bluuebunny/crossref_metadata_2025 huggingface dataset:
Preparation and Characterization of Highly Elastic Foams with Enhanced Electromagnetic Wave Absorption Based On Ethylene-Propylene-Diene-Monomer Rubber Filled with Barium Titanate/Multiwall Carbon Nanotube Hybrid
Authors: Bizhani Hasti , Katbab Ali Asghar , Lopez-Hernandez Emil , Miranda Jose Miguel , Lopez-Manchado Miguel A. , Verdejo Raquel
Published: October 2020

Hybrid ethylene-propylene-diene-monomer (EPDM) nanocomposite foams were produced via compression molding with enhanced electromagnetic wave absorption efficiency. The hybrid filler, consisting of 20 phr ferroelectric barium titanate (BT) and various loading fractions of multi-wall carbon nanotubes (MWCNTs), synergistically increased the electromagnetic (EM) wave absorption characteristics of the EPDM foam. Accordingly, while the EPDM foam filled with 20 phr BT was transparent to the EM wave within the frequency range of 8.2–12.4 GHz (X-band), the hybrid EPDM nanocomposite foam loaded with 20 phr BT and 10 phr MWCNTs presented a total shielding effectiveness (SE) of ~22.3 dB compared to ~16.0 dB of the MWCNTs (10 phr). This synergistic effect is suggested to be due to the segregation of MWCNT networks within the cellular structure of EPDM, resulting in enhanced electrical conductivity, and also high dielectric permittivity of the foam imparted by the BT particles. Moreover, the total SE of the BT/MWCNTs loaded foam samples remained almost unchanged when subjected to repeated bending due to the elastic recovery behavior of the crosslinked EPDM foamed nanocomposites.
Theoretical Modeling of Chemical Equilibrium in Weak Polyelectrolyte Layers on Curved Nanosystems
Authors: Gonzalez Solveyra Estefania , Nap Rikkert J. , Huang Kai , Szleifer Igal
Published: October 2020

Surface functionalization with end-tethered weak polyelectrolytes (PE) is a versatile way to modify and control surface properties, given their ability to alter their degree of charge depending on external cues like pH and salt concentration. Weak PEs find usage in a wide range of applications, from colloidal stabilization, lubrication, adhesion, wetting to biomedical applications such as drug delivery and theranostics applications. They are also ubiquitous in many biological systems. Here, we present an overview of some of the main theoretical methods that we consider key in the field of weak PE at interfaces. Several applications involving engineered nanoparticles, synthetic and biological nanopores, as well as biological macromolecules are discussed to illustrate the salient features of systems involving weak PE near an interface or under (nano)confinement. The key feature is that by confining weak PEs near an interface the degree of charge is different from what would be expected in solution. This is the result of the strong coupling between structural organization of weak PE and its chemical state. The responsiveness of engineered and biological nanomaterials comprising weak PE combined with an adequate level of modeling can provide the keys to a rational design of smart nanosystems.
Study on the Synthesis and Thermal Stability of Silicone Resin Containing Trifluorovinyl Ether Groups
Authors: Huang Rui , Yao Jinshui , Mu Qiuhong , Peng Dan , Zhao Hui , Yang Zhizhou
Published: October 2020

Silicone resin is a high-temperature resistant material with excellent performance. The improvement of its thermal stability has always been the pursuit of researchers. In this paper, a sequence of silicone resins containing trifluorovinyl ether groups were prepared by the co-hydrolysis-polycondensation of methyl alkoxysilane monomers and {4-[trifluorovinyl(oxygen)]phenyl}methyldiethoxysilane. The structures of the silicone resins were characterized by FT-IR and 1H NMR. The curing process of them was studied by DSC and FT-IR spectra, and results showed that the curing of the resins included the condensation of the Si-OH groups and the [2 + 2] cyclodimerization reaction of the TFVE groups, which converted to perfluorocyclobutane structure after curing. The thermal stability and thermal degradation behavior of them was studied by TGA and FT-IR spectra. Compared with the pure methyl silicone resin, silicone resins containing TFVE groups showed better thermal stability under both N2 and air atmosphere. Their hydrophobic properties were characterized by contact angle test. Results showed that PFCB structure also improved the hydrophobicity of the silicone resin.
Recent Development of Microfluidic Technology for Cell Trapping in Single Cell Analysis: A Review
Authors: Deng Yilin , Guo Ying , Xu Bin
Published: October 2020

