bluuebunny/crossref_metadata_2025 huggingface dataset:
Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning
Authors: Qin Shengwu , Guo Xu , Sun Jingbo , Qiao Shuangshuang , Zhang Lingshuai , Yao Jingyu , Cheng Qiushi , Zhang Yanqing
Published: September 2021

Using convolutional neural network (CNN) methods and satellite images for landslide identification and classification is a very efficient and popular task in geological hazard investigations. However, traditional CNNs have two disadvantages: (1) insufficient training images from the study area and (2) uneven distribution of the training set and validation set. In this paper, we introduced distant domain transfer learning (DDTL) methods for landslide detection and classification. We first introduce scene classification satellite imagery into the landslide detection task. In addition, in order to more effectively extract information from satellite images, we innovatively add an attention mechanism to DDTL (AM-DDTL). In this paper, the Longgang study area, a district in Shenzhen City, Guangdong Province, has only 177 samples as the landslide target domain. We examine the effect of DDTL by comparing three methods: the convolutional CNN, pretrained model and DDTL. We compare different attention mechanisms based on the DDTL. The experimental results show that the DDTL method has better detection performance than the normal CNN, and the AM-DDTL models achieve 94% classification accuracy, which is 7% higher than the conventional DDTL method. The requirements for the detection and classification of potential landslides at different disaster zones can be met by applying the AM-DDTL algorithm, which outperforms traditional CNN methods.
Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence
Authors: de Vries Robin , Egger Matthias , Mani Thomas , Lebreton Laurent
Published: September 2021

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls.
Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China
Authors: Wang Yanjun , Wang Mengjie , Huang Bo , Li Shaochun , Lin Yunhao
Published: September 2021

At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.
Impacts of Saline-Alkali Land Improvement on Regional Climate: Process, Mechanisms, and Implications
Authors: Yu Lingxue , Yang Jiuchun , Bu Kun , Liu Tingxiang , Jiao Yue , Li Guangshuai , Pu Luoman , Zhang Shuwen
Published: September 2021

Studying land use change and its associated climate effects is important to understand the role of human activities in the regulation of climate systems. By coupling remote sensing measurements with a high-resolution regional climate model, this study evaluated the land surface changes and corresponding climate impact caused by planting rice on saline-alkali land in western Jilin (China). Our results showed that paddy field expansion became the dominant land use change in western Jilin from 2015 to 2019, 25% of which was converted from saline-alkali land; this percentage is expected to increase in the near future. We found that saline-alkali land reclamation to paddy fields significantly increased the leaf area index (LAI), particularly in July and August, whereas it decreased albedo, mainly in May and June. Our simulation results showed that planting rice on saline-alkali land can help decrease the air temperature and increase the relative humidity. The temperature and humidity effects showed different magnitudes during the growing season and were most significant in July and August, followed by September and June. The nonradiative process, rather than the radiative process, played a dominant role in regulating the regional climate in this case, and the biophysical competition between evapotranspiration (ET) and albedo determined the temperature and relative humidity response differences during the growing season.
Variations in the Effects of Landscape Patterns on the Urban Thermal Environment during Rapid Urbanization (1990–2020) in Megacities
Authors: Ye Haipeng , Li Zehong , Zhang Ninghui , Leng Xuejing , Meng Dan , Zheng Ji , Li Yu
Published: September 2021

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.
Spatial Variations in Terrestrial Water Storage with Variable Forces across the Yellow River Basin
Authors: Zhou Meilin , Wang Xiaolei , Sun Lin , Luo Yi
Published: September 2021

