bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal Characteristics of Slinky-Coil Ground Heat Exchanger with Discrete Double Inclined Ribs
Authors: Ariwibowo Teguh Hady , Miyara Akio
Published: August 2020

The slinky ground heat exchanger (GHE) is the most widely utilized horizontal-type GHE, however, this GHE has a low curvature coil. The GHE has poor thermal mixing, especially at a low flowrate. At this flowrate, the coil heat exchanger has similar performance to a straight tube heat exchanger. Discrete double-inclined ribs (DDIR) are well known for their good thermal mixing by generating a vortex in straight tubes. In this paper, a numerical analysis of thermal performance for the plain coil and DDIR coil is discussed. It was found that the thermal performance of the DDIR coil was slightly higher than that of the plain coil in laminar flow. In turbulent flow, the DDIR coil was superior to the plain coil only in the first 149-min operation. The first 60-min analysis shows that in laminar flow, the average heat transfer rate in the plain coil is 59 W/m and in the DDIR coil is 60.1 W/m. In turbulent flow, the average heat transfer rate is 62 W/m, and the plain coil is 62.3 W/m. The copper DDIR coil material produced a better heat transfer rate than that of the composite and High-Density Polyethylene (HDPE). Sandy clay has the highest heat transfer rate. The influence of ground thermal conductivity on the performance of the GHE is more dominant than convection in the DDIR coil.
Evaluating Multi-Angle Photochemical Reflectance Index and Solar-Induced Fluorescence for the Estimation of Gross Primary Production in Maize
Authors: Chen Jinghua , Zhang Qian , Chen Bin , Zhang Yongguang , Ma Li , Li Zhaohui , Zhang Xiaokang , Wu Yunfei , Wang Shaoqiang , A. Mickler Robert
Published: August 2020

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance > 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.
Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”
Authors: Jeon Gwanggil , Bellandi Valerio , Chehri Abdellah
Published: August 2020

This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue.
Multiparameter Elastic Full Waveform Inversion of Ocean Bottom Seismic Four-Component Data Based on A Modified Acoustic-Elastic Coupled Equation
Authors: Sun Minao , Jin Shuanggen
Published: August 2020

Ocean bottom seismometer (OBS) can record both pressure and displacement data by modern marine seismic acquisitions with four-component (4C) sensors. Elastic full-waveform inversion (EFWI) has shown to recover high-accuracy parameter models from multicomponent seismic data. However, due to limitation of the standard elastic wave equation, EFWI can hardly simulate and utilize the pressure components. To remedy this problem, we propose an elastic full-waveform inversion method based on a modified acoustic-elastic coupled (AEC) equation. Our method adopts a new misfit function to account for both 1C pressure and 3C displacement data, which can easily adjust the weight of different data components and eliminate the differences in the order of magnitude. Owing to the modified AEC equation, our method can simultaneously generate pressure and displacement records and avoid explicit implementation of the boundary condition at the seabed. Besides, we also derive a new preconditioned truncated Gauss–Newton algorithm to consider the Hessian associated with ocean bottom seismic 4C data. We analyze the multiparameter sensitivity kernels of pressure and displacement components and use two numerical experiments to demonstrate that the proposed method can provide more accurate multiparameter inversions with higher resolution and convergence rate.
Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China
Authors: Mao Wanliu , Lu Debin , Hou Li , Liu Xue , Yue Wenze
Published: August 2020

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.
Comparative Analysis of the Effect of the Loading Series from GFZ and EOST on Long-Term GPS Height Time Series
Authors: Wu Shuguang , Nie Guigen , Meng Xiaolin , Liu Jingnan , He Yuefan , Xue Changhu , Li Haiyang
Published: August 2020