Microfluidic technology has emerged from the MEMS (Micro-Electro-Mechanical System)-technology as an important research field. During the last decade, various microfluidic technologies have been developed to open up a new era for biological studies. To understand the function of single cells, it is very important to monitor the dynamic behavior of a single cell in a living environment. Cell trapping in single cell analysis is urgently demanded There have been some review papers focusing on drug screen and cell analysis. However, cell trapping in single cell analysis has rarely been covered in the previous reviews. The present paper focuses on recent developments of cell trapping and highlights the mechanisms, governing equations and key parameters affecting the cell trapping efficiency by contact-based and contactless approach. The applications of the cell trapping method are discussed according to their basic research areas, such as biology and tissue engineering. Finally, the paper highlights the most promising cell trapping method for this research area.
The Spiritual Heart
Authors: Anderson Micheline R.
Published: October 2020

The heart has been a symbol within ancient philosophy and spiritual practice for personal consciousness, wisdom, intuition and love. In recent decades, scientists with growing interest in spirituality have built a strong case for the beneficial relationship between religiosity/spirituality and physical health. Explanations for this connection have included associated health behaviors that negatively impact cardiovascular health but have failed to adequately explain away this consistent association. Here, we suggest a central and bidirectional relationship between the heart, the “Master Organ,” and the phenomenology of spiritual experience. Further, we provide existing evidence for a synergistic, salutogenic relationship between robust cardiac function and spiritual wellbeing that may offer a roadmap to spiritual, psychological and physical recovery and health at the individual, interpersonal and global level.
SAFDet: A Semi-Anchor-Free Detector for Effective Detection of Oriented Objects in Aerial Images
Authors: Fang Zhenyu , Ren Jinchang , Sun He , Marshall Stephen , Han Junwei , Zhao Huimin
Published: October 2020

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned horizontal proposals and the interference from complex backgrounds. To tackle these issues, region of interest transformer and attention models were proposed, yet they are extremely computationally intensive. To this end, we propose a semi-anchor-free detector (SAFDet) for object detection in aerial images, where a rotation-anchor-free-branch (RAFB) is used to enhance the foreground features via precisely regressing the OBB. Meanwhile, a center-prediction-module (CPM) is introduced for enhancing object localization and suppressing the background noise. Both RAFB and CPM are deployed during training, avoiding increased computational cost of inference. By evaluating on DOTA and HRSC2016 datasets, the efficacy of our approach has been fully validated for a good balance between the accuracy and computational cost.
Integrated Multi-Sensor Real Time Pile Positioning Model and Its Application for Sea Piling
Authors: Xie Yilin , Wang Qing , Yao Lianbi , Meng Xiaolin , Yang Yusong
Published: October 2020

The traditional pile positioning method for offshore piling uses the intersection of lines of sight with two or three theodolites. This method has certain limits, including using post-mission pile positioning, being time-consuming and lacking position accuracy. A novel pile positioning model using four kinds of sensors (GNSS—Global Navigation Satellite System receivers, tiltmeters, laser rangefinders and calibrated CCD cameras) for sea piling was developed. Firstly, with Real Time Kinematics (RTK) GNSS and tiltmeter data, the piling ship’s position and attitude was achieved in real time, and then the coordinates of the pile center in the Ship Fixed Coordinate System (SFCS) were calculated by a laser rangefinder and a CCD camera data. Finally, using the coordinate transformation, the coordinates of the pile center construction were figured out and used to guide the pile movement to the right place in real time. Because of the poor RTK GNSS vertical accuracy (normally 2–3 cm) and complex piling ship structure, it is difficult to get the accurate penetration value per hammering, which is a very important parameter for structural engineers. A Scale Invariant Feature Transform (SIFT) algorithm was created to get the pixel difference between the two pile images captured before and after one hammering, respectively, which was then used to calculate the penetration. A case study on the piling ship named “YangShanHao” with the sensors and algorithms was also described and discussed in the paper. The results showed the high accuracy of the proposed position model and the pile sinking distance of the pixel, thanks to the SIFT algorithm.
A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data
Authors: Genzano Nicola , Pergola Nicola , Marchese Francesco
Published: October 2020

