bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Animal ByProducts Fertilization on Durum Wheat in Mediterranean Conditions: Preliminary Results
Authors: Mulè Paolo , Dettori Marco , Carboni Gianluca
Published: August 2020

This study aims to evaluate the effects of new-BioFertilizing Amendments (BFAs) deriving from fast organic matter decomposition of Animal ByProducts (ABPs) in comparison with ordinary soil organic amendments (compost), mineral N-fertilizers and no fertilization, on durum wheat development and production in a field trial under Mediterranean conditions. Results showed taller plants with heavier spikes and greater vigor in plots fertilized with BFAs when compared to no fertilization and N-fertilization, respectively. Likewise, BFAs fertilization resulted in higher protein content, gluten content, protein yields and higher values of yellow index with respect to no fertilization and N-fertilization. In contrast, lower values for test weight in correspondence of BFAs fertilization as well as no statistically significant differences on grain yield and gluten index were found. These preliminary results suggest that replacing N-fertilization with BFAs can be effective to ensure crop quality and yield stability in Mediterranean conditions.
Anion-Dominated Copper Salicyaldimine Complexes—Structures, Coordination Mode of Nitrate and Decolorization Properties toward Acid Orange 7 Dye
Authors: Tsai Meng-Jung , Tsai Chi-Jou , Lin Ken , Wu Jing-Yun
Published: August 2020

A salicyaldimine ligand, 3-tert-butyl-4-hydroxy-5-(((pyridin-2-ylmethyl)imino)methyl)benzoic acid (H2Lsalpyca) and two Cu(II)−salicylaldimine complexes, [Cu(HLsalpyca)Cl] (1) and [Cu(HLsalpyca)(NO3)]n (2), have been synthesized. Complex 1 has a discrete mononuclear structure, in which the Cu(II) center is in a distorted square-planar geometry made up of one HLsalpyca− monoanion in an NNO tris-chelating mode and one Cl− anion. Complex 2 adopts a neutral one-dimensional zigzag chain structure propagating along the crystallographic [010] direction, where the Cu(II) center suits a distorted square pyramidal geometry with a τ value of 0.134, consisted of one HLsalpyca− monoanion as an NNO tris-chelator and two NO3− anions. When the Cu∙∙∙O semi coordination is taken into consideration, the nitrato ligand bridges two Cu(II) centers in an unsymmetrical bridging-tridentate with a μ, κ4O,O′:O′,O″ coordination. Clearly, anion herein plays a critical role in dominating the formation of discrete and polymeric structures of copper salicyaldimine complexes. Noteworthy, complex 2 is insoluble but highly stable in H2O and various organic solvents (CH3OH, CH3CN, acetone, CH2Cl2 and THF). Moreover, complex 2 shows good photocatalytic degradation activity and recyclability to accelerate the decolorization rate and enhance the decolorization performance of acid orange 7 (AO7) dye by hydrogen peroxide (H2O2) under daylight.
A Microscopically Motivated Model for Particle Penetration into Swollen Biological Networks
Authors: Arzi Roni Sverdlov , Sosnik Alejandro , Cohen Noy
Published: August 2020

Biological gels (bio-gels) are hydrated polymer networks that serve diverse biological functions, which often lead to intentional or unintentional exposure to particulate matter. In this work, we derive a microscopically motivated framework that enables the investigation of penetration mechanisms into bio-gels. We distinguish between two types of mechanisms: spontaneous (unforced) penetration and forced penetration. Using experimental data available in the literature, we exploit the proposed model to characterize and compare between the microstructures of respiratory, intestinal, and cervicovaginal mucus and two types of biofilms. Next, we investigate the forced penetration process of spherical and ellipsoidal particles into a locally quadrilateral network. The proposed framework can be used to improve and complement the analysis of experimental findings in vitro, ex vivo, and in vivo. Additionally, the insights from this work pave the way towards enhanced designs of nano-medicines and allow the assessment of risk factors related to the nano-pollutants exposure.
Shape Memory Polymer Foam with Programmable Apertures
Authors: Walter Mario , Friess Fabian , Krus Martin , Zolanvari Seyed Mohammad Hassan , Grün Gunnar , Kröber Hartmut , Pretsch Thorsten
Published: August 2020

