bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt
Authors: Ibrahim Mahmoud A. , Al-Najafi Amenah
Published: November 2020

In this paper, we study and investigate the spread of the coronavirus disease 2019 (COVID-19) in Iraq and Egypt by using compartmental, logistic regression, and Gaussian models. We developed a generalized SEIR model for the spread of COVID-19, taking into account mildly and symptomatically infected individuals. The logistic and Gaussian models were utilized to forecast and predict the numbers of confirmed cases in both countries. We estimated the parameters that best fit the incidence data. The results provide discouraging forecasts for Iraq from 22 February to 8 October 2020 and for Egypt from 15 February to 8 October 2020. To provide a forecast of the spread of COVID-19 in Iraq, we present various simulation scenarios for the expected peak and its timing using Gaussian and logistic regression models, where the predicted cases showed a reasonable agreement with the officially reported cases. We apply our compartmental model with a time-periodic transmission rate to predict the possible start of the second wave of the COVID-19 epidemic in Egypt and the possible control measures. Our sensitivity analyses of the basic reproduction number allow us to conclude that the most effective way to prevent COVID-19 cases is by decreasing the transmission rate. The findings of this study could therefore assist Iraqi and Egyptian officials to intervene with the appropriate safety measures to cope with the increase of COVID-19 cases.
Biological Evaluation of Azetidine-2-One Derivatives of Ferulic Acid as Promising Anti-Inflammatory Agents
Authors: Drăgan Maria , Stan Cătălina Daniela , Iacob Andreea Teodora , Dragostin Oana Maria , Boancă Mihaela , Lupuşoru Cătălina Elena , Zamfir Carmen Lăcrămioara , Profire Lenuţa
Published: November 2020

The purpose of this study was to evaluate the in vivo biological potential of new azetidine-2-one derivatives of ferulic acid (6a–f). First, the in vivo acute toxicity of azetidine-2-one derivatives of ferulic acid on Swiss white mice was investigated and, based on the obtained results, it can be stated that the studied derivatives belong to compounds with moderate toxicity. The in vivo anti-inflammatory potential of these derivatives was determined in a model of acute inflammation induced by carrageenan in rats and in a chronic inflammation model induced in rats using the granuloma test. In the acute inflammation model, all the studied compounds had a maximum anti-inflammatory effect 24 h after administration, which suggests that these compounds may be classified, from a pharmacokinetic point of view, in the category of long-acting compounds. The most active compound in the series was found to be compound 6b. In the case of the chronic inflammation model, it was observed that the studied compounds (6a–f) reduced the formation of granulation tissue compared to the control group, having an intense effect of inhibiting the proliferative component. The most important inhibitory effect of inhibiting the proliferative component was recorded for compound 6b. Additionally, the investigation of liver function was performed by determining the serum levels of liver enzymes aspartate transaminase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and bilirubin (total and direct). The results showed that, in the series of azetidin-2-one derivatives, the liver enzymes concentration values were close to those recorded for the reference anti-inflammatories (diclofenac sodium and indomethacin) and slightly higher compared to the values for the healthy control group. At the end of the experiment, the animals were euthanized and fragments of liver, lung, and kidney tissue were taken from all groups in the study. These were processed for histopathological examination, and we noticed no major changes in the groups treated with the azetidine 2-one derivatives of ferulic acid compared to the healthy groups.
A Modified Geometrical Optical Model of Row Crops Considering Multiple Scattering Frame
Authors: Ma Xu , Liu Yong
Published: November 2020

The canopy reflectance model is the physical basis of remote sensing inversion. In canopy reflectance modeling, the geometric optical (GO) approach is the most commonly used. However, it ignores the description of a multiple-scattering contribution, which causes an underestimation of the reflectance. Although researchers have tried to add a multiple-scattering contribution to the GO approach for forest modeling, different from forests, row crops have unique geometric characteristics. Therefore, the modeling approach originally applied to forests cannot be directly applied to row crops. In this study, we introduced the adding method and mathematical solution of integral radiative transfer equation into row modeling, and on the basis of improving the overlapping relationship of the gap probabilities involved in the single-scattering contribution, we derived multiple-scattering equations suitable for the GO approach. Based on these modifications, we established a row model that can accurately describe the single-scattering and multiple-scattering contributions in row crops. We validated the row model using computer simulations and in situ measurements and found that it can be used to simulate crop canopy reflectance at different growth stages. Moreover, the row model can be successfully used to simulate the distribution of reflectances (RMSEs < 0.0404). During computer validation, the row model also maintained high accuracy (RMSEs < 0.0062). Our results demonstrate that considering multiple scattering in GO-approach-based modeling can successfully address the underestimation of reflectance in the row crops.
An Advanced Multipath Mitigation Method Based on Trend Surface Analysis
Authors: Wang Zhiren , Chen Wen , Dong Danan , Zhang Chenglong , Peng Yu , Zheng Zhengqi
Published: November 2020

