bluuebunny/crossref_metadata_2025 huggingface dataset:
Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices
Authors: Marino Stefano , Alvino Arturo
Published: January 2020

Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic traits of ten winter wheat cultivars. Five VIs groups were identified and the ground truth yield-related data were analyzed for clusters validation. The yield data revealed a value of 6.91 t ha−1 for the first cluster with the highest VIs value and a decrease of −12%, −21%, and −27% for the 2nd, 3rd, and 4th clusters; respectively; the 5th cluster; with the lowest VIs value showed the lower yield values (4 t ha−1). Agronomic traits, such as dry biomass, spike numbers, and weight were grouped according to VIs clusters and analyzed and showed the same trends. The analysis of spatial distribution and agronomic data of the ten cultivars within the single clusters highlighted that the most productive varieties showing a greater value of spike weight and numbers and a greater presence of areas with high values of VIs and vice versa the less productive once, though two cultivars showed productions not linked to cluster classification and high data range variability were recorded. Cluster identified by high-resolution UAV vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification.
Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
Authors: Park Haemi , Takeuchi Wataru , Ichii Kazuhito
Published: January 2020

Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (Re), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (GWT) by incorporating LST and precipitation into the Keetch–Byram Drought Index (KBDI). The GWT was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (R2) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (Re) based on a relationship between the in situ GWT and flux-tower-observed Re. Fire emissions are a large direct source of CO2 from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) Re, GPP, FE, and NBE from whole peatlands in the study area (6°N–11°S, 95–141°E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. Re and FE were influenced by El Niño, and the value of the NBE was also high in the El Niño period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of Re with delineations of types of anthropogenic impacts (e.g., drainages and fires).
Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas
Authors: Masoom Akriti , Kosmopoulos Panagiotis , Bansal Ankit , Kazadzis Stelios
Published: January 2020

Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.
Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images
Authors: Ma Haojie , Liu Yalan , Ren Yuhuan , Wang Dacheng , Yu Linjun , Yu Jingxian
Published: January 2020

Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images.
Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds
Authors: Szostak Marta , Pietrzykowski Marcin , Likus-Cieślik Justyna
Published: January 2020

This paper investigates the possibility of using fusion Sentinel-2 imageries (2016, ESA) and light detection and ranging (LiDAR) point clouds for the automation of land cover mapping with a primary focus on detecting and monitoring afforested areas and deriving precise information about the spatial (2D and 3D) characteristics of vegetation for reclaimed areas. The study was carried out for reclaimed areas – two former sulfur mines located in Southeast Poland, namely, Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation, and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The current land use and land cover (LULC) classes at the Machów and Jeziórko former sulfur mines are derived based on Sentinel-2 image processing, and confirmed the applied type of reclamation for both analysed areas. The following LULC classes showed a significant spatial range: broad-leaved forest, coniferous forest, and transitional woodland shrub. The progress of afforested areas, not only in terms of the occupied area, but also in terms of the growth of trees and shrubs, was confirmed. The results of the study showed differences in vegetation parameters, namely, height and canopy cover. Various stages of vegetation growth were also observed. This indicates an ongoing process of vegetation development, as an effect of the reclamation treatment for these areas.
Seasonal Characteristics of Disdrometer-Observed Raindrop Size Distributions and Their Applications on Radar Calibration and Erosion Mechanism in a Semi-Arid Area of China
Authors: Xie Zongxu , Yang Hanbo , Lv Huafang , Hu Qingfang
Published: January 2020