Terrestrial water storage (TWS) variations are a result of the interconnected impact of various variables including climate, hydrology, ecology, and anthropogenic activities. Previous studies have indicated that climate factors (e.g., precipitation and potential evapotranspiration), vegetation restoration, and water withdrawals (irrigational and industrial water use) are the major determinants of TWS depletion across the Yellow River Basin (YRB). However, few studies have provided explicit information about the main forcing variables that determine spatiotemporal variations in TWS and the synergies among these factors. This study explored the explicit understanding of hydro-climatic and socio-ecological determinants and the key interacting processes that affected the TWS variations across the Yellow River Basin in northern China. The multivariate adaptive regression splines model was employed to establish the relationship function of the long-term trends for the dependent (TWS) and independent (explanatory) variables consisting of normalized difference vegetation index (NDVI), hydro-climate, and human water withdrawal. The long-term trends estimated from the MARS model reproduced the ones calculated by Gravity Recovery and Climate Experiment gravity satellites, with a determination coefficient (R2) of 0.83 and a mean absolute error (MAE) of 1.2 mm. The results showed that precipitation, minimum temperature, runoff, base flow, water withdrawal for electricity, and NDVI were the main drivers of the spatiotemporal variations in the TWS, of which minimum temperature and runoff played a considerable role in TWS variations through the interplay with other variables. The critical values of the trend for interactive variables, which could alter the acting direction of the synergy on the TWS, were also estimated. In view of the connotation of interactive variables, we suggested that spatiotemporal variations in TWS resulted from the coupling of the hydrological energy system, hydrological ecosystem, and hydrological system in the YRB, of which the hydrological system plays the most significant role, followed by the hydrological ecosystem.
Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy
Authors: Xue Jie , Anderson Martha C. , Gao Feng , Hain Christopher , Yang Yun , Knipper Kyle R. , Kustas William P. , Yang Yang
Published: September 2021

Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products.
EddyGraph: The Tracking of Mesoscale Eddy Splitting and Merging Events in the Northwest Pacific Ocean
Authors: Tian Fenglin , Li Zhijiao , Yuan Zhonghao , Chen Ge
Published: September 2021

This paper proposes an algorithm named EddyGraph for tracking mesoscale eddy splitting and merging events. Twenty-seven years (January 1993–December 2019) of sea level anomaly (SLA) data are analyzed in the Northwest Pacific Ocean (105°E–165°W, 0°N–60°N). First, we propose a multilevel eddy identification method based on SLA to obtain an eddytree data set, representing a spatial topological tree structure of closed SLA contours with mononuclear eddies, multicore eddies and eddy seeds as the leaf nodes and eddygroups (reflecting the spatial topological relationship among eddies) as the intermediate nodes. The EddyGraph tracking algorithm is applied to the eddytree data set, which results in eddy-directed acyclic graphs (Eddy-DAGs). Only eddies contained within a common eddygroup are tracked as sources in merging events or sinks in splitting events. Furthermore, we extract typical splitting and merging events and composite the sea surface temperature anomalies (SSTAs) inside the eddygroups and eddies during these events. The results confirm that merging eddies in the same eddygroup degenerate into a single eddy and that a splitting eddy evolves into eddies within the same parent eddygroup. Moreover, we match a merging event of cyclonic eddies with in situ data of both drifters and loopers in Lagrangian trajectories. Finally, we present EddyGraph, a data set of mesoscale eddy tracking in the Northwest Pacific Ocean (105°E–165°W, 0°N–60°N).
An Integration of UAV-Based Photogrammetry and 3D Modelling for Rockfall Hazard Assessment: The Cárcavos Case in 2018 (Spain)
Authors: Gallo Ilenia G. , Martínez-Corbella Mónica , Sarro Roberto , Iovine Giulio , López-Vinielles Juan , Hérnandez Mario , Robustelli Gaetano , Mateos Rosa María , García-Davalillo Juan Carlos
Published: September 2021

An example of the combined use of UAV photogrammetry and rockfall numerical simulation is described. A case of fragmental rockfall occurred on 17 November 2018 in Cárcavos, a site located in the Spanish municipality of Ayna (Albacete). The event caused a great social alarm as some infrastructure was affected. By using Unmanned Aerial Vehicle (UAV) photogrammetry, a high-resolution 3D model has been generated from point cloud data, and distribution and size of the fragmented rocks (more than 600 boulders) determined. The analysis has been performed through numerical simulations to: (1) reproduce the paths followed by the real blocks; and (2) estimate the speed and energy of the blocks, together with their heights, impacts and stopping points. Accordingly, source areas have been identified, including the potential source areas and unstable blocks on the slope. In addition, the exposed elements at risk (buildings, facilities, infrastructures, etc.) have been identified, and the effectiveness of mitigation measures against future events evaluated.
Difference Curvature Multidimensional Network for Hyperspectral Image Super-Resolution
Authors: Zhang Chi , Zhang Mingjin , Li Yunsong , Gao Xinbo , Qiu Shi
Published: September 2021