Among various ways to eliminate the multipath effect in high-precision global navigation satellite system positioning, the multipath hemispherical map (MHM) is a typical multipath correction method based on spatial domain repeatability, which is suitable for not only static environments, but also some dynamic carriers, such as ships and aircraft. So, it has notable advantages and is widely used. The MHM method divides the sky into grids according to the azimuth and elevation angles of satellite, and calculates the average of the residuals within the grid points as its multipath calibration value. It is easy to implement, but it will inevitably lead to excessive or insufficient multipath correction in the grid. The trend surface analysis-based multipath hemispherical map (T-MHM) method makes up for this deficiency by performing trend surface analysis on the multipath spatial changes within the grid points. However, the effectiveness of T-MHM is limited and less capable of resisting noise interference due to the multicollinearity between the independent variables caused by the special spatial distribution of multipath sampling and the overfitting problem caused by ignoring the multipath anisotropy. Thus, we proposed an improved multipath elimination method named AT-MHM (advanced trend surface analysis-based multipath hemispherical model), which cautiously judges the occurrence of the above problems and gives corresponding solutions. This was extended to double-difference mode, which expands the scope of application. The performance of AT-MHM in GPS pseudorange multipath mitigation was verified on geodetic receiver and low-cost receiver in a strong multipath environment with high occlusion.
Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label
Authors: Wang Jiaxin , H. Q. Ding Chris , Chen Sibao , He Chenggang , Luo Bin
Published: November 2020

Image segmentation has made great progress in recent years, but the annotation required for image segmentation is usually expensive, especially for remote sensing images. To solve this problem, we explore semi-supervised learning methods and appropriately utilize a large amount of unlabeled data to improve the performance of remote sensing image segmentation. This paper proposes a method for remote sensing image segmentation based on semi-supervised learning. We first design a Consistency Regularization (CR) training method for semi-supervised training, then employ the new learned model for Average Update of Pseudo-label (AUP), and finally combine pseudo labels and strong labels to train semantic segmentation network. We demonstrate the effectiveness of the proposed method on three remote sensing datasets, achieving better performance without more labeled data. Extensive experiments show that our semi-supervised method can learn the latent information from the unlabeled data to improve the segmentation performance.
Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States
Authors: Tadesse Tsegaye , Hollinger David , Bayissa Yared , Svoboda Mark , Fuchs Brian , Zhang Beichen , Demissie Getachew , Wardlow Brian , Bohrer Gil , Clark Kenneth , Desai Ankur , Gu Lianhong , Noormets Asko , Novick Kimberly , Richardson Andrew
Published: November 2020

Monitoring drought impacts in forest ecosystems is a complex process because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.
PlaNet: A Neural Network for Detecting Transverse Aeolian Ridges on Mars
Authors: Nagle-McNaughton Timothy , McClanahan Timothy , Scuderi Louis
Published: November 2020

Transverse aeolian ridges (TARs) are unusual bedforms on the surface of Mars. TARs are common but sparse on Mars; TAR fields are small, rarely continuous, and scattered, making manual mapping impractical. There have been many efforts to automatically classify the Martian surface, but they have never explicitly located TARs successfully. Here, we present a simple adaptation of the off-the-shelf neural network RetinaNet that is designed to identify the presence of TARs at a 50-m scale. Once trained, the network was able to identify TARs with high precision (92.9%). Our model also shows promising results for applications to other surficial features like ripples and polygonal terrain. In the future, we hope to apply this model more broadly and generate a large database of TAR distributions on Mars.
Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods
Authors: Li Xinchuan , Luo Juhua , Jin Xiuliang , He Qiaoning , Niu Yun
Published: November 2020

