bluuebunny/crossref_metadata_2025 huggingface dataset:
Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN
Authors: Chuang Tzu-Yi , Han Jen-Yu , Jhan Deng-Jie , Yang Ming-Der
Published: June 2020

Moving object detection and tracking from image sequences has been extensively studied in a variety of fields. Nevertheless, observing geometric attributes and identifying the detected objects for further investigation of moving behavior has drawn less attention. The focus of this study is to determine moving trajectories, object heights, and object recognition using a monocular camera configuration. This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry. The camera motion effects are first eliminated to detect objects that contain actual movement, and a moving object recognition process is employed to recognize the object classes and to facilitate the estimation of their geometric attributes. Thus, this information can further contribute to the investigation of object moving behavior. To evaluate the effectiveness of the proposed scheme quantitatively, first, an experiment with indoor synthetic configuration is conducted, then, outdoor real-life data are used to verify the feasibility based on recall, precision, and F1 index. The experiments have shown promising results and have verified the effectiveness of the proposed method in both laboratory and real environments. The proposed approach calculates the height and speed estimates of the recognized moving objects, including pedestrians and vehicles, and shows promising results with acceptable errors and application potential through existing PTZ camera images at a very low cost.
Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data
Authors: Eskandari Saeedeh , Reza Jaafari Mohammad , Oliva Patricia , Ghorbanzadeh Omid , Blaschke Thomas
Published: June 2020

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.
Investigating the Effects of Land Use and Land Cover on the Relationship between Moisture and Reflectance Using Landsat Time Series
Authors: Tollerud Heather J. , Brown Jesslyn F. , Loveland Thomas R.
Published: June 2020

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly ( p < 0.01 ) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships.
Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution
Authors: Ren Zhehao , Liu Yufu , Chen Bin , Xu Bing
Published: June 2020

Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management.
Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization
Authors: Zhao Hengqian , Yang Chenghai , Guo Wei , Zhang Lifu , Zhang Dongyan
Published: June 2020

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.
A Tile-Based Framework with a Spatial-Aware Feature for Easy Access and Efficient Analysis of Marine Remote Sensing Data
Authors: Ye Weiwen , Zhang Feng , He Xianqiang , Bai Yan , Liu Renyi , Du Zhenhong
Published: June 2020

Marine remote sensing (MRS) data provide an important tool for advancing global change research. However, the existing product service practices are insufficient for meeting the needs of a full-experience online application. This paper introduces a framework named SatANA, which is unified by a data tiling method with a spatial-aware feature, for integrated and intelligent improvements in visualization, storage and computing. The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading and distributed computing optimized indexing. To verify its feasibility and effectiveness, we applied this framework to a platform called SatCO2, which is devoted to providing convenient access to and the efficient utilization of MRS data.
Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities
Authors: Muckenhuber Stefan , Holzer Hannes , Bockaj Zrinka
Published: June 2020

Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach that takes material properties and corresponding lidar capabilities into account. The considered material property is the incidence angle dependent reflectance of the illuminated material in the infrared spectrum and the considered lidar property its capability to detect a material with a certain reflectance up to a certain range. A new material classification for lidar modelling in the automotive context is suggested, distinguishing between 7 material classes and 23 subclasses. To measure angle dependent reflectance in the infrared spectrum, a new measurement device based on a time of flight camera is introduced and calibrated using Lambertian targets with defined reflectance values at

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. Reflectance measurements of 9 material subclasses are presented and 488 spectra from the NASA ECOSTRESS library are considered to evaluate the new measurement device. The parametrisation of the lidar capabilities is illustrated by presenting a lidar measurement campaign with a new Infineon lidar prototype and relevant data from 12 common lidar types.
Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas
Authors: de Meester Jasper , Storch Tobias
Published: June 2020

Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications.
Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping
Authors: Bagley Stuart A. , Atkinson Jonathan A. , Hunt Henry , Wilson Michael H. , Pridmore Tony P. , Wells Darren M.
Published: June 2020

High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.
Investigation of the Effect of Process Parameters on Bone Grinding Performance Based on On-Line Measurement of Temperature and Force Sensors
Authors: Zhang Lihui , Zou Lei , Wen Donghui , Wang Xudong , Kong Fanzhi , Piao Zhongyu
Published: June 2020