In order to investigate the effect of different loading models on the nonlinear variations in Global Positioning System (GPS) height time series, the characteristics of annual signals (amplitude and phase) of GPS time series, loading series from Deutsche GeoForschungsZentrum, Germany (GFZ) and School and Observatory of Earth Sciences, France (EOST) at 633 global GPS stations are processed and analyzed. The change characteristics of the root mean square (RMS) reduction rate, annual amplitude and phase of GPS time series after environmental loading corrections (ELCs) are then detected. Results show that ELCs have a positive effect on the reduction in the nonlinear deformation contained in most GPS stations around the world. RMS reduction rates are positive at 82.6% stations after GFZ correction and 87.4% after EOST correction, and the average reduction rates of all stations are 10.6% and 15.4%, respectively. As for the environmental loading series from GFZ and EOST, their average annual amplitudes are 2.7 and 3.1 mm, which explains ~40% annual amplitude of GPS height time series (7.2 mm). Further analysis of some specific stations indicates that the annual phase difference between GPS height time series and the environmental loading series is an important reason that affects the reduction rates of the RMS and annual amplitude. The linear relationship between the annual phase difference and the annual amplitude reduction rate is significant. The linear fitting results show that when there is no annual phase difference between GPS and loading series, the reduction rates of the RMS and annual amplitude will increase to the maximum of 15.6% and 41.6% for GFZ, and 22.0% and 46.6% for EOST.
Identification of Tyre and Plastic Waste from Combined Copernicus Sentinel-1 and -2 Data
Authors: Page Robert , Lavender Samantha , Thomas Dean , Berry Katie , Stevens Susan , Haq Mohammed , Udugbezi Emmanuel , Fowler Gillian , Best Jennifer , Brockie Iain
Published: August 2020

As a result of tightened waste regulation across Europe, reports of waste crime have been on the rise. Significant stockpiles of tyres and plastic materials have been identified as a threat to both human and environmental health, leading to water and livestock contamination, providing substantial fuel for fires, and cultivating a variety of disease vectors. Traditional methods of identifying illegal stockpiles usually involve laborious field surveys, which are unsuitable for national scale management. Remotely-sensed investigations to tackle waste have been less explored due to the spectrally variable and complex nature of tyres and plastics, as well as their similarity to other land covers such as water and shadow. Therefore, the overall objective of this study was to develop an accurate classification method for both tyre and plastic waste to provide a viable platform for repeatable, cost-effective, and large-scale monitoring. An augmented land cover classification is presented that combines Copernicus Sentinel-2 optical imagery with thematic indices and Copernicus Sentinel-1 microwave data, and two random forests land cover classification algorithms were trained for the detection of tyres and plastics across Scotland. Testing of the method identified 211 confirmed tyre and plastic stockpiles, with overall classification accuracies calculated above 90%.
New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment
Authors: Vernimmen Ronald , Hooijer Aljosja , Pronk Maarten
Published: August 2020

No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km2 of land below 10, 5, and 2 m +MSL. The 0.93 million km2 of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available.
Global Temperature Sensing for an Operating Power Transformer Based on Raman Scattering
Authors: Liu Yunpeng , Li Xinye , Li Huan , Fan Xiaozhou
Published: August 2020

Traditional monitoring methods cannot obtain the overall thermal information for power transformers. To solve this problem, a distributed fiber optic sensor (DFOS) was creatively applied inside an operating 35 kV power transformer by highly integrating with the electromagnetic wires. Then, the transformer prototype with totally global sensing capability was successfully developed and it was qualified for power grid application through the strict ex-factory tests. The as designed optical fiber sensor works stably all the time with a temperature accuracy of ±0.2 °C and spatial positioning accuracy of 0.8 m. Based on the obtained internal temperature distribution, Gaussian convolution was further applied for the signal processing and hereby, the hotspots for all the windings and iron cores could be accurately traced. The hottest points were located at 89.1% (55 °C) of the high voltage winding height and 89.7% (77.5 °C) of the low voltage winding height. The actual precise hotspot location corrected the traditional cognition on the transformer windings and it would serve as an essential reference for the manufactures. This new nondestructive internal sensing and condition monitoring method also exhibits a promising future for the DFOS applying in the high-voltage electrical apparatus industry.
Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation
Authors: Muttillo Mirco , Stornelli Vincenzo , Alaggio Rocco , Paolucci Romina , Di Battista Luca , de Rubeis Tullio , Ferri Giuseppe
Published: August 2020