In this work, a novel type of polyester urethane urea (PEUU) foam is introduced. The foam was produced by reactive foaming using a mixture of poly(1,10–decamethylene adipate) diol and poly(1,4–butylene adipate) diol, 4,4′-diphenylmethane diisocyanate, 1,4–butanediol, diethanolamine and water as blowing agent. As determined by differential scanning calorimetry, the melting of the ester-based phases occurred at temperatures in between 25 °C and 61 °C, while the crystallization transition spread from 48 °C to 20 °C. The mechanical properties of the foam were simulated with the hyperplastic models Neo-Hookean and Ogden, whereby the latter showed a better agreement with the experimental data as evidenced by a Pearson correlation coefficient R² above 0.99. Once thermomechanically treated, the foam exhibited a maximum actuation of 13.7% in heating-cooling cycles under a constant external load. In turn, thermal cycling under load-free conditions resulted in an actuation of more than 10%. Good thermal insulation properties were demonstrated by thermal conductivities of 0.039 W·(m·K)−1 in the pristine state and 0.052 W·(m·K)−1 in a state after compression by 50%, respectively. Finally, three demonstrators were developed, which closed an aperture or opened it again simply by changing the temperature. The self-sufficient material behavior is particularly promising in the construction industry, where programmable air slots offer the prospect of a dynamic insulation system for an adaptive building envelope.
Evaluating the Absolute Calibration Accuracy and Stability of AIRS Using the CMC SST
Authors: Aumann Hartmut H. , Broberg Steven E. , Manning Evan M. , Pagano Thomas S. , Wilson Robert C.
Published: August 2020

We compare the daily mean and standard deviation of the difference between the sea surface skin temperature (SST) derived from clear sky Atmospheric InfraRed Sounder (AIRS) data from seven atmospheric window channels between 2002 and 2020 and collocated Canadian Meteorological Centre (CMC) SST data from the tropical oceans. After correcting the mean difference for cloud contamination and diurnal effects, the remaining bias relative to the CMC SST, is reasonably consistent with estimates of the AIRS absolute accuracy based on the uncertainty of the pre-launch calibration. The time series of the bias produces trends well below the 10 mK/yr level required for climate change evaluations. The trends are in the 2 mK/yr range for the five window channels between 790 and 1231 cm−1, and +5 mK/yr for the shortwave channels. Between 2002 and 2020, the time series of the standard deviation of the difference between the AIRS SST and the CMC SST dropped fairly steadily to below 0.4 K in several AIRS window channels, a level previously only seen in gridded SST products relative to the Argo buoys.
A Case Study on the Noncontact Inventory of the Oldest European Cast-iron Bridge Using Terrestrial Laser Scanning and Photogrammetric Techniques
Authors: Kwiatkowski Jacek , Anigacz Wojciech , Beben Damian
Published: August 2020

Conventional measurement technologies of transportation infrastructures consist of discrete surveys which can be inconvenient in practice. Furthermore, data obtained using these methods are restricted to several points (or elements) placed on the observed structures. Modern survey techniques—for example, terrestrial laser scanning (TLS) and photogrammetric—allow for the surveying of quasi-continuous surfaces of examined structures. The examined object is an historic cast-iron suspension bridge in Ozimek (south of Poland). The bridge was constructed in 1825–1827 and constitutes the oldest European bridge of this type. The surveys were conducted using TLS and digital photogrammetric techniques. The data obtained were compared with traditional survey results (reference data) and the project. The achieved effects of the measurements show that the discrepancies between the applied techniques (TLS and photogrammetry) and reference methods varied only within several millimeters and can be regarded as satisfactory. Better compliance was obtained for TLS than photogrammetry. The main benefits of the applied techniques include reducing time in the field and obtaining a three-dimensional model of the structure that has satisfactory accuracy.
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network
Authors: Ding Yifan , Cheng Xiao , Liu Jiping , Hui Fengming , Wang Zhenzhan , Chen Shengzhe
Published: August 2020

The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.
Evaluating the Quality of TLS Point Cloud Colorization
Authors: Julin Arttu , Kurkela Matti , Rantanen Toni , Virtanen Juho-Pekka , Maksimainen Mikko , Kukko Antero , Kaartinen Harri , Vaaja Matti T. , Hyyppä Juha , Hyyppä Hannu
Published: August 2020