This study investigates the effect of process parameters on neurosurgical bone grinding performance using a miniature surgical diamond wheel. Bone grinding is an important procedure in the expanded endonasal approach for removing the cranial bone and access to the skull base tumor via nasal corridor. Heat and force are generated during the grinding process, which may cause thermal and mechanical damage to the adjacent tissues. This study investigates the effect of grinding process parameters (including the depth of cut, feed rate, and spindle speed) on the bone grinding performance using temperature and force measurement sensors in order to optimize the grinding process. An orthogonal experimental design with a standard orthogonal array, L9 (33), is selected with each parameter in three levels. The experimental results have been statistically analyzed using the range and variance analysis methods in order to determine the importance order of the process parameters. The results indicate that the effect of the cutting depth on the grinding temperature and normal force is the largest, while the effect of the spindle speed on the tangential force is the largest. A high spindle speed would make the temperature rise to a certain extent; however, it significantly reduces the grinding force. At a certain spindle speed, a lower depth of cut and feed rate help to reduce the grinding temperature and force.
A New Label-Free and Contactless Bio-Tomographic Imaging with Miniaturized Capacitively-Coupled Spectroscopy Measurements
Authors: Ma Gege , Soleimani Manuchehr
Published: June 2020

A new bio-imaging method has been developed by introducing an experimental verification of capacitively coupled resistivity imaging in a small scale. This paper focuses on the 2D circular array imaging sensor as well as a 3D planar array imaging sensor with spectroscopic measurements in a wide range from low frequency to radiofrequency. Both these two setups are well suited for standard containers used in cell and culture biological studies, allowing for fully non-invasive testing. This is true as the capacitive based imaging sensor can extract dielectric spectroscopic images from the sample without direct contact with the medium. The paper shows the concept by deriving a wide range of spectroscopic information from biological test samples. We drive both spectra of electrical conductivity and the change rate of electrical conductivity with frequency as a piece of fundamentally important information. The high-frequency excitation allows the interrogation of critical properties that arise from the cell nucleus.
Photon Counting LIDAR Based on True Random Coding
Authors: Yu Yang , Liu Bo , Chen Zhen , Hua Kangjian
Published: June 2020

In this paper, a true random coding photon counting LIDAR is described, in which a Gm-APD (Geiger mode avalanche photodiode) acts as the true random sequence signal generator. The true random coding method not only improves the anti-crosstalk capability of the system, but also greatly reduces the 1-bit missed detection caused by the limited Gm-APD count rate. The experiment verifies the feasibility of the true random sequence used in a photon counting LIDAR ranging system, and a simple and intuitive evaluation model of true random sequence autocorrelation is proposed. Finally, the influence of system parameters (mean echo photon number, mean pulse count density, sequence length, mean noise count) on detection probability is explored. In general, this paper proves that the true random code photon counting LIDAR is an effective target detection method, and provides a new idea for the research of an anti-crosstalk LIDAR system.
A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation
Authors: Wang Bo , Shahzad Muhammad , Zhu Xianglin , Rehman Khalil Ur , Uddin Saad
Published: June 2020

l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used as a process model in the non-linear model predictive control to predict product concentration. GWO is also used to solve the non-convex optimization problem in non-linear model predictive control (GWO-NMPC) for calculating optimal future inputs. The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear model predictive control (PSO-NMPC) to validate their effectiveness. The comparative results show that the prediction accuracy, adaptability, real-time tracking ability, overall error and control precision of GWO-based predictive control is better compared to PSO-based predictive control.
Neural Network Self-Tuning Control for a Piezoelectric Actuator
Authors: Li Wenjun , Zhang Chen , Gao Wei , Zhou Miaolei
Published: June 2020

Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.
Terahertz Time-Domain Polarimetry in Reflection for Film Characterization
Authors: van Frank Sandrine , Leiss-Holzinger Elisabeth , Pfleger Michael , Rankl Christian
Published: June 2020