Raindrop size distributions (DSDs) are the microphysical characteristics of raindrop spectra. Rainfall characterization is important to: (1) provide information on extreme rate, thus, it has an impact on rainfall related hazard; (2) provide data for indirect observation, model and forecast; (3) calibrate and validate the parameters in radar reflectivity-rainfall intensity (Z-R) relationships (quantitative estimate precipitation, QPE) and the mechanism of precipitation erosivity. In this study, the one-year datasets of raindrop spectra were measured by an OTT Parsivel-2 Disdrometer placed in Yulin, Shaanxi Province, China. At the same time, four TE525MM Gauges were also used in the same location to check the disdrometer-measured rainfall data. The theoretical formula of raindrop kinetic energy-rainfall intensity (KE-R) relationships was derived based on the DSDs to characterize the impact of precipitation characteristics and environmental conditions on KE-R relationships in semi-arid areas. In addition, seasonal rainfall intensity curves observed by the disdrometer of the area with application to erosion were characterized and estimated. The results showed that after quality control (QC), the frequencies of raindrop spectra data in different seasons varied, and rainfalls with R within 0.5–5 mm/h accounted for the largest proportion of rainfalls in each season. The parameters in Z-R relationships (Z = aRb) were different for rainfall events of different seasons (a varies from 78.3–119.0, and b from 1.8–2.1), and the calculated KE-R relationships satisfied the form of power function KE = ARm, in which A and m are parameters derived from rainfall shape factor μ. The sensitivity analysis of parameter A with μ demonstrated the applicability of the KE-R formula to different precipitation processes in the Yulin area.
Passive Detection of Moving Aerial Target Based on Multiple Collaborative GPS Satellites
Authors: Liu Mingqian , Gao Zhiyang , Chen Yunfei , Song Hao , Li Yuting , Gong Fengkui
Published: January 2020

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance–velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite.
A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5
Authors: Li Lianfa
Published: January 2020

Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.
Skeletal Muscle Myofibrillar Protein Abundance Is Higher in Resistance-Trained Men, and Aging in the Absence of Training May Have an Opposite Effect
Authors: Vann Christopher G. , Roberson Paul. A. , Osburn Shelby C. , Mumford Petey W. , Romero Matthew A. , Fox Carlton D. , Moore Johnathon H. , Haun Cody , Beck Darren T. , Moon Jordan R. , Kavazis Andreas N. , Young Kaelin C. , Badisa Veera L. D. , Mwashote Benjamin M. , Ibeanusi Victor , Singh Rakesh K. , Roberts Michael D.
Published: January 2020

Resistance training generally increases skeletal muscle hypertrophy, whereas aging is associated with a loss in muscle mass. Interestingly, select studies suggest that aging, as well as resistance training, may lead to a reduction in the abundance of skeletal muscle myofibrillar (or contractile) protein (per mg tissue). Proteomic interrogations have also demonstrated that aging, as well as weeks to months of resistance training, lead to appreciable alterations in the muscle proteome. Given this evidence, the purpose of this small pilot study was to examine total myofibrillar as well as total sarcoplasmic protein concentrations (per mg wet muscle) from the vastus lateralis muscle of males who were younger and resistance-trained (denoted as YT, n = 6, 25 ± 4 years old, 10 ± 3 self-reported years of training), younger and untrained (denoted as YU, n = 6, 21 ± 1 years old), and older and untrained (denoted as OU, n = 6, 62 ± 8 years old). The relative abundances of actin and myosin heavy chain (per mg tissue) were also examined using SDS-PAGE and Coomassie staining, and shotgun proteomics was used to interrogate the abundances of individual sarcoplasmic and myofibrillar proteins between cohorts. Whole-body fat-free mass (YT > YU = OU), VL thickness (YT > YU = OU), and leg extensor peak torque (YT > YU = OU) differed between groups (p < 0.05). Total myofibrillar protein concentrations were greater in YT versus OU (p = 0.005), but were not different between YT versus YU (p = 0.325). The abundances of actin and myosin heavy chain were greater in YT versus YU (p < 0.05) and OU (p < 0.001). Total sarcoplasmic protein concentrations were not different between groups. While proteomics indicated that marginal differences existed for individual myofibrillar and sarcoplasmic proteins between YT versus other groups, age-related differences were more prominent for myofibrillar proteins (YT = YU > OU, p < 0.05: 7 proteins; OU > YT = YU, p < 0.05: 11 proteins) and sarcoplasmic proteins (YT = YU > OU, p < 0.05: 8 proteins; OU > YT&YU, p < 0.05: 29 proteins). In summary, our data suggest that modest (~9%) myofibrillar protein packing (on a per mg muscle basis) was evident in the YT group. This study also provides further evidence to suggest that notable skeletal muscle proteome differences exist between younger and older humans. However, given that our n-sizes are low, these results only provide a preliminary phenotyping of the reported protein and proteomic variables.
Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS
Authors: Chen Shaopei , Zhuang Dachang
Published: January 2020

