In MSR-3DCNN, we expand the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension, and combine 3D convolution and residual connection; therefore, it can better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands. Experimental results on four benchmark datasets show the effectiveness of the proposed approach and its superiority as compared with some state-of-the-art (SOTA) HSI classification methods.
Analyzing the Suitability of Remotely Sensed ET for Calibrating a Watershed Model of a Mediterranean Montane Forest
Authors: Jepsen Steven M. , Harmon Thomas C. , Guan Bin
Published: March 2021

The ability to spatially characterize runoff generation and forest health depends partly on the accuracy and resolution of evapotranspiration (ET) simulated by numerical models. A possible strategy to increase the accuracy and resolution of numerically modeled ET is the use of remotely sensed ET products as an observational basis for parameter estimation (model calibration) of those numerical models. However, the extent to which that calibration strategy leads to a realistic representation of ET, relative to ground conditions, is not well understood. We examined this by comparing the spatiotemporal accuracy of ET from a remote sensing product, MODIS MOD16A2, to that from a watershed model (SWAT) calibrated to flow measured at an outlet streamgage. We examined this in the upper Kings River watershed (3999 km2) of California’s Sierra Nevada, a snow-influenced watershed in a Mediterranean climate. We assessed ET accuracies against observations from three eddy-covariance flux towers at elevations of 1160–2700 m. The accuracy of ET from the stream-calibrated watershed model surpassed that of MODIS in terms of Nash-Sutcliffe efficiency (+0.36 versus −0.43) and error in elevational trend (+7.7% versus +81%). These results indicate that for this particular experiment, an outlet streamgage would provide a more effective observational basis than remotely sensed ET product for watershed-model parameter estimation. Based on analysis of ET-weather relationships, the relatively large errors we found in MODIS ET may be related to weather-based corrections to water limitation not representative of the hydrology of this snow-influenced, Mediterranean-climate area.
Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network
Authors: Chen Wenjing , Zheng Xiangtao , Lu Xiaoqiang
Published: March 2021

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.
Photogrammetric Digital Surface Model Reconstruction in Extreme Low-Light Environments
Authors: Roncella Riccardo , Bruno Nazarena , Diotri Fabrizio , Thoeni Klaus , Giacomini Anna
Published: March 2021

Digital surface models (DSM) have become one of the main sources of geometrical information for a broad range of applications. Image-based systems typically rely on passive sensors which can represent a strong limitation in several survey activities (e.g., night-time monitoring, underground survey and night surveillance). However, recent progresses in sensor technology allow very high sensitivity which drastically improves low-light image quality by applying innovative noise reduction techniques. This work focuses on the performances of night-time photogrammetric systems devoted to the monitoring of rock slopes. The study investigates the application of different camera settings and their reliability to produce accurate DSM. A total of 672 stereo-pairs acquired with high-sensitivity cameras (Nikon D800 and D810) at three different testing sites were considered. The dataset includes different camera configurations (ISO speed, shutter speed, aperture and image under-/over-exposure). The use of image quality assessment (IQA) methods to evaluate the quality of the images prior to the 3D reconstruction is investigated. The results show that modern high-sensitivity cameras allow the reconstruction of accurate DSM in an extreme low-light environment and, exploiting the correct camera setup, achieving comparable results to daylight acquisitions. This makes imaging sensors extremely versatile for monitoring applications at generally low costs.
Special Issue “Remote-Sensing-Based Urban Planning Indicators”
Authors: Kuffer Monika , Pfeffer Karin , Persello Claudio
Published: March 2021

We are living in an urban age [...]
An Infrared Thermography Approach to Evaluate the Strength of a Rock Cliff
Authors: Loche Marco , Scaringi Gianvito , Blahůt Jan , Melis Maria , Funedda Antonio , Da Pelo Stefania , Erbì Ivan , Deiana Giacomo , Meloni Mattia , Cocco Fabrizio
Published: March 2021