In the last decades, the applications of structural monitoring are moving toward the field of civil engineering and infrastructures. Nevertheless, if the structures have damages, it does not mean that they have a complete loss of functionality, but rather that the system is no longer in an optimal condition so that, if the damage increases, the structure can collapse. Structural Health Monitoring (SHM), a process for the identification of damage, periodically collects data from suitable sensors that allow to characterize the damage and establishes the health status of the structure. Therefore, this monitoring will provide information on the structure condition, mostly about its integrity, in a short time, and, for infrastructures and civil structures, it is necessary to assess performance and health status. The aim of this work is to design an Internet of Things (IoT) system for Structural Health Monitoring to find possible damages and to see how the structure behaves over time. For this purpose, a customized datalogger and nodes have been designed. The datalogger is able to acquire the data coming from the nodes through RS485 communication and synchronize acquisitions. Furthermore, it has an internal memory to allow for the post-processing of the collected data. The nodes are composed of a digital triaxial accelerometer, a general-purpose microcontroller, and an external memory for storage measures. The microcontroller communicates with an accelerometer, acquires values, and then saves them in the memory. The system has been characterized and the damage indicator has been evaluated on a testing structure. Experimental results show that the estimated damage indicator increases when the structure is perturbed. In the present work, the damage indicator increased by a maximum value of 24.65 when the structure is perturbed by a 2.5 mm engraving.
Stability-Guaranteed and High Terrain Adaptability Static Gait for Quadruped Robots
Authors: Hao Qian , Wang Zhaoba , Wang Junzheng , Chen Guangrong
Published: August 2020

Stability is a prerequisite for legged robots to execute tasks and traverse rough terrains. To guarantee the stability of quadruped locomotion and improve the terrain adaptability of quadruped robots, a stability-guaranteed and high terrain adaptability static gait for quadruped robots is addressed. Firstly, three chosen stability-guaranteed static gaits: intermittent gait 1&2 and coordinated gait are investigated. In addition, then the static gait: intermittent gait 1, which is with the biggest stability margin, is chosen to do a further research about quadruped robots walking on rough terrains. Secondly, a position/force based impedance control is employed to achieve a compliant behavior of quadruped robots on rough terrains. Thirdly, an exploratory gait planning method on uneven terrains with touch sensing and an attitude-position adjustment strategy with terrain estimation are proposed to improve the terrain adaptability of quadruped robots. Finally, the proposed methods are validated by simulations.
Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar
Authors: He Mi , Nian Yongjian , Xu Luping , Qiao Lihong , Wang Wenwu
Published: August 2020

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.
Robust Fault Estimation Using the Intermediate Observer: Application to the Quadcopter
Authors: Nguyen Ngoc Phi , Huynh Tuan Tu , Do Xuan Phu , Xuan Mung Nguyen , Hong Sung Kyung
Published: August 2020

In this paper, an actuator fault estimation technique is proposed for quadcopters under uncertainties. In previous studies, matching conditions were required for the observer design, but they were found to be complex for solving linear matrix inequalities (LMIs). To overcome these limitations, in this study, an improved intermediate estimator algorithm was applied to the quadcopter model, which can be used to estimate actuator faults and system states. The system stability was validated using Lyapunov theory. It was shown that system errors are uniformly ultimately bounded. To increase the accuracy of the proposed fault estimation algorithm, a magnitude order balance method was applied. Experiments were verified with four scenarios to show the effectiveness of the proposed algorithm. Two first scenarios were compared to show the effectiveness of the magnitude order balance method. The remaining scenarios were described to test the reliability of the presented method in the presence of multiple actuator faults. Different from previous studies on observer-based fault estimation, this proposal not only can estimate the fault magnitude of the roll, pitch, yaw, and thrust channel, but also can estimate the loss of control effectiveness of each actuator under uncertainties.
Knowledge-Based Decision Support in Healthcare via Near Field Communication
Authors: Loseto Giuseppe , Scioscia Floriano , Ruta Michele , Gramegna Filippo , Ieva Saverio , Pinto Agnese , Scioscia Crescenzio
Published: August 2020