Terahertz time-domain spectroscopy is a useful technique to characterize layered samples and thin films. It gives access to their optical properties and thickness. Such measurements are done in transmission, which requires access to the sample from opposite sides. In reality this is not always possible. In such cases, reflection measurements are the only option, but they are more difficult to implement. Here we propose a method to characterize films in reflection geometry using a polarimetric approach based on the identification of Brewster angle and modeling of the measured signal to extract the refractive index and thickness of the sample. The technique is demonstrated experimentally on an unsupported single layer thin film sample. The extracted optical properties and thickness were in good agreement with established transmission terahertz spectroscopy measurements. The new method has the potential to cover a wide range of applications, both for research and industrial purposes.
Internet of Robotic Things in Smart Domains: Applications and Challenges
Authors: Romeo Laura , Petitti Antonio , Marani Roberto , Milella Annalisa
Published: June 2020

With the advent of the Fourth Industrial Revolution, Internet of Things (IoT) and robotic systems are closely cooperating, reshaping their relations and managing to develop new-generation devices. Such disruptive technology corresponds to the backbone of the so-called Industry 4.0. The integration of robotic agents and IoT leads to the concept of the Internet of Robotic Things, in which innovation in digital systems is drawing new possibilities in both industrial and research fields, covering several domains such as manufacturing, agriculture, health, surveillance, and education, to name but a few. In this manuscript, the state-of-the-art of IoRT applications is outlined, aiming to mark their impact on several research fields, and focusing on the main open challenges of the integration of robotic technologies into smart spaces. IoRT technologies and applications are also discussed to underline their influence in everyday life, inducing the need for more research into remote and automated applications.
Optical Camera Communications for IoT–Rolling-Shutter Based MIMO Scheme with Grouped LED Array Transmitter
Authors: Teli Shivani Rajendra , Matus Vicente , Zvanovec Stanislav , Perez-Jimenez Rafael , Vitek Stanislav , Ghassemlooy Zabih
Published: June 2020

In optical camera communications (OCC), the provision of both flicker-free illumination and high data rates are challenging issues, which can be addressed by utilizing the rolling-shutter (RS) property of the image sensors as the receiver (Rx). In this paper, we propose an RS-based multiple-input multiple-output OCC scheme for the Internet of things (IoT) application. A simplified design of multi-channel transmitter (Tx) using a 7.2 × 7.2 cm2 small 8 × 8 distributed light emitting diode (LED) array, based on grouping of LEDs, is proposed for flicker-free transmission. We carry out an experimental investigation of the indoor OCC system by employing a Raspberry Pi camera as the Rx, with RS capturing mode. Despite the small area of the display, flicker-free communication links within the range of 20–100 cm are established with data throughput of 960 to 120 bps sufficient for IoT. A method to extend link spans up to 1.8 m and the data throughput to 13.44 kbps using different configurations of multi-channel Tx is provided. The peak signal-to-noise ratio of ~14 and 16 dB and the rate of successfully received bits of 99.4 and 81% are measured for the shutter speeds of 200 and 800 µs for a link span of 1 m, respectively.
A “Global–Local” Visual Servo System for Picking Manipulators
Authors: Shi Yinggang , Zhang Wei , Li Zhiwen , Wang Yong , Liu Li , Cui Yongjie
Published: June 2020

During the process of automated crop picking, the two hand–eye coordination operation systems, namely “eye to hand” and “eye in hand” have their respective advantages and disadvantages. It is challenging to simultaneously consider both the operational accuracy and the speed of a manipulator. In response to this problem, this study constructs a “global–local” visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo. Using tomato picking as an example, experiments were conducted to obtain the accuracies of judgment and range of fruit maturity, and the scenario of fruit-bearing was simulated over an area where the operation was ongoing to examine the rate of success of the system in terms of continuous fruit picking. The results show that the global–local visual servo picking system had an average accuracy of correctly judging fruit maturity of 92.8%, average error of fruit distance measurement in the range 0.485 cm, average time for continuous fruit picking of 20.06 s, and average success rate of picking of 92.45%.
A Stand-Alone Smart Camera System for Online Post-Earthquake Building Safety Assessment
Authors: Hsu Ting-Yu , Kuo Xiang-Ju
Published: June 2020