The mechanical strength is a fundamental characteristic of rock masses that can be empirically related to a number of properties and to the likelihood of instability phenomena. Direct field acquisition of mechanical information on tall cliffs, however, is challenging, particularly in coastal and alpine environments. Here, we propose a method to evaluate the compressive strength of rock blocks by monitoring their thermal behaviour over a 24-h period by infrared thermography. Using a drone-mounted thermal camera and a Schmidt (rebound) hammer, we surveyed granitoid and aphanitic blocks in a coastal cliff in south-east Sardinia, Italy. We observed a strong correlation between a simple cooling index, evaluated in the hours succeeding the temperature peak, and strength values estimated from rebound hammer test results. We also noticed different heating-cooling patterns in relation to the nature and structure of the rock blocks and to the size of the fractures. Although further validation is warranted in different morpho-lithological settings, we believe the proposed method may prove a valid tool for the characterisation of non-directly accessible rock faces, and may serve as a basis for the formulation, calibration, and validation of thermo-hydro-mechanical constitutive models.
Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR
Authors: Rudge Mitchel L. M. , Levick Shaun R. , Bartolo Renee E. , Erskine Peter D.
Published: March 2021

The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.
Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model
Authors: Choi Wonei , Lee Hanlim , Kim Daewon , Kim Serin
Published: March 2021

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.
From Standard Weather Stations to Virtual Micro-Meteorological Towers in Ungauged Sites: Modeling Tool for Surface Energy Fluxes, Evapotranspiration, Soil Temperature, and Soil Moisture Estimations
Authors: Celis Jorge A. , Moreno Hernan A. , Basara Jeffrey B. , McPherson Renee A. , Cosh Michael , Ochsner Tyson , Xiao Xiangming
Published: March 2021

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available.
Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner
Authors: Adams Tyler , Bruton Richard , Ruiz Henry , Barrios-Perez Ilse , Selvaraj Michael G. , Hays Dirk B.
Published: March 2021

Challenges in rapid prototyping are a major bottleneck for plant breeders trying to develop the needed cultivars to feed a growing world population. Remote sensing techniques, particularly LiDAR, have proven useful in the quick phenotyping of many characteristics across a number of popular crops. However, these techniques have not been demonstrated with cassava, a crop of global importance as both a source of starch as well as animal fodder. In this study, we demonstrate the applicability of using terrestrial LiDAR for the determination of cassava biomass through binned height estimations, total aboveground biomass and total leaf biomass. We also tested using single LiDAR scans versus multiple registered scans for estimation, all within a field setting. Our results show that while the binned height does not appear to be an effective method of aboveground phenotyping, terrestrial laser scanners can be a reliable tool in acquiring surface biomass data in cassava. Additionally, we found that using single scans versus multiple scans provides similarly accurate correlations in most cases, which will allow for the 3D phenotyping method to be conducted even more rapidly than expected.
CubeSat Observation of the Radiation Field of the South Atlantic Anomaly
Authors: Kovář Pavel , Sommer Marek
Published: March 2021

The movement of the South Atlantic Anomaly has been observed since the end of the last century by many spacecrafts equipped with various types of radiation detectors. All satellites that have observed the drift of the South Atlantic Anomaly have been exclusively large missions with heavy payload equipment. With the recent rapid progression of CubeSats, it can be expected that the routine monitoring of the South Atlantic Anomaly will be taken over by CubeSats in the future. We present one-and-a-half years of observations of the South Atlantic Anomaly radiation field measured by a CubeSat in polar orbit with an elevation of 540 km. The position is calculated by an improved centroid method that takes into account the area of the grid. The dataset consists of eight campaigns measured at different times, each with a length of 22 orbits (~2000 min). The radiation data were combined with GPS position data. We detected westward movement at 0.33°/year and southward movement at 0.25°/year. The position of the fluence maximum featured higher scatter than the centroid position.
Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios
Authors: Pałka Filip , Książek Wojciech , Pławiak Paweł , Romaszewski Michał , Książek Kamil
Published: March 2021

This study is focused on applying genetic algorithms (GAs) to model and band selection in hyperspectral image classification. We use a forensic-inspired data set of seven hyperspectral images with blood and five visually similar substances to test GA-optimised classifiers in two scenarios: when the training and test data come from the same image and when they come from different images, which is a more challenging task due to significant spectral differences. In our experiments, we compare GA with a classic model optimisation through a grid search. Our results show that GA-based model optimisation can reduce the number of bands and create an accurate classifier that outperforms the GS-based reference models, provided that, during model optimisation, it has access to examples similar to test data. We illustrate this with experiments highlighting the importance of a validation set.
A Signal Processing Algorithm of Two-Phase Staggered PRI and Slow Time Signal Integration for MTI Triangular FMCW Multi-Target Tracking Radars
Authors: Tang Taiwen , Wu Chen , Elangage Janaka
Published: March 2021

