bluuebunny/crossref_metadata_2025 huggingface dataset:
The Constellation of Agents: An Often Overlooked Aspect in the Comparison of Deuteronomy and Ancient Near Eastern Treaties
Authors: Krause Joachim J.
Published: March 2023

Ever since the pioneering studies of George Mendenhall, Klaus Baltzer, Dennis McCarthy, and Moshe Weinfeld, the structural analogies between Deuteronomy and ancient Near Eastern treaties have been a key issue in the scholarly study of the book. More recently, the hypothesis that Deuteronomy 13* and 28* could even represent a Hebrew rendering of the Neo-Assyrian Succession Treaties of Esarhaddon has prompted a yet intensified investigation of the matter. Yielding nuanced models to account for the traditio-historical pluriformity of features in Deuteronomy vis-à-vis the various strands of tradition found in late Hittite and Neo-Assyrian, as well as Aramaic comparative evidence, this latter discussion has arguably once again broadened the horizon. In any case, it only emphasizes that reading Deuteronomy against the background of the ancient Near Eastern treaty tradition more broadly provides an indispensable perspective when it comes to the literary genesis of Deuteronomy as we have it. What is more, it also opens a window on its interpretation. At the same time, however, it can also lead to certain misconceptions, for as much as major parts of Deuteronomy are modeled on a treaty, Deuteronomy is no treaty. The comparative perspective thus requires one to heed both commonalities and differences. This article focuses on one such difference, namely the constellation of agents. It is typical of ancient Near Eastern treaties that the contracting parties agree to delegate the task of safeguarding the treaty to a third party constituted by deities. It is these gods who figure as “witnesses” of the curses that the contracting parties call upon themselves if they should act contrary to the treaty, the term “witness” also denoting, according to the semantics of ancient Near Eastern treaty discourse, “agent of the sanctions.” Hence, the agreed upon sanctions are conceived of as coming into effect without the further involvement of the contracting parties. In fact, this particular feature is the operating principle which makes an ancient Near Eastern treaty work. In Deuteronomy, however, it does not apply. While intriguingly enough there are certain entities in the close context of the curse sections which are called “witnesses,” none of them can truly figure as a witness in the sense described above, for none of them is a deity; neither is there an attempt made to charge these “witnesses” with putting into effect the curse sanctions. Often overlooked, this aspect has significant ramifications for the understanding of the curses in Deuteronomy and the treaty style structure in general.
Forgotten Traces of the Buddhist Incantation Spell Practice from Early Korea: Amulet Sheets of the Incantation of Wish-Fulfillment (Mahāpratisarā) from Silla
Authors: Han Joung Ho , Kim Youn-mi
Published: March 2023

Through an investigation of two recently discovered paper sheets of the Incantation of Wish-Fulfillment from the Silla kingdom, this paper reveals that early Korea had more diverse forms of dhāraṇī practices than previously assumed. Through analyses of these incantation sheets, this paper contributes toward filling the gap in our current understanding of the material practice pertaining to the Incantation of Wish-Fulfillment of medieval East Asia. Previously, all known traces of material dhāraṇīs from early Korea, with just a few exceptions, were related to the Sūtra of the Pure Light Incantation enshrined in the relic crypts of pagodas—a practice that has little connection to contemporaneous Chinese dhāraṇī practice. However, the newly discovered Incantation of Wish-Fulfillment sheets, whose date this paper infers to be between the eighth and ninth century, show that Unified Silla had a dhāraṇī practice closely linked to coeval Chinese practice. The Incantation of Wish-Fulfillment sheets from Silla show the modification and continuation of Chinese dhāraṇī practice. Unlike the Chinese amulet sheets of the Incantation of Wish-Fulfillment that were buried in tombs, the Silla amulet sheets were likely enshrined in one of the pagodas erected on Mount Nam in Silla’s capital. At the same time, they were placed in the pagoda to wish for good afterlives of the soldiers who died at the battle, suggesting that they had a mortuary function similar to those buried in Chinese tombs.
Adaptive-Attention Completing Network for Remote Sensing Image
Authors: Huang Wenli , Deng Ye , Hui Siqi , Wang Jinjun
Published: March 2023

