bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility of Ceilometers Data to Estimate Radiative Forcing Values: Application to Different Conditions around the COVID-19 Lockdown Period
Authors: Barragan Ruben , Molero Francisco , Granados-Muñoz María José , Salvador Pedro , Pujadas Manuel , Artíñano Begoña
Published: November 2020

In this study, the feasibility of using ceilometer signals to retrieve radiative forcing values is evaluated. The Global Atmospheric Model (GAME) radiative transfer model is used to estimate the shortwave and longwave radiative forcing using an aerosol parameterization based on AERONET data and vertical profiles from a Lufft CHM-15k Nimbus ceilometer. First, eight cases confirmed as dusty days are analyzed to check the feasibility of using ceilometer profiles to feed GAME. The obtained radiative forcing estimates are in good agreement with the literature showing negative values in the short wave (SW) (cooling effect) and positive values in the long wave (LW) (heating effect), both at all levels. As in the literature, radiative forcing estimates show a strong dependence on variations in the aerosol optical depth (AOD), solar zenith angle (θz), surface temperature (ST), and single scattering albedo at 440 nm (SSA440). Thus, GAME can be fed using ceilometer measurements obtaining reliable results. Then, as the temporal evolution of the AOD440 between 27 January and 15 June compared to the 6-year weekly AERONET AOD440 average (from 2014 to 2019) shows a decrease because of the lockdown imposed in Spain due to the COVID-19, a total of 37 radiative forcing calculations without African dust, divided into 8 scenarios, are performed in order to check the effect of the lockdown measures in the radiative forcing. It is shown that the decrease in the AOD, during the lockdown, caused a decrease in the cooling effect in the SW spectral range at all levels. Besides, the increase in the ST increased the heating effect of the aerosols in the LW at the top of the atmosphere and the presence of pollution and absorbing particles (SSA440 < 0.90) caused an increase of the heating effect in the LW at the surface. Therefore, the observed variations in the radiative forcing estimates before and during the lockdown are directly related with the decrease in emissions of aerosols related to human activities.
A Spectral Acceleration Approach for the Spherical Harmonics Discrete Ordinate Method
Authors: Doicu Adrian , Efremenko Dmitry S. , Trautmann Thomas
Published: November 2020

A spectral acceleration approach for the spherical harmonics discrete ordinate method (SHDOM) is designed. This approach combines the correlated k-distribution method and some dimensionality reduction techniques applied on the optical parameters of an atmospheric system. The dimensionality reduction techniques used in this study are the linear embedding methods: principal component analysis, locality pursuit embedding, locality preserving projection, and locally embedded analysis. Through a numerical analysis, it is shown that relative to the correlated k-distribution method, PCA in conjunction with a second-order of scattering approximation yields an acceleration factor of 12. This implies that SHDOM equipped with this acceleration approach is efficient enough to perform spectral integration of radiance fields in inhomogeneous multi-dimensional media.
Mapping Forest Fire Risk—A Case Study in Galicia (Spain)
Authors: Novo Ana , Fariñas-Álvarez Noelia , Martínez-Sánchez Joaquín , González-Jorge Higinio , Fernández-Alonso José María , Lorenzo Henrique
Published: November 2020

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.
A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images
Authors: Kim Bowoo , Suh Dongjun
Published: November 2020

Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data.
Sensitivities of Quantitative Precipitation Forecasts for Typhoon Soudelor (2015) near Landfall to Polarimetric Radar Data Assimilation
Authors: Tsai Chih-Chien , Chung Kao-Shen
Published: November 2020

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating various combinations of radar variables are carried out for the purpose of improving a 6 h deterministic forecast for the most intense period. The results of the control simulation expose three sources of the observation operator errors, including the raindrop shape-size relation, the limitations for ice-phase hydrometeors, and the melting ice model. Nevertheless, polarimetric radar data assimilation with the unadjusted observation operator can still improve the analyses, especially rainwater, and consequent QPFs for this typhoon case. The different impacts of assimilating reflectivity, differential reflectivity, and specific differential phase are only distinguishable at the lower levels of convective precipitation areas where specific differential phase is found most helpful. The positive effect of radar data assimilation on QPFs can last three hours in this study, and further improvement can be expected by optimizing the observation operator in the future
Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning
Authors: Yuan Xiuliang , Uchenna Ochege Friday , De Maeyer Philippe , Kurban Alishir
Published: November 2020