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.
Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool
Authors: Aranguren Marta , Castellón Ander , Aizpurua Ana
Published: August 2020

Nitrogen (N) splitting is critical to achieving high crop yields without having negative effects on the environment. Monitoring crop N status throughout the wheat growing season is key to finding the balance between crop N requirements and fertilizer needs. Three soft winter wheat fertilization trials under rainfed conditions in Mediterranean climate conditions were monitored with a RapidScan CS-45 (Holland Scientific, Lincoln, NE, USA) instrument to determine the normalized difference vegetation index (NDVI) values at the GS30, GS32, GS37, and GS65 growth stages. The threshold NDVI values in the Cezanne variety were 0.7–0.75 at the GS32, GS37, and GS65 growing stages. However, for the GS30 growing stage, a threshold value could not be established precisely. At this stage, N deficiency may not affect wheat yield, as long as the N status increases at GS32 stage and it is maintained thereafter. Following the NDVI dynamic throughout the growing season could help to predict the yields at harvest time. Therefore, the ΣNDVI from GS30 to GS65 explains about 80% of wheat yield variability. Therefore, a given yield could be achieved with different dynamics in wheat NDVI values throughout the growing cycle. The determined ranges of the NDVI values might be used for developing new fertilization strategies that are able to adjust N fertilization to wheat crop needs.
Contrasting Effects of Temperature and Precipitation on Vegetation Greenness along Elevation Gradients of the Tibetan Plateau
Authors: Wang Yan , Peng Dailiang , Shen Miaogen , Xu Xiyan , Yang Xiaohua , Huang Wenjiang , Yu Le , Liu Liangyun , Li Cunjun , Li Xinwu , Zheng Shijun , Zhang Helin
Published: August 2020

The Tibetan Plateau (TP) is one of the most sensitive regions to global climate warming, not only at the inter-annual time scale but also at the altitudinal scale. We aim to investigate the contrasting effects of temperature and precipitation on vegetation greenness at different altitudes across the TP. In this study, interannual and elevational characteristics of the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation were examined during the growing season from 1982 to 2015. We compared the elevational movement rates of the isolines of NDVI, temperature, and precipitation, and the sensitivities of elevational NDVI changes to temperature and precipitation. The results show that from 1982 to 2015, the elevational variation rate of isolines for NDVI mismatched with that for temperature and precipitation. The elevational movements of NDVI isolines were mostly controlled by precipitation at elevations below 2400 m and by the temperature at elevations above 2400 m. Precipitation appears to plays a role similar to temperature, and even a more effective role than the temperature at low elevations, in controlling elevational vegetation greenness changes at both spatial and interannual scales in the TP. This study highlights the regulation of temperature and precipitation on vegetation ecosystems along elevation gradients over the whole TP under global warming conditions.
An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment
Authors: Ilori Christopher O. , Knudby Anders
Published: August 2020

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.
Automated Method for Delineating Harvested Stands Based on Harvester Location Data
Authors: Melkas Timo , Riekki Kirsi , Sorsa Juha-Antti
Published: August 2020

The data produced by cut-to-length harvesters provide new large-scale data source for event-based update of national forest stand inventory by Finnish Forest Centre. This study aimed to automate geoprocessing, which generates delineations of operated areas from harvester location data. Automated algorithms were developed and tested with a dataset of 455 harvested objects, recorded during harvestings. In automated stand delineation, the location points are clustered, the stand points are identified and external strip roads are separated. Then, stand polygons are produced. To validate the results, automatic delineations were compared to 57 observed delineations from field measurements and aerial images. A detailed comparison method was developed to study the correspondence. Stand polygonization parameter was adjusted and areal correspondence with 1% error on average was obtained for stands over 0.75 ha. Good stand shape agreement was observed. Overall, the automated method worked well, and the operative stand delineations were found suitable for updating the forest inventory data. To modify the operative stands towards forest inventory stands, a balancing algorithm is introduced to create a solid, unique stand boundary between overlapping stands. This algorithm is beneficial for upkeep of stand networks. In addition, the Global Navigation Satellite System (GNSS) accuracy of the harvesters was examined and estimated numerically.
Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria
Authors: Nachappa Thimmaiah , Ghorbanzadeh Omid , Gholamnia Khalil , Blaschke Thomas
Published: August 2020