In this paper, a novel signal processing algorithm for mitigating the radar blind speed problem of moving target indication (MTI) for frequency modulated continuous wave (FMCW) multi-target tracking radars is proposed. A two-phase staggered pulse repetition interval (PRI) solution is introduced to the FMCW radar system. It is implemented as a time-varying MTI filter using twice the hardware resources as compared to a uniform PRI MTI filter. The two-phase staggered PRI FMCW waveform is still periodic with a little more than twice the period of the uniform PRI radar. We also propose a slow time signal integration scheme for the radar detector using the post-fast Fourier transformation Doppler tracking loop. This scheme introduces 4.77 dB of extra signal processing gain to the signal before the radar detector compared with the original uniform PRI FMCW radar. The validation of the algorithm is done on the field programmable logic array in the loop test bed, which accurately models and emulates the target movement, line of sight propagation and radar signal processing. A simulation run of tracking 16 s of the target movement near or at the radar blind speed shows that the total degradation from the raw post-fast Fourier transformation received signal to noise ratio is about 2 dB. With a 20 dB post-processing signal to noise ratio of the proposed algorithm for the moving target at around a 20 km range and with about a −3.5 dB m2 radar cross section at a 1.5 GHz carrier frequency, the tracking errors of the two-dimensional angles with a 4×4 digital phased array are less than 0.2 degree. The range tracking error is about 28 m.
Development of a Non-Linear Bi-Directional Vortex-Induced Piezoelectric Energy Harvester with Magnetic Interaction
Authors: Su Wei-Jiun , Wang Zong-Siang
Published: March 2021

In this study, magnetic force is introduced to the design of a bi-directional U-shaped piezoelectric energy harvester for vortex-induced vibrations. The theoretical model of the beam structure is derived based on the Euler–Bernoulli beam theory. The vortex-induced vibration and the non-linear magnetic force are modeled according to the Rayleigh oscillator and the charge model, respectively. A prototype is fabricated and tested in two orthogonal directions under vortex-induced vibrations in a wind tunnel. Up and down wind-speed sweeps are carried out to investigate the non-linear responses of the harvester. The distance between the magnets and the length of the side beams are adjusted to examine the influence of the magnetic force on the lock-in region and voltage output of the harvester. Overall, the harvester shows strong non-linearity in the horizontal excitations. After adding magnets to the system, significant improvement of the lock-in region and the peak voltage is noticed in the horizontal mode under both up and down sweeps.
Optimized Tapered Fiber Decorated by Ag Nanoparticles for Raman Measurement with High Sensitivity
Authors: Li Tao , Yu Zhinan , Wang Zhengkun , Zhu Yong , Zhang Jie
Published: March 2021

A tapered fiber decorated by Ag nanoparticles is prepared as a surface-enhanced Raman scattering (SERS) substrate. There are two key parameters during the preparation process, the fiber cone angle and the density of decorated AgNPs on the fiber tip surface. Their theoretical analysis on the forming mechanism and the optimization process is studied in detail. The tapered fibers with angles from 0.5 to 30° are successfully prepared, with a chemical method in a small tube using a bending interface. AgNPs with different densities are decorated on the surface of the tapered fibers with an electrostatic adsorption method. The optimized tapered fiber SERS probe with an angle of 12° and AgNPs density of 26.67% provides the detection of Rhodamine 6G (R6G) with 10−10 mol/L.
Study of the Seismoelectric Effect of the Second Kind Using Molecular Sensors
Authors: Potylitsyn Vadim , Kudinov Danil , Dmitry Alekseev , Kokhonkova Ekaterina , Kurkov Sergey , Egorov Ivan , Pliss Aleksandra
Published: March 2021