This paper takes the metro network of Guangzhou as a case study, and provides a quantitative analysis of the historical development of the network from 1999 to 2018. Particularly, the evolution of the topological structure of the Guangzhou Metro Network (GMN) is evaluated and characterized through the integration of geographic information system (GIS) and complex network analysis. The results show that: (1) The metro network of Guangzhou possesses the basic characteristics of small-world network, (2) with the development of GMN, the network complexity is increased and the spatial dispersion of the nodes tends to ease, but the average travel time and transfer rate continues to rise up, leading to the decreasing of the network transmission efficiency and the scattering of the nodes, (3) a good fault tolerance of the overall metro network of Guangzhou is revealed, but the spatial variance is observed, (4) the peak of degree centrality (DC) of the nodes is gradually moving northward along “Kecun Station–Guangzhou railway station–Jiahe Wanggang station”, while the peak of betweenness centrality (BC) is changing from “Kecun station” to “Jiahe Wanggang station”, and Jiahe Wanggang station has evolved into the most critical node in the current metro network of Guangzhou. In conclusion, this study should provide the scientific basis and significant decision-making support to the planning and operation management of GMN.
Transformational Collaboration for the SDGs: The Alianza Shire’s Work to Provide Energy Access in Refugee Camps and Host Communities
Authors: Moreno-Serna Jaime , Sánchez-Chaparro Teresa , Mazorra Javier , Arzamendi Ander , Stott Leda , Mataix Carlos
Published: January 2020

The potential for achieving transformation through partnerships is central to the 2030 Agenda for Sustainable Development. However, information on experiences that explore the processes that might generate systemic change is generally lacking. This article uses the Collaborative Value Creation (CVC) framework to analyze the transformational prospects of the Alianza Shire, the first multi-stakeholder partnership for humanitarian action in Spain. The partnership, which aims to develop innovative energy access solutions in refugee camps situated in the Shire region of northern Ethiopia is assessed from its creation in 2014 to the present with regard to four key partnership features: organizational engagement, resources and activities, partnership dynamics and impact. Our findings suggest that while the CVC framework is a useful tool for analyzing the evolution of a partnership to a transformative phase, additional information is required on the important role played by a partnership facilitator in assisting this process. This inquiry aims to build upon the CVC analysis by identifying and addressing some of the barriers faced by the Alianza Shire and other partnerships in attaining transformational outcomes and proposing two key enablers that can assist progression towards this: a facilitating organization that ensures the creation of collaborative shared value and an aspirational strategy for achieving significant systemic change.
Teaching Down to Earth—Service-Learning Methodology for Science Education and Sustainability at the University Level: A Practical Approach
Authors: Hernández-Barco Míriam , Sánchez-Martín Jesús , Blanco-Salas José , Ruiz-Téllez Trinidad
Published: January 2020

The Bologna Process and the European Higher Education area require the application of new active methodologies in the classroom that place the student at the center of his or her learning process. In the present work, we analyze the application of a Service-Learning (SL) methodology in the context of a Final Degree Dissertation (FDD) in the degree in Environmental Sciences at the University of Extremadura (Spain). The project deals with an isolated Kichwa community in Ecuador and involves the development of alternative science education materials for the capacitation of in-service science teachers. This paper evaluates how an FDD carried out according to Service-Learning (SL) principles can help in the acquisition of so-called “soft skills” and how these can be focused in the promotion of the sustainable development goal (SDG) knowledge and achievement. To this end, a qualitative study of the experience and a deep evaluation, followed by a final reflection, were carried out. According to the preliminary results, we can conclude that Higher Education should include SDGs in its teaching praxis and could do this successfully using the SL methodology.
A Novel Closed-Loop Supply Chain Network Design Considering Enterprise Profit and Service Level
Authors: Jiang Guanshuang , Wang Qi , Wang Ke , Zhang Qianyu , Zhou Jian
Published: January 2020

Increasing concerns for sustainable development have motivated the study of closed-loop supply chain network design from a multidimensional perspective. To cope with such issues, this paper presents a general closed-loop supply chain network comprising various recovery options and further formulates a multi-objective mixed-integer linear programming model considering enterprise profit and service level simultaneously. Within this model, market segmentation is also considered to meet real-world operating conditions. Moreover, an ε -constraint method and two interactive fuzzy approaches are applied to find a global optimum for this model together with the decisions on the numbers, locations, and capacities of the facilities, as well as the material flow through the network. Ultimately, numerical experiments are conducted to demonstrate the viability and effectiveness of both the proposed model and solution approaches.
Measuring and Integrating Risk Management into Green Innovation Practices for Green Manufacturing under the Global Value Chain
Authors: Sun Yingying , Bi Kexin , Yin Shi
Published: January 2020