We live in a sphere that has unpredictable and multifaceted landscapes that make the risk arising from several incidences that are omnipresent. Floods and landslides are widespread and recurring hazards occurring at an alarming rate in recent years. The importance of this study is to produce multi-hazard exposure maps for flooding and landslides for the federal State of Salzburg, Austria, using the selected machine learning (ML) approach of support vector machine (SVM) and random forest (RF). Multi-hazard exposure maps were established on thirteen influencing factors for flood and landslides such as elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), geology, lithology, rainfall, land cover, distance to roads, distance to faults, and distance to drainage. We classified the inventory data for flood and landslide into training and validation with the widely used splitting ratio, where 70% of the locations are used for training, and 30% are used for validation. The accuracy assessment of the exposure maps was derived through ROC (receiver operating curve) and R-Index (relative density). RF yielded better results for both flood and landslide exposure with 0.87 for flood and 0.90 for landslides compared to 0.87 for flood and 0.89 for landslides using SVM. However, the multi-hazard exposure map for the State of Salzburg derived through RF and SVM provides the planners and managers to plan better for risk regions affected by both floods and landslides.
An Electrical Resistivity Method of Characterizing Hydromechanical and Structural Properties of Compacted Loess During Constant Rate of Strain Compression
Authors: Qin Pengju , Liu Yubo , Song Zhiwei , Ma Fuli , Wang Yongbao , Zhang Xiao , Miao Chenxi , Dong Xiaoqiang
Published: August 2020

Hydromechanical and structural properties of compacted loess have a significant impact on the stability and reliability of subbase and subgrade, which needs to be quickly determined in the field and laboratory. Hence, an electrical resistivity method was used to characterize the hydromechanical and structural properties of compacted loess during constant rate of strain compression. In the present work, compacted loess samples with a dry density of 1.7 g/cm3, a diameter of 64 mm, a height of 10 mm and different water content ranging from 5–25% were prepared. The constant rate of strain (CRS) tests were conducted by a developed oedometer cell equipped with a pair of horizontal circular electrodes (diameter of 20 mm) and vertical rectangular electrodes (width of 3.5 mm) to determine the electrical resistivity of compacted loess. The results showed that as average water content increases, plastic compression indices increase from 0.220 to 0.350 and the elastic compression indices increase from 0.0152 to 0.030, but they decrease to 0.167 and 0.010 and yield stress decreases from 381.28 kPa to 72.35 kPa. Moreover, as vertical strain increases, the variation trend of average formation factor and average shape factor for the lower water content decreases but increases for the maximum water content, and the anisotropy index first decrease and then tend to increase slightly, which indicates that the structural properties of unsaturated and saturated samples during compression exhibits different trend and the anisotropy of samples tend to be stable as vertical strain increases. As the water content increases, the average formation factor and average shape factor decrease, but the anisotropy index first decreases then increases, suggesting that water content has a significant impact on these electrical indices. More important, The coefficients of average formation factor decrease from 33.830 to −1.698 and the coefficients of average shape factor decrease from 8.339 to −0.398 as water content increases, whereas there is less variation for the coefficient of anisotropic index with a value of 2.190. An equation correlating average formation factor and water content and vertical strain is regressed to characterize the hydromechanical properties of compacted loess by measuring its impedance, which can be used to evaluate the stability of compacted loessic ground and subgrade.
Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes
Authors: Liu Luhang , Zhang Qiang , Wei Dazhong , Li Gang , Wu Hao , Wang Zhipeng , Guo Baozhu , Zhang Jiyang
Published: August 2020

Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality, large individual differences and difficulty in processing streaming data. To overcome these issues, we propose a new method named Chaotic Ensemble of Online Recurrent Extreme Learning Machine (CE-ORELM) for temperature prediction of control moment gyroscopes. By means of the CE-ORELM model, this proposed method is capable of dynamic prediction of temperature. The performance of the method was tested by real temperature data acquired from actual CMGs. Experimental results show that this method has high prediction accuracy and strong adaptability to the on-orbital temperature data with sudden variations. These superiorities indicate that the proposed method can be used for temperature prediction of control moment gyroscopes.
Structural Model Identification Using a Modified Electromagnetism-Like Mechanism Algorithm
Authors: Feng Zhouquan , Ye Zhengtao , Wang Wenzan , Lin Yang , Chen Zhengqing , Hua Xugang
Published: August 2020