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.
Precise Orbit Determination of the China Seismo-Electromagnetic Satellite (CSES) Using Onboard GPS and BDS Observations
Authors: Qing Yun , Lin Jian , Liu Yang , Dai Xiaolei , Lou Yidong , Gu Shengfeng
Published: October 2020

The Global Navigation Satellite System (GNSS) occultation receiver onboard the China Seismo-Electromagnetic Satellite (CSES) can provide dual-frequency observations for both GPS and BDS-2 satellites. In this study, the data quality and orbit determination performance of the CSES are assessed. Severe data loss of about 30% is observed in GPS P2/L2 data, resulting in only 11% of epochs possessing six to eight useful GPS satellites. Due to fewer channels being allocated for BDS signals, less than 5% of epochs have more than three useful BDS satellites. Precise orbit determination (POD) of CSES is firstly carried out using GPS data. The results indicate that the orbit overlap differences improved from 3.65 cm to 2.8 cm in 3D root mean square (RMS) by antenna phase center correction. CSES orbits are then derived from the BDS only, and combined GPS and BDS data. BDS-based POD indicates that adding BDS geostationary Earth orbit (GEO) satellites could dramatically degrade the orbit accuracy. When excluding BDS GEO satellites, the orbit overlap differences of BDS-based and combined POD are 23.68 cm and 2.73 cm in 3D, respectively, while the differences compared with GPS-based POD are 14.83 cm and 1.05 cm, respectively. The results suggest that the obtained orbit can satisfy centimeter-level requirements. Given that large GPS tracking losses occurred and few channels are allocated for BDS signals, it is expected that POD performance can be further improved by increasing the number of dual-frequency observations.
Modeling River Discharge Using Automated River Width Measurements Derived from Sentinel-1 Time Series
Authors: Mengen David , Ottinger Marco , Leinenkugel Patrick , Ribbe Lars
Published: October 2020

Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018.
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques
Authors: Osco Lucas Prado , Junior José Marcato , Ramos Ana Paula Marques , Furuya Danielle Elis Garcia , Santana Dthenifer Cordeiro , Teodoro Larissa Pereira Ribeiro , Gonçalves Wesley Nunes , Baio Fábio Henrique Rojo , Pistori Hemerson , Junior Carlos Antonio da Silva , Teodoro Paulo Eduardo
Published: October 2020

Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.
A Novel Radar Detection Method for Sensing Tiny and Maneuvering Insect Migrants
Authors: Wang Rui , Cai Jiong , Hu Cheng , Zhou Chao , Zhang Tianran
Published: October 2020

The use of radar to monitor insect migration is of great significance for pest control and biological migration mechanism research. However, migrating insects usually have small radar-cross-section (RCS) and are accompanied by maneuvering. The current radar detection algorithms mainly have contradictions in detection performance and computational complexity. So it is difficult for traditional radar detection algorithms to detect them effectively. Hence, a novel coherent integration detection algorithm based on dynamic programming (DP) and fractional Fourier transforming (FrFT) is proposed. By combining the advantages of DP and FrFT, the proposed DP-FrFT method can quickly search the target track, and simultaneously perform parameters estimation and motion compensation, achieving high integration gain with relatively low time consumption. The high efficiency of the method is verified with a large number of simulations and sufficient field experiments.
Intercomparison of Remote Sensing Retrievals: An Examination of Prior-Induced Biases in Averaging Kernel Corrections
Authors: Nguyen Hai , Hobbs Jonathan
Published: October 2020