The article is devoted to the study of the potential possibilities of using molecular-electronic sensors of seismic waves for field work using the seismoelectric method to explore the hydrocarbon deposits. The introduction provides an analytical review of the current state of research based on data from science magazines and patents. It is shown that at present, seismoelectric effects are at the stage of experimental implementation into the practice of field work for oil and gas geophysical prospecting. Further in the article, theoretical estimates and results of mathematical modeling of the manifestation of seismoelectric (SE) phenomena in the regions of hydrocarbon anomalies are presented, numerical estimates of the values of the seismic and secondary electromagnetic fields are given. The analysis of the results (on a tank and real gas condensate field) showed that the use of molecular-electronic geophones, which have a higher sensitivity and operate in a wider frequency range (up to 0.1 Hz), allows one to obtain higher signal-to-noise ratio. Thus, it has been experimentally established that the use of molecular sensors for recording seismic electric effects when searching for deposits is more preferable when carrying out field work.
Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks
Authors: Seok Woojoon , Lee Kwang Jin , Cho Dongrae , Roh Jongryun , Kim Sayup
Published: March 2021

Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.
Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach
Authors: Duong Soan T. M. , Phung Son Lam , Bouzerdoum Abdesselam , Ang Sui Paul , Schira Mark M.
Published: March 2021

Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-end deep learning framework, named TS-Net, for susceptibility artifact correction (SAC) in a pair of 3D EPI images with reversed phase-encoding directions. The proposed TS-Net comprises a deep convolutional network to predict a displacement field in three dimensions to overcome the limitation of existing methods, which only estimate the displacement field along the dominant-distortion direction. In the training phase, anatomical T1-weighted images are leveraged to regularize the correction, but they are not required during the inference phase to make TS-Net more flexible for general use. The experimental results show that TS-Net achieves favorable accuracy and speed trade-off when compared with the state-of-the-art SAC methods, i.e., TOPUP, TISAC, and S-Net. The fast inference speed (less than a second) of TS-Net makes real-time SAC during EPI image acquisition feasible and accelerates the medical image-processing pipelines.
Effect of Low-Doses of Gamma Radiation on Electric Arc-Induced Long Period Fiber Gratings
Authors: Mesonero-Santos Patricia , Fernández-Medina Ana , Coelho Luis C. C. , Viveiros Duarte , Jorge Pedro A. , Belenguer Tomás , López Heredero Raquel
Published: March 2021

This work presents an experimental study on the effects of gamma radiation on Long Period Fiber Gratings (LPFGs) in a low-dose test campaign to evaluate their eventual degradation. The study was carried out with standard single-mode fibers where the grating was inscribed using the Electric-Arc Discharge (EAD) technique. Before the gamma campaign, a detailed optical characterization was performed with repeatability tests to verify the accuracy of the setup and the associated error sources. The gamma-induced changes up to a dose of 200 krad and the recovery after radiation were monitored with the Dip Wavelength Shift (DWS). The results show that the gamma sensitivity for a total dose of 200 krad is 11 pm/krad and a total DWS of 2.3 nm has been observed with no linear dependence. Post-radiation study shows that recovery from radiation-induced wavelength shift is nearly complete in about 4000 h. Experimental results show that the changes suffered under gamma irradiation of these LPFGs are temporary making them a good choice as sensors in space applications.
Development of Electrochemical DNA Biosensor for Equine Hindgut Acidosis Detection
Authors: Davies Joshua , Thomas Carol , Rizwan Mohammad , Gwenin Christopher
Published: March 2021