A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction–repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original algorithm, a new local search strategy, new charge and force calculation formulas, new particle movement and updating rules are proposed. The test results of benchmark functions show that the modified EM algorithm has better accuracy and faster convergence rate than the original EM algorithm and the particle swarm optimization (PSO) algorithm. In order to investigate the applicability of this approach in parameter identification of structural models, one numerical truss model and one experimental shear-building model are presented as illustrative examples. The identification results show that this approach can achieve remarkable parameter identification even in the case of large noise contamination and few measurements. The modified EM algorithm can also be used to solve other optimization problems.
Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models
Authors: Prabhu Ghanashyama , O’Connor Noel E. , Moran Kieran
Published: August 2020

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g., their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutional Neural Networks (CNNs). We investigated CNN models based on the AlexNet architecture and found that the performance was better than the traditional approaches, for exercise recognition (overall F1-score of 97.18%) and repetition counting (±1 error among 90% observed sets). To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. Also, we make the INSIGHT-LME dataset publicly available to encourage further research.
Interactive Timeline Approach for Contextual Spatio-Temporal ECT Data Investigation
Authors: Romanowski Andrzej , Chaniecki Zbigniew , Koralczyk Aleksandra , Woźniak Mikołaj , Nowak Adam , Kucharski Przemysław , Jaworski Tomasz , Malaya Maja , Rózga Paweł , Grudzień Krzysztof
Published: August 2020

This paper presents a novel approach to a complex process of electrical capacitance tomography (ECT) measurement data analysis. ECT is frequently employed for non-invasive monitoring of industrial process phenomena. Proposed methodology is based on the premeditated integration of the spatial and temporal relations inherent in the measurement records into the workflow of the analysis procedure. We propose a concept of interactive timeline that enables arranging data visualization according to the user’s current focus along the process of analysis. We evaluated the proposed method using a prototype system in a task-based user study conducted with a group of domain experts. The evaluation is based on gravitational silo flow measurement datasets. Proposed prototype system enables diverse data manipulation in a more natural way allowing the user to switch back and forth between space and time domains along the data analysis trail. Experiments with the prototype system showed that the accuracy and completion times have significantly improved in comparison to the performance measured in the baseline condition. Additionally, the participants reported decreased physical load with improved efficiency measured with NASA task load index. Finally, a short discussion coupled with directions for the future of interactive spatio-temporal ECT measurement data analysis conclude the paper.
Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke
Authors: Holder Amara L. , Mebust Anna K. , Maghran Lauren A. , McGown Michael R. , Stewart Kathleen E. , Vallano Dena M. , Elleman Robert A. , Baker Kirk R.
Published: August 2020

Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM2.5 = 295 µg/m3). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r2 = 0.52–0.95), but overpredicted PM2.5 concentrations (normalized root mean square errors, NRMSE = 80–167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM2.5 concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor-specific smoke correction equation.
Chi-Square and Student Bridge Distributions and the Behrens–Fisher Statistic
Authors: Richter Wolf-Dieter
Published: August 2020

We prove that the Behrens–Fisher statistic follows a Student bridge distribution, the mixing coefficient of which depends on the two sample variances only through their ratio. To this end, it is first shown that a weighted sum of two independent normalized chi-square distributed random variables is chi-square bridge distributed, and secondly that the Behrens–Fisher statistic is based on such a variable and a standard normally distributed one that is independent of the former. In case of a known variance ratio, exact standard statistical testing and confidence estimation methods apply without the need for any additional approximations. In addition, a three pillar bridges explanation is given for the choice of degrees of freedom in Welch’s approximation to the exact distribution of the Behrens–Fisher statistic.
Enhancing Sustainable Urban Regeneration through Smart Technologies: An Assessment of Local Urban Regeneration Strategic Plans in Korea
Authors: Kim Hyun Woo , Aaron McCarty Dakota , Lee Jaekyung
Published: August 2020