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.
Neckio: Motivating Neck Exercises in Computer Workers
Authors: Markopoulos Panos , Shen Xiaoyu , Wang Qi , Timmermans Annick
Published: August 2020

Neck pain is common among computer workers who may spend too much time in a static posture facing their display. Regular breaks and variety in one’s posture can help to prevent discomfort and pain. In order to understand how to support computer workers to do so regularly, we surveyed a convenience sample of computer workers (N = 130) regarding their work habits and their attitudes towards neck exercises at the workplace. The survey showed that they are highly motivated, but not able to comply with a neck exercise program. To address this challenge, we designed Neckio, a system that is aimed at encouraging posture variation and facilitating neck exercises at work. Neckio consists in an interactive application and a wireless angulation sensing appliance that can be mounted on the headset that office workers often use for reasons of privacy. Next to providing an interactive exercise program suitable for the workplace, its design places emphasis on an engaging user experience. We report a short-term user experience valuation of Neckio in an actual office environment (N = 10). Participants rated the overall user experience positively and reported to be intrinsically motivated to do the neck exercises. These results indicate the potential of the Neckio as a behavior change support technology to reduce the risk of developing neck pain in computer workers.
Research on the Gravity Disturbance Compensation Terminal for High-Precision Position and Orientation System
Authors: Zhu Zhuangsheng , Tan Hao , Jia Yue , Xu Qifei
Published: August 2020

The Position and Orientation System (POS) is the core device of high-resolution aerial remote sensing systems, which can obtain the real-time object position and collect target attitude information. The goal of exceeding 0.015°/0.003° of its real-time heading/attitude measurement accuracy is unlikely to be achieved without gravity disturbance compensation. In this paper, a high-precision gravity data architecture for gravity disturbance compensation technology is proposed, and a gravity database with accuracy better than 1 mGal is constructed in the test area. Based on the “Block-Time Variation” Markov Model (B-TV-MM), a gravity disturbance compensation device is developed. The gravity disturbance compensation technology is applied to POS products for the first time, and is applied in the field of aerial remote sensing. Flight test results show that the heading accuracy and attitude accuracy of POS products are improved by at least 6% and 16%, respectively. The device can be used for the gravity disturbance compensation of various inertial technology products.
Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks
Authors: Kim Jung-Hwa , Jeong Jin-Woo
Published: August 2020

In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user’s intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered one of the most effective indicators among recently developed interaction techniques (e.g., face orientation estimation, body tracking, and gesture recognition). Previous gaze estimation techniques, however, are known to be effective only in a controlled lab environment under normal lighting conditions. In this study, we propose a novel deep learning-based approach to achieve a successful gaze estimation under various low-light conditions, which is anticipated to be more practical for smart interaction scenarios. The proposed approach utilizes a generative adversarial network (GAN) to enhance users’ eye images captured under low-light conditions, thereby restoring missing information for gaze estimation. Afterward, the GAN-recovered images are fed into the convolutional neural network architecture as input data to estimate the direction of the user gaze. Our experimental results on the modified MPIIGaze dataset demonstrate that the proposed approach achieves an average performance improvement of 4.53%–8.9% under low and dark light conditions, which is a promising step toward further research.
Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV–Vis Spectrometry and SPSE-XGBoost Method
Authors: Cheng Fei , Yang Chunhua , Zhou Can , Lan Lijuan , Zhu Hongqiu , Li Yonggang
Published: August 2020