As two competitive pathways of surface energy partitioning, latent (LE) and sensible (H) heat fluxes are expected to be strongly influenced by climate change and wide spread of global greening in recent several decades. Quantifying the surface energy fluxes (i.e., LE and H) variations and controlling factors is still a challenge because of the discrepancy in existing models, parameterizations, as well as driven datasets. In this study, we assessed the ability of random forest (RF, a machine learning method) and further predicted the global surface energy fluxes (i.e., LE and H) by combining FLUXNET observations, climate reanalysis and satellite-based leaf area index (LAI). The results show that the surface energy fluxes variations can be highly explained by the established RF models. The coefficient of determination (R2) ranges from 0.66 to 0.89 for the LE, and from 0.53 to 0.90 for the H across 10 plant functional types (PFTs), respectively. Meanwhile, the root mean square error (RMSE) ranges from 12.20 W/m2 to 21.94 W/m2 for the LE and from 12.05 W/m2 to 22.34 W/m2 for the H at a monthly scale, respectively. The important influencing factors in building RF models are divergent with respect to LE and H, but the solar radiation is common to both LE and H and to all 10 PFTs in this study. We also found a contrasting trend of LE and H: a positive trend in LE and a negative trend in H during 1982–2016 and these contrasting trends are dominated by the elevated CO2 concentration level. Our study suggested an important role of the CO2 concentration in determining surface energy partitioning which is needed to be considered in future studies.
Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification
Authors: Hsu Pai-Hui , Zhuang Zong-Yi
Published: November 2020

Point cloud classification is an important task in point cloud data analysis. Traditional point cloud classification is conducted primarily on the basis of specific handcrafted features with a specific classifier and is often capable of producing satisfactory results. However, the extraction of crucial handcrafted features hinges on sufficient knowledge of the field and substantial experience. In contrast, while powerful deep learning algorithms possess the ability to learn features automatically, it normally requires complex network architecture and a considerable amount of calculation time to attain better accuracy of classification. In order to combine the advantages of both the methods, in this study, we integrated the handcrafted features, whose benefits were confirmed by previous studies, into a deep learning network, in the hopes of solving the problem of insufficient extraction of specific features and enabling the network to recognise other effective features through automatic learning. This was done to achieve the performance of a complex model by using a simple model and fulfil the application requirements of the remote sensing domain. As indicated by the experimental results, the integration of handcrafted features into the simple and fast-calculating PointNet model could generate a classification result that bore comparison with that generated by a complex network model such as PointNet++ or KPConv.
SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction
Authors: Guo Ziyu , Mei Guangxu , Liu Shijun , Pan Li , Bian Lei , Tang Hongwu , Wang Diansheng
Published: November 2020

There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.
High-Resolution Ultrasound Imaging Enabled by Random Interference and Joint Image Reconstruction
Authors: Ni Pavel , Lee Heung-No
Published: November 2020

In ultrasound, wave interference is an undesirable effect that degrades the resolution of the images. We have recently shown that a wavefront of random interference can be used to reconstruct high-resolution ultrasound images. In this study, we further improve the resolution of interference-based ultrasound imaging by proposing a joint image reconstruction scheme. The proposed reconstruction scheme utilizes radio frequency (RF) signals from all elements of the sensor array in a joint optimization problem to directly reconstruct the final high-resolution image. By jointly processing array signals, we significantly improved the resolution of interference-based imaging. We compare the proposed joint reconstruction method with popular beamforming techniques and the previously proposed interference-based compound method. The simulation study suggests that, among the different reconstruction methods, the joint reconstruction method has the lowest mean-squared error (MSE), the best peak signal-to-noise ratio (PSNR), and the best signal-to-noise ratio (SNR). Similarly, the joint reconstruction method has an exceptional structural similarity index (SSIM) of 0.998. Experimental studies showed that the quality of images significantly improved when compared to other image reconstruction methods. Furthermore, we share our simulation codes as an open-source repository in support of reproducible research.
Design and Analysis of a Novel Piezoceramic Stack-based Smart Aggregate
Authors: Lu Guangtao , Zhu Xin , Wang Tao , Hao Zhiqiang , Tan Bohai
Published: November 2020