Computer vision-based approaches are very useful for dynamic displacement measurement, damage detection, and structural health monitoring. However, for the application using a large number of existing cameras in buildings, the computational cost of videos from dozens of cameras using a centralized computer becomes a huge burden. Moreover, when a manual process is required for processing the videos, prompt safety assessment of tens of thousands of buildings after a catastrophic earthquake striking a megacity becomes very challenging. Therefore, a decentralized and fully automatic computer vision-based approach for prompt building safety assessment and decision-making is desired for practical applications. In this study, a prototype of a novel stand-alone smart camera system for measuring interstory drifts was developed. The proposed system is composed of a single camera, a single-board computer, and two accelerometers with a microcontroller unit. The system is capable of compensating for rotational effects of the camera during earthquake excitations. Furthermore, by fusing the camera-based interstory drifts with the accelerometer-based ones, the interstory drifts can be measured accurately even when residual interstory drifts exist. Algorithms used to compensate for the camera’s rotational effects, algorithms used to track the movement of three targets within three regions of interest, artificial neural networks used to convert the interstory drifts to engineering units, and some necessary signal processing algorithms, including interpolation, cross-correlation, and filtering algorithms, were embedded in the smart camera system. As a result, online processing of the video data and acceleration data using decentralized computational resources is achieved in each individual smart camera system to obtain interstory drifts. Using the maximum interstory drifts measured during an earthquake, the safety of a building can be assessed right after the earthquake excitation. We validated the feasibility of the prototype of the proposed smart camera system through the use of large-scale shaking table tests of a steel building. The results show that the proposed smart camera system had very promising results in terms of assessing the safety of steel building specimens after earthquake excitations.
Microwave Sensors Based on Resonant Elements
Authors: Martín Ferran , Vélez Paris , Gil Marta
Published: June 2020

This paper highlights interest in the implementation of microwave sensors based on resonant elements, the subject of a special issue in the journal. A classification of these sensors on the basis of the operating principle is presented, and the advantages and limitations of the different sensor types are pointed out. Finally, the paper summarizes the different contributions to the special issue.
Evaluation of Soil Management Effect on Crop Productivity and Vegetation Indices Accuracy in Mediterranean Cereal-Based Cropping Systems
Authors: Orsini Roberto , Fiorentini Marco , Zenobi Stefano
Published: June 2020

Mostly, precision agriculture applications include the acquisition and elaboration of images, and it is fundamental to understand how farmers’ practices, such as soil management, affect those images and relate to the vegetation index. We investigated how long-term conservation agriculture practices, in comparison with conventional practices, can affect the yield components and the accuracy of five vegetation indexes. The experimental site is a part of a long-term experiment established in 1994 and is still ongoing that consists of a rainfed 2-year rotation with durum wheat and maize, where two unfertilized soil managements were repeated in the same plots every year. This study shows the superiority of no tillage over conventional tillage for both nutritional and productive aspects on durum wheat. The soil management affects the vegetation indexes’ accuracy, which is related to the nitrogen nutrition status. No-tillage management, which is characterized by a higher content of soil organic matter and nitrogen availability into the soil, allows obtaining a higher accuracy than the conventional tillage. So, the users of multispectral cameras for precision agriculture applications must take into account the soil management, organic matter, and nitrogen content.
A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN
Authors: Waris Asim , Zia ur Rehman Muhammad , Niazi Imran Khan , Jochumsen Mads , Englehart Kevin , Jensen Winnie , Haavik Heidi , Kamavuako Ernest Nlandu
Published: June 2020

Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.
The Effects of Physical Education on Motor Competence in Children and Adolescents: A Systematic Review and Meta-Analysis
Authors: Lorås Håvard
Published: June 2020