How to solve the contradiction between economic growth and ecological environmental protection is a practical problem that should be solved urgently at present. The development of green technology in the manufacturing industry must rely on technology innovation. However, the process of implementing green innovation in the manufacturing industry is full of high uncertainty and risk. First, the green innovation risks were divided into global green R&D risk, global green manufacturing risk, global green marketing risk, and global green service risk from the perspective of the process. Then, this study established a management criteria system of green innovation risk identification in the manufacturing industry under the global value chain (GVC). Furthermore, three methods were applied to identify the green innovation risk of the manufacturing industry under the GVC. Finally, this paper put forward the countermeasures to the green innovation risk of the manufacturing industry under the GVC. The empirical research results of this paper are as follows: From the perspective of the green innovation process, four risks are classified in this study, namely, global green R&D risk, global green manufacturing risk, global green marketing risk, and global green service risk. Among the four stages of green innovation risk, green marketing risk is the highest, followed by green service risk, and green R&D risk and green manufacturing risk are the least. Global green service risk and green R&D risk can be reduced mainly through risk diversification and risk reduction. Global green manufacturing risk and green marketing risk can be reduced mainly through risk diversification and secondary through risk reduction.
Localizing the Sustainable Development Goals Through the Lens of Urban Resilience: Lessons and Learnings from 100 Resilient Cities and Cape Town
Authors: Croese Sylvia , Green Cayley , Morgan Gareth
Published: January 2020

Urban resilience is increasingly seen as essential to managing the risks and challenges arising in a globally changing, connected, and urbanized world. Hence, cities are central to achieving a range of global development policy commitments adopted over the past few years, ranging from the Paris Climate Agreement to the Sustainable Development Goals (SDGs). However, knowledge of the ways in which cities are going about implementing resilience or of how such efforts can practically contribute to the implementation of global agendas is still limited. This paper discusses the experience of cities that were members of the 100 Resilient Cities (100RC) network, an entity pioneered by The Rockefeller Foundation. It reviews the resilience strategies developed by 100RC members to show that 100RC cities are increasingly aligning their resilience work to global development policies such as the SDGs. It then draws on the case of the city of Cape Town in South Africa to illustrate the process of developing a resilience strategy through 100RC tools and methodologies including the City Resilience Framework (CRF) and City Resilience Index (CRI) and its alignment to the SDGs and reflects on lessons and learnings of Cape Town’s experience for the global city network-policy nexus post-2015.
Start Walking! How to Boost Sustainable Mode Choice—Psychological Measures to Support a Shift from Individual Car Use to More Sustainable Traffic Modes
Authors: Risser Ralf , Sucha Matus
Published: January 2020

Transport is one of the sectors that contributes to global warming. To tackle this issue, one of the most important tasks is to enhance/support a mode shift from individual car use to more sustainable traffic modes. To achieve this, psychology-based measures are needed. We need to understand what keeps people from walking, and what motivates people who do walk to do so, or to define the barriers and the preconditions that are needed for walking or cycling. These preconditions are perceived in a different way by different people. As the motivation of individual citizens differs greatly, the preconditions have to be targeted precisely. In this paper a scheme is presented that deals with this issue—i.e., to enhance/support a mode shift to sustainable traffic modes with the following steps: To identify and improve the preconditions for walking; to communicate information, and; to provide opportunities or incentives. As a theoretical background we use a classical marketing model presented by Philip Kotler, which has strong connections with communication and social psychology. Through the case of supporting sustainable traffic modes—the product which will be “sold”—we present the different steps of the model (information, product, communication, incentives, and distribution). With all aspects we emphasize that a holistic approach (a combination of all these aspects and steps) is necessary for a successful marketing process that in fact makes citizens “buy” the idea of walking (more). The main suggestion of this paper is to implement the steps suggested in it in the frame of a pilot project in a city or a province and then to evaluate. Evidence that the suggested measures work when applied appropriately and—as strongly underlined—in a concerted manner can only be achieved by trying in practice.
Nature-Based Solution for Reducing CO2 Levels in Museum Environments: A Phytoremediation Study for the Leonardo da Vinci’s “Last Supper”
Authors: Salvatori Elisabetta , Gentile Chiara , Altieri Antonella , Aramini Fabio , Manes Fausto
Published: January 2020