Excessive discharge of heavy metal ions will aggravate environment pollution and threaten human health. Thus, it is of significance to real-time detect metal ions and control discharge in the metallurgical wastewater. We developed an accurate and rapid approach based on the singular perturbation spectrum estimator and extreme gradient boosting (SPSE-XGBoost) algorithms to simultaneously determine multi-metal ion concentrations by UV–vis spectrometry. In the approach, the spectral data is expanded by multi-order derivative preprocessing, and then, the sensitive feature bands in each spectrum are extracted by feature importance (VI score) ranking. Subsequently, the SPSE-XGBoost model are trained to combine multi-derivative features and to predict ion concentrations. The experimental results indicate that the developed “Expand-Extract-Combine” strategy can not only overcome problems of background noise and spectral overlapping but also mine the deeper spectrum information by integrating important features. Moreover, the SPSE-XGBoost strategy utilizes the selected feature subset instead of the full-spectrum for calculation, which effectively improves the computing speed. The comparisons of different data processing methods are conducted. It outcomes that the proposed strategy outperforms other routine methods and can profoundly determine the concentrations of zinc, copper, cobalt, and nickel with the lowest RMSEP. Therefore, our developed approach can be implemented as a promising mean for real-time and on-line determination of multi-metal ion concentrations in zinc hydrometallurgy.
The Effects of Mixed Hardwood Biochar, Mycorrhizae, and Fertigation on Container Tomato and Pepper Plant Growth
Authors: Yu Ping , Li Qiansheng , Huang Lan , Qin Kuan , Niu Genhua , Gu Mengmeng
Published: August 2020

Biochar (BC) has the potential as a peat moss alternative for container plant growth. Three experiments were conducted to evaluate the effects of mixed hardwood BC, compost types, mycorrhizae, and fertigation on container-grown tomato and pepper growth. In experiment 1 (Exp1), BC at 50%, 70%, and 90% (vol.) were mixed with 5% vermicompost (VC) with the rest being a commercial peat moss-based substrate (CS) and fertigated at 200 or 300 mg L−1 N. In experiment 2 (Exp2), 80% BC was mixed with chicken manure compost (CM; 5% or 10%) and CS and fertigated at 100 or 200 mg L−1 N. In experiment 3 (Exp3), 90% BC was blended with CS and fertigated at 200 or 300 mg L−1 N. Mixes in all the three experiments were added with or without mycorrhizae. Results showed that, compared with CS, in Exp1 tomato and pepper plants grown in BC-VC mixes had similar soil-plant analyses development (SPAD), growth index (GI), and total dry weight (TDW); in Exp2 and Exp3, plants in BC mixes (80% or 90%) had lower GI and TDW. In conclusion, BC (≤70%) amended with VC mixes could be used for container tomato and pepper production without negatively affecting plant growth, while BC (80%, 90%) mixes could have some negative impacts on plant growth.
Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China
Authors: Zhang Shumei , Zhang Wenshi , Wang Ying , Zhao Xiaoyu , Song Peihao , Tian Guohang , Mayer Audrey L.
Published: August 2020

Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.
Motivational Factors in Intergenerational Sustainability Dilemma: A Post-Interview Analysis
Authors: Shahen Mostafa E. , Masaya Wada , Kotani Koji , Saijo Tatsuyoshi
Published: August 2020