This study develops multiple evaluation indexes in the context of sustainable urban regeneration through introducing smart technologies/infrastructures and assesses 63 local urban regeneration strategic plans by using the content analysis method. A total of 107 indexes are developed based on the four aspects (economy, society and culture, environment, and livability) of sustainability. From our findings, the average plan quality score of 54 local governments’ plans is 17.5 out of 50, with the metropolitan governments’ plans averaging 16.8, which indicates that the plans currently sampled do not sufficiently reflect the basic concepts of sustainable and smart urban regeneration. The contents of most of the plans generally focus on specific sectors, such as society, culture, and housing, whereas smart technology-related information and policies are relatively deficient. Among the five plan components (factual bases, goals/objectives, policies/strategies, implementation, coordination) reviewed, the implementation component receives the highest score, while indicators related to action strategies are mentioned least often. In particular, the results reveal that indexes relating to the energy and transportation sectors are not frequently mentioned; as such, each municipality is recommended to work to increase awareness of smart technologies and policies. For urban regeneration projects to be sustainable, multi-faceted policies must be implemented by various stakeholders with a long-term perspective. The results of this study can be used as a base for local planners and decision-makers when adopting and supplementing existing regeneration plans, and can contribute to promoting more sustainable urban regeneration through actively adopting various smart technologies initiatives.
Understanding of Sustainability and Education for Sustainable Development among Pre-Service Biology Teachers
Authors: Bezeljak Petra , Scheuch Martin , Torkar Gregor
Published: August 2020

Sustainable development (SD) is one of the global and central aims of today’s politics. As stated in Agenda 21, education must play an essential role in achieving a sustainable society. The present research is focused on Slovenian and Austrian biology teacher students’ understanding of SD and education for sustainable development (ESD). The research was carried out at the University of Ljubljana and the University of Vienna. Altogether, 60 Slovenian and 60 Austrian pre-service biology teachers participated in the questionnaire-based study. Pre-service biology teachers answered a set of Likert-type and open survey questions. Less than half of the pre-service biology teachers from Slovenia and Austria had a good understanding of the environmental aspects of SD, but they lack understanding of the interconnections between the environmental, economic and social dimensions related to SD. They describe and connect ESD with environmental education and environmental awareness. Students from both countries know some pedagogical principles of ESD, such as active learning and transformative education. Analysis with the sustainable development goals (SDGs) in focus showed that only some of them were mentioned by the teacher students. The results of the research contribute to the evaluation and development of curriculum for middle and high school biology teachers.
Factors Affecting the Community Acceptance of Onshore Wind Farms: A Case Study of the Zhongying Wind Farm in Eastern China
Authors: Guan Jinjin , Zepp Harald
Published: August 2020

The conflict between wind energy expansion and local environmental protection has attracted attention from society and initiated a fierce discussion about the community acceptance of wind farms. There are various empirical studies on factors affecting the public acceptance of wind farms, but little concerning the correlation and significance of factors, especially in a close distance to the wind farms. This paper aims to identify, classify, and analyze the factors affecting community acceptance through literature review, questionnaire, variance analysis, and linear regression analysis. A total of 169 questionnaires was conducted in 17 villages around the Zhongying Wind Farm in Zhejiang Province, China. The factors are categorized into four groups: Location-related factors, demographic factors, environmental impact factors, and public participation factors. Through the analysis of variance (ANOVA) and linear regression analysis, the outcome shows the universal rule of community acceptance under the Chinese social background. Finally, recommendations for improving wind farm planning procedures are put forward.
Practical Analysis of BIM Tasks for Modular Construction Projects in South Korea
Authors: Lee Myungdo , Lee Dongmin , Kim Taehoon , Lee Ung-Kyun
Published: August 2020

Building information modeling (BIM) and modular construction are important technologies for construction industry sustainability. This study proposes a relational matrix of key activities and BIM tasks of modular construction projects to analyze practical BIM tasks in Korea. To achieve this objective, 11 key activities and eight BIM tasks are identified through a comprehensive literature review and expert interviews. Then, the relational matrix of key activities and BIM tasks is proposed, and the BIM tasks in the matrix are analyzed in terms of necessity and efficiency using 5-point Likert scales. Finally, the matrix with the BIM utilization index is suggested. As a result, the average BIM utilization index is 0.80 in the off-site phase, and the index results show that 3D shop drawings have the highest index. In the on-site phase, the average BIM UI is 0.73 and the integration of a 4D model with quantity take-off is the most efficient at 0.85. Additionally, from the decision-maker’s perspective, the priority through the index presented helps in making decisions and in practical BIM execution planning. The proposed matrix is a practical reference for decision-makers considering the application of BIM in modular projects, and it contributes to a sustainable construction industry.
Intellectual Structure of the Belt and Road Initiative Research: A Scientometric Analysis and Suggestions for a Future Research Agenda
Authors: Cao Mingchun , Alon Ilan
Published: August 2020