A novel piezoceramic stack-based smart aggregate (PiSSA) with piezoceramic wafers in series or parallel connection is developed to increase the efficiency and output performance over the conventional smart aggregate with only one piezoelectric patch. Due to the improvement, PiSSA is suitable for situations where the stress waves easily attenuate. In PiSSA, the piezoelectric wafers are electrically connected in series or parallel, and three types of piezoelectric wafers with different electrode patterns are designed for easy connection. Based on the theory of piezo-elasticity, a simplified one-dimensional model is derived to study the electromechanical, transmitting and sensing performance of PiSSAs with the wafers in series and parallel connection, and the model was verified by experiments. The theoretical results reveal that the first resonance frequency of PiSSAs in series and parallel decreases as the number or thickness of the PZT wafers increases, and the first electromechanical coupling factor increases firstly and then decrease gradually as the number or thickness increases. The results also show that both the first resonance frequency and the first electromechanical coupling factor of PiSSA in series and parallel change no more than 0.87% as the Young’s modulus of the epoxy increases from 0.5 to 1.5 times 3.2 GPa, which is helpful for the fabrication of PiSSAs. In addition, the displacement output of PiSSAs in parallel is about 2.18–22.49 times that in series at 1–50 kHz, while the voltage output of PiSSAs in parallel is much less than that in parallel, which indicates that PiSSA in parallel is much more suitable for working as an actuator to excite stress waves and PiSSA in series is suitable for working as a sensor to detect the waves. All the results demonstrate that the connecting type, number and thickness of the PZT wafers should be carefully selected to increase the efficiency and output of PiSSA actuators and sensors. This study contributes to providing a method to investigate the characteristics and optimize the structural parameters of the proposed PiSSAs.
Advances in Electrochemical Impedance Spectroscopy Detection of Endocrine Disruptors
Authors: Zamfir Lucian-Gabriel , Puiu Mihaela , Bala Camelia
Published: November 2020

Endocrine disruptors (EDs) are contaminants that may mimic or interfere with the body’s hormones, hampering the normal functions of the endocrine system in humans and animals. These substances, either natural or man-made, are involved in development, breeding, and immunity, causing a wide range of diseases and disorders. The traditional detection methods such as enzyme linked immunosorbent assay (ELISA) and chromatography are still the golden techniques for EDs detection due to their high sensitivity, robustness, and accuracy. Nevertheless, they have the disadvantage of being expensive and time-consuming, requiring bulky equipment or skilled personnel. On the other hand, early stage detection of EDs on-the-field requires portable devices fulfilling the Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment free, Deliverable to end users (ASSURED) norms. Electrochemical impedance spectroscopy (EIS)-based sensors can be easily implemented in fully automated, sample-to-answer devices by integrating electrodes in microfluidic chips. The latest achievements on EIS-based sensors are discussed and critically assessed.
Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone
Authors: Zhu Hongyu , Xia Linyuan , Wu Dongjin , Xia Jingchao , Li Qianxia
Published: November 2020

The emergence of dual frequency global navigation satellite system (GNSS) chip actively promotes the progress of precise point positioning (PPP) technology in Android smartphones. However, some characteristics of GNSS signals on current smartphones still adversely affect the positioning accuracy of multi-GNSS PPP. In order to reduce the adverse effects on positioning, this paper takes Huawei Mate30 as the experimental object and presents the analysis of multi-GNSS observations from the aspects of carrier-to-noise ratio, cycle slip, gradual accumulation of phase error, and pseudorange residual. Accordingly, we establish a multi-GNSS PPP mathematical model that is more suitable for GNSS observations from a smartphone. The stochastic model is composed of GNSS step function variances depending on carrier-to-noise ratio, and the robust Kalman filter is applied to parameter estimation. The multi-GNSS experimental results show that the proposed PPP method can significantly reduce the effect of poor satellite signal quality on positioning accuracy. Compared with the conventional PPP model, the root mean square (RMS) of GPS/BeiDou (BDS)/GLONASS static PPP horizontal and vertical errors in the initial 10 min decreased by 23.71% and 62.06%, respectively, and the horizontal positioning accuracy reached 10 cm within 100 min. Meanwhile, the kinematic PPP maximum three-dimensional positioning error of GPS/BDS/GLONASS decreased from 16.543 to 10.317 m.
A Novel Reconstruction Method for Measurement Data Based on MTLS Algorithm
Authors: Gu Tianqi , Hu Chenjie , Tang Dawei , Luo Tianzhi
Published: November 2020