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.
Tropical Cyclone Climatology from Satellite Passive Microwave Measurements
Authors: Yang Song , Bankert Richard , Cossuth Joshua
Published: November 2020

The satellite passive microwave (PMW) sensor brightness temperatures (TBs) of all tropical cyclones (TCs) from 1987–2012 have been carefully calibrated for inter-sensor frequency differences, center position fixing using the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) scheme, and application of the Backus–Gilbert interpolation scheme for better presentation of the TC horizontal structure. With additional storm motion direction and the 200–850 hPa wind shear direction, a unique and comprehensive TC database is created for this study. A reliable and detailed climatology for each TC category is analyzed and discussed. There is significant annual variability of the number of storms at hurricane intensity, but the annual number of all storms is relatively stable. Results based on the analysis of the 89 GHz horizontal polarization TBs over oceans are presented in this study. An eyewall contraction is clearly displayed with an increase in TC intensity. Three composition schemes are applied to present a reliable and detailed TC climatology at each intensity category and its geographic characteristics. The global composition relative to the North direction is not able to lead a realistic structure for an individual TC. Enhanced convection in the down-motion quadrants relative to direction of TC motion is obvious for Cat 1–3 TCs, while Cat 4–5 TCs still have a concentric pattern of convection within 200 km radius. Regional differences are evident for weak storms. Results indicate the direction of TC movement has more impact on weak storms than on Cat 4–5 TCs. A striking feature is that all TCs have a consistent pattern of minimum TBs at 89 GHz in the downshear left quadrant (DSLQ) for the northern hemisphere basins and in the downshear right quadrant (DSRQ) for the southern hemisphere basin, regarding the direction of the 200–850 hPa wind shear. Tropical depression and tropical storm have the minimum TBs in the downshear quadrants. The axis of the minimum TBs is slightly shifted toward the vertical shear direction. There is no geographic variation of storm structure relative to the vertical wind shear direction except over the southern hemisphere which shows a mirror image of the storm structure over the northern hemisphere. This study indicates that regional variation of storm structure relative to storm motion direction is mainly due to differences of the vertical wind shear direction among these basins. Results demonstrate the direction of the 200–850 hPa wind shear plays a critical role in TC structure.
Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery
Authors: Landuyt Lisa , Verhoest Niko E. C. , Van Coillie Frieke M. B.
Published: November 2020

The European Space Agency’s Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available.
An OSM Data-Driven Method for Road-Positive Sample Creation
Authors: Dai Jiguang , Li Chengcheng , Zuo Yuqiang , Ai Haibin
Published: November 2020

Determining samples is considered to be a precondition in deep network training and learning, but at present, samples are usually created manually, which limits the application of deep networks. Therefore, this article proposes an OpenStreetMap (OSM) data-driven method for creating road-positive samples. First, based on the OSM data, a line segment orientation histogram (LSOH) model is constructed to determine the local road direction. Secondly, a road homogeneity constraint rule and road texture feature statistical model are constructed to extract the local road line, and on the basis of the local road lines with the same direction, a polar constraint rule is proposed to determine the local road line set. Then, an iterative interpolation algorithm is used to connect the local road lines on both sides of the gaps between the road lines. Finally, a local texture self-similarity (LTSS) model is implemented to determine the road width, and the centerpoint autocorrection model and random sample consensus (RANSAC) algorithm are used to extract the road centerline; the road width and road centerline are used to complete the creation of the road-positive samples. Experiments are conducted on different scenes and different types of images to demonstrate the proposed method and compare it with other approaches. The results demonstrate that the proposed method for creating road-positive samples has great advantages in terms of accuracy and integrity.
Can GNSS-R Detect Abrupt Water Level Changes?
Authors: Tabibi Sajad , Francis Olivier
Published: November 2020

