bluuebunny/crossref_metadata_2025 huggingface dataset:
Water Quality Variability and Related Factors along the Yangtze River Using Landsat-8
Authors: He Yang , Jin Shuanggen , Shang Wei
Published: June 2021

Chlorophyll-a (Chl-a), total nitrogen (TN), and total phosphorus (TP) are important indicators to evaluate water environmental quality. Monitoring water quality and its variability can help control water pollution. However, traditional monitoring techniques of water quality are time-consuming and laborious, and can mostly conduct with sample point-to-point at the edge of lakes and rivers. In this study, an empirical (regression-based) model is proposed to retrieve Chl-a, TN, and TP concentrations in the Yangtze River by Landsat-8 images from 2014 to 2020. The spatial-temporal distribution and variability of water quality in the whole Yangtze River are analyzed in detail. Furthermore, the driving forces of water quality variations are explored. The results show that the mean absolute percentage error (MAPE) of the water quality parameters are 25.88%, 4.3%, and 8.37% for Chl-a, TN, and TP concentrations, respectively, and the root mean square errors (RMSE) are 0.475 μg/L, 0.110 mg/L, and 0.01 mg/L, respectively. The concentrations of Chl-a, TN, and TP in the upstream of the Yangtze River are lower than those in the midstream and downstream. These water quality parameters have a seasonal cycle with a maximum in summer and minimum in winter. The hydrological and meteorological factors such as water level, flow, temperature, and precipitation are positively correlated with Chl-a, TN, and TP concentrations. The larger the impervious surface and cropland area, the greater the cargo handling capacity (CHC), higher ratio of TP to TN will further pollute the water. The methods and results provide essential information to evaluate and control water pollution in the Yangtze River.
An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring
Authors: Zhang Ruicheng , Gao Chengfa , Zhao Qing , Peng Zihan , Shang Rui
Published: June 2021

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.
Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?
Authors: Sanderman Jonathan , Savage Kathleen , Dangal Shree R. S. , Duran Gabriel , Rivard Charlotte , Cavigelli Michel A. , Gollany Hero T. , Jin Virginia L. , Liebig Mark A. , Omondi Emmanuel Chiwo , Rui Yichao , Stewart Catherine
Published: June 2021

A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring.
Interannual Variability of Atmospheric CH4 and Its Driver Over South Korea Captured by Integrated Data in 2019
Authors: Kenea Samuel Takele , Lee Haeyoung , Joo Sangwon , Li Shanlan , Labzovskii Lev , Chung Chu-Yong , Kim Yeon-Hee
Published: June 2021

Understanding the temporal variability of atmospheric methane (CH4) and its potential drivers can advance the progress toward mitigating changes to the climate. To comprehend interannual variability and spatial characteristics of anomalous CH4 mole fractions and its drivers, we used integrated data from different platforms such as in situ measurements and satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Greenhouse Gases Observing SATellite (GOSAT)) retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Republic of Korea, ranging from −16.8 to 31.3 ppb yr−1 as captured in situ through 2015–2020 and 3.9 to 16.4 ppb yr−1 detected by GOSAT through 2014–2019, respectively. High growth rates were discerned in 2016 (31.3 ppb yr−1 and 13.4 ppb yr−1 from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr−1 and 16.4 ppb yr−1 from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015–2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature according to the Noah Land Surface Model. The stable isotopic composition of 13C/12C in CH4 (δ13-CH4) collected by flask-air sampling at AMY during 2014–2019 supported the soil methane hypothesis. The intercept of the Keeling plot for summer and autumn were found to be −53.3‰ and −52.9‰, respectively, which suggested isotopic signature of biogenic emissions. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal. Such changes in the biogenic signal were affected by the variations of soil temperature and soil moisture. We looked more closely at the variability of XCH4 and the relationship with soil properties. The result indicated a spatial distribution of interannual variability, as well as the captured elevated anomaly over the southwest of the domain in autumn 2019, up to 70 ppb, which was largely explained by the combined effect of soil temperature and soil moisture changes, indicating a pixel-wise correlation of XCH4 anomaly with those parameters in the range of 0.5–0.8 with a statistical significance (p < 0.05). This implies that the soil-associated drivers are able to exert a large-scale influence on the regional distribution of CH4 in Korea.
Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm
Authors: Mohammed Maged , Riad Khaled , Alqahtani Nashi
Published: June 2021

Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m3 palm−1, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m−3) and T-BIS (1.44 kg m−3) methods was significantly higher compared to the TSI (0.531 kg m−3). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions.
Computer Vision for 3D Perception and Applications
Authors: Poggi Matteo , Moeslund Thomas B.
Published: June 2021

Effective 3D perception of an observed scene greatly enriches the knowledge about the surrounding environment and is crucial to effectively develop high-level applications for various purposes [...]
HUMANNET—A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction
Authors: Kulikajevas Audrius , Maskeliunas Rytis , Damasevicius Robertas , Scherer Rafal
Published: June 2021

Majority of current research focuses on a single static object reconstruction from a given pointcloud. However, the existing approaches are not applicable to real world applications such as dynamic and morphing scene reconstruction. To solve this, we propose a novel two-tiered deep neural network architecture, which is capable of reconstructing self-obstructed human-like morphing shapes from a depth frame in conjunction with cameras intrinsic parameters. The tests were performed using on custom dataset generated using a combination of AMASS and MoVi datasets. The proposed network achieved Jaccards’ Index of 0.7907 for the first tier, which is used to extract region of interest from the point cloud. The second tier of the network has achieved Earth Mover’s distance of 0.0256 and Chamfer distance of 0.276, indicating good experimental results. Further, subjective reconstruction results inspection shows strong predictive capabilities of the network, with the solution being able to reconstruct limb positions from very few object details.
Emitter Location with Azimuth and Elevation Measurements Using a Single Aerial Platform for Electronic Support Missions
Authors: Khalaf-Allah Mohamed
Published: June 2021

Passive ground emitter geolocation techniques are essential to electronic warfare systems, as they provide threat warnings in hostile environments, while ensuring the electronic silence of the mission platform. Geolocation of enemy emitters indicates the position of and type of adversary troops, and allows for the use of guided munition against enemy targets. Three-dimensional geolocation solutions based on least squares and particle filter estimation, using only azimuth and elevation measurements, were considered. Three batch-processing and one instantaneous solution algorithms, i.e., using a single pulse or a single observation point, were developed and investigated. The performance of the proposed solutions was demonstrated by simulations. Results showed that the batch-processing solutions achieved acceptable accuracies with a sufficient number of observation points. The performance degraded with fewer observation points. The instantaneous geolocation solution improved performance with increasing observation points, i.e., working in the sequential mode, and therefore could approach the accuracy of the batch-processing solutions.
Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
Authors: Kim Haksu
Published: June 2021

As worldwide vehicle CO2 emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.
RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs
Authors: Yang Jung-Cheng , Lin Chun-Jung , You Bing-Yuan , Yan Yin-Long , Cheng Teng-Hu
Published: June 2021

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.
A Scalable Implementation of Anonymous Voting over Ethereum Blockchain
Authors: Song Jae-Geun , Moon Sung-Jun , Jang Ju-Wook
Published: June 2021

We considered scalable anonymous voting on the Ethereum blockchain. We identified three major bottlenecks in implementation: (1) division overflow in encryption of voting values for anonymity; (2) large time complexity in tallying, which limited scalability in the number of candidates and voters; and (3) tallying failure due to “no votes” from registered voters. Previous schemes failed at tallying if one (or more) registered voters did not send encrypted voting values. Algorithmic solutions and implementation details are provided. An experiment using Truffle and Remix running on a desktop PC was performed for evaluation. Our scheme shows great reduction in gas, which measures the computational burden of smart contracts to be executed on Ethereum. For instance, our scheme consumed 1/53 of the gas compared to a state-of-the-art solution for 60 voters. Time complexity analysis shows that our scheme is asymptotically superior to known solutions. In addition, we propose a solution to the tallying failure due to the “no vote” from registered voters.
Fast Real-Time Model Predictive Control for a Ball-on-Plate Process
Authors: Zarzycki Krzysztof , Ławryńczuk Maciej
Published: June 2021

This work is concerned with an original ball-on-plate laboratory process. First, a simplified process model based on state–space process description is derived. Next, a fast state–space MPC algorithm is discussed. Its main advantage is computational simplicity: the manipulated variables are found on-line using explicit formulas with parameters calculated off-line; no real-time optimization is necessary. Software and hardware implementation details of the considered MPC algorithm using the STM32 microcontroller are presented. Tuning of the fast MPC algorithm is discussed. To show the efficacy of the MPC algorithm, it is compared with the classical PID and LQR controllers.
Salient Region Guided Blind Image Sharpness Assessment
Authors: Liu Siqi , Yu Shaode , Zhao Yanming , Tao Zhulin , Yu Hang , Jin Libiao
Published: June 2021