An intergenerational sustainability dilemma (ISD) is a situation of whether or not a person sacrifices herself for future sustainability. However, little is known about what people consider while making a decision under ISD. This paper analyzes motivational factors for people to decide under ISD, hypothesizing that the factors can be different with or without perspective-taking of future generations. One-person basic ISD game (ISDG) along with post-interviews are instituted where a lineup of individuals is organized as a generational sequence. Each individual chooses an unsustainable (or sustainable) option with (without) irreversibly costing future generations in 36 situations. A future ahead and back (FAB) mechanism is applied as a treatment for perspective-taking of future generations where each individual is asked to take the next generation’s position and to make a request about the choice that he/she wants the current generation to choose, and next, he/she makes the actual decision from the original position. By analyzing the post-interview contents with text-mining techniques, the paper finds that individuals mostly consider how previous generations had behaved in basic ISDG as the main motivational factor. However, individuals in FAB treatment are induced to put more weight on the possible consequences of their decisions for future generations as motivational factors. The findings suggest that perspective-taking of future generations through FAB mechanism enables people to change not only their behaviors but also motivational factors, enhancing ISD.
What Drives Continuance Intention towards Social Media? Social Influence and Identity Perspectives
Authors: Ruangkanjanases Athapol , Hsu Shu-Ling , Wu Yenchun Jim , Chen Shih-Chih , Chang Jo-Yu
Published: August 2020

With the growth of social media communities, people now use this new media to engage in many interrelated activities. As a result, social media communities have grown into popular and interactive platforms among users, consumers and enterprises. In the social media era of high competition, increasing continuance intention towards a specific social media platform could transfer extra benefits to such virtual groups. Based on the expectation-confirmation model (ECM), this research proposed a conceptual framework incorporating social influence and social identity as key determinants of social media continuous usage intention. The research findings of this study highlight that: (1) the social influence view of the group norms and image significantly affects social identity; (2) social identity significantly affects perceived usefulness and confirmation; (3) confirmation has a significant impact on perceived usefulness and satisfaction; (4) perceived usefulness and satisfaction have positive effects on usage continuance intention. The results of this study can serve as a guide to better understand the reasons for and implications of social media usage and adoption.
Bacterial and Fungal Community Dynamics and Shaping Factors During Agricultural Waste Composting with Zeolite and Biochar Addition
Authors: Wu Xin , Ren Liheng , Luo Lin , Zhang Jiachao , Zhang Lihua , Huang Hongli
Published: August 2020

Bacterial and fungal communities play significant roles in waste biodegradation and nutrient reservation during composting. Biochar and zeolite were widely reported to directly or indirectly promote microbial growth. Therefore, the effects of zeolite and biochar on the abundance and structure of bacterial and fungal communities and their shaping factors during the composting of agricultural waste were studied. Four treatments were carried out as follows: Run A as the control without any addition, Run B with zeolite (5%), Run C with biochar (5%), and Run D with zeolite (5%) and biochar (5%), respectively. The bacterial and fungal community structures were detected by high-throughput sequencing. Redundancy analysis was used for determining the relationship between community structure and physico-chemical parameters. The results indicated that the addition of biochar and zeolite changed the physico-chemical parameters (e.g., pile temperature, pH, total organic matter, ammonium, nitrate, and water-soluble carbon) during the composting process. Zeolite and biochar significantly changed the structure and diversity of bacterial and fungal populations. Moreover, the bacterial community rather than the fungal community was sensitive to the biochar and zeolite addition during the composting process. Community phylogenetic characteristics showed that Nocardiopsaceae, Bacillaceae, Leuconostocaceae, Phyllobacteriaceae, and Xanthomonadaceae were the predominant bacterial species at the family-level. Chaetomiaceae and Trichocomaceae were the two most dominant fungal species. The pH, total organic matter, and nitrate were the most important factors affecting the bacterial and fungal population changes during the composting process.
A Conceptual Model for Green Human Resource Management: Indicators, Differential Pathways, and Multiple Pro-Environmental Outcomes
Authors: Ari Engin , Karatepe Osman M. , Rezapouraghdam Hamed , Avci Turgay
Published: August 2020