The pH drop in the hindgut of the horse is caused by lactic acid-producing bacteria which are abundant when a horse’s feeding regime is excessively carbohydrate rich. This drop in pH below six causes hindgut acidosis and may lead to laminitis. Lactic acid-producing bacteria Streptococcus equinus and Mitsuokella jalaludinii have been found to produce high amounts of L-lactate and D-lactate, respectively. Early detection of increased levels of these bacteria could allow the horse owner to tailor the horse’s diet to avoid hindgut acidosis and subsequent laminitis. Therefore, 16s ribosomal ribonucleic acid (rRNA) sequences were identified and modified to obtain target single stranded deoxyribonucleic acid (DNA) from these bacteria. Complementary single stranded DNAs were designed from the modified target sequences to form capture probes. Binding between capture probe and target single stranded deoxyribonucleic acid (ssDNA) in solution has been studied by gel electrophoresis. Among pairs of different capture probes and target single stranded DNA, hybridization of Streptococcus equinus capture probe 1 (SECP1) and Streptococcus equinus target 1 (SET1) was portrayed as gel electrophoresis. Adsorptive stripping voltammetry was utilized to study the binding of thiol modified SECP1 over gold on glass substrates and these studies showed a consistent binding signal of thiol modified SECP1 and their hybridization with SET1 over the gold working electrode. Cyclic voltammetry and electrochemical impedance spectroscopy were employed to examine the binding of thiol modified SECP1 on the gold working electrode and hybridization of thiol modified SECP1 with the target single stranded DNA. Both demonstrated the gold working electrode surface was modified with a capture probe layer and hybridization of the thiol bound ssDNA probe with target DNA was indicated. Therefore, the proposed electrochemical biosensor has the potential to be used for the detection of the non-synthetic bacterial DNA target responsible for equine hindgut acidosis.
Assessing the Capability and Potential of LiDAR for Weed Detection
Authors: Shahbazi Nooshin , Ashworth Michael B. , Callow J. Nikolaus , Mian Ajmal , Beckie Hugh J. , Speidel Stuart , Nicholls Elliot , Flower Ken C.
Published: March 2021

Conventional methods of uniformly spraying fields to combat weeds, requires large herbicide inputs at significant cost with impacts on the environment. More focused weed control methods such as site-specific weed management (SSWM) have become popular but require methods to identify weed locations. Advances in technology allows the potential for automated methods such as drone, but also ground-based sensors for detecting and mapping weeds. In this study, the capability of Light Detection and Ranging (LiDAR) sensors were assessed to detect and locate weeds. For this purpose, two trials were performed using artificial targets (representing weeds) at different heights and diameter to understand the detection limits of a LiDAR. The results showed the detectability of the target at different scanning distances from the LiDAR was directly influenced by the size of the target and its orientation toward the LiDAR. A third trial was performed in a wheat plot where the LiDAR was used to scan different weed species at various heights above the crop canopy, to verify the capacity of the stationary LiDAR to detect weeds in a field situation. The results showed that 100% of weeds in the wheat plot were detected by the LiDAR, based on their height differences with the crop canopy.
Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals
Authors: Verdejo Espinosa Ángeles , Lopez Ruiz José Luis , Mata Mata Francisco , Estevez Macarena Espinilla
Published: March 2021

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people’s health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health? (c) What sustainable development indicators and objectives can be assessed in the applications and projects analyzed?
What Is Interdisciplinarity in the Study of Sustainable Destination Development?
Authors: Persson-Fischer Ulrika , Liu Shuangqi
Published: March 2021

A large body of evidence suggests that sustainable destination development (SDD) is not only multidisciplinary but interdisciplinary as its research involves the integration of knowledge, methods, theories or disciplines. The word inter- is a “dangerous” one as it implies a “dangerous connection” attempting to reconcile irreconcilable people (i.e., North institutions and South institutions), but it is also very inclusive as, for example, economic behavior is related to social background and cultural issues. Although a common view is that SDD is interdisciplinary, what disciplines does it cross exactly? With the attendant “semantic confusion”, research on SDD is working in different directions, but what exactly does the existing research take as its object of study? What are the leading themes and perspectives in the field? How do we evaluate these diversification efforts? Trying to add one more seems redundant. We believe that after nearly two decades of productive scholarship, it is now time to try to identify some potential paradigms in SDD. A content-analysis-based literature review to explore previous studies is undoubted of value, as these diverse efforts point to current trends in SDD research. Therefore, we conducted an exploratory and descriptive analysis of the literature on SDD from 2015–2020 to provide specific indications for its interdisciplinary character. As a result, a total of 175 articles in 31 crucial journals from 2015 to 2020 are reviewed. Based on content analysis, five leading themes and five leading perspectives in the SDD literature were identified. We adopted an immanent critique method to discuss our findings. We appeal for consensus instead of definition and balance instead of choice in the discourse of SDD. We suggest ways in which past academic research can be used smartly and point out some important but neglected areas to stimulate a more creative research production.
Dynamic Effect of Oil Resources on Environmental Quality: Testing the Environmental Kuznets Curve Hypothesis for Selected African Countries
Authors: Ouédraogo Mohamed , Peng Daiyan , Chen Xi , Hashmi Shujahat Haider , Sall Mamoudou Ibrahima
Published: March 2021