Global navigation satellite system reflectometry (GNSS-R) uses signals of opportunity in a bi-static configuration of L-band microwave radar to retrieve environmental variables such as water level. The line-of-sight signal and its coherent surface reflection signal are not separate observables in geodetic GNSS-R. The temporally constructive and destructive oscillations in the recorded signal-to-noise ratio (SNR) observations can be used to retrieve water-surface levels at intermediate spatial scales that are proportional to the height of the GNSS antenna above the water surface. In this contribution, SNR observations are used to retrieve water levels at the Vianden Pumped Storage Plant (VPSP) in Luxembourg, where the water-surface level abruptly changes up to 17 m every 4-8 h to generate a peak current when the energy demand increases. The GNSS-R water level retrievals are corrected for the vertical velocity and acceleration of the water surface. The vertical velocity and acceleration corrections are important corrections that mitigate systematic errors in the estimated water level, especially for VPSP with such large water-surface changes. The root mean square error (RMSE) between the 10-min multi-GNSS water level time series and water level gauge records is 7.0 cm for a one-year period, with a 0.999 correlation coefficient. Our results demonstrate that GNSS-R can be used as a new complementary approach to study hurricanes or storm surges that cause abnormal rises of water levels.
Reconstruction of Periodic Band Limited Signals from Non-Uniform Samples with Sub-Nyquist Sampling Rate
Authors: Wang Dongxiao , Liu Xiaoqin , Wu Xing , Wang Zhihai
Published: November 2020

Important state parameters, such as torque and angle obtained from the servo control and drive system, can reflect the operating condition of the equipment. However, there are two problems with the information obtained through the network from the control and drive system: the low sampling rate, which does not meet the sampling theorem and the nonuniformity of the sampling points. By combing equivalent sampling and nonuniform signal reconstruction theory, this paper proposes a reconstruction method for signal obtained from servo system in periodic reciprocating motion. Equivalent sampling combines the low rate and nonuniform samples from multiple periods into one single period, so that the equivalent sampling rate is far increased. Then, the nonuniform samples with high density are further resampled to meet the reconstruction conditions. This step can avoid the amplitude error in the reconstructed signal and increase the possibility of successful reconstruction. Finally, the reconstruction formula derived from basis theory is applied to recover the signal. This method has been successfully verified by the simulation signal of the robot swing process and the actual current signal collected on the robot arm testbed.
Classification of Variable Foundation Properties Based on Vehicle–Pavement–Foundation Interaction Dynamics
Authors: Kim Robin Eunju
Published: November 2020

The dynamic interaction between vehicle, roughness, and foundation is a fundamental problem in road management and also a complex problem, with their coupled and nonlinear behavior. Thus, in this study, the vehicle–pavement–foundation interaction model was formulated to incorporate the mass inertia of the vehicle, stochastic roughness, and non-uniform and deformable foundation. Herein, a quarter-car model was considered, a filtered white noise model was formulated to represent the road roughness, and a two-layered foundation was employed to simulate the road structure. To represent the non-uniform foundation, stiffness and damping coefficients were assumed to vary either in a linear or in a quadratic manner. Subsequently, an augmented state-space representation was formulated for the entire system. The time-varying equation governing the covariance of the response was solved to examine the vehicle response, subject to various foundation properties. Finally, a linear discriminant analysis method was employed for classifying the foundation types. The performance of the classifier was validated by test sets, which contained 100 cases for each foundation type. The results showed an accuracy of over 90%, indicating that the machine learning-based classification of the foundation had the potential of using vehicle responses in road managements.
Blade Rub-Impact Fault Identification Using Autoencoder-Based Nonlinear Function Approximation and a Deep Neural Network
Authors: Prosvirin Alexander E. , Piltan Farzin , Kim Jong-Myon
Published: November 2020

A blade rub-impact fault is one of the complex and frequently appearing faults in turbines. Due to their nonlinear and nonstationary nature, complex signal analysis techniques, which are expensive in terms of computation time, are required to extract valuable fault information from the vibration signals collected from rotor systems. In this work, a novel method for diagnosing the blade rub-impact faults of different severity levels is proposed. Specifically, the deep undercomplete denoising autoencoder is first used for estimating the nonlinear function of the system under normal operating conditions. Next, the residual signals obtained as the difference between the original signals and their estimates by the autoencoder are computed. Finally, these residual signals are used as inputs to a deep neural network to determine the current state of the rotor system. The experimental results demonstrate that the amplitudes of the residual signals reflect the changes in states of the rotor system and the fault severity levels. Furthermore, these residual signals in combination with the deep neural network demonstrated promising fault identification results when applied to a complex nonlinear fault, such as a blade-rubbing fault. To test the effectiveness of the proposed nonlinear-based fault diagnosis algorithm, this technique is compared with the autoregressive with external input Laguerre proportional-integral observer that is a linear-based fault diagnosis observation technique.
Digital Cultural Heritage Preservation in Art Painting: A Surface Roughness Approach to the Brush Strokes
Authors: Mironova Anna , Robache Frederic , Deltombe Raphael , Guibert Robin , Nys Ludovic , Bigerelle Maxence
Published: November 2020