The reconstruction of missing pixels is essential for remote sensing images, as they often suffer from problems such as covering, dead pixels, and scan line corrector (SLC)-off. Image inpainting techniques can solve these problems, as they can generate realistic content for the unknown regions of an image based on the known regions. Recently, convolutional neural network (CNN)-based inpainting methods have integrated the attention mechanism to improve inpainting performance, as they can capture long-range dependencies and adapt to inputs in a flexible manner. However, to obtain the attention map for each feature, they compute the similarities between the feature and the entire feature map, which may introduce noise from irrelevant features. To address this problem, we propose a novel adaptive attention (Ada-attention) that uses an offset position subnet to adaptively select the most relevant keys and values based on self-attention. This enables the attention to be focused on essential features and model more informative dependencies on the global range. Ada-attention first employs an offset subnet to predict offset position maps on the query feature map; then, it samples the most relevant features from the input feature map based on the offset position; next, it computes key and value maps for self-attention using the sampled features; finally, using the query, key and value maps, the self-attention outputs the reconstructed feature map. Based on Ada-attention, we customized a u-shaped adaptive-attention completing network (AACNet) to reconstruct missing regions. Experimental results on several digital remote sensing and natural image datasets, using two image inpainting models and two remote sensing image reconstruction approaches, demonstrate that the proposed AACNet achieves a good quantitative performance and good visual restoration results with regard to object integrity, texture/edge detail, and structural consistency. Ablation studies indicate that Ada-attention outperforms self-attention in terms of PSNR by 0.66%, SSIM by 0.74%, and MAE by 3.9%, and can focus on valuable global features using the adaptive offset subnet. Additionally, our approach has also been successfully applied to remove real clouds in remote sensing images, generating credible content for cloudy regions.
Global Comparison of Leaf Area Index Products over Water-Vegetation Mixed Heterogeneous Surface Network (HESNet-WV)
Authors: Liu Chang , Li Jing , Liu Qinhuo , Xu Baodong , Dong Yadong , Zhao Jing , Mumtaz Faisal , Gu Chenpeng , Zhang Hu
Published: March 2023

Spatial land surface heterogeneities are widespread at various scales and represent a great challenge to leaf area index (LAI) retrievals and product validations. In this paper, considering the mixed water and vegetation pixels prevalent at moderate and low resolutions, we propose a methodological framework for conducting global comparisons of heterogeneous land surfaces based on criterion setting and a global search of high-resolution data. We construct a global network, Heterogeneous Surface Network aiming Water and Vegetation Mixture (HESNet-WV), comprised of three vegetation types: grassland, evergreen broadleaf forests (EBFs), and evergreen needle forests (ENFs). Validation is performed using the MCD15A3H Global 500-m/4-day and GLASS 500-m/8-day LAI products. As the water area fraction (WAF), LAI values and LAI inversion errors increase in the MODIS and GLASS products, the GLASS product errors (relative LAI error (RELAI): 94.43%, bias: 0.858) are lower than the MODIS product errors (RELAI: 124.05%, bias: 1.209). The result indicates that the proposed framework can be applied to evaluate the accuracy of LAI values in mixed water-vegetation pixels in different global LAI products.
Tree-Species Classification and Individual-Tree-Biomass Model Construction Based on Hyperspectral and LiDAR Data
Authors: Qiao Yifan , Zheng Guang , Du Zihan , Ma Xiao , Li Jiarui , Moskal L. Monika
Published: March 2023

Accurate classification of tree species is essential for forest resource monitoring, management, and conservation. Based on the classification of tree species, the biomass model at the individual-tree scale of each tree species can be accurately estimated, which can improve the estimation efficiency of individual-tree biomass. In this study, we first extracted four categories of indicators: canopy height model, spectral features, vegetation indices, and texture features from airborne-laser-scanning (ALS) data and hyperspectral data. We used these features as inputs to the random forest algorithm and screened out the optimal variable combination for tree-species classification, with an overall accuracy of 84.4% (kappa coefficient = 0.794). Then, we used ALS data to perform tree segmentation in forest plots to extract tree height, crown size, crown projected area, and crown volume. According to multivariate nonlinear fitting, the parameters of the individual-tree structure were introduced into the constant allometric ratio (CAR) biomass model to establish the biomass models of three tree species: Douglas fir, Red alder, and Bigleaf maple. The results showed that the model-fitting effects were improved after introducing the crown parameters. In addition, we also found that better tree segmentation results led to more accurate structural parameters.
High-Resolution Semantic Segmentation of Woodland Fires Using Residual Attention UNet and Time Series of Sentinel-2
Authors: Shirvani Zeinab , Abdi Omid , Goodman Rosa C.
Published: March 2023

Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) > 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models—especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.
Correction: Erfanifard et al. Assessment of Iran’s Mangrove Forest Dynamics (1990–2020) Using Landsat Time Series. Remote Sens. 2022, 14, 4912
Authors: Erfanifard Yousef , Nasirabad Mohsen Lotfi , Stereńczak Krzysztof
Published: March 2023

In the original article [...]
High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset
Authors: Yan Yang , Yang Jiajie , Li Baoguo , Qin Chengzhi , Ji Wenjun , Xu Yan , Huang Yuanfang
Published: March 2023

The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.
Modulation of the Marine Environment in the Natal Bight
Authors: Jury Mark R.
Published: March 2023

Modulation of the marine environment in the Natal Bight (~29.1°S, 31.6°E) was studied using daily high-resolution climate reanalysis products and monthly satellite green- and red-band reflectance in the period 2002–2022. The KwaZulu-Natal shelf edge is characterized by a narrow band of upwelling next to the warm Agulhas Current. Strong, reversing longshore winds ~7 m/s and meandering poleward flow ~1 m/s pulse the system, but along the leeward coast that forms the Natal Bight, environmental conditions are buffered by a weak cyclonic gyre. Wind and current shear create a shadow zone that aggregates plankton, recycles nutrients, and sustains marine resources. The seasonal cycle is of high amplitude: the surface heat balance reaches +70 W/m2 in December, followed by river discharges ~3 M m3/yr of fresh nutrient-rich water that peak in February. This induces a buoyant surface layer that inhibits wind wave turbulence during summer. By contrast, winter (June–August) cooling −95 W/m2 and frequent cyclonic storminess deepen the mixed layer from 25 to 65 m, enabling wind wave turbulence to reach the seafloor (Tugela Bank). Red-band reflectance increases 3-fold from summer to winter and is significantly correlated with net heat balance −0.54, daily wave heights > 2.5 m +0.51, mixed layer depth +0.47, sea surface temp −0.41, and wind vorticity −0.39. Daily longshore winds from the northeast and southwest were, unexpectedly, most amplified in spring (August–October). The seasonality exhibits sequential effects that supports year-round marine nutrification in the Natal Bight. Intra-seasonal fluctuations were related to meandering of the Agulhas Current and changes in longshore winds and shelf waves that impart significant pulsing of near-shore currents at 4–9-day periods. Although the cyclonic gyre in the Natal Bight spins up and down, SST variance was found to be relatively low in its center, where external influences are buffered. Considering linear trends for winds and runoff and surface temperature over the period 1950–2021, we found that northeasterlies increased, runoff decreased, and inshore sea surface temperatures have warmed slowly relative to the adjacent land surface temperature. New insights derive from the use of monthly satellite red-band reflectance and daily 10 km climate reanalysis fields to understand how air–land–sea fluxes modulate the marine environment in the Natal Bight.
Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models
Authors: He Yunlin , Ma Jiangming , Zhang Changshun , Yang Hao
Published: March 2023

In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon loss area was larger than the carbon increase area. The carbon storage in the ecological priority scenario in 2035 is the highest, reaching 874.76 × 106 t. The aboveground carbon storage (ACG) is the main carbon pool in Guilin. Most of the regions with high carbon storage are located in the northwest and northeast of Guilin. No matter what scenario, the carbon storage in the main urban area is maintained at a low level; (3) In 2035, the distribution of carbon storage in Guilin has a strong spatial positive correlation, with more hot spots than cold spots. The high-value areas of carbon storage are concentrated in the northwest and east, whereas the low-value areas are concentrated in the urban area of Guilin.
PolSAR Image Building Extraction with G0 Statistical Texture Using Convolutional Neural Network and Superpixel
Authors: Li Mei , Shen Qikai , Xiao Yun , Liu Xiuguo , Chen Qihao
Published: March 2023

Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle noise of SAR images will affect the accuracy of building extraction. This paper proposes a novel building extraction approach from PolSAR images with statistical texture and polarization features by using a convolutional neural network and superpixel. A feature space that is sensitive to building, including G0 statistical texture and PualiRGB features, is constructed and used as CNN input. Considering that the building boundary of the CNN classification result is inaccurate due to speckle noise, the simple linear iterative cluster (SLIC) superpixel is utilized to constrain the building extraction result. Finally, the effectiveness of the proposed method has been verified by experimenting with PolSAR images from three different sensors, including ESAR, GF-3, and RADARSAT-2. Experiment results show that compared with the other five PolSAR building extraction methods including threshold, SVM, RVCNN, and PFDCNN, our method without superpixel constraint, the F1-score of this method is the highest, reaching 84.22%, 91.24%, and 87.49%, respectively. The false alarm rate of this method is at least 10% lower and the F1 index is at least 6% higher when the building extraction accuracy is comparable. Further, the discussion and method parameter analysis results show that increasing the use of G0 statistical texture parameters can improve building extraction accuracy and reduce false alarms, and the introduction of superpixel constraints can further reduce false alarms.
A Comprehensive Survey on SAR ATR in Deep-Learning Era
Authors: Li Jianwei , Yu Zhentao , Yu Lu , Cheng Pu , Chen Jie , Chi Cheng
Published: March 2023

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field.
Deep Churn Prediction Method for Telecommunication Industry
Authors: Saha Lewlisa , Tripathy Hrudaya Kumar , Gaber Tarek , El-Gohary Hatem , El-kenawy El-Sayed M.
Published: March 2023

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively.
Analysis of Students’ Online Learning Engagement during the COVID-19 Pandemic: A Case Study of a SPOC-Based Geography Education Undergraduate Course
Authors: Zhu Xuemei , Gong Qian , Wang Qi , He Yongjie , Sun Ziqi , Liu Feifei
Published: March 2023

With the long-lasting impact of the COVID-19 pandemic, online learning has gradually become one of the mainstream learning methods in Chinese universities. The effectiveness of online learning is significantly influenced by learning engagement, and studies into this topic can help learners by providing them with process-based learning support and focused teaching interventions. Based on the online learning environment, this research constructs an online learning engagement analysis model. Additionally, this study explores the relationship between students’ online learning engagement and their online learning performance by taking the Secondary School Geography Curriculum Standards and Textbooks Research, a small-scale private online course (SPOC) of the geography education undergraduate course at Nanjing Normal University, as an example. The findings are as follows: In the cognitive engagement dimension, only “analyze” is significantly positively correlated with learning performance; in the behavioral engagement dimension, the “number of question and answer (Q&A) topic posts,” the “replies to others,” and the “teachers’ replies” are all significantly positively correlated with learning performance. In terms of the emotional engagement dimension, “curiosity” and “pleasure” are positively correlated with learning performance; as for the social engagement dimension, “point centrality” and “intermediary centrality” are positively correlated with learning performance. The findings of this case study reveal that the student’s engagement in higher-order cognitive learning is obviously insufficient. Students’ online learning performance can be enhanced both by behavioral engagement in knowledge reprocessing and positive emotional engagement. Further research should be focused on finding ways to increase students’ enthusiasm for social engagement.
Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example
Authors: Zhu Shengyuan , Wu Yinglei , Ma Xiaoshuang
Published: March 2023