Salient regions provide important cues for scene understanding to the human vision system. However, whether the detected salient regions are helpful in image blur estimation is unknown. In this study, a salient region guided blind image sharpness assessment (BISA) framework is proposed, and the effect of the detected salient regions on the BISA performance is investigated. Specifically, three salient region detection (SRD) methods and ten BISA models are jointly explored, during which the output saliency maps from SRD methods are re-organized as the input of BISA models. Consequently, the change in BISA metric values can be quantified and then directly related to the difference in BISA model inputs. Finally, experiments are conducted on three Gaussian blurring image databases, and the BISA prediction performance is evaluated. The comparison results indicate that salient region input can help achieve a close and sometimes superior performance to a BISA model over the whole image input. When using the center region input as the baseline, the detected salient regions from the saliency optimization from robust background detection (SORBD) method lead to consistently better score prediction, regardless of the BISA model. Based on the proposed hybrid framework, this study reveals that saliency detection benefits image blur estimation, while how to properly incorporate SRD methods and BISA models to improve the score prediction will be explored in our future work.
Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing
Authors: de Oliveira Gabriel Silva , Marcato Junior José , Polidoro Caio , Osco Lucas Prado , Siqueira Henrique , Rodrigues Lucas , Jank Liana , Barrios Sanzio , Valle Cacilda , Simeão Rosângela , Carromeu Camilo , Silveira Eloise , André de Castro Jorge Lúcio , Gonçalves Wesley , Santos Mateus , Matsubara Edson
Published: June 2021

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.
Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson’s Disease
Authors: Corrà Marta Francisca , Atrsaei Arash , Sardoreira Ana , Hansen Clint , Aminian Kamiar , Correia Manuel , Vila-Chã Nuno , Maetzler Walter , Maia Luís
Published: June 2021

Accurate assessment of Parkinson’s disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.
Quality of Service Provision in Fog Computing: Network-Aware Scheduling of Containers
Authors: Caminero Agustín C. , Muñoz-Mansilla Rocío
Published: June 2021

State-of-the-art scenarios, such as Internet of Things (IoT) and Smart Cities, have recently arisen. They involve the processing of huge data sets under strict time requirements, rendering the use of cloud resources unfeasible. For this reason, Fog computing has been proposed as a solution; however, there remains a need for intelligent allocation decisions, in order to make it a fully usable solution in such contexts. In this paper, a network-aware scheduling algorithm is presented, which aims to select the fog node most suitable for the execution of an application within a given deadline. This decision is made taking the status of the network into account. This scheduling algorithm was implemented as an extension to the Kubernetes default scheduler, and compared with existing proposals in the literature. The comparison shows that our proposal is the only one that can execute all the submitted jobs within their deadlines (i.e., no job is rejected or executed exceeding its deadline) with certain configurations in some of the scenarios tested, thus obtaining an optimal solution in such scenarios.
Inkjet-Printed and Electroplated 3D Electrodes for Recording Extracellular Signals in Cell Culture
Authors: Grob Leroy , Rinklin Philipp , Zips Sabine , Mayer Dirk , Weidlich Sabrina , Terkan Korkut , Weiß Lennart J. K. , Adly Nouran , Offenhäusser Andreas , Wolfrum Bernhard
Published: June 2021

Recent investigations into cardiac or nervous tissues call for systems that are able to electrically record in 3D as opposed to 2D. Typically, challenging microfabrication steps are required to produce 3D microelectrode arrays capable of recording at the desired position within the tissue of interest. As an alternative, additive manufacturing is becoming a versatile platform for rapidly prototyping novel sensors with flexible geometric design. In this work, 3D MEAs for cell-culture applications were fabricated using a piezoelectric inkjet printer. The aspect ratio and height of the printed 3D electrodes were user-defined by adjusting the number of deposited droplets of silver nanoparticle ink along with a continuous printing method and an appropriate drop-to-drop delay. The Ag 3D MEAs were later electroplated with Au and Pt in order to reduce leakage of potentially cytotoxic silver ions into the cellular medium. The functionality of the array was confirmed using impedance spectroscopy, cyclic voltammetry, and recordings of extracellular potentials from cardiomyocyte-like HL-1 cells.
A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography
Authors: Lehtonen Eero , Teuho Jarmo , Koskinen Juho , Jafari Tadi Mojtaba , Klén Riku , Siekkinen Reetta , Rives Gambin Joaquin , Vasankari Tuija , Saraste Antti
Published: June 2021