In recent years, convolutional-neural-network-based methods have been introduced to the field of hyperspectral image super-resolution following their great success in the field of RGB image super-resolution. However, hyperspectral images appear different from RGB images in that they have high dimensionality, implying a redundancy in the high-dimensional space. Existing approaches struggle in learning the spectral correlation and spatial priors, leading to inferior performance. In this paper, we present a difference curvature multidimensional network for hyperspectral image super-resolution that exploits the spectral correlation to help improve the spatial resolution. Specifically, we introduce a multidimensional enhanced convolution (MEC) unit into the network to learn the spectral correlation through a self-attention mechanism. Meanwhile, it reduces the redundancy in the spectral dimension via a bottleneck projection to condense useful spectral features and reduce computations. To remove the unrelated information in high-dimensional space and extract the delicate texture features of a hyperspectral image, we design an additional difference curvature branch (DCB), which works as an edge indicator to fully preserve the texture information and eliminate the unwanted noise. Experiments on three publicly available datasets demonstrate that the proposed method can recover sharper images with minimal spectral distortion compared to state-of-the-art methods. PSNR/SAM is 0.3–0.5 dB/0.2–0.4 better than the second best methods.
PUMA Applied to Time Delay Estimation for Processing GPR Data over Debonded Pavement Structures
Authors: Tchana Tankeu Bachir , Baltazart Vincent , Wang Yide , Guilbert David
Published: September 2021

In this paper, principal-singular-vector utilization for modal analysis (PUMA) was adapted to perform time delay estimation on ground-penetrating radar (GPR) data by taking into account the shape of the transmitted GPR signal. The super-resolution capability of PUMA was used to separate overlapping backscattered echoes from a layered pavement structure with some embedded debondings. The well-known root-MUSIC algorithm was selected as a benchmark for performance assessment. The simulation results showed that the proposed PUMA performs very well, especially in the case where the sources are totally coherent, and it requires much less computational time than the root-MUSIC algorithm.
Comparative Analysis of PM2.5 and O3 Source in Beijing Using a Chemical Transport Model
Authors: Wen Wei , Shen Song , Liu Lei , Ma Xin , Wei Ying , Wang Jikang , Xing Yi , Su Wei
Published: September 2021

For many years, Beijing has suffered from severe air pollution. At present, fine particulate matter (PM2.5) pollution in the winter and ozone (O3) pollution in the summer constitute serious environmental problems. In this study, the combination of a comprehensive air quality model with particulate matter source apportionment technology (CAMx-PAST) and monitoring data was used for the high-spatial resolution source apportionment of secondary inorganic components (SNA: SO42−, NO3−, and NH4+) in PM2.5; their corresponding precursor gases (SO2, NO2, and NH3); and O3 in the winter and summer over Beijing. Emissions from residents, industry, traffic, agriculture, and power accounted for 54%, 25%, 14%, 5%, and 2% of PM2.5 in the winter, respectively. In the summer, the emissions from industry, traffic, residents, agriculture, and power accounted for 42%, 24%, 20%, 10%, and 4% of PM2.5, respectively. The monthly transport ratio of PM2.5 was 27% and 46% in the winter and summer, respectively. The regional transport of residential and industrial emissions accounted for the highest proportion of PM2.5. The regional transport of emissions had a significant effect on the SO42− and NO3− concentrations, whereas SO2 and NO2 pollution were mainly affected by local emissions, and NH4+ and NH3 were mainly attributed to agricultural emissions. Industrial and traffic sources were two major emission sectors that contributed to O3 pollution in Beijing. The monthly transport ratios of O3 were 31% and 65% in the winter and summer, respectively. The high-spatial resolution regional source apportionment results showed that emissions from Langfang, Baoding, and Tangshan had the greatest impact on Beijing’s air pollution. This work’s methods and results will provide scientific guidance to support the government in its decision-making processes to manage the PM2.5 and O3 pollution issues.
Moving Car Recognition and Removal for 3D Urban Modelling Using Oblique Images
Authors: Yang Chong , Zhang Fan , Gao Yunlong , Mao Zhu , Li Liang , Huang Xianfeng
Published: September 2021