There is a growing interest in cultural heritage preservation. The notion of HyperHeritage highlights the creation of new means of communication for the perception and data processing in cultural heritage. This article presents the Digital Surface HyperHeritage approach, an academic project to identify the topography of art painting surfaces at the scale at which the elementary information of sensorial rendering is contained. High-resolution roughness and imaging measurement tools are then required. The high-resolution digital model of painted surfaces provides a solid foundation for artwork-related information and is a source of many potential opportunities in the fields of identification, conservation, and restoration. It can facilitate the determination of the operations used by the artist in the creative process and allow art historians to define, for instance, the meaning, provenance, or authorship of a masterpiece. The Digital Surface HyperHeritage approach also includes the development of a database for archiving and sharing the topographic signature of a painting.
Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models
Authors: Rezapour Mahdi , Ksaibati Khaled
Published: November 2020

With tremendous associated economic and social costs of crashes, researchers have been trying not only to identify the factors affecting crashes, but also to estimate those coefficients in the most accurate ways. Estimating model coefficients without accounting for a correct distribution would result in biased and erroneous results. This risk especially holds true when modeling skewed equivalent property damage only (EPDO) crashes with a preponderance of zeroes. The distribution of EPDO is known for not being modeled with known distributions such as Poisson or negative binomial distributions. This issue is highlighted in particular for a mountainous state like Wyoming that has very low traffic levels and a severely high crash rate. In addition, we included barriers in the model that did not experience any crashes but did suffer from being under-designed by geometric architects, thereby adding to the number of zero count observations. Various models with different distributional characteristics were considered and compared in this study. Comparisons were not just made across models in terms of their goodness of fit, but the estimated coefficients were also compared to see the impact of considering the wrong distributional assumptions on model parameter estimates. As the objectives of this study are to implement the identified results for optimization purposes and locate hazardous locations that could host future crashes, the results highlight accurate model estimations and the consequences of a failure to account for the right distributions. After conducting different goodness-of-fit measures, a hurdle model was proposed in this study to accommodate observations with zero crashes, and to account for a sparse distribution of EPDO crashes in the state of Wyoming. For the hurdle model, binary logistic regression was used to account for observations with zero crashes, while the negative binomial method was considered for non-zero observations. The findings of this study have direct implications on the allocation of limited funds for policymakers in Wyoming, as optimization could be conducted on the geometric characteristics of traffic barriers in future studies.
The Effects of Fare-Free Public Transport: A Lesson from Frýdek-Místek (Czechia)
Authors: Štraub Daniel
Published: November 2020

The fare-free public transport policy (FFPT), the characteristic feature of which is abolishing fares in public transport, influences the transport system in favour of one means of transport. By joining the academic disputes over the outcomes of the FFPT policy, the aim of this paper is to understand the effects of the FFPT on overall mobility strategies and on the dynamics of the transport system where the policy is implemented. To do so, the paper is analysing a concrete example of the FFPT practice in Frýdek-Místek (Czechia) by conducting surveys with the residents and interviews with the city planning authorities. The results show that the FFPT influences mobility strategies and dynamics in the given transport system, which imprints in an increased use of public transport. However, the FFPT does not represent a universal tool of urban and transport planning and its implementation into practice should take place in synergy with other transport (dis)incentives.
Assessing Importance of Disaster Preparedness Factors for Sustainable Disaster Risk Management: The Case of the Slovak Republic
Authors: Titko Michal , Ristvej Jozef
Published: November 2020