We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 ± 0.23 1/min, and the absolute amplitude error of 0.24 ± 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.
An Adaptive Energy Saving Algorithm for an RSSI-Based Localization System in Mobile Radio Sensors
Authors: Olesiński Adam , Piotrowski Zbigniew
Published: June 2021

In localization systems based on the emission of reference radio signals, an important issue related to the reliability of sensor operation is the problem of operating time and power of the emitted reference radio signal. There are many localization methods that have proven useful in practice and that use a reference radio signal for this purpose. In the issue of determining the location of radio emitters, various radio signal propagation models are used to determine the effective range and distance of the sensor-receiver from the radio emitter. This paper presents an adaptive power control algorithm for a transmitter, as a reference emitter, operating in power-saving mode. An important advantage of the presented solution is the adjustment of the localization system accuracy at the assumed level of energy radiated by radio emitters based on the RSSI signal received power estimation.
Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column
Authors: Niño-Adan Iratxe , Landa-Torres Itziar , Manjarres Diana , Portillo Eva , Orbe Lucía
Published: June 2021

Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.
Difficult Times: The Division of Domestic Work under Lockdown in France
Authors: Dominguez-Folgueras Marta
Published: June 2021

The lockdowns enforced in many countries to contain the spread of COVID-19 had important consequences for the domestic sphere. This paper analyzes the division of domestic work among heterosexual couples in France during the lockdown. In particular, we analyzed the role of time constraints and availability and expected to find a more egalitarian division of domestic work among couples in which the man had more time than his partner due to not working or working from home. We used data from the ELIPSS panel, a representative survey of the French population, and ran OLS regressions on the division of domestic work among 406 couples. The results show that men’s time availability was associated with a more egalitarian division of domestic work, even if gender inequalities persisted. However, we did not find any clear differences between men who did not work and men who worked from home, leading us to hypothesize that men’s presence at home is an important factor.
A Review on Bitumen Aging and Rejuvenation Chemistry: Processes, Materials and Analyses
Authors: Prosperi Emiliano , Bocci Edoardo
Published: June 2021

During the last decades, extensive research has been carried out on using reclaimed asphalt pavement (RAP) material in the production of hot recycled mix asphalt. Unfortunately, the aged, stiff, and brittle binder in the RAP typically increases the mixture stiffness and can therefore cause fatigue and low-temperature damages. In the scientific literature, there are many studies concerning the aging and rejuvenation of bitumen, but there is a lack of up-to-date reviews that bring them together, especially those facing the phenomena from a chemical point of view. In this paper, a recap of the chemical aspects of virgin, aged, and rejuvenated bitumen is proposed in order to provide a useful summary of the state of the art, with the aim of both encouraging the use of an increasing quantity of RAP in hot mix asphalt and trying to give indications for further research.
Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut
Authors: Taner Alper , Öztekin Yeşim Benal , Duran Hüseyin
Published: June 2021

In evaluating agricultural products, knowing the specific product varieties is important for the producer, the industrialist, and the consumer. Human labor is widely used in the classification of varieties. It is generally performed by visual examination of each sample by experts, which is very laborious and time-consuming with poor sensitivity. There is a need in commercial hazelnut production for a rapid, non-destructive and reliable variety classification in order to obtain quality nuts from the orchard to the consumer. In this study, a convolutional neural network, which is one of the deep learning methods, was preferred due to its success in computer vision. A total of 17 widely grown hazelnut varieties were classified. The proposed model was evaluated by comparing with pre-trained models. Accuracy, precision, recall, and F1-Score evaluation metrics were used to determine the performance of classifiers. It was found that the proposed model showed a better performance than pre-trained models in terms of performance evaluation criteria. The proposed model was found to produce 98.63% accuracy in the test set, including 510 images. This result has shown that the proposed model can be used practically in the classification of hazelnut varieties.
Environmental Impact of the Shadow Economy, Globalisation, Trade and Market Size: Evidence Using Linear and Non-Linear Methods
Authors: Shao Jinhua , Tillaguango Brayan , Alvarado Rafael , Ochoa-Moreno Santiago , Alvarado-Espejo Johanna
Published: June 2021