With the progress of photogrammetry and computer vision technology, three-dimensional (3D) reconstruction using aerial oblique images has been widely applied in urban modelling and smart city applications. However, state-of-the-art image-based automatic 3D reconstruction methods cannot effectively handle the unavoidable geometric deformation and incorrect texture mapping problems caused by moving cars in a city. This paper proposes a method to address this situation and prevent the influence of moving cars on 3D modelling by recognizing moving cars and combining the recognition results with a photogrammetric 3D modelling procedure. Through car detection using a deep learning method and multiview geometry constraints, we can analyse the state of a car’s movement and apply a proper preprocessing method to the geometrically model generation and texture mapping steps of 3D reconstruction pipelines. First, we apply the traditional Mask R-CNN object detection method to detect cars from oblique images. Then, a detected car and its corresponding image patch calculated by the geometry constraints in the other view images are used to identify the moving state of the car. Finally, the geometry and texture information corresponding to the moving car will be processed according to its moving state. Experiments on three different urban datasets demonstrate that the proposed method is effective in recognizing and removing moving cars and can repair the geometric deformation and error texture mapping problems caused by moving cars. In addition, the methods proposed in this paper can be applied to eliminate other moving objects in 3D modelling applications.
Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning
Authors: Pranga Joanna , Borra-Serrano Irene , Aper Jonas , De Swaef Tom , Ghesquiere An , Quataert Paul , Roldán-Ruiz Isabel , Janssens Ivan A. , Ruysschaert Greet , Lootens Peter
Published: September 2021

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.
Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends
Authors: Kishcha Pavel , Starobinets Boris , Lechinsky Yury , Alpert Pinhas
Published: September 2021

This study was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km × 1 km resolution records on board Terra and Aqua satellites and in-situ measurements during the period (2003–2019). In spite of the presence of increasing atmospheric warming, in summer when evaporation is maximal, in fresh-water Lake Kinneret, satellite data revealed the absence of surface water temperature (SWT) trends. The absence of SWT trends in the presence of increasing atmospheric warming is an indication of the influence of increasing evaporation on SWT trends. The increasing water cooling, due to the above-mentioned increasing evaporation, compensated for increasing heating of surface water by regional atmospheric warming, resulting in the absence of SWT trends. In contrast to fresh-water Lake Kinneret, in the hypersaline Dead Sea, located ~100 km apart, MODIS records showed an increasing trend of 0.8 °C decade−1 in summer SWT during the same study period. The presence of increasing SWT trends in the presence of increasing atmospheric warming is an indication of the absence of steadily increasing evaporation in the Dead Sea. This is supported by a constant drop in Dead Sea water level at the rate of ~1 m/year from year to year during the last 25-year period (1995–2020). In summer, in contrast to satellite measurements, in-situ measurements of near-surface water temperature in Lake Kinneret showed an increasing trend of 0.7 °C  decade−1.
Correction: Kulk et al. Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sens. 2020, 12, 826
Authors: Kulk Gemma , Platt Trevor , Dingle James , Jackson Thomas , Jönsson Bror F. , Bouman Heather A. , Babin Marcel , Brewin Robert J. W. , Doblin Martina , Estrada Marta , Figueiras Francisco G. , Furuya Ken , González-Benítez Natalia , Gudfinnsson Hafsteinn G. , Gudmundsson Kristinn , Huang Bangqin , Isada Tomonori , Kovač Žarko , Lutz Vivian A. , Marañón Emilio , Raman Mini , Richardson Katherine , Rozema Patrick D. , van de Poll Willem H. , Segura Valeria , Tilstone Gavin H. , Uitz Julia , van Dongen-Vogels Virginie , Yoshikawa Takashi , Sathyendranath Shubha
Published: September 2021