This work investigates the possibility of implementing a nature-based solution (NBS) based on the photosynthetic process of Laurus nobilis L. (common laurel), for reducing peak CO2 concentrations in an air-tight museum environment, namely the Refectory of the Santa Maria delle Grazie Church (Milan, Italy), home of Leonardo da Vinci’s painting “Last Supper”. The phytoremediation potential of laurel plants was evaluated at CO2 ≅ 1000 ppm under controlled environmental conditions. Furthermore, light-saturated net assimilation (Pnmax) was measured at two CO2 concentrations (380 and 1000 ppm) during the growing season. Steady-state gas exchanges were not affected by elevated CO2 in the short-term, while Pnmax was significantly increased, also showing higher values in spring and autumn, and a reduction during summer. Our estimated CO2 removal rates indicate that, in order to control visitors’ respiratory CO2 emissions in view of an increase in visitor numbers in the Refectory, a possible NBS in the form of an external greenhouse, connected to the HVAC system of the museum, should allocate from 58 to 112 young laurel plants, depending on their seasonal phytoremediation capacity. These results, although preliminary, allow to hypothesize the possibility of controlling CO2 indoors through a combination of traditional air-cleaning systems and a properly designed NBS, thus increasing the sustainability of air-tight museum environments.
How Are Wood and Non-Wood Forest Products Utilized in the Czech Republic? A Preliminary Assessment of a Nationwide Survey on the Bioeconomy
Authors: Purwestri Ratna C. , Hájek Miroslav , Šodková Miroslava , Jarský Vilém
Published: January 2020

The Czech forests occupy 33.7% of the total country area; thus, wood and non-wood forest products (NWFPs) are important resources for the country. To date, the country has not adopted a forest bioeconomy strategy. A forest bioeconomy is defined as all activities that relate to the forest ecosystem services (FES). This study aimed to provide an initial evaluation regarding the use of forest products and related factors, and to make recommendations on developing wood consumption and promoting other FES for the adoption of a forest bioeconomy strategy in the country. The research study was part of a nationwide survey in June 2019. An online panel of 1050 respondents aged 18–65 years old was recruited based on a quota sampling procedure. Wood products were the most preferred material for furniture (96.3%) and building materials (46.3%). In total, 38.6% of Czech residents used wood as a source of energy, mostly in the form of firewood. It is challenging to switch the practice from using fossil-based heating to wood boiler energy source. The further development of wood into products with a high added value is recommended. Picking mushrooms and berries were among the popular activities in relation to NWFPs. The promotion of wood and NWFPs is encouraged, starting with increasing awareness and knowledge of the strength of the forest-based sector as a renewable energy resource and the importance of FES, using different channels as sources of information.
Mapping the Roman Water Supply System of the Wadi el Melah Valley in Gafsa, Tunisia, Using Remote Sensing
Authors: Bachagha Nabil , Luo Lei , Wang Xinyuan , Masini Nicola , Moussa Tababi , Khatteli Houcine , Lasaponara Rosa
Published: January 2020

In recent years, very high-resolution satellite remote-sensing tools have been progressively used in archaeological prospecting to acquire information and improve documentation. Satellite remote sensing has also benefited from technical improvements, including better spectral and spatial resolution of sensors, which have facilitated the detection and discovery of unknown archaeological areas. This paper focuses on investigations conducted using multi-spectral satellite remote-sensing data of the ancient canal systems of the Wadi el Melah Valley (WMV) in southern Tunisia. The area used to be part of a huge military defense system along the desert border. This paper describes the use of GeoEye-1 and Ziyuan-3 satellite remote-sensing data to reveal ancient Roman canals, which were part of an advanced hydraulic system devised to capture runoff water and cope with the lack of water in the area. In general, this research provides new information on some essential sections of the Roman walled defense system Limes (Fossatum) in the southern part of the empire, where we study previously undetected sites.
Surface Dust and Aerosol Effects on the Performance of Grid-Connected Photovoltaic Systems
Authors: Roumpakias Elias , Stamatelos Tassos
Published: January 2020