Regarding the prognoses of disasters in the future connected with climate change, disaster risk management (DRM) is becoming one of the decisive elements of sustainable development. The possibility of involving the general public to DRM implementation is, currently, a frequently discussed topic. In particular, population preparedness for the involvement of the public is questionable and, therefore, the understanding of the conditions that facilitate public preparedness for disasters is decisive. This article presents the results of research that investigates the factors affecting (1) the objective preparedness of the population, as well as (2) the subjective perception of our preparedness for disasters. The statistical analysis discovered that both sides of the public’s preparedness depended especially on their experience with disasters, the awareness of the possible risks and appropriate procedures to solve situations, and the economic potential of the households. The results emphasize the need to support the process of increasing the awareness of risks and the possible preventive procedures that can be carried out before disasters by the public, including the more economically vulnerable groups. In this area, the collaboration of the responsible authorities and general public is very desirable. Therefore, our study and its results can serve as a support for creating the DRM policies and sustainable development.
Abandonment or Regeneration and Re-Use? Factors Affecting the Usage of Farm Premises in Different Social Spaces of the Rural
Authors: Navrátil Josef , Krejčí Tomáš , Martinát Stanislav , Pícha Kamil , Klusáček Petr , Škrabal Jaroslav , Osman Robert
Published: November 2020

Nearly every village in Central and Eastern European countries with heavily collectivized agriculture has its collective farm premises that encompass substantial parts of the village area, were built in the sixties, and now are unable to be used in former ways. The aim of the paper is to identify indicators that are relevant for spatial disparities in the utilization of agricultural premises thirty years after the fall of the Iron Curtain. The location and its area of all pre-1989 rural farm premises within two NUTS 3 regions of the Czech Republic with its current uses were identified, and differences in present uses were tested against agri-natural and socio-economic characteristics (of the municipalities where rural farm premises are located) obtained from national databases. From a global point of view, socio-economic characteristics of municipalities were found to be exceedingly more important than agri-natural—thus, changing of uses is rather dependent on socio-economic context than on geographical preconditions of agriculture. Surprisingly, agricultural use or re-use can be primarily found in municipalities not suitable for intensive agriculture located in the fodder crops and potatoes areas of agricultural production with the highest shares of permanent grassland on agricultural land. On the other hand, areas with the best preconditions for agriculture tend to re-use former farm premises for non-agricultural production.
YouTube Dominance in Sustainability of Gaining Knowledge via Social Media in University Setting—Case Study
Authors: Černá Miloslava , Borkovcová Anna
Published: November 2020

The study deals with sustainability of social software applications in a university setting focusing primarily on the YouTube platform. The aim of this paper is to identify the potential of YouTube media in serving as a supportive educational tool. To meet the objective, researchers carried out an exploratory study based on mixed quantitative–qualitative methods. A mental knowledge model was designed as a starting point where crucial aspects of gaining knowledge were visualized. Sustainability of social media was analyzed in three defined sections encompassing three areas reflecting the needs of the educational process: social media as a tool of communication, repository of study materials and a tool for testing. The affective learning domain was highlighted in the study; one of the key explored categories was the category of satisfaction as a students’ motivation mover. Based on the findings, YouTube as an absolute winner was consequently analyzed in detail, focusing on three areas: satisfaction, kinds of activities on this platform and spent time. The discussion raises questions on pitfalls of social media utilization; this chapter also brings the latest experience from the time hit by coronavirus and related quarantine which showed incontestable benefits of social media in education and proved their irreplaceable role.
Risk Influence Analysis Assessing the Profitability of Large Photovoltaic Plant Construction Projects
Authors: Serrano-Gomez Luis , Muñoz-Hernandez Jose Ignacio
Published: November 2020