Reconstruction methods for discrete data, such as the Moving Least Squares (MLS) and Moving Total Least Squares (MTLS), have made a great many achievements with the progress of modern industrial technology. Although the MLS and MTLS have good approximation accuracy, neither of these two approaches are robust model reconstruction methods and the outliers in the data cannot be processed effectively as the construction principle results in distorted local approximation. This paper proposes an improved method that is called the Moving Total Least Trimmed Squares (MTLTS) to achieve more accurate and robust estimations. By applying the Total Least Trimmed Squares (TLTS) method to the orthogonal construction way in the proposed MTLTS, the outliers as well as the random errors of all variables that exist in the measurement data can be effectively suppressed. The results of the numerical simulation and measurement experiment show that the proposed algorithm is superior to the MTLS and MLS method from the perspective of robustness and accuracy.
Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats
Authors: Hassan Taimur , Shafay Muhammad , Akçay Samet , Khan Salman , Bennamoun Mohammed , Damiani Ernesto , Werghi Naoufel
Published: November 2020

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.
Foot-Worn Inertial Sensors Are Reliable to Assess Spatiotemporal Gait Parameters in Axial Spondyloarthritis under Single and Dual Task Walking in Axial Spondyloarthritis
Authors: Soulard Julie , Vaillant Jacques , Balaguier Romain , Baillet Athan , Gaudin Philippe , Vuillerme Nicolas
Published: November 2020

The aim of this study was (1) to evaluate the relative and absolute reliability of gait parameters during walking in single- and dual-task conditions in patients with axial spondyloarthritis (axSpA), (2) to evaluate the absolute and relative reliability of dual task effects (DTE) parameters, and (3) to determine the number of trials required to ensure reliable gait assessment, in patients with axSpA. Twenty patients with axSpa performed a 10-m walk test in single- and dual-task conditions, three times for each condition. Spatiotemporal, symmetry, and DTE gait parameters were calculated from foot-worn inertial sensors. The relative reliability (intraclass correlation coefficients-ICC) and absolute reliability (standard error of measurement-SEM and minimum detectable change-MDC) were calculated for these parameters in each condition. Spatiotemporal gait parameters showed good to excellent reliability in both conditions (0.59 < ICC < 0.90). The reliability of symmetry and DTE parameters was low. ICC, SEM, and MDC were better when using the mean of the second and the third trials. Spatiotemporal gait parameters obtained from foot-worn inertial sensors assessed in patients with axSpA in single- and dual-task conditions are reliable. However, symmetry and DTE parameters seem less reliable and need to be interpreted with caution. Finally, better reliability of gait parameters was found when using the mean of the 2nd and the 3rd trials.
Survey on Security Threats in Agricultural IoT and Smart Farming
Authors: Demestichas Konstantinos , Peppes Nikolaos , Alexakis Theodoros
Published: November 2020

The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector.
Effect of Surface and Interfacial Tension on the Resonance Frequency of Microfluidic Channel Cantilever
Authors: Abraham Rosmi , Khan Faheem , Bukhari Syed A. , Liu Qingxia , Thundat Thomas , Chung Hyun-Joong , Kim Chun Il
Published: November 2020