Appropriate levels of motor competence are an integrated part of individuals’ health-related fitness, and physical education is proposed as an important context for developing a broad range of motor skills. The aim of the current study was to apply meta-analyses to assess the effectiveness of curriculum-based physical education on the development of the overall motor competence of children and adolescents. Studies were located by searching seven databases and included according to predefined criteria. Random effects models using the standardized effect size (Hedges’ g) were used to aggregate results, including an examination of heterogeneity and inconsistency. The meta-analysis included 20 studies, and a total of 38 effect sizes were calculated. A statistically significant improvement in motor competence following curriculum-based physical education compared to active control groups was observed in children and adolescents (g = −0.69, 95% CI −0.91 to −0.46, n = 23). Participants’ ages, total time for physical education intervention, and type of motor competence assessment did not appear to be statistically significant moderators of effect size. Physical education with various curricula can, therefore, increase overall motor competence in children and adolescents.
The Distribution of Rural Accommodation in Extremadura, Spain-between the Randomness and the Suitability Achieved by Means of Regression Models (OLS vs. GWR)
Authors: Sánchez-Martín José-Manuel , Gurría-Gascón José-Luis , Rengifo-Gallego Juan-Ignacio
Published: June 2020

There are multiple types of regression, the essential task of which is the obtaining of models which, starting from a set of regressive values, are capable of finding explanations for the variability of a dependent. However, in many cases, the territorial criterion is not considered to be a noteworthy factor of analysis, owing to which this deficiency has encouraged the arising of spatial statistics. Nevertheless, given the variety of regressions, it is not clear which can best be adapted to the analysis of tourism. In this sector, when the supply of accommodation is analysed, it is understood that it must be strongly related to the presence of resources, owing to which it has been taken as an example of an application between two differentiated regression techniques: ordinary least squares (OLS) and geographically weighted regression (GWR), with the objective of determining which of the two is best adapted to this type of analysis. The model has been drawn up based on various methods, although it has been shown that it is more efficient to resort to the declared preferences of the rural tourist, with the starting point being a survey made of the tourists. These aspects have been taken as independent variables with the aim of explaining the distribution of accommodation establishments. The results obtained show that the configuration of the spatial relations between the variable included in the model encourages the explanation of the latter, owing to which GWR is much more suitable than OLS, even when a system as complex as the distribution of accommodation establishments is analysed. Likewise, it is noteworthy that the distribution of accommodation does not also follow the guidelines marked by demand; far from it, it appears that in some areas, it is of a random nature.
Three Dimensions of Transformative Impact and Capacity: A Conceptual Framework Applied in Social Innovation Practice
Authors: Strasser Tim , de Kraker Joop , Kemp René
Published: June 2020

This article empirically applies, tests, and refines a conceptual framework that articulates three dimensions of transformative impact and transformative capacity: depth, width, and length. This responds to the need for a more precise conceptual language to describe these terms and operationalize them in a way that is useful for practitioners in social innovation networks. By applying this framework in diverse cases of social innovation networks, we demonstrate how the framework can serve to identify and assess transformative impacts and the capacities needed to bring about these impacts. Our findings include 1. empirical substantiations, 2. refinements, and 3. interaction effects among the elements of the framework. We also subjected the framework to an appraisal by practitioners in social innovation networks regarding the recognizability of the framework elements and usefulness for practice. The framework was generally perceived as very meaningful and valuable for social innovation practitioners as a way to understand, assess, strategically design and evaluate their transformation efforts. Drawing on feedback by practitioners, we offer recommendations for further research and development of the framework to improve its usefulness in practice.
Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach
Authors: Raghupathi Viju , Ren Jie , Raghupathi Wullianallur
Published: June 2020

Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders’ perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience.
Practical Head-Outflow Relationship Definition Methodology That Accounts for Varied Water-Supply Methods
Authors: Chang Dong Eil , Yoo Do Guen , Kim Joong Hoon
Published: June 2020