Since the article “Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades” by Kulk et al [...]
Evaluation and Analysis of Whole-Body Vibration Exposure during Soil Tillage Operation
Authors: Singh Amandeep , Samuel Siby , Singh Harwinder , Kumar Yash , Prakash Chander
Published: September 2021

This study investigated whole-body vibration (WBV) response in real field harrowing operations at different tractor ride conditions i.e., average speed, front harrow pin angle (FHPA), and rear harrow pin distance (RHPD). Taguchi’s L27 orthogonal array was used to formulate a systematic design of experiments. WBV exposure was measured along the three translational axes to compute overall daily vibration magnitude i.e., A(8). Tractor’s seat isolation capacity was assessed in terms of Seat Effective Amplitude Transmissibility i.e., SEAT%. Raw acceleration data was analysed to obtain dominant frequencies using Fast Fourier Transform (FFT). A(8) was found to range between 0.43 to 0.87 m/s2 in the experimental trials. Seat isolation capacity was found to be poor in 89% of the experiments i.e., SEAT% > 100%. Average speed and FHPA was found to have a significant impact (p ≤ 0.05) on A(8) and SEAT%. FFT response showed a range of primary and secondary dominant peaks within a frequency range of 0.2 to 11 Hz. In conclusion, the majority of experimental trials (67%) exceeded the Directive2002/44EU recommended exposure action value (EAV) limit i.e., 0.5 m/s2. The harrowing operation was found to exhibit vibration exposure at low frequencies in the vicinity of natural frequencies of the human body and may consequently affect ride comfort.
Food and Nutrition Myths among Future Secondary School Teachers: A Problem of Trust in Inadequate Sources of Information
Authors: Moreno-Rodríguez Vanessa P. , Sánchez-Cabrero Roberto , Abad-Mancheño Alfonso , Juanes-García Almudena , Martínez-López Fernando
Published: September 2021

The Internet and social networks are full of nutrition information, offering people guidance to make healthy eating choices. These sources always present themselves as a gateway to reliable information on healthy eating; however, too often this is not the case. Far from being trustworthy, there are usually plenty of food myths. A food myth is a widespread false belief about food, nutrition, and eating facts that gives rise to certain behaviors, from fashionable trends to diets. Academic training is a valuable tool to combat food myths and the pseudoscience linked to them, but educators must participate in this battle. To test this idea, we analyzed the prevalence of nine highly popular food myths held by 201 secondary school Spanish teachers. The aim was to assess whether expertise in science areas prevents teachers from falling into these food misconceptions. Our study results showed that food myths are held regardless of specialty area. The power of the media in popularizing and spreading nutrition myths among educators may be the cause, even more potent than academic training. We conclude that since scientific knowledge is not enough to erase food myths, we need further actions if we aim to prevent the problems that food myths may cause.
“We Are Peronists, We Are Organic”: Discipline, Authority, and Loyalty in Argentine Populism
Authors: Fierman Julia Beth
Published: September 2021

Since 1945, Argentine politics has been largely defined by Peronism, a populist movement established by General Juan Perón. While the ideology of Peronism has shifted and swerved over its seven-decade history, its central emphasis on loyalty has remained constant. This paper examines the notion of “organicity” (organicidad), a Peronist conception of obedience, to elucidate how populist movements valorize discipline and loyalty in order to unify their ranks around sentiment and ritual in the absence of more stable programmatic positions. The original sense of “organicity”, as Perón developed it in his early writings, equated to strict military notions of discipline, obedience, and insubordination. In other words, Perón understood loyalty as an organic conception of discipline that consisted of both unyielding deference for the leader and unwavering commitment to the Peronist Movement. Yet, at particular moments in Argentine political history, Peronist militants either find organicity and loyalty to be intrinsically incompatible, or vocalize definitions of organicity that seem to question the top-down structure of the movement celebrated in Perón’s writings. As a result, among Peronists there is disagreement over what it means to behave organically and loyally. This article draws on extensive ethnographic fieldwork among Peronist militants to argue that populism’s authoritarian preoccupation with fealty attempts to obscure the internal contradictions that result from its lack of clear ideological commitments. However, an emphasis on loyalty cannot produce eternally harmonious uniformity. As Peronists come to view those holding alternate interpretations of their doctrine as heretical and traitorous, their accusations against their comrades reveal the intrinsic fragility of populist unity.
Qualification (Mis)Match for Upper Secondary and Higher Education
Authors: Neycheva Mariya
Published: September 2021