This paper examines the environmental Kuznets curve (EKC) theory, augmenting the role of oil resources and energy consumption in carbon dioxide (CO2) emissions using the annual data of 11 African oil-producing countries from 1980 to 2014. We apply advanced panel cointegration and panel autoregressive distributive lag (ARDL) techniques coupled with Granger non-causality analysis to account for cross-sectional dependence and heterogeneity. The results of the augmented mean group (AMG) reveal that oil resources abundance degrades the environmental quality in Angola while abating CO2 emissions in Algeria, Gabon, Morocco, and Nigeria. Contrarily, energy consumption escalates pollution in the Congo Democratic Republic (COD), Côte d’Ivoire (CIV), Gabon, Morocco, and Tunisia. Our findings support the EKC hypothesis only in Cameroon, CIV, and Nigeria while exhibiting a U-shaped curve in Algeria and Morocco. Causality analysis unveils that oil resources Granger cause energy consumption, suggesting the balance between renewable and non-renewable energy sources. The current study has important policy implications for promoting green technology, economic diversification, service sector, and green investments.
Climate Change Mitigation Pathways for the Aviation Sector
Authors: Hasan Md Arif , Mamun Abdullah Al , Rahman Syed Masiur , Malik Karim , Al Amran Md. Iqram Uddin , Khondaker Abu Nasser , Reshi Omer , Tiwari Surya Prakash , Alismail Fahad Saleh
Published: March 2021

Even though the contribution of the aviation sector to the global economy is very notable, it also has an adverse impact on climate change. Improvements have been made in different areas (i.e., technology, sustainable aviation fuel, and design) to mitigate these adverse effects. However, the rate of improvement is small compared to the increase in the demand for air transportation. Hence, greenhouse gas emissions in the aviation sector are steadily increasing and this trend is expected to continue unless adequately addressed. In this context, this study examined the following: (i) the factors that affect the growth of aviation, (ii) trends in greenhouse gas emissions in the sector, (iii) trends in energy demand, (iv) mitigation pathways of emissions, (v) mitigation challenges for the International Civil Aviation Organization, (vi) achievements in mitigating emissions, (vii) barriers against mitigating emissions, and (viii) approaches of overcoming barriers against emissions mitigation. This study finds that continued research and development efforts targeting aircraft fuel burn efficiency are crucial in reducing greenhouse gas emissions. Although biofuels are promising for the reduction of aviation emissions, techniques to reduce NOx emissions could enhance large-scale deployment. Pragmatic market-based mechanisms, such as the Emissions Trading Scheme (ETS) and/or carbon tax must be enforced on a global scale to capitalize on a collective stakeholder effort to curb CO2 emissions. The findings of this study will help in understanding the emissions and energy consumption scenarios, which will provide a comprehensive package of mitigation pathways to overcome future emissions reduction challenges in the aviation sector.
Mobility as a Service and Public Transport: A Rapid Literature Review and the Case of Moovit
Authors: Santos Georgina , Nikolaev Nikolay
Published: March 2021

Mobility as a Service (MaaS) is often proposed as a tool for achieving sustainable mobility and, in particular, increasing the share of public transport trips in cities. In this paper we conduct a rapid review of the literature on MaaS and, using Moovit as a case study, we explore the association between the popularity of searches in Google using the term “Moovit” and the share of workers that commute by public transport. The exercise focuses on metropolitan areas in the United States over the period 2010 to 2019. We find a positive correlation, and we speculate that metropolitan areas with pre-existing higher shares of workers commuting by public transport tend to be metropolitan areas where use of Moovit is more likely.
Life Cycle and Economic Analyses of the Removal of Pesticides and Pharmaceuticals from Municipal Wastewater by Anodic Oxidation
Authors: Surra Elena , Correia Manuela , Figueiredo Sónia , Silva Jaime Gabriel , Vieira Joana , Jorge Sandra , Pazos Marta , Sanromán Maria Ángeles , Lapa Nuno , Delerue-Matos Cristina
Published: March 2021