The global energy system is in a phase of change for power generation technologies which involve traditional fossil fuel-based technologies to renewable energy-based systems, thanks to lower construction costs, mainly for photovoltaic energy, and changes in countries’ energy policies. In the case of Spain, both factors have led to a reactivation of renewable technologies, which can be found from the data on requests for access and connection to the electricity transmission network that are being processed in Red Eléctrica de España (REE). The requests that were granted access to the network exceeded 100 GW of power in November 2019 alone, and the companies which made the requests must commence electricity production by 2025. During the early stage of approval considerations, it is necessary to carry out an influence study of the risks that can already be identified, as this would enable determining the effects of these risks on the project’s main financial parameters. Based on a risk identification for similar prior projects, experts are typically asked to make their judgments on the influence of such risks on the main economic variables of a project, focusing on the project’s cost, time, and scope. By applying the fuzzy sets, these judgments can be transformed into triangular values that, through Monte Carlo simulation, allow us to assess the influence of these risks on the main financial parameters: the net present value (NPV), internal rate of return (IRR), and payback (PB); as a result of obtaining these parameters, a response to project risks can be planned. To check the functionality of the model, it was applied to a case study involving a construction project for a 250 MW photovoltaic plant located in Murcia (Spain). The application of this methodology allowed us to determine which evaluation criteria are most appropriate based on the philosophy of the PMO (Project Management Office) and the data that were obtained.
Prioritizing Agricultural Patches for Reforestation to Improve Connectivity of Habitat Conservation Areas: A Guide to Grain-to-Green Project
Authors: Ren Zhouqiao , Zhan Wanxin , Yue Qiaobing , He Jianhua
Published: November 2020

Landscape connectivity can largely affect the level of biodiversity and it is a key concern in conservation planning. Considering that protected areas (PAs) may become functionally isolated “islands” under rapid land-use change, there is an urgent need to expand and connect protected areas to prevent further biodiversity loss and improve PAs effectiveness. The Grain-to-Green Project (GTGP) is the largest reforestation program worldwide with the aim of controlling water and soil loss; however, the opportunities for connectivity gains through GTGP have been widely ignored. Here we provided a three-stage hierarchical framework incorporating soil erosion analysis, cropland suitability analysis and network analysis to prioritize agricultural patches for reforestation under the scheme of GTGP. The potential reforestation patches were identified in the first two stages. Then, four different GTGP strategies were designed, and a set of network metrics were used to determine the best strategy and prioritize patches that significantly enhance PAs connectivity. A typical GTGP region, Wanzhou district (Southwest China), was taken as the study area. We found that: (1) the agricultural patches with high reforestation suitability cover an area of c. 40 km2 (1% of the study area); and (2) the efficiency of GTGP strategies varies by species, species with intermediate and high mobility benefit more from a strategy that continuously adds reforestation patches close to PAs, while for species with low dispersal ability, the amount of patches added should also be taken into account to decide the appropriate strategy. We conclude that our framework can provide guidance to restore PAs connectivity with limited land resources in the context of GTGP.
A Case Study of Applying Open Educational Practices in Higher Education during COVID-19: Impacts on Learning Motivation and Perceptions
Authors: Zhang Xiangling , Tlili Ahmed , Huang Ronghuai , Chang Tingwen , Burgos Daniel , Yang Junfeng , Zhang Jiacai
Published: November 2020

Open Educational Resources (OER) have been researched for a long time in the open education field. Researchers are now shifting their focus from resources to practices for delivering open education, an area called Open Educational Practices (OEP). However, there is little information in the related literature regarding the design of an OEP-based course or the impact of these types of courses. Therefore, this study designs a new OEP-based course at a public university for teaching family education during the COVID-19 pandemic. It also investigates its impact on learning motivation and teachers’ perceptions. In this context, a practical pilot experiment using both qualitative and quantitative methods was conducted. Specifically, 36 learners participated in this experiment. The obtained findings highlight: (1) an innovative design framework for OEP-based courses that teachers can refer to in their contexts; (2) that learners had a high motivation level in terms of knowledge achievements, individual connection and engagement when taking the OEP-based course; and (3) several advantages and challenges of the OEP-based course from the teacher’s and learners’ perspectives. For instance, the teacher reported the fear of losing control over the learning process when applying OEP. The findings of this paper can help researchers and educators in adopting OEP in higher education especially in times of crises, as well as increase the sustainability of OEP, hence contributing to open education development.
Sustainable Business Models–Canvas for Sustainability, Evaluation Method, and Their Application to Additive Manufacturing in Aircraft Maintenance
Authors: Cardeal Gonçalo , Höse Kristina , Ribeiro Inês , Götze Uwe
Published: November 2020