The expansion of education all over the world is expected to improve economic and social development. However, the oversupply of educated labor force might bring unfavorable consequences for the labor market and long-run growth prospects. In this regard the purpose of this paper is two-fold. First, it aims at summarizing the main channels and mechanisms through which education–job mismatch could impact the changes of per capita income. Second, the study presents empirical evidence on that impact by differentiating between qualification mismatch among workers having completed tertiary education and those with upper secondary education. The sample comprises the EU member countries between 2000 and 2019. The results suggest that whereas the higher percentage of the properly matched labor force increases the steady-state level of per capita output for both educational levels being considered, the effect of qualification mismatch is either negative or insignificant. There is some evidence that overeducation among higher education graduates exhibits a stronger negative effect on economic activity in comparison with overeducation among workers with upper secondary education.
Bacterial Movement in Subsurface Soil during Winter Irrigation of Reclaimed Wastewater
Authors: Ding Guannan , Mancl Karen , Lee Jiyoung , Tuovinen Olli H.
Published: September 2021

Processes to remove and inactivate Escherichia coli from wastewater effluents and drainage are complex and interrelated. The objective of this study was to determine if irrigation of undisinfected wastewater effluents in the winter moves bacteria to surface water through subsurface drainage, posing a public health risk. The central Ohio study site, an open meadow constructed in the 1970s, is irrigated with lagoon effluents each summer. The irrigated area has subsurface drainage that collects for discharge in one spot. Undisinfected wastewater from a stabilization pond was irrigated for the first time in the winter of 2013/2014. E. coli was measured in the subsurface discharge during the irrigated winter season and compared to the non-irrigated previous winter season. Soil temperature and moisture were also monitored. E. coli moved to subsurface drains when the water table was above the drain. E. coli also moved to subsurface drains when the shallow soil temperature dropped to near freezing. With less winter sunlight and minimal evapotranspiration, the soil stayed moist near field capacity. Temperature appears to be the most important factor in limiting natural inactivation in subsurface soil and allowing the movement of E. coli in undisinfected wastewater effluents to the subsurface drainage systems. The results show that winter reuse of undisinfected wastewater does pose a public health risk to surface water through subsurface drainage. Therefore, disinfection of wastewater effluents used for irrigation is strongly recommended.
Measuring the Degree of Balance between Urban and Tourism Development: An Analytical Approach Using Cellular Data
Authors: Shi Cheng , Liu Mengyang , Ye Yu
Published: September 2021

This study presents an analytical approach for measuring the degree of balance between urban and tourism development, which has been previously analyzed qualitatively and was difficult to measure. With the help of 1012 million cellular data records generated by 20 million users in two weeks, we tracked the behavior of residents, commuters, and tourists at a set of historical conservation areas in central Shanghai. We calculated the degree of balance and visualized it via ternary graphs. Moreover, the relationships between key urban features derived from multi-sourced urban data and balanced degrees of tourism development were analyzed via multinomial logistic analysis. Insights gained from this analysis will help to achieve a more scientific decision-making process toward balanced urban development for historical conservation area. Achievements in this study contribute to the development of human-centered planning through providing continuous measurements of an “unmeasurable” quality.
Rheological Behavior and Strength Characteristics of Cement Paste and Mortar with Fly Ash and GGBS Admixtures
Authors: Arularasi V. , Thamilselvi P. , Avudaiappan Siva , Saavedra Flores Erick I. , Amran Mugahed , Fediuk Roman , Vatin Nikolai , Karelina Maria
Published: September 2021