A large number of grid-connected Photovoltaic parks of different scales have been operating worldwide for more than two decades. Systems’ performance varies with time, and an important factor that influences PV performance is dust and ambient aerosols. Dust accumulation has significant effects depending the region, and—on the other hand—understanding the role of absorption or scattering in particular wavelengths from aerosols is a challenging task. This paper focuses on performance analysis of a grid-connected PV system in Central Greece, aiming to study these effects. The methodology of analysis follows three directions, namely, PR computations, use of mathematical model’s prediction as reference value, and normalized efficiency calculation. These metrics are correlated with the levels of dust accumulation on PV panels’ surfaces and the ambient aerosol mass concentration. The results show that only heavily soiled surfaces have significant impact on PV performance and, particularly, a decrease of 5.6%. On the other hand, light or medium soiling have negligible impact on PV performance. On the other hand, the impact of ambient aerosol concentration levels on PV efficiency is more complex and requires further study. Aerosol scattering of different wavelengths can possibly affect PV efficiency, however, this fact may be related to the specific spectral response of PV cells.
Analysis on the Higher Education Sustainability in China Based on the Comparison between Universities in China and America
Authors: He Yong-Ming , Pei Yu-Long , Ran Bin , Kang Jia , Song Yu-Ting
Published: January 2020

To find and solve the problems existing in the development of higher education in China, the input–output, scale of higher education, students’ tuition and teachers’ income of Chinese and American universities are compared. The results show that the investment in top universities in China is similar to that in the United States, but the average student budget is much less, and the output is not comparable to that of American universities. The scale of higher education is much larger than that of the United States, and the growth rate is far more than demand. College tuition should be increased, with the absolute tuition only 5.93% of income, and relative tuition is 20.21% of that in the United States. College teachers are underpaid, earning only approximately 20% of what their peers earn in the United States. Therefore, for higher education sustainability, the paper puts forward the development direction of higher education in China, which is to control the expansion scale of colleges and universities, and to increase students’ tuition and teachers’ salary.
Perceived Organizational Performance in Recruiting and Retaining Employees with Respect to Different Generational Groups of Employees and Sustainable Human Resource Management
Authors: Gyurák Babeľová Zdenka , Stareček Augustín , Koltnerová Kristína , Cagáňová Dagmar
Published: January 2020

Organizational performance is considered as the essence of an industrial enterprises’ existence. However, in terms of performance, it is important not only to achieve results, but also maintain and develop the potential of an enterprise. When assessing performance, industrial management often omits the organizational success in managing human resources and developing human resources potential. The main aim of this paper is to present the research results focused on perceived organizational performance in recruiting and retaining employees with respect to different generations of employees in industrial enterprises. For the purposes of the analysis, the authors of the paper have designed a valid and reliable collection tool (research questionnaire). The research sample consisted of N = 1471 respondents, employees of industrial enterprises in the Slovak Republic. The research result shows that different generations of employees perceive differently selected issues of organizational performance. Testing the first research hypothesis has proved that there is a significant difference between employees from different generational groups in the perception of relationships among employees. The testing of the second research hypothesis confirmed that there is a statistically significant relationship between selected issues of perceived organizational performance.
Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems
Authors: Mahmoud Karar , Abdel-Nasser Mohamed , Mustafa Eman , M. Ali Ziad
Published: January 2020

Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.
Beyond the Indicators: Improving Science, Scholarship, Policy and Practice to Meet the Complex Challenges of Sustainability
Authors: Renn Ortwin , Chabay Ilan , van der Leeuw Sander , Droy Solène
Published: January 2020

Many teams have developed a wide range of numerical or categorical indicators of progress in the implementation of the SDG targets. But these indicators cannot identify why target goals have not been accomplished, whether or how they do or do not do justice to the social and cultural context in which they are applied, and how newly emerging social dynamics affect indicators. Nor do they provide means for resolving conflicting values and making balanced trade-offs. Our starting point in examining why we have not been successful in progressing towards sustainability is that the sustainability conundrum is primarily a societal, rather than an environmental problem. Our present emphasis is to maintain our way of life while minimizing its impact, hoping that such a minimization strategy would make the world more sustainable. Reducing for example the extent of pollution but keeping the same industries alive would not be sufficient for a transformation towards sustainability. Instead we should ask “How did we come to this point and what practices, in our societies and in our science, need to change to make progress towards sustainability?” To answer these questions, one needs to go much further back than usual in the history of western societies to identify the societal, scientific, technological and environmental co-evolutionary dynamics that have brought us to the current conundrum. And the fact that most societal challenges are of the “wicked” kind, as well as the need to decide among many societal options and many future pathways that may lead to positive results require that we seriously engage in using “Complex Systems” approaches. It is up to our scientific community to identify these pathways, and we need to move fast!
Competencies in Education for Sustainable Development: Emerging Teaching and Research Developments
Authors: Cebrián Gisela , Junyent Mercè , Mulà Ingrid
Published: January 2020