Several pesticides and pharmaceuticals (PP) have been detected in the effluent of a full-scale Portuguese Wastewater Treatment Plant (WWTP). Their presence contributed to the environmental burdens associated with the existing treatment of the Municipal Wastewater (MWW) in the impact categories of Human Carcinogenicity, Non-Carcinogenicity, and Freshwater toxicities on average by 85%, 60%, and 90%, respectively (ReciPe2016 and USEtox methods). The environmental and economic assessment of the installation of an Anodic Oxidation (AO) unit for PPs’ removal was performed through Life Cycle and Economic Analysis, considering two types of anodes, the Boron-Doped Diamond (BDD) and the Mixed Metal Oxides (MMO). The operation of the AO unit increased the environmental burdens of the system by 95% on average (USEtox), but these impacts can be partially compensated by the avoided the production of non-renewable energy in the Portuguese electricity mix by biogas cogeneration at the WWTP. If the construction of the AO unit and the manufacturing of the electrodes are considered, the Human and Freshwater Toxicities are often higher than the environmental benefits derived from the PPs’ removal. On the economic side, the MMO configuration is clearly more advantageous, whereas BDD is environmentally more favorable. The issue of the presence of PP in MWW effluents has to be addressed as an integrated solution both improving upstream PP’s management and adopting PP’s removal technologies strongly supported by renewable energies. Further insights are needed for the assessment of fate and of the environmental effects of PP in the sludge.
The Ethics of Farm Animal Biotechnology from an Anthropological Perspective
Authors: Aerni Philipp
Published: March 2021

Over the past 11,000 years, humans have domesticated a wide range of animals for different purposes designed to serve the human economy, society, and religious activities. The resulting mutual dependence between humans and their domestic partners created anthropogenic landscapes designed to sustain and protect their members. In this paper, we review the literature on the latest insights in interdisciplinary anthropological research on the evolution of animal domestication and breeding and put them in the context of the contemporary ethical debate on animal welfare and the application of modern biotechnology to animal breeding. Opponents of the use of animal biotechnology tend to see breeders often as enablers of industrial farming that would seek selective business advantage at the expense of the environment and animal welfare. Many applications of animal biotechnology may, however, also help to address environmental and animal welfare concerns in an effective way. Moreover, recent archeological and genetic research findings on the history of animal domestication reveal a distinctive kind of mutualism in the human–animal relationship based on a gradual co-evolutionary process with clear benefits for both parties in the relationship. These insights challenge the popular Neo-Darwinian account of unilateral adaptation only benefiting the more powerful party. Instead, they support the hypothesis that humans do not just adapt, but actively shape the environment through cultural niche construction (CNC) that also involves care and protection for domesticated animals. These empirical findings should also be taken into account in the contemporary ethical debate on animal welfare, which has become increasingly detached from the real-world efforts to improve animal welfare through best practices.
New Classification Method to Evaluate Pollution Levels of Sewage Contaminated Lakes
Authors: Rao Sudhakar M. , Mogili Nitish Venkateswarlu
Published: March 2021

Monitoring water quality to minimize deterioration of a lake’s functionality is important, as several Indian lakes are exposed to sewage contamination. Public health laboratories, citizen scientists, and volunteers in developing nations often find it difficult to perform elaborate tests to monitor the water quality of freshwater systems. Developing a classification method to evaluate the pollution status of sewage-contaminated lakes using limited tests will expand environmental monitoring of freshwater systems and contribute valuable data to the regional and global repository. Four classes of lake pollution ranging from unpolluted (class 1) to mixed wastewater (class 4) were identified based on the distribution of data points in the K+ (potassium) versus COD (chemical oxygen demand) scatter chart. As pH, EC (electrical conductivity), turbidity, and DO (dissolved oxygen) are deteriorated by sewage contamination, these parameters were also incorporated in the proposed pollution classification table. Data of unpolluted and sewage polluted Indian lakes were employed to compile the limiting range of parameters in the proposed lake pollution classification. The five parameters (K+, pH, EC, DO, turbidity) required to categorize lake pollution (class 1 to 4) can be measured with equipment costing 800–1000 USD, while COD can be measured at 5 USD/sample in laboratories.
Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries
Authors: Murayama Yuji , Simwanda Matamyo , Ranagalage Manjula
Published: March 2021

The international statistics show that the global urban population will increase by up to 68% by 2050 [...]