The topic of sustainable business models is growing in literature and in the industry, driving companies to search for opportunities to improve their impact on the three pillars of sustainability—profit, people, and planet (economic, social, and environmental). However, the process of developing sustainable business models is often complex, due to conflicting objectives from the three dimensions of sustainability. This paper presents a procedure model that supports the design and assessment of business models with a sustainable perspective, by integrating a new business model canvas for sustainability (BMCS) and an evaluation method to assess it. A comprehensive assessment is proposed, performed in a life cycle perspective. The proposed model is applied and validated with a real case study, based on a new business model for an aircraft maintenance, repair, and overhaul company. The case is based on shifting from traditional maintenance, repair, and overhaul activities to adopting additive manufacturing as an activity that allows manufacturing optimized spare parts with benefits for the costumer. The results show the application of the procedure model on a specific case study, as well as the potential of additive manufacturing as a driver for more sustainable business models in the aircraft maintenance sector.
Family Coping Strategies During Finland’s COVID-19 Lockdown
Authors: Salin Milla , Kaittila Anniina , Hakovirta Mia , Anttila Mari
Published: November 2020

During spring 2020, the COVID-19 pandemic and global lockdowns fundamentally changed families’ everyday lives. This study aims to examine how families with children coped during the COVID-19 lockdown in Finland and what kind of coping strategies they developed. An online survey including both qualitative and quantitative questions was conducted between April and May 2020 to gather Finnish families’ experiences during the COVID-19 lockdown. In this study, we focus on information from one open-ended question, and Huston’s social-ecological theory was used as an analytical framework. The results showed that Finnish families employed coping strategies on three levels: macroenvironmental, relationship, and individual. This supports the argument that to better understand families’ coping strategies, the macrosocietal environment surrounding families, their relationships, and the interactions between family members, as well as individual decisions and attitudes, should be taken into account.
Conceptualizing Audit Fatigue in the Context of Sustainable Supply Chains
Authors: Khalid Muhammad Kamran , Agha Mujtaba Hassan , Shah Syed Tasweer Hussain , Akhtar Muhammad Naseer
Published: November 2020

Organizations rely heavily on audits and compliance related activities to prove their competency, credibility, and firm performance. Sustainability audits encompass entire supply chains and are very complex due to, firstly, the global nature of supply chains and, secondly, the expansive scope of sustainability, which may include financial, manufacturing, social, and environmental audits. Adding to this dilemma is the absence of a consensus on standards related to sustainability, resulting in differences, variations, and multiple interpretations. While the frequency, complexity, and scope of audits has increased, unfortunately so has the incident of audit fraud, which has seen increasing media coverage in recent times, often implicating major multinationals and their supply chains. We posit that this trend of increasing audit activity is causing “audit fatigue”, which, in turn, may influence the audit outcome, i.e., either audit fraud or a clean audit. This study proposes that audit fatigue is a genuine issue faced by organizations and needs to be conceptualized.
Development of Renewable Energy in China, USA, and Brazil: A Comparative Study on Renewable Energy Policies
Authors: Muhammed Gaafar , Tekbiyik-Ersoy Neyre
Published: November 2020

Countries differ in terms of their socio-economic development, population growth, and energy consumption. Many countries still depend on conventional energy to supply enough energy source for their demand, while some have made considerable progress in making the transition to renewable/sustainable energy sources. Owing to the increasing demand and drawbacks of conventional energy sources, policies can play a major role in encouraging and increasing the uptake of renewable energy (RE) technologies. In this paper, a general overview on the RE activities in the three leading countries—China, Brazil, and the United States of America (USA)—is presented. Moreover, a comparative analysis on the implementations of the RE support policies is conducted. The linear regression analysis technique is applied to develop several models for the three countries in order to investigate the effect of different policies on RE. The main contributions of this study are establishing a link between RE support policies and RE development (in terms of the installed capacity) in the three countries under study, and providing models that can be used in estimating RE development using RE policy data. In addition to this, some models are developed to investigate the relationship between RE installed capacity and the patents. The linear regression analyses suggest that RE policies promote the development of RE installed capacity in the three countries in different proportions. For example, it is found that each additional wind policy will increase the RE wind capacity in China, Brazil, and the USA by 1.63, 0.689, and 1.576 GW, respectively. Moreover, the economic instruments turn out to be more effective in promoting the RE installed capacity in the USA and Brazil, while the policy support and regulatory instruments are the most influential policy categories in China. Furthermore, the linear regression analyses indicate the existence of a positive significant relation between the number of patents and the total RE installed capacity in the three countries.