China’s Belt and Road Initiative (BRI) has attracted increasing attention worldwide since its inception in 2013. Although the research is fast increasing in terms of publications, there is a lack of knowledge about the intellectual structure, research trends, and appropriate theories underpinning. Using bibliometric data from 1583 articles from 2013 to 2019 in the Web of Science and Scopus databases, we used bibliometric techniques and scientometric analyses to identify and analyze the intellectual structure, hotspots, and emerging trends in this field. We identified 18 clusters that we categorized into three groups for thematic discussion. Research gaps and future research directions were identified and proposed after qualitative content analysis. By providing the big picture of the latest research on BRI since it was initiated, this paper serves as a one-stop shop for multi-disciplinary research on the topic.
Exploring Cultivation Path of Building Information Modelling in China: An Analysis from the Perspective of an Innovation Ecosystem
Authors: Yang Yingnan , Zhang Yidan , Xie Hongming
Published: August 2020

Ecosystem theory provides a new perspective for studying the development of the architecture engineering and construction (AEC) industry in the age of information and communication technology (ICT). As an extremely ICT innovation, building information modelling (BIM) not only brings technical benefits to the AEC industry, but changes the innovation paradigm of the AEC industry towards an innovation ecosystem, which improve productivity and sustainability throughout the project life cycle. This article contributes to innovation ecosystem theory by exploring the structure of the BIM ecosystem and deriving its cultivation path. Then, as the leading city in China for developing BIM technologies, Shanghai was selected as the case study to elaborate on the cultivation path of the BIM ecosystem. The results indicate that three layers identified in the structure contribute to the understanding of the boundaries, units, and analytical focus of the BIM ecosystem, with the BIM platform being the core layer. This topology structure, with the BIM platform as the hub, promotes interdependency and symbiosis among participants in the cultivation of the BIM ecosystem, supporting the birth, expansion, maturity, re-innovation (or extinction), and sustainable development of the BIM ecosystem. This research complements and extends literature on the BIM ecosystem, and provides implications as to the construction, cultivation, and sustainability of BIM ecosystems for emerging economy firms.
Evaluating the Performance of the Government Venture Capital Guiding Fund Using the Intuitionistic Fuzzy Analytic Hierarchy Process
Authors: Xu Jianjun , Yu Lijie , Gupta Rakesh
Published: August 2020

The performance evaluation of the government venture capital guiding fund (GVCGF) has come into focus in the field of venture capital. Most of the existing studies, such as whether the GVCGF has guided social capital to start-up enterprises and has played its due role in the process of enterprise growth and innovation, are all based on relevant work under the framework of econometric analysis. Unlike in these existing studies, we construct the performance analysis model of the GVCGF from four dimensions, including the standardization development of the guidance fund, the risk control ability, and the leverage and the support effects under the framework of a multi-attribute decision-making analysis. Taking a GVCGF project in Ningbo City, China, as an example, we comprehensively evaluate the development performance of the GVCGF using the intuitionistic fuzzy analytic hierarchy process (IFAHP). The results show that the development performance of the GVCGF is at a “relatively high” level. Compared with the traditional analytic hierarchy process (AHP), the IFAHP effectively avoids the false, enlarged influence caused by data subjectivity and evaluation uncertainty. This study provides a feasible analytical framework for the application of the IFAHP in other project performance evaluations.
Redesign of the Curing Area of the Tire Manufacturing Process
Authors: Stîngă Florin , Severin Irina , Mitrache Ioana Alina , Lascu Elena
Published: August 2020