A cement paste or mortar is composed of a mineral skeleton with micron to millimeter-sized grains, surrounded by water filaments. The cohesion or shear resistance in the cement paste and mortar is caused by capillary forces of action. In the case of mortar mixes, there is friction between the particles. Therefore, the mortar mixture shows both friction between particles and cohesion, while the paste shows only cohesion, and the friction between particles is negligible. The property of the cement paste is greatly influenced by the rheological characteristics like cohesion and internal angle friction. It is also interesting that when studying the rheology of fresh concrete, the rheological behavior of cement paste and mortar has direct applicability. In this paper, the rheological characteristics of cement paste and mortar with and without mineral admixtures, that is, fly ash and ground granulated blast-furnace slag (GGBS), were studied. A cement mortar mix with a cement-to-sand ratio of 1:3 was investigated, including fly ash replacement from 10% to 40%, and GGBS from 10% to 70% of the weight of the cement. A suitable blend of fly ash, GGBS, and ordinary Portland cement (OPC) was also selected to determine rheological parameters. For mortar mixtures, the flow table was conducted for workability studies. The flexural and split tensile strength tests were conducted on various mortar mixtures for different curing times. The results indicate that in the presence of a mineral mixture of fly ash and GGBS, the rheological behavior of paste and mortar is similar. Compared with OPC-GGBS-based mixtures, both cement with fly ash and ternary mixtures show less shear resistance or impact resistance. The rheological behavior of the mortar also matches the rheological behavior in the flow table test. Therefore, it is easy to use the vane shear test equipment to conduct cohesion studies to understand the properties of cement paste and mortar using mineral admixtures. The strength results show that the long-term strength of GGBS-based mixtures and ternary mixed mixtures is better than that of fly-ash-based mixtures. For all mixtures, the strength characteristics are greatest at a w/b ratio of 0.6.
Application of Fuzzy Logic to Evaluate the Economic Impact of COVID-19: Case Study of a Project-Oriented Travel Agency
Authors: Thalmeiner Gergő , Gáspár Sándor , Barta Ákos , Zéman Zoltán
Published: September 2021

The aim of the study is to create a performance evaluation controlling model to evaluate the performance of tourism enterprises as a function of the economic effects of COVID-19. As a result of the significant change in demand resulting from the economic environment, expectations and cyclicality caused by the pandemic, the assessment of organization performance has become subjective. Under these changed environmental conditions, most of the methods used by tourism companies to evaluate performance are not effective enough. In our research, we illustrated a controlling model based on fuzzy logic through a case study. By applying the model, it becomes possible to evaluate project-oriented tourism organizations according to different standardized norms. Our model considers the subjectivity derived from measurability and goal setting. We point out that the performance of organizations operating in the tourism industry significantly influenced by COVID-19 can be subjectively assessed during the pandemic period and thus depends on the analytical context. By evaluating the performance of tourism organizations along internal organizational goals, more relevant information content and more informed managerial decision support can be achieved.
Volatile Fatty Acid Production from Food Waste Leachate Using Enriched Bacterial Culture and Soil Bacteria as Co-Digester
Authors: Pham Van Hong Thi , Ahn Jeongyoon , Kim Jaisoo , Lee Sangbeom , Lee Ingyu , Kim Sungchul , Chang Soonwoong , Chung Woojin
Published: September 2021

The production of volatile fatty acids (VFAs) from waste stream has been recently getting attention as a cost-effective and environmentally friendly approach in mechanical–biological treatment plants. This is the first study to explore the use of a functional bacterium, AM5 isolated from forest soil, which is capable of enhancing the production of VFAs in the presence of soil bacteria as a co-digester in non-strict anaerobic fermentation processes of food waste leachates. Batch laboratory-scale trials were conducted under thermophilic conditions at 55 °C and different pH values ranging from approximately 5 to 11, as well as under uncontrolled pH for 15 days. Total solid content (TS) and volatile solid content (VS) were observed with 58.42% and 65.17% removal, respectively. An effluent with a VFA concentration of up to 33,849 mg/L (2365.57 mg/g VS; 2244.45 mg/g chemical oxygen demand (COD)-VFA VS; 1249 mg/g VSremoved) was obtained at pH 10.5 on the second day of the batch culture. The pH resulted in a significant effect on VFA concentration and composition at various values. Additionally, all types of VFAs were produced under pH no-adjustment (approximately 5) and at pH 10.5. This research might lead to interesting questions and ideas for further studies on the complex metabolic pathways of microbial communities in the mixture of a soil solution and food waste leachate.
Adaptation and Returns from Improved Indigenous Small Ruminants in Climatically Challenged Smallholder Systems of Kenya
Authors: Sila Winfred , Gachuiri Charles K. , Recha John W. , Audho James , Ojango Julie M. K.
Published: September 2021