In remote sensing applications, Optimal Estimation (OE) retrievals are sometimes compared to independent OE retrievals of the same process. This intercomparison is often done in instrument validation, where retrievals are compared with data from a separate validation instrument, and it is sometimes done in data assimilation, where data from multiple instruments need to be adjusted to the “same footing.” In these cases, the two different retrievals are compared using an adjustment that is colloquially known as the averaging kernel correction. A general misconception in the existing literature is that this averaging kernel correction removes any bias introduced by prior misspecification by either (or both) of the two comparative OE retrievals. In this paper, we will analytically show that this is not the case and the averaging kernel correction process implicitly “shifts” both OE retrievals to a common comparison prior. We will also show that there is generally a non-zero bias that is proportional to the difference between this comparison prior mean and the true (but unobserved) mean state, which has large implications for retrieval validation and data assimilation in remote sensing. Finally, to better characterize OE retrievals and retrieval intercomparisons, we will make some recommendations for mitigating this prior-induced bias in intercomparison of OE retrievals.
An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle
Authors: Yang Yang , Pan Ping , Jiang Xingang , Zheng Shuanghua , Zhao Yongjian , Yang Yi , Zhong Songyi , Peng Yan
Published: October 2020

The development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence of the sea waves, we propose a stacking model of one-dimensional convolutional neural network and long short-term memory neural network predicting the attitude of the USV. The data from experiments by “Jinghai VII” USV developed by Shanghai University, China, under levels 1–4 sea conditions are used to train and test the network. The results show that the stabilized platform with the proposed prediction method can keep the launching angle of the launching mechanism constant by regulating the pitching joint and rotation joint under the random influence from the wave. Finally, the efficiency and effectiveness of the L&RS are demonstrated by the successful application in actual environments.
Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only
Authors: Hsu Yan-Cheng , Li Yung-Hui , Chang Ching-Chun , Harfiya Latifa Nabila
Published: October 2020

Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor cost, researchers endeavor to establish a generalized BP estimation model using only PPG signals. In this paper, we propose a deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation. The effectiveness and accuracy of our proposed model was evaluated by the root mean square error (RMSE), mean absolute error (MAE), the Association for the Advancement of Medical Instrumentation (AAMI) standard and the British Hypertension Society (BHS) standard. Experimental results showed that the RMSEs in systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 4.643 mmHg and 3.307 mmHg, respectively, across 9000 subjects, with 80.63% of absolute errors among estimated SBP records lower than 5 mmHg and 90.19% of absolute errors among estimated DBP records lower than 5 mmHg. We demonstrated that our proposed model has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works.
Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification
Authors: Mukaroh Afifatul , Le Thi-Thu-Huong , Kim Howon
Published: October 2020

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.
Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems
Authors: Zhang Anqi , He Yihai , Han Xiao , Li Yao , Yang Xiuzhen , Zhang Zixuan
Published: October 2020

For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.
Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch
Authors: Han Dong , Bashar Syed Khairul , Mohagheghian Fahimeh , Ding Eric , Whitcomb Cody , McManus David D. , Chon Ki H.
Published: October 2020

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
Authors: Roh Bong-Soo , Han Myoung-Hun , Ham Jae-Hyun , Kim Ki-Il
Published: October 2020

Although various unmanned aerial vehicle (UAV)-assisted routing protocols have been proposed for vehicular ad hoc networks, few studies have investigated load balancing algorithms to accommodate future traffic growth and deal with complex dynamic network environments simultaneously. In particular, owing to the extended coverage and clear line-of-sight relay link on a UAV relay node (URN), the possibility of a bottleneck link is high. To prevent problems caused by traffic congestion, we propose Q-learning based load balancing routing (Q-LBR) through a combination of three key techniques, namely, a low-overhead technique for estimating the network load through the queue status obtained from each ground vehicular node by the URN, a load balancing scheme based on Q-learning and a reward control function for rapid convergence of Q-learning. Through diverse simulations, we demonstrate that Q-LBR improves the packet delivery ratio, network utilization and latency by more than 8, 28 and 30%, respectively, compared to the existing protocol.
Parametric Electromagnetic Analysis of Radar-Based Advanced Driver Assistant Systems
Authors: Vermiglio Simona , Champaney Victor , Sancarlos Abel , Daim Fatima , Kedzia Jean Claude , Duval Jean Louis , Diez Pedro , Chinesta Francisco
Published: October 2020

Efficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave propagation. Because of the very high frequency at which these devices operate, with the associated extremely small wavelength, very fine meshes are needed to accurately discretize the electromagnetic equations. Thus, the computational cost of each numerical solution for a given choice of the design or operation parameters, is high (CPU time consuming and needing significant computational resources) compromising the efficiency of standard optimization algorithms. In order to alleviate the just referred difficulties the present paper proposes an approach based on the use of reduced order modeling, in particular the construction of a parametric solution by employing a non-intrusive formulation of the Proper Generalized Decomposition, combined with a powerful phase-angle unwrapping strategy for accurately addressing the electric and magnetic fields interpolation, contributing to improve the design, the calibration and the operational use of those systems.
Congruence Effects on the Effectiveness of Sponsorship of Sport Event Websites: An Experimental Approach
Authors: Aguiló-Lemoine Àngela Elisa , Rejón-Guardia Francisco , García-Sastre María Antonia
Published: October 2020

Emerging online marketing strategies are an opportunity for the sport sponsorship industry as a way of complementing traditional methods. However, in-depth attention has not been given to the study of congruence effects on the effectiveness of sponsorship of sport event websites, and specifically to study the role and effects of sponsor logos. The main aim of this study was to evaluate the congruence effect of sponsor brands featured on the website of a sports event on sponsorship effectiveness in visual, attitude-related and behavioural terms, using an eye-tracker to monitor memory activation and changes in attitudes and intentions. In study 1, the role of congruence on website sponsorship was analysed, using real brands sponsoring the ninth edition of the “Mallorca 312” Cycletourist Tour (42 participants). In study 2, the congruence of fictitious brands was analysed on the effects of website sponsorship of the 37th edition of the MAPFRE (competitions brand name) Copa del Rey regatta (101 participants). Congruence is preferable to incongruence in sponsor brands, except when the sponsorship aims to boost a recall of new market brands. The results validate the importance of managing congruence levels in the online sponsorship of sports events due to the influence on sponsorship effectiveness and its impact on cognitive processing.
Healthy Cities, New Technologies and Sustainability: A Collaborative Mapping of Informal Sport Activity in the Public Space of Cities as an Innovative Tool for Understanding City Sport Phenomena
Authors: Cornax-Martín Marta , Nebot-Gómez de Salazar Nuria , Rosa-Jiménez Carlos , Luque-Gil Ana
Published: October 2020

Urban public space has become one of the main infrastructures for informal sports in cities. However, despite the high impact of that practice, local records only show the sports activity at the urban spaces specifically designed and regulated for that purpose. More information about where this practice arises and what attributes of urban space promote it would allow the adoption of specific measures in urban and sports policies. This paper proposes a methodology mainly based on the mapping of this informal sport activity and urban places where it appears by local communities and sportspeople. These collaborative maps are supported by the use of geographical information system (GIS) technologies and surveys on local communities. The research establishes a double objective of identifying urban spaces where citizens perform outdoor sports and deepen the knowledge of the physical attributes of public spaces that promote sports and local demands related to public space requirements. This methodology has been tested in the city of Malaga, the European City of Sport 2020, as a reference of a city with a growing outdoors’ sport activity. The main contribution is focused on the use of new tools that offer subjective information: opinions and habits of citizens in relation to sport urban practice. This information—which is difficult to obtain through other resources—should be considered for the design of urban and sports policies according to citizens’ demands.
Energy Analysis of a Novel Ejector-Compressor Cooling Cycle Driven by Electricity and Heat (Waste Heat or Solar Energy)
Authors: Riaz Fahid , Tan Kah Hoe , Farooq Muhammad , Imran Muhammad , Lee Poh Seng
Published: October 2020