The bending resonance of micro-sized resonators has been utilized to study adsorption of analyte molecules in complex fluids of picogram quantity. Traditionally, the analysis to characterize the resonance frequency has focused solely on the mass change, whereas the effect of interfacial tension of the fluid has been largely neglected. By observing forced vibrations of a microfluidic cantilever filled with a series of alkanes using a laser Doppler vibrometer (LDV), we studied the effect of surface and interfacial tension on the resonance frequency. Here, we incorporated the Young–Laplace equation into the Euler–Bernoulli beam theory to consider extra stress that surface and interface tension exerts on the vibration of the cantilever. Based on the hypothesis that the near-surface region of a continuum is subject to the extra stress, thin surface and interface layers are introduced to our model. The thin layer is subject to an axial force exerted by the extra stress, which in turn affects the transverse vibration of the cantilever. We tested the analytical model by varying the interfacial tension between the silicon nitride microchannel cantilever and the filled alkanes, whose interfacial tension varies with chain length. Compared with the conventional Euler–Bernoulli model, our enhanced model provides a better agreement to the experimental results, shedding light on precision measurements using micro-sized cantilever resonators.
Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
Authors: Kim Dae-Yeon , Choi Dong-Sik , Kim Jaeyun , Chun Sung Wan , Gil Hyo-Wook , Cho Nam-Jun , Kang Ah Reum , Woo Jiyoung
Published: November 2020

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.
Noninvasive Measurement of Time-Varying Arterial Wall Elastance Using a Single-Frequency Vibration Approach
Authors: Wang Jia-Jung , Liu Shing-Hong , Tseng Wei-Kung , Chen Wenxi
Published: November 2020

The arterial wall elastance is an important indicator of arterial stiffness and a kind of manifestation associated with vessel-related disease. The time-varying arterial wall elastances can be measured using a multiple-frequency vibration approach according to the Voigt and Maxwell model. However, such a method needs extensive calculation time and its operating steps are very complex. Thus, the aim of this study is to propose a simple and easy method for assessing the time-varying arterial wall elastances with the single-frequency vibration approach. This method was developed according to the simplified Voigt and Maxwell model. Thus, the arterial wall elastance measured using this method was compared with the elastance measured using the multiple-frequency vibration approach. In the single-frequency vibration approach, a moving probe of a vibrator was induced with a radial displacement of 0.15 mm and a 40 Hz frequency. The tip of the probe directly contacted the wall of a superficial radial artery, resulting in the arterial wall moving 0.15 mm radially. A force sensor attached to the probe was used to detect the reactive force exerted by the radial arterial wall. According to Voigt and Maxwell model, the wall elastance (Esingle) was calculated from the ratio of the measured reactive force to the peak deflection of the displacement. The wall elastances (Emultiple) measured by the multiple-frequency vibration approach were used as the reference to validate the performance of the single-frequency approach. Twenty-eight healthy subjects were recruited in the study. Individual wall elastances of the radial artery were determined with the multiple-frequency and the single-frequency approaches at room temperature (25 °C), after 5 min of cold stress (4 °C), and after 5 min of hot stress (42 °C). We found that the time-varying Esingle curves were very close to the time-varying Emultiple curves. Meanwhile, there was a regression line (Esingle = 0.019 + 0.91 Emultiple, standard error of the estimate (SEE) = 0.0295, p < 0.0001) with a high correlation coefficient (0.995) between Esingle and Emultiple. Furthermore, from the Bland–Altman plot, good precision and agreement between the two approaches were demonstrated. In summary, the proposed approach with a single-frequency vibrator and a force sensor showed its feasibility for measuring time-varying wall elastances.
Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition
Authors: Jun Kyungkoo , Choi Soonpil
Published: November 2020

Human activity recognition (HAR) works have mostly focused on the activities of adults. However, HAR is typically beneficial to the safety and wellness of newborn or infants because they have difficulties in verbal communication. The activities of infants are different from those of adults in terms of its types and intensity. Hence, it is necessary to study the behavior of infants separately. We study newborn and infant activity recognition by analyzing accelerometer data from the sensors attached to body. We aim to classify four types of activities: sleeping, moving in agony, moving in normal condition, and movement by external force. For this work, we collected 11 h videos and corresponding sensor data from 10 infant subjects. For recognition, we propose an end-to-end deep model using autoencoder and k-means clustering, which is trained in an unsupervised way. From a set of performance tests, our model can achieve 0.96 in balanced accuracy and F-1 score of 0.95.
Evolving Patterns of Aggression: Investigating the Structure of Gang Violence during the Era of Civil Gang Injunctions
Authors: Bichler Gisela , Norris Alexis , Ibarra Citlalik
Published: November 2020