Achieving reliable pressure-driven analysis (PDA) results that account for anomalies within water-pipe networks requires a head-outflow relationship (HOR) model that can calculate supply flow rate according to the supply pressure at each node. Many studies have suggested HOR models, but a methodology to define HORs that considers the actual residential environment of users and differing water-supply methods for residential buildings has not yet been proposed. This study considered water-use data from buildings and actual differences in residential environments in a surveyed area (including building heights, topography, and water systems within buildings) and water-supply methods (indirect/direct water supply, existence of a pump within buildings) to develop a methodology and derive a representative HOR for the target area for PDA. Further, a representative HOR was determined for each block by applying the developed methodology for two blocks with similar residential environments but different water-use patterns. It confirmed that the HOR induced through this process could provide high water-supply performance despite a low supply head and needed to reflect the diversity of the water-supply method. The proposed HOR-definition methodology can be easily applied in water-pipe network design and operation processes and ensure objectivity and rationality of HOR selection to yield reliable PDA results.
Antecedents of Employees’ Goal Orientation and the Effects of Goal Orientation on E-Learning Outcomes: The Roles of Intra-Organizational Environment
Authors: Li Der-Chiang , Tsai Ching-Yeh
Published: June 2020

Organizational learning is beneficial for the maintenance and development of an organization’s long-term competitive advantages. However, organizational learning can be achieved only through the learning carried out by individual members. Therefore, employees’ learning plays a critical role in companies that pursue sustainable management. E-learning allows employees to learn without any time or space constraints, which strengthens and improves organizational learning capacity. The participants of this study were employees in small and medium-sized enterprises. This study explored and identified the ways in which these employees’ perceived intra-organizational environment (including the organization’s commitment to learning and a competitive psychological climate) affects their learning outcomes through their goal orientations and computer anxiety (CA). A questionnaire was administered to the employees who have used an Enterprise Resource Planning (ERP) e-learning platform. The results indicated that an organization’s commitment to learning and its competitive psychological climate affect e-learning outcomes, with employees’ goal orientations and CA acting as mediator variables. Furthermore, in contrast to past studies, this study found that the organization’s competitive psychological climate could strengthen employees’ learning motivation (i.e., learning goal orientation) and their desire to perform well (i.e., proving goal orientation), thus improving their e-learning outcomes. Finally, the empirical results were used to develop recommendations for high-ranking executives, department managers, and human resource departments.
Trade-Offs Analysis of Ecosystem Services for the Grain for Green Program: Informing Reforestation Decisions in a Mountainous Headwater Region, Northeast China
Authors: Li Xiufen , Tian Yichen , Gao Tian , Jin Lei , Li Shuangtian , Zhao Dan , Zheng Xiao , Yu Lizhong , Zhu Jiaojun
Published: June 2020

The effects of forest restoration on ecosystem services and their trade-offs are increasingly discussed by environmental managers and ecologists, but few demonstrations have analyzed ecosystem service trade-offs with a view to informing afforestation choices. Here, we examined how the Grain for Green Program (GGP), an ambitious reforestation program in China, affected ecosystem services. We quantified regulating services and provisioning service in the potential scenarios, which were developed to improve ecosystem services better. The results indicated the GGP drove 14.5% of land-use/land-cover from 2000 to 2015, and all the regulating services increased. Prioritizing reforestations in steep-sloped and riparian farmlands can promote flood mitigation, water purification, and soil retention services by 62.7%, 25.5%, and 216.1% as compared with 2015 levels, respectively, suggesting that the improvements strongly depend on afforestation locations. Driven by the new GGP policy, a high proportion of economic forest increased provisioning service (272.2%), but at the expense of decreases in soil retention (−25.1%), flood mitigation (−11.4%), water purification (−36.6%), and carbon storage (−48.5%). We identified a suitable scenario that would reduce the trade-offs, which associated with afforestation types and their spatial allocation. Identifying priority areas of afforestation types can inform the GGP policy to assure sustainable and broader benefits.
Sustainable Use of the Littoral by Traditional People of Barbados and Bahamas
Authors: Stoffle Brent , Stoffle Richard , Van Vlack Kathleen
Published: June 2020

This paper is about the traditional people of Barbados and The Bahamas, in the Caribbean and their sustainable adaptations to the littoral, which included both marine and terrestrial components. Traditional people are defined as having lived in a sustainable way in an environment for five generations, the littoral is described here as an ecological zone at the sea’s edge, which is composed of hundreds of medicine and food plants and animals, and resilient adaptations are understood with the environmental multiplicity model. The analysis is based on more than a thousand site intercept interviews conducted by the authors and their research teams. These data argue that culturally based patterns of sustainable food use and environmental preservation can be understood from generations of successful adaptations of traditional people.