The achievement of the Sustainable Development Goals (SDG) related to the environment requires identifying new sources of environmental degradation. In this research, we examine the impact of the underground economy on polluting gas emissions. This relationship was estimated, including the role of globalization, trade, and market size. Using annual data from the World Bank and the International Monetary Fund, we found that, in the short term, the underground economy had a negative effect on global environmental pollution and a long-term negative impact. In the long term, the cointegration results indicate a long-term relationship between the series included in the investigation. The existence of a long-term relationship between the variables implies that as the underground economy increases, the emissions of polluting gases also change. In the long term, policymakers can use the black economy as an instrument to influence environmental pollution. Likewise, we found a threshold effect in the index of globalization, trade, and market size. The existence of a threshold effect implies that from a threshold, the impact of globalization, trade, and the size of the market on polluting emissions is more significant. Therefore, the environmental policy must consider these aspects to achieve greater effectiveness of regulation in favor of the environment. The results were stable, including the dependence of the cross-sections and the heterogeneity in the slope of the panel. Actions to mitigate polluting gas emissions should regulate informal and clandestine activities and take advantage of globalization and trade to improve the practices of companies and individuals.
Can Space–Time Shifting of Activities and Travels Mitigate Hyper-Congestion in an Emerging Megacity, Bangkok? Effects on Quality of Life and CO2 Emission
Authors: Achariyaviriya Witsarut , Hayashi Yoshitsugu , Takeshita Hiroyuki , Kii Masanobu , Vichiensan Varameth , Theeramunkong Thanaruk
Published: June 2021

Many megacities in the world, especially Bangkok, are facing severe congestion in road traffic and public transport, particularly during peak hours. This situation (a) worsens the quality of life, (b) releases emissions causing air pollution and climate change, and (c) subsequently creates requests for massive investment in transport infrastructure, which easily exceed the budget’s limit. Instead of solving the problem by supply-side strategies, applying ICT-based solutions to reform people’s daily activities, particularly commuting and working behaviors, on the demand-side is an alternative solution. As a promising solution, Mobility as a Service (MaaS) introduces ICT to persuade people to use public modes of transport. However, modal shift is a partial solution to mitigate traffic congestion. With technological advancements in communication, people become more flexible in their activities in terms of place and time aspects. MaaS should not only induce a transport mode shift but also a workplace shift and working time shift by extending the planning service in daily activity-travel level, exploiting people’s flexibilities for an activity’s place and time to manage travel demand. This paper proposes the QOL-MaaS as the extended MaaS to support space–time shift of activity-travel and reveals its potential impacts on traffic congestion, quality of life, and CO2 emission.
Abandoned Mine Lands Reclamation by Plant Remediation Technologies
Authors: Peco Jesús D. , Higueras Pablo , Campos Juan A. , Esbrí José M. , Moreno Marta M. , Battaglia-Brunet Fabienne , Sandalio Luisa M.
Published: June 2021

Abandoned mine lands (AMLs), which are considered some of the most dangerous anthropogenic activities in the world, are a source of hazards relating to potentially toxic elements (PTEs). Traditional reclamation techniques, which are expensive, time-consuming and not well accepted by the general public, cannot be used on a large scale. However, plant-based techniques have gained acceptance as an environmentally friendly alternative over the last 20 years. Plants can be used in AMLs for PTE phytoextraction, phytostabilization, and phytovolatilization. We reviewed these phytoremediation techniques, paying particular attention to the selection of appropriate plants in each case. In order to assess the suitability of plants for phytoremediation purposes, the accumulation capacity and tolerance mechanisms of PTEs was described. We also compiled a collection of interesting actual examples of AML phytoremediation. On-site studies have shown positive results in terms of soil quality improvement, reduced PTE bioavailability, and increased biodiversity. However, phytoremediation strategies need to better characterize potential plant candidates in order to improve PTE extraction and to reduce the negative impact on AMLs.
Green Purchasing Behaviour towards Compostable Coffee Pods
Authors: Visser Rebecca , Dlamini Siphiwe
Published: June 2021