The 2030 Agenda for Sustainable Development clearly reflects the urgency to embed the principles of education for sustainable development (ESD) into all levels of education. ESD, understood as an integral part of quality education and all educational institutions, from preschool to higher education and in nonformal and informal education, can and should foster the development of sustainability competencies. This Special Issue entitled “Competencies in Education for Sustainable Development” responds to this urgency and the papers presented deliver recent developments in the field of ESD competencies. They focus on various perspectives: systematic literature reviews; curriculum developments; meta-analysis of the interrelation between sustainability content, competencies developed and pedagogical approaches used; testing and validation of evaluation tools and processes for sustainability competencies; and the development of conceptual models focused on different subject areas and educational levels (higher education, primary education, rural schools, external support services to schools, and extracurricular activities). The contributions indicate how the field of ESD competencies in the area of sustainability has become a major focus in recent years. Further research efforts need to be put into operationalizing sustainability competencies and developing tools to measure and evaluate students’ and educators’ competencies development.
3D Printed Personalized Corneal Models as a Tool for Improving Patient’s Knowledge of an Asymmetric Disease
Authors: Velázquez Jose S. , Cavas Francisco , Bolarín José M. , Alió Jorge L.
Published: January 2020

Additive manufacturing is a vanguard technology that is currently being used in several fields in medicine. This study aims to evaluate the viability in clinical practice of a patient-specific 3D model that helps to improve the strategies of the doctor-patient assistance. Data obtained from a corneal topographer were used to make a virtual 3D model by using CAD software, to later print this model by FDM and get an exact replica of each patient’s cornea in consultation. Used CAD and printing software were open-source, and the printing material was biodegradable and its cost was low. Clinic users gave their feedback by means of a survey about their feelings when perceiving with their senses their own printed cornea. There was 82 surveyed, 73.8% (9.74; SD: 0.45) of them considered that the model had helped them a lot to understand their disease, expressing 100% of them their intention of taking home the printed model. The majority highlighted that this new concept improves both quality and clinical service in consultation. Custom-made individualized printed models allow a new patient-oriented perspective that may improve the communication strategy from the ophthalmologist to the patient, easing patient’s understanding of their asymmetric disease and its later treatment.
Disformal Transformations in Modified Teleparallel Gravity
Authors: Golovnev Alexey , Guzmán María José
Published: January 2020

In this work, we explore disformal transformations in the context of the teleparallel equivalent of general relativity and modified teleparallel gravity. We present explicit formulas in components for disformal transformations of the main geometric objects in these theories such as torsion tensor, torsion vector and contortion. Most importantly, we consider the boundary term which distinguishes the torsion scalar from the Ricci scalar. With that we show for f ( T ) gravity that disformal transformations from the Jordan frame representation are unable to straightforwardly remove local Lorentz breaking terms that characterize it. However, we have shown that disformal transformations have interesting properties, which can be useful for future applications in scalar-torsion gravity models, among others.
SET Pulse Characterization and SER Estimation in Combinational Logic with Placement and Multiple Transient Faults Considerations
Authors: Paliaroutis Georgios Ioannis , Tsoumanis Pelopidas , Evmorfopoulos Nestor , Dimitriou George , Stamoulis Georgios I.
Published: January 2020

Integrated circuit susceptibility to radiation-induced faults remains a major reliability concern. The continuous downscaling of device feature size and the reduction in supply voltage in CMOS technology tend to worsen the problem. Thus, the evaluation of Soft Error Rate (SER) in the presence of multiple transient faults is necessary, since it remains an open research field. In this work, a Monte-Carlo simulation-based methodology is presented taking into consideration the masking mechanisms and placement information. The proposed SER estimation tool exploits the results of a Single Event Transient (SET) pulse characterization process with HSPICE to obtain an accurate assessment of circuit vulnerability to radiation. A new metric, called Glitch Latching Probability, which represents the impact of the masking effects on a SET, is introduced to identify gate sensitivity and, finally, experimental results on a set of ISCAS’ 89 benchmarks are presented.