Low-grade heat is abundantly available as solar thermal energy and as industrial waste heat. Non concentrating solar collectors can provide heat with temperatures 75–100 °C. In this paper, a new system is proposed and analyzed which enhances the electrical coefficient of performance (COP) of vapour compression cycle (VCC) by incorporating low-temperature heat-driven ejectors. This novel system, ejector enhanced vapour compression refrigeration cycle (EEVCRC), significantly increases the electrical COP of the system while utilizing abundantly available low-temperature solar or waste heat (below 100 °C). This system uses two ejectors in an innovative way such that the higher-pressure ejector is used at the downstream of the electrically driven compressor to help reduce the delivery pressure for the electrical compressor. The lower pressure ejector is used to reduce the quality of wet vapour at the entrance of the evaporator. This system has been modelled in Engineering Equation Solver (EES) and its performance is theoretically compared with conventional VCC, enhanced ejector refrigeration system (EERS), and ejection-compression system (ECS). The proposed EEVCRC gives better electrical COP as compared to all the three systems. The parametric study has been conducted and it is found that the COP of the proposed system increases exponentially at lower condensation temperature and higher evaporator temperature. At 50 °C condenser temperature, the electrical COP of EEVCRC is 50% higher than conventional VCC while at 35 °C, the electrical COP of EEVCRC is 90% higher than conventional VCC. For the higher temperature heat source, and hence the higher generator temperatures, the electrical COP of EEVCRC increases linearly while there is no increase in the electrical COP for ECS. The better global COP indicates that a small solar collector will be needed if this system is driven by solar thermal energy. It is found that by using the second ejector at the upstream of the electrical compressor, the electrical COP is increased by 49.2% as compared to a single ejector system.
Digital Tools and Personal Learning Environments: An Analysis in Higher Education
Authors: García-Martínez José-Antonio , Rosa-Napal Francisco-César , Romero-Tabeayo Isabel , López-Calvo Sara , Fuentes-Abeledo Eduardo-José
Published: October 2020

The effective use of Information and Communications Technology (ICT) and the discussion surrounding its educational contributions in formal settings are key elements in the analysis of personal learning environments (PLE). The aim of this study was to analyze the tools that students use to access information, create content, and share and interact in the framework of higher education. The study took a quantitative approach, using an ex post facto, transactional design. Data collection was via the application of a questionnaire to a stratified probabilistic sample (n = 1187) of university students on different courses at the National University (Costa Rica). Analysis of the data showed moderate use of tools in students’ PLEs. Students made more frequent use of resources aimed at accessing information, followed by applications for sharing and interacting, and, to a lesser extent, content creation. We also found significant differences in the use of tools depending on sex, previous education in technology, and academic performance. We recommend the inclusion of open, flexible learning strategies in university education which incorporate the various technological resources available in the digital era to ensure the development of PLEs and lifelong learning.
Systematic Modeling of Municipal Wastewater Activated Sludge Process and Treatment Plant Capacity Analysis Using GPS-X
Authors: Mu’azu Nuhu Dalhat , Alagha Omar , Anil Ismail
Published: October 2020