In this study, the production efficiency of a company’s manufacturing flow decreased, and therefore specific improvement actions on the tire manufacturing process were implemented. Additionally, the process improvement plan started with a deeper understanding of the activities, by using the SIPOC method (suppliers, inputs, process, outputs, customers). In order to identify the root-causes and the opportunities for improvement, quality instruments, such as the Ishikawa and Pareto quality tools, were used. The tire manufacturing process was reorganized using the 5S method, then the process was standardized and maintained through specific working procedures drafted on the standard operating procedure format. A particular device was designed for unlocking spring vents. Finally, the implementation of the redesign project shows added value as the entire manufacturing process improved by identifying weaknesses and correcting them. In addition to this, a different objective of the same company used in this case study is linked to adapting to market regulation requirements (the Economic Commission of Europe), with the aim to remain competitive. Therefore, well-defined processes and a strategy based on continuous improvement are needed. The methodology may be transferred to other industries.
Risk-Intolerant but Risk-Taking—Towards a Better Understanding of Inconsistent Survey Responses of the Euro Area Households
Authors: Kochaniak Katarzyna , Ulman Paweł
Published: August 2020

The sustainable development of the EU internal market for retail financial services is based on the rules of ‘suitability’, ‘know your client’, and ‘know your product’. The rules ensure that financial institutions (including banks) offer retail clients only products and services that are adequate to their purposes and preferences, including risk tolerance. Our study, however, concerns households for which the above rules are not valid, since they declare risk aversion and possess risky assets. According to the European Union Markets in Financial Instruments Directive and Regulation (MiFID II and MiFIR), the inconsistent information they provide within survey questions should classify them to more compound suitability assessment procedures. In the study, we use nationally representative data for 16 euro area countries from the second wave of the Eurosystem Household Finance and Consumption Survey. Using logit regression, we identify sets of socio-demographic and socio-economic characteristics conducive to the possession of risky assets by risk-averse households in individual countries. To assess their similarity, we use the hierarchical taxonomic method with Ward’s formula. The results of the study showed that risky assets were primarily possessed by risk-averse households that were characterised by high income, including from self-employment, and reference persons having a university degree and at least 55 years of age. The significance of their other characteristics was mainly shaped at the national level. The clear similarity of sets of the characteristics was confirmed only for a few pairs of countries. The information inconsistency that may result from erroneous self-assessments of being risk-averse was recognised in all countries and most often concerned high-income households with reference persons being males with a university degree. In 11 countries, the reason for this inconsistency could also be the inadequacy of assets held, also among senior households. The results provide insights for practitioners and policy. Identification of households providing inconsistent information to financial institutions, with the recognition of its reasons based on easily verifiable characteristics, may prove helpful in suitability assessments. The results confirming the similarity of household profiles requiring special attention between countries may be useful for entities operating cross-border. Due to the collection of information on risk aversion based on the single question self-classification method, conclusions regarding the restrictions of its use should also be considered relevant. In turn, policy implications may relate to consumer protection, since significant fractions of risk-averse households indeed participate in risky assets. Moreover, in selected countries, the risk-averse senior households were recognised as susceptible to making wrong investment decisions.
Protected Landscapes in Spain: Reasons for Protection and Sustainability of Conservation Management
Authors: Marine Nicolas , Arnaiz-Schmitz Cecilia , Herrero-Jáuregui Cristina , de la O Cabrera Manuel Rodrigo , Escudero David , Schmitz María F.
Published: August 2020

Landscape conservation efforts in many European countries focus on cultural landscapes, which are part of the cultural identity of people, have a great heritage significance, improve the living standards of local populations and provide valuable cultural biodiversity. However, despite a wide arrange of protective measures, the management of preserved areas is seldom effective for the protection of cultural landscapes. Through a multi-approach analysis, we characterise the main heritage attributes of 17 Protected Landscapes in Spain and assess their management effectiveness by quantifying the evolution of the spatial pattern inside and outside protected landscapes. Our method has proven useful to quantitatively describe the spatial-temporal patterns of change of the protected and unprotected landscapes studied. We highlight the following results: (i) the concepts of uniqueness and naturalness are not appropriate to preserve cultural landscapes; (ii) the land protection approach currently adopted is not useful for the protection of cultural landscapes, particularly of the most rural ones; (iii) the landscapes studied with greater rural features can be considered as “paper parks”. We recommend that different protection measures focused on the needs and desires of the rural population are taken into account in order to protect cultural landscapes that are shaped by traditional rural activities.