There is a growing awareness of green human resource management (GHRM) in the environmental sustainability research in hospitality and tourism literature. The extant literature also delineates review studies about GHRM. Despite the existence of these studies, there is still a paucity of research about a parsimonious model that includes the underlying mechanisms linking GHRM to multiple pro-environmental behaviors. In view of such a gap, using the ability-motivation-opportunity framework, social exchange theory, social information processing and reformulation of attitude theories, and the happy-productive thesis as the theoretical underpinnings, our study proposes a conceptual model of GHRM. Specifically, our study links GHRM to organizationally valued pro-environmental behaviors such as task-related pro-environmental behaviors, proactive pro-environmental behaviors, green recovery performance, and green voice behavior through the mediating roles of work engagement and job satisfaction. In addition, our search of the relevant literature highlights eight indicators of GHRM such as green selective staffing, green training, green empowerment, green rewards, green career opportunities, green teamwork, green work-life balance, and green participation in decision making. Theoretical contributions and avenues for future research are discussed in the paper.
Implications of COVID-19 on the Labor Market of Saudi Arabia: The Role of Universities for a Sustainable Workforce
Authors: Al-Youbi Abdulrahman O. , Al-Hayani Abdulmonem , Rizwan Ali , Choudhry Hani
Published: August 2020

Governments all over the world are taking preventive measures to contain the spread of COVID-19. However, these measures have caused both long- and short-term effects on the socioeconomic situation of many countries. Due to lockdowns and business shutdowns, people are becoming unemployed or are working on reduced wages, creating a unique type of career shock in the global job market. Moreover, this phenomenon also produces a negative reflux among workers, encouraging a new skill set for this unprecedented time. The present study aimed to investigate the implications of COVID-19 on the labor market of Saudi Arabia. Data were collected with the help of a questionnaire from both public and private sector employers (n = 234) to inquire about their perceptions of the new skill set required in the changing business environment during and after pandemics. The data were analyzed with the help of descriptive statistics as well as simple and companion regression. The results indicate that the healthcare, service and education sectors have quickly transformed themselves from conventional to remote forms of working and consider virtual skills, autonomous working and effective communication the most important skills for their workforce during the current and the postpandemic scenarios. Interviews were then conducted with educational leaders to develop a conceptual framework by integrating both qualitative and quantitative analysis of the surveys. The results of the study are beneficial for the educational leadership of higher education institutions (HEIs) to better align their educational programs with changing market needs. By doing so, they not only increase the sustainability of the workforce but also minimize the impact of COVID-19 on the Saudi labor market.
Effect of Mindfulness on the Stress–Recovery Balance in Professional Soccer Players during the Competitive Season
Authors: Holguín-Ramírez Joaquín , Ramos-Jiménez Arnulfo , Quezada-Chacón José Trinidad , Cervantes-Borunda Mónica Sofía , Hernández-Torres Rosa Patricia
Published: August 2020

Professional athletes are subjected to constant stress that often leads them to exceed their capacities and lose their homeostasis, which without a proper recovery program can lead to injury, chronic fatigue, and overtraining. This work examines the effect of six weeks of a psychological intervention called Mindful Sports Performance Enhancement (MSPE), on the stress–recovery balance in professional soccer players during a competitive season. Methods: The RESTQ-76 Sport psychometric questionnaire and heart rate variability (HRV) were used as psychometric and physiological evaluation methods. Under a longitudinal case–control study and having complied with bioethical procedures, 42 professional soccer players (22 control without treatment and 20 experimental), age 17 ± 1 year, weight 63 ± 11 kg, and height 172 ± 7 cm, were analyzed. Results: RESTQ-76 Sport increased the stress–recovery balance and global recovery (p < 0.5), but decreased global stress. The stress–recovery balance values measured by the nonlinear indicators of the HRV: SD1, SD2, SS, and S:PS, were not modified. Conclusions: Six weeks of MSPE improves the stress–recovery balance in third-division professional soccer players during the competitive season, reduces stress, and increases recovery. These positive effects were not observed in the nonlinear indicators of the HRV: SD1, SD2, SS, and S:PS.
Systems of Food and Systems of Violence: An Intervention for the Special Issue on “Community Self Organisation, Sustainability and Resilience in Food Systems”
Authors: Ujuaje Mama D. , Chang Marina
Published: August 2020