Mathematical modeling has become an indispensable tool for sustainable wastewater management, especially for the simulation of complex biochemical processes involved in the activated sludge process (ASP), which requires a substantial amount of data related to wastewater and sludge characteristics as well as process kinetics and stoichiometry. In this study, a systematic approach for calibration of the activated sludge model one (ASM1) model for a real municipal wastewater ASP was undertaken in GPS-X. The developed model was successfully validated while meeting the assumption of the model’s constant stoichiometry and kinetic coefficients for any plant influent compositions. The influences of vital ASP parameters on the treatment plant performance and capacity analysis for meeting local discharge limits were also investigated. Lower influent chemical oxygen demand in mgO2/L (COD) could inhibit effective nitrification and denitrification, while beyond 250 mgO2/L, there is a tendency for effluent quality to breach the regulatory limit. The plant performance can be satisfactory for handling even higher influent volumes up to 60,000 m3/d and organic loading when Total Suspended Solids/Volatile Suspended Solids (VSS/TSS) and particulate COD (XCOD)/VSS are maintained above 0.7 and 1, respectively. The wasted activated sludge (WAS) has more impact on the effluent quality compared to recycle activated sludge (RAS) with significant performance improvement when the WAS was increased from 3000 to 9000 m3/d. Hydraulic retention time (HRT) > 6 h and solids retention time (SRT) < 7 days resulted in better plant performance with the SRT having greater impact compared with HRT. The plant performance could be sustained for a quite appreciable range of COD/5-day Biochemical Oxygen Demand (BOD5 in mgO2/L) ratio, Mixed Liquor Suspended Solid (MLSS) of up to 6000 mg/L, and when BOD5/total nitrogen (TN) and COD/TN are comparatively at higher values. This work demonstrated a systematic approach for estimation of the wastewater treatment plant (WWTP) ASP parameters and the high modeling capabilities of ASM1 in GPS-X when respirometry tests data are lacking.
Effects of Droughts on Vegetation Condition and Ecosystem Service Delivery in Data-Poor Areas: A Case of Bobirwa Sub-District, Limpopo Basin and Botswana
Authors: Mugari Ephias , Masundire Hillary , Bolaane Maitseo
Published: October 2020

Understanding the effects of droughts on vegetation and ecosystem services (ES) is important for climate change adaptation. However, drought occurrence varies across space and time. We examined drought dynamics and impacts on vegetation and ES in the semi-arid Limpopo Basin of Botswana. Weather station precipitation, remotely sensed normalized difference vegetation index (NDVI) and participatory mapping exercises provided data for the analyses. Results show that between 1980 and 2015, rainfall anomaly indices of potential drought years ranged between −4.38 and −0.12. The longest spell of below-average rainfall occurred between 1992 and 1996. On average, drought events lasted for 1.9 years and recurred every 2.3 years. Although the overall drought frequency was 3.7 times in every 5 years, drought prevalence increased to 50%, 60% and 70% between 1981–1990, 1991–2000, and 2001–2010, respectively. The wet season average vegetation condition index between 2000 and 2015 revealed the occurrence of severe-to-extreme droughts in 2002–2003, 2005, 2008–2009 and 2012–2013 and light-to-moderate droughts in 2004, 2006–2007 and 2011, giving a drought prevalence of 73.3%. The increased frequency and severity of droughts is diminishing natural vegetation, crop productivity and several provisioning ES through moisture stress and drought-induced agricultural expansions. There exists an urgent need for smallholder irrigation development in Bobirwa sub-district to improve crop productivity and reduce the drought-induced conversion of woodlands to agriculture.
When Do CEOs Engage in CSR Activities? Performance Feedback, CEO Ownership, and CSR
Authors: Kim Minji , Kim Tohyun
Published: October 2020

The growing importance of corporate social responsibility (CSR) for firms’ sustainability has been spurring scholarly attempts at identifying the antecedents of CSR activities. This study examines how CEO ownership differentially affects firms’ CSR activities in response to their performance relative to aspirations. Building upon the agency theory and the performance feedback model, we argue that CEOs with greater ownership are relatively more likely to increase their firms’ CSR activities than those with smaller ownership as their firms’ performance further decreases below or increases above their aspiration levels, because they typically have more discretionary power and a greater pool of behavioral alternatives. Our fixed-effects analysis of the panel data from the U.S. crude petroleum and natural gas industry between 1995 and 2016 found that there is a systematic difference in how firms’ CSR activities are adjusted in response to negative performance feedback depending on the levels of CEO ownership, but no significant difference in the face of positive performance feedback. The overall results imply that the CEOs tend to care about CSR only when performance is near their target and not so much when performance is far below or above the target, especially if their stock ownership is low.
Optimal Power Dispatch and Reliability Analysis of Hybrid CHP-PV-Wind Systems in Farming Applications
Authors: Gbadamosi Saheed Lekan , Nwulu Nnamdi I.
Published: October 2020