Mapping the structural characteristics of attack behavior, this study explores how violent conflict evolved with the implementation of civil gang injunctions (CGIs). Networks were generated by linking defendants and victims named in 963 prosecutions involving street gangs active in the City of Los Angeles (1998–2013). Aggregating directed ties to 318 groups associated with the combatants, we compare four observations that correspond with distinct phases of CGI implementation—development (1998–2001), assent (2002–2005), maturity (2006–2009), and saturation (2010–2013). Using a triad census to calculate a ratio of simple patterns (retaliation, directed lines, and out-stars) to complex three-way interactions, we observed that CGIs were associated with a substantive thickening of conflict—greater complexity was found in conflict relations over time. Dissecting the nature of change, stochastic actor-oriented models (SAOMs) show that enjoined gangs are more likely to initiate transitive closure. The findings suggest that crime control efforts must make regular adjustments in response to the evolving structure of gang interactions.
Prioritizing Critical Management Strategies to Improving Construction Productivity: Empirical Research in Singapore
Authors: Hwang Bon-Gang , Li Yu-Shan , Shan Ming , Chua Jia-En
Published: November 2020

Although various types of management strategies have been proposed to improve construction productivity, they are seldom prioritized based on contribution. This study aims to bridge the knowledge gap to prioritize the critical management strategies that can help improve productivity in the construction industry. To achieve this goal, a comprehensive literature review was conducted first, to develop a conceptual framework of construction management strategies that are assumed to have positive relationships with productivity performance. A questionnaire was then developed and disseminated to 53 Singapore-based companies for data collection. The collected data were analyzed using partial least squares structural equation modelling. Results showed that “planning and communication” is the most critical management strategy category for productivity improvement, followed by “logistics,” “project strategic management”, “human resources”, and “on-site management.” This study contributes to the current body of knowledge by prioritizing critical management strategies that can help the construction industry improve productivity. Findings are likewise beneficial for the industry practitioners as well, as they can help deepen their understanding of the role played by these management strategies on increasing productivity and thereby formulate the best combinations for their own companies.
Environmental Enhancement through High-Rise Building Refurbishment
Authors: Rahmawati Yani , Utomo Christiono , Muhamad Sukri Nur Suhailah , Yasinta Rezi Berliana , Al-Aidrous Al-Hussein Mohammed Hassan
Published: November 2020

Property investment is always enticing, especially the high-rise residential property. The promise of the raising future value of it keeps investors attracted. Urbanisation also contributes to the massive development in this business, and it is hardly ignored. To run the business and to be able to compete with others, developers have to keep the cost attracted for the residents/buyers. The property needs to be well designed to satisfy the economical capital expenditure as well as low operation and maintenance cost. The high density of this development in an urban area makes developers tend to have low awareness of practising sustainability. Inconsistent evaluation, as well as unwell-defined assessment for sustainability, worsen the situation. This research aims to investigate and develop performance evaluation criteria of sustainability for high-rise residential building. Both qualitative and quantitative approaches were implemented through surveys. Three main factors, which are the promotion of a healthy environment, comfortability of residents and energy-saving, found to be the main categories of criteria to be considered. Based on validation through a case study, it is found that providing easy access to public transport is the most critical criteria for promoting a healthy environment. Besides, the usage of low Volatile Organic Compound (VOC) paint can give comfort to residents because it provides healthy indoor air quality. Lastly, the usage of LED bulbs and natural light supports energy saving. The criteria are then used to develop the decision-making model in selecting the best alternative for the building refurbishment, by using Analytical Hierarchy Process (AHP). Application of the decision model in a case study reveals that repainting the building using low VOC paint become the best fit option for enhancing the environment. Developers may implement this in their policy for the redevelopment of their building. The result is limited to the aggregation value; thus, future research is directing into coalition and negotiation among stakeholders by applying payoff optimum and agreement options and also automation in selecting the best technical solution.
Conditioning Factors of Sustainability of Dual Vocational Educational Training in Andalusia (Spain): Case Study of Three Educational Centres
Authors: Pozo-Llorente María Teresa , Poza-Vilches María de Fátima
Published: November 2020