This intervention critiques the rationale which underpins the authority of the food system as a context for sustainability, resilience and self-organisation. We apply learning from embodied practice, in particular The Food Journey©, to demonstrate the existence of harm and trauma arising from the overrepresentation of the liberal model of Man as constituting the only reality of humanity. This model has, in reality been a colonial, capitalising force of violent dispossession. It is this context that has produced global circulations of agricultural produce, systematised by a colonialism which violates the integrity of all that it encounters as different. Colonialities of being, power and knowledge extract and exploit globally both people and places as legacies of colonialism and perpetuate an abyssal divide between worlds. We unsettle and reconfigure both geopolitical contemporary and historic accounts of food-related narratives. We do this to help reveal how the ‘food system’ is actually a mainly Euro-American-centred narrative of dispossession, presented as universal. We propose the use of decolonial tools that are pluriversal, ecological and embodied as a means of interrogating the present system design, including its academic and field practice. The embrace of decolonial tools have the potential to take us beyond mere emancipation, cutting through old definitions and understandings of how food sovereignty, farm production, land justice and food itself are understood and applied as concepts. The outcome—as a continuous process of engagement, learning and redefinition—can then lead us towards a relational pluriverse as an expression of freedom and full nourishment for all humans and for the Earth, which is, in itself, a necessary healing.
Classroom Learning and the Perception of Social Responsibility Amongst Graduate Students of Management Accounting
Authors: Castilla-Polo Francisca , Ruiz-Rodríguez María Consuelo , Moreno Alonso , Licerán-Gutiérrez Ana , Cámara de la Fuente Macario , Chamorro Rufián Eva , Cano-Rodríguez Manuel
Published: August 2020

This study analyzes how learning about social responsibility (SR) can modify the perceptions of university students about the importance of responsible behavior on the part of companies. To this end, a questionnaire was designed and administered to Management Accounting students before (n = 128) and after (n = 71) receiving two training activities on SR. The descriptive results obtained testify to the importance of SR in the views of the sampled students, both before and after receiving the specific learning in SR. In this latter moment, students demonstrated a vision highly committed to the need for SR to be part of the economic agenda. The results also show that there was a significant change in the perception of SR and its implications in terms of benefits and costs before and after receiving the training. All of this suggests that SR training has partially modified students’ perceptions of SR. This paper provides important insights that could be leveraged by university and business school managers for the purpose of designing or modifying curricula related to SR. At the same time, it evaluates the potential of SR learning as a tool for modifying attitudes.
Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning
Authors: Zhao Shuanfeng , Wang Chao , Wei Pei , Zhao Qingqing
Published: August 2020

At present, the recognition of vehicle flow is mainly achieved with an artificial statistical method or by intelligent recognition based on video. The artificial method requires a large amount of manpower and time, and the existing video-based vehicle flow recognition methods are only applicable to straight roads. Therefore, a deep recognition model (DERD) for urban road vehicle flow is proposed in this paper. Learning from the characteristic that the cosine distance between the feature vectors of the same target in different states is in a fixed range, we designed a deep feature network model (D-CNN) to extract the feature vectors of all vehicles in the traffic flow and to intelligently determine the real-time statistics of vehicle flow based on the change of distance between vectors. A detection and tracking model was built to ensure the stability of the feature vector extraction process and to obtain the behavior trajectory of the vehicle. Finally, we combined the behavior and the number of vehicle flows to achieve the deep recognition of vehicle flow. After testing with videos recorded in actual scenes, the experimental results showed that our method can intelligently achieve the deep recognition of urban road vehicle flow. Compared with the existing methods, our approach shows higher accuracy and faster real-time performance.