Renewable energy sources (RES) are seen as potential alternative energy sources for rural communities to meet energy demand where electricity supply is inaccessible. Wind and Photo-Voltaic (PV) power is seen as mature and sustainable alternatives for rural electrification. This paper discusses the optimal power dispatch for hybrid combined heat and power (CHP), wind, PV and battery systems with a view to determining the operation of the hybrid system for farming applications. This is accomplished by considering the basic power system probability concepts to assess the performance of the reliability indices. The proposed mathematical model seeks to minimize the system operation costs from CHP. The developed model was validated on five case studies with the same load profile, solar radiation, wind speed and CHP generating unit parameters and solved using a CPLEX solver embedded in Algebraic Modelling Language. The sensitivity analysis performed indicates that the hybrid system achieved a higher reliability as compared to other case studies. The result shows 48% of energy cost reduction is achievable when considering the proposed hybrid CHP, wind, PV and battery system as compared to energy supply via CHP.
Acid-Modified and Unmodified Natural Clay Deposits for In Situ Immobilization and Reducing Phytoavailability of Molybdenum in a Sandy Loam Calcareous Soil
Authors: Alrashidi Saleh H. , Sallam Abdelazeem S. , Usman Adel R. A.
Published: October 2020

Molybdenum (Mo) in basic soils has high bioavailability and plant toxicity. This study aimed to investigate the effect of increasing Mo concentration on its availability and toxicity threshold in alfalfa plants grown in sandy loam calcareous soils, and the potential use of raw and acid- modified clay deposits as soil additives to immobilize Mo and reduce its phytoavailability. Raw clay deposits (RCD) were treated with H2SO4 to produce acid-modified clay deposits (AMCD). The first experiment was performed using soils treated with 0, 0.1, 1, 10, 50, and 100 mg Mo kg−1. The second experiment was conducted with soils treated with 10 or 50 mg Mo kg−1 and amended with RCD and AMCD at application rates of 0, 2.5, 5, and 10% (w/w). After harvesting, water-soluble Mo, ammonium bicarbonate-diethylenetriaminepentaacetic acid (AB-DTPA)-extractable Mo, and shoot Mo content as well as dry matter were measured. The results showed that water-soluble Mo, AB-DTPA-extractable Mo, and shoot Mo concentration increased at higher Mo soil addition. AMCD had a stronger influence on Mo immobilization and reduction effect on plant shoots compared to RCD, depending on soil Mo concentration and application rate. Applying AMCD decreased soil pH but increased salinity levels. The shoot dry matter significantly increased in soils amended with RCD and/or AMCD compared to control soils; with the highest improvement recorded for RCD at 10%. It was concluded that AMCD is an efficient immobilizing agent to reduce Mo mobility and its phytoavailability in calcareous soils. Additionally, both AMCD and especially RCD were able to create favorable conditions for plant growth.
Use of Soil Enzymes as Indicators for Contaminated Soil Monitoring and Sustainable Management
Authors: Lee Sang-Hwan , Kim Min-Suk , Kim Jeong-Gyu , Kim Soon-Oh
Published: October 2020

Diagnosis of the risk of soil pollution and the performance of measures for the recovery of contaminated soil requires proper monitoring of the extent of soil function damage and its recovery process. Soil enzymes reveal ecosystem perturbations, are sensitive to management choices, and have been used as indicators of biogeochemical cycles, organic matter degradation, and soil remediation processes. Thus, enzymes can indicate, along with other physical or chemical properties, soil quality. In this paper, we review the effects of soil pollutants [toxic trace elements (TTE), and petroleum hydrocarbons (PHC)] on enzymatic activities and evaluate the usefulness of soil enzyme’s activity for monitoring recovery processes in contaminated soil. Soil enzymes can be said to be a powerful means of monitoring to properly indicate the degree of deterioration of soil quality caused by soil pollution and to diagnose the process of functional recovery of contaminated soil. Further research is needed to establish the quantitative relationships between the soil physico-chemical properties and enzyme activity and the effect of soil remediation on the functional recovery of soil-related to soil quality.