Improved climate-resilient strains of indigenous sheep and goats were introduced in the Nyando basin of western Kenya in 2013. This study evaluated the performance of the breeds five years after their first introduction, and their contribution to household revenues. Red Maasai and Red Maasai x Dorper sheep and Galla goats introduced in Nyando adapted to the environment and retained performance levels exhibited in their original environments. They have been widely adopted by the farmers and are used for upgrading local breeds through crossbreeding, yielding offspring that are 50% heavier than local breeds at one year of age. Costs for producing the small ruminants tend to increase with land size owned, mainly due to higher costs for managing the health of more livestock. The costs of producing goats are significantly higher than for sheep. Revenues accrued from goat milk contribute to 10% of the revenue from goats. The introduced breeds provided a significant productivity lift in the local livestock population, resulting in higher returns to the smallholder farmers. For long-term sustainability of the productivity gains, a community-based selective mating program using reference sire flocks with an overall goal of good growth, adaptability, and milk production should be adopted.
B Corps’ Social Media Communications during the COVID-19 Pandemic: Through the Lens of the Triple Bottom Line
Authors: Mann Manveer , Byun Sang-Eun , Ginder Whitney
Published: September 2021

The COVID-19 pandemic and rising demand for transparency has heightened the importance of sustainability communications on social media to generate deeper stakeholder engagement. Although B Corporations (B Corps), businesses committed to the triple bottom line (TBL), could serve as a catalyst for sustainable development, little is known about how they communicate on social media during a crisis. Therefore, we examined social media communications of B Corps to (1) identify salient topics and themes, (2) analyze how these themes align with the TBL, and (3) evaluate social media performance against industry benchmarks. We focused on the apparel, footwear, and accessories (AFA) sectors in the U.S. and chose Twitter, a platform known for crisis communication. Using a qualitative method, we found four topics and 21 underlying themes. Topics related to social/environmental issues and COVID-19 were most dominant, followed by product/brand promotions. Further classification of specific themes and cases from a TBL perspective demonstrated that, overall, B Corps in the AFA sectors leveraged various approaches to promote balance between each TBL dimension. Lastly, although collectively B Corps exceeded some of the Twitter industry benchmarks, at an individual level, most brands had room for improvement to build a stronger community and promote synergy among the three pillars of the TBL.
Sustainable Education Using New Communication Technology: Assessment with Analytical Hierarchy Process (AHP)
Authors: Wang Xin-Yu , Li Guang , Tu Jih-Fu , Khuyen Nguyen Thi To , Chang Chun-Yen
Published: September 2021

This study explores how new communication technology is implemented in education by mainly focusing on the teacher’s role. With a questionnaire and interview surveys, the analytic hierarchy process (AHP) was carried out to understand the factors that affect implementing new communication technology in education. New technologies such as the fifth-generation (5G) technology contribute to the realization of ubiquitous and effective learning. Effective adoption of this technology for education is pedagogically based on teachers’ capability and determination to improve students’ learning activities. The results indicate that teachers and students prefer traditional teaching methods to the new technological methods, with a high weight recorded for the “maintaining the traditional teaching tools” criteria in the solution layer. The importance of the criteria layer shows that there are possibilities of implementing new technologies into education with appropriate support. When considering teachers’ effort, time spent, and resources used to prepare adequate materials, teachers are hesitant about using new technology. However, support helps to implement new communication technology successfully into education, especially teaching. Even with the many advantages of new technology, such as 5G, its problems prohibit teachers from actively using new technology. To provide a basic understanding of how to encourage teachers to successfully implement new technology into education, especially teaching, the results of this study help promote the applications used for sustainable education to narrow the educational divide.