From the incorporation of the Dual Vocational Educational Training (dual VET) in the Spanish educational panorama in 2012, and in the midst of the economic and financial crisis, this dual VET educational scheme has been a political priority of national and regional governments, who see it as a strategy for socioeconomic growth, employability, and youth entrepreneurship framed under an educational sustainability model. Despite the growing number of companies involved in the scheme, this model of educational training has not been free of debate and controversy. This study focuses on the autonomous community of Andalusia with the objective of identifying—using a complementary methodological perspective—the key factors that condition the successful implementation of dual VET in this region based on the experience of three educational centres and the opinions of those educators involved. Some of the weaknesses of dual VET identified in the autonomous community include the ambiguity of regulations, budgetary sustainability, governance models, the relationship between the training offered and industry, the involvement of companies, and the recognition of the teaching staff involved.
Spatiotemporal Analysis of Environmental Factors on the Birdstrike Risk in High Plateau Airport with Multi-Scale Research
Authors: Shao Quan , Zhou Yan , Zhu Pei
Published: November 2020

The aircrafts’ engine performance deteriorates sharply during the take-off and landing at high plateau airport. This situation increases the take-off or landing distance, aggravating the hidden danger of birdstrikes at high plateau airport. This paper first used GIS to classify and rasterize the bird data and calculated the monthly Birdstrike Risk Index (BRI) within 6, 13, and 25 km radii of Lhasa Airport, based on the bird observation data of Tibet and the birdstrike data of Lhasa Airport from 2015 to 2019. The spatiotemporal relationships between the BRI and the environmental factors around Lhasa Airport were compared by the Geographically or Temporally Weighted Regression (GWR or TWR) model and Geographically and Temporally Weighted Regression (GTWR) model. The results showed that the temporal nonstationary effect of environmental factors was more significant than that of spatial nonstationary at Lhasa Airport. Besides, the composition of land types had positive impacts on birdstrike risk within the 6 km radius, and this scope was broader than that of the plain airport. Within the 13 km and 25 km ranges, the water distribution and the altitude during dry season also positively impacted birdstrike risk. Moreover, the key factor to birdstrike risk was the water distribution in December.
Trade-Offs between Sugarcane Straw Removal and Soil Organic Matter in Brazil
Authors: Morais Maristela C. , Siqueira-Neto Marcos , Guerra Henrique P. , Satiro Lucas S. , Soltangheisi Amin , Cerri Carlos E. P. , Feigl Brigitte J. , Cherubin Maurício R.
Published: November 2020

Environmental benefits from bioenergy production derived from sugarcane crop residues (straw) can be lost by soil organic matter depletion resulting from excessive straw removal rates from fields. Soil organic carbon stock is the core for sustaining soil health, supporting nutrient cycling, and sequestering carbon dioxide. To find out that how much sugarcane straw can be removed from the field to produce bioenergy without changes in soil C concentrations, we investigated effects of straw removal rates (total, moderate, and no removal of sugarcane straw) on soil carbon and nitrogen fractions in an Oxisol and an Ultisol in southeastern Brazil for two years. Soil C and N fractions were affected by increased rates of straw removal at the second year. In the Oxisol, total straw removal decreased labile and microbial-C by ~30% and soil C stock by 20% compared to no straw removal. No removal decreased microbial-N and total N stock by ~15% and ~20%, respectively. In the Ultisol, no straw removal resulted in increases in C stock by >10% and labile and microbial-C by ~20% related to total straw removal. Total straw removal showed more microbial-N (~10%) and total-N stock (~25%) compared to no straw removal. The moderate straw removal intensity (i.e., 8 to 10 Mg ha−1 of straw) may control the straw-C release to soil by straw decomposition. This study suggests that excessive straw removal rates should be avoided, preventing SOM depletion and consequently, soil health degradation. Moderate straw removal seems to be a promising strategy, but long-term soil C monitoring is fundamental to design more sustainable straw management and bioenergy production systems.
Power Hardware in-the-Loop Testing to Analyze Fault Behavior of Smart Inverters in Distribution Networks
Authors: Ustun Taha Selim , Sugahara Shuichi , Suzuki Masaichi , Hashimoto Jun , Otani Kenji
Published: November 2020