The field of green marketing and purchase behaviour has been growing in the last couple of years; consequently, there is a need to understand this behaviour among coffee consumers. Coffee is considered one of the widely consumed beverages in the world. There is heightened consciousness towards the environmental awareness of the product of compostable coffee pods. This study aims to fill this gap by providing a holistic conceptual framework to determine which factors influence green purchasing behaviour with regard to compostable coffee pods and whether gender and income have an impact. To achieve this aim, structural equation modelling (SEM) will be utilised on SmartPLS from data collected from 133 South African coffee pod consumers using an online survey. The findings indicated that interpersonal influence and altruistic values have a significant positive influence on environmental attitude and that environmental attitude has a significant positive effect on green purchasing behaviour. However, it was found that no significant relationship was observed between environmental knowledge and environmental attitude. Regarding the influence of gender and income as mediators, no influence was found in the relationship between environmental attitude and green purchasing behaviour. However, income level partially mediates this relationship. The results will provide practical solutions for marketers and policymakers.
Determinant of University Students’ Choices and Preferences of Agricultural Sub-Sector Engagement in Cameroon
Authors: Mkong Cynthia J. , Abdoulaye Tahirou , Dontsop-Nguezet Paul Martin , Bamba Zoumana , Manyong Victor , Shu Godlove
Published: June 2021

Although the agri-food sector has a huge potential to offer attractive employment opportunities for Africa’s burgeoning youth, a negative perception of agriculture persists among Cameroonian youths, such as in many other African countries. The paper assesses the determinants of university students’ choices and preferences for agricultural sub-sector engagement in Cameroon. A multistage random sampling technique was used to select 550 students from two state universities. We used the SWOT analysis to evaluate students’ perceptions of challenges and opportunities within the agricultural sector in Cameroon, the binomial probit analysis to assess the determinants of students’ choices of agriculture as a university major, and an ordered probit analysis to evaluate the determinants of students’ preferences of agricultural sub-sector engagement. Findings reveal that choice of agriculture as a university major is significantly determined by sex, pre-university farming experience, pre-university academic background, mother’s level of education, and household income. Likewise, preference of agricultural sub-sector engagement is significantly determined by the student’s level of study and location of a childhood home. Improving the attractiveness of, and working conditions in, the agricultural sector could increase youth engagement in agribusiness and rural economic activities. The results also reinforce the need to increase household income in Cameroon, as this could lift families out of poverty and offer them more economic opportunities. Policies that regulate levels of education can equally be used to allocate human resources into different agricultural sub-sectors, subject to felt needs.
Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models
Authors: Mulero-Palencia Sofía , Álvarez-Díaz Sonia , Andrés-Chicote Manuel
Published: June 2021

In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO2 emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.
The Impact of Social Media Use on Consumers’ Restaurant Consumption Experiences: A Qualitative Study
Authors: Hwang Jooyoung , Eves Anita , Stienmetz Jason L.
Published: June 2021

Travellers have high standards and regard restaurants as important travel attributes. In the tourism and hospitality industry, the use of developed tools (e.g., smartphones and location-based tablets) has been popularised as a way for travellers to easily search for information and to book venues. Qualitative research using semi-structured interviews based on the face-to-face approach was adopted for this study to examine how consumers’ restaurant selection processes are performed with the utilisation of social media on smartphones. Then, thematic analysis was adopted. The findings of this research show that the adoption of social media on smartphones is positively related with consumers’ gratification. More specifically, when consumers regard that process, content and social gratification are satisfied, their intention to adopt social media is fulfilled. It is suggested by this study that consumers’ restaurant decision-making process needs to be understood, as each stage of the decision-making process is not independent; all the stages of the restaurant selection process are organically connected and influence one another.
An Australian Perspective on Local Government Investment in Electric Vehicle Charging Infrastructure
Authors: Dwyer Scott , Moutou Claudine , Nagrath Kriti , Wyndham Joseph , McIntosh Lawrence , Chapman Dean
Published: June 2021

Electric vehicle (EV) adoption is growing worldwide with increasing market pull from consumers and market push from manufacturers of vehicles and charging equipment, as well as others in the supply chain. Governments have begun developing policies to support EV uptake and local governments, in particular, are examining what role they should play. In Australia, a large country with low population density, EV uptake has been slower in comparison to other similar economies. This paper discusses the status of EV charging infrastructure deployment in Australia with regards to local governments, by considering the extent to which they are relied upon for the deployment of such technology and what motivates them to act. It also covers the work undertaken by the authors with one local government in developing an EV charging infrastructure business model that will help the local community adopt and benefit from EVs. An applied use of the business canvas methodology adapted to suit local government interests is presented to assess the risks and benefits that different business models offer. The paper offers insights into the strategic and pragmatic responsibilities local governments balance in seeking to expand the EV charging infrastructure in their jurisdiction.