Deep penetration of distributed generators have created several stability and operation issues for power systems. In order to address these, inverters with advanced capabilities such as frequency and reactive power support the grid. Known also as Smart Inverters (SIs), these devices are highly dynamic and contribute to the power flow in the system. Notwithstanding their benefits, such dynamic devices are new to distribution networks. Power system operators are very reluctant toward such changes as they may cause unknown issues. In order to alleviate these concerns and facilitate SIs integration to the grid, behavior studies are required. To that end, this paper presents a power hardware-in-the-loop test set up and tests that are performed to study fault behavior of SIs connected to distribution networks. The details of the software model, SI integration with the real-time simulator, test results, and their analyses are presented. This experience shows that it is not trivial to connect such novel devices with simulation environments. Adjustments are required on both software and hardware fronts on a case-by-case basis. The encountered integration issues and their solutions are presented herein. The fault behavior of the SI with respect to the fault location is documented. It is observed that for faults that are close to SIs, momentary cessation of generation is observed. This needs to be tackled by device manufacturers as this phenomenon is very detrimental to health of a power system under fault conditions. Extensive PHIL test results show that several factors affect the fault behavior of an SI: fault location and its duration, SI mode of operation as well as extra devices housed in the casing. These results and their in-depth analyses are presented for a thorough understanding of SI behavior under fault conditions.
Type of Entrepreneurial Activity and Sustainable Development Goals
Authors: Venâncio Ana , Pinto Inês
Published: November 2020

In this study, we conduct an exploratory study with the aim to investigate whether the type of entrepreneurial activity contributes to the achievement of sustainable development goals (SDGs) in its five dimensions (people, prosperity, planet, peace, and partnership). In addition, we evaluate whether foreign direct investment (FDI) strengthens or reduces these relations. To do so, we apply a multivariate analysis to a sample of 67 countries and find that entrepreneurship contributes negatively to the achievement of SDGs. This effect is mostly due to necessity and non-innovative entrepreneurships, and is evident in the people, prosperity, and partnership dimensions. Nonetheless, FDI helps to diminish this negative effect, as it improves the relation between entrepreneurships, particularly necessity entrepreneurships, and SDG achievement. The main dimension which experiences an improvement due to FDI is people.
Autogenous Shrinkage, Strength, and Hydration Heat of Ultra-High-Strength Paste Incorporating Nano-Zirconium Dioxide
Authors: Zhang Guang-Zhu , Lee Han-Seung , Wang Xiao-Yong
Published: November 2020

Ultra-high-strength paste (UHSP) combined with nanomaterials has been extensively studied. However, the research on nano-ZrO2 is limited. In this study, UHSP with various nano-ZrO2 contents is analyzed. The motivation of this study is to clarify the effects of nano-ZrO2 on the hydration products, strength, autogenous shrinkage, and hydration heat of UHSPs. The water-to-binder ratio (w/b) of the specimens is 0.2. The nano-ZrO2 content is 0, 1.5, and 3 wt.%. The strength is measured at the age of 3, 7, and 28 days. The hydration heat is measured from the mixing stage to 3 days. The hydration products are analyzed by X-ray diffraction (XRD) and thermogravimetric analysis (TG). The autogenous shrinkage is measured from the mixing stage for 7 days using a new experimental device. The new experimental device can measure autogenous shrinkage, internal relative humidity, and internal temperature simultaneously. The following conclusions can be drawn based on the experimental studies: (1) Two stages were noticed in the autogenous shrinkage of UHSPs: a variable-temperature stage and a room-temperature stage. The cut-off point of these two stages occurred in roughly 1.5 days. Furthermore, in the room-temperature stage, there was a straight-line relationship between the autogenous shrinkage and internal relative humidity. (2) With the increase of the nano-ZrO2 amount, the compressive strength at 3 days, 7 days, and 4 weeks increased. (3) With the nano-ZrO2 increasing, the flow decreased. (4) With the nano-ZrO2 increasing, the hydration heat increased due to the physical nucleation effect of the nano-ZrO2. Furthermore, the nano-ZrO2 used in this study was chemically inert and did not take part in the cement hydration reaction based on the XRD, differential thermal, and TG data. This paper is of great significance for the development of high-strength cementitious materials doped with nano-ZrO2.