Rapid and accurate monitoring of algal blooms using remote sensing techniques is an effective means for the prevention and control of algal blooms. Traditional methods often have difficulty achieving the balance between interpretative accuracy and efficiency. The advantages of a deep learning method bring new possibilities to the rapid and precise identification of algal blooms using images. In this paper, taking Chaohu Lake as the study area, a dual U-Net model (including a U-Net network for spring and winter and a U-Net network for summer and autumn) is proposed for the identification of algal blooms using remote sensing images according to the different traits of the algae in different seasons. First, the spectral reflection characteristics of the algae in Chaohu Lake in different seasons are analyzed, and sufficient samples are selected for the training of the proposed model. Then, by adding an attention gate architecture to the classical U-Net framework, which can enhance the capability of the network on feature extraction, the dual U-Net model is constructed and trained for the identification of algal blooms in different seasons. Finally, the identification results are obtained by inputting remote sensing data into the model. The experimental results show that the interpretation accuracy of the proposed deep learning model is higher than 90% in most cases with the fastest processing time being less than 10 s, which achieves much better performance than the traditional supervised classification method and also outperforms the single U-Net model using data of whole year as the training samples. Furthermore, the profiles of algal blooms are well-captured.
Land Use Conflicts and Synergies on Agricultural Land in Brandenburg, Germany
Authors: Unger Martin , Lakes Tobia
Published: March 2023

The growing and multiple interests in land as a resource has led to an increase in locally or regionally clashing land use interests on agricultural land which may result in conflicts or open up possibilities for synergies. Urbanization, food production, renewable energy production, environmental protection, and climate protection are known as key land use interests in many regions. The objective of our study is to identify and map land use conflicts, land use synergies, and areas with land use synergy potentials in the federal state of Brandenburg, Germany. We have combined different methods: an analysis of statistical data, an online survey with farmers, a primary document analysis (articles, court documents, policy documents, position papers), and a GIS-based spatial analysis. In our Brandenburg case study, we have identified the use of agricultural land for renewable energy production and environmental protection as the most relevant land use interests leading to conflict situations. We show that land use synergies can make a significant contribution to achieving environmental and climate protection goals, as well as sustainable development. Through the site-adapted and targeted establishment of agroforestry systems, agricultural areas with agri-photovoltaic systems and agricultural parcels with integrated nonproductive areas may lead to land use synergies. Our study contributes to a better understanding of the occurrence of land use conflicts and land use synergies. We highlight the potential for targeted and sustainable environmental and climate protection through the promotion of land use synergies as a result of establishing agroforestry systems and agricultural parcels with agri-photovoltaic systems and integrated nonproductive areas. Our results provide a basis for agricultural policy to promote land use systems that contribute to environmental and climate protection.
Predicting the Impact of Climate Change on the Distribution of Rhipicephalus sanguineus in the Americas
Authors: Sánchez Pérez Marcos , Feria Arroyo Teresa Patricia , Venegas Barrera Crystian Sadiel , Sosa-Gutiérrez Carolina , Torres Javier , Brown Katherine A. , Gordillo Pérez Guadalupe
Published: March 2023

Climate change may influence the incidence of infectious diseases including those transmitted by ticks. Rhipicephalus sanguineus complex has a worldwide distribution and transmits Rickettsial infections that could cause high mortality rates if untreated. We assessed the potential effects of climate change on the distribution of R. sanguineus in the Americas in 2050 and 2070 using the general circulation model CanESM5 and two shared socioeconomic pathways (SSPs), SSP2-4.5 (moderate emissions) and SSP2-8.5 (high emissions). A total of 355 occurrence points of R. sanguineus and eight uncorrelated bioclimatic variables were entered into a maximum entropy algorithm (MaxEnt) to produce 50 replicates per scenario. The area under the curve (AUC) value for the consensus model (>0.90) and the partial ROC value (>1.28) indicated a high predictive capacity. The models showed that the geographic regions currently suitable for R. sanguineus will remain stable in the future, but also predicted increases in habitat suitability in the Western U.S., Venezuela, Brazil and Bolivia. Scenario 4.5 showed an increase in habitat suitability for R. sanguineus in tropical and subtropical regions in both 2050 and 2070. Habitat suitability is predicted to remain constant in moist broadleaf forests and deserts but is predicted to decrease in flooded grasslands and savannas. Using the high emissions SSP5-8.5 scenario, habitat suitability in tropical and subtropical coniferous forests and temperate grasslands, savannas, and shrublands was predicted to be constant in 2050. In 2070, however, habitat suitability was predicted to decrease in tropical and subtropical moist broadleaf forests and increase in tropical and subtropical dry broadleaf forests. Our findings suggest that the current and potential future geographic distributions can be used in evidence-based strategies in the design of control plans aimed at reducing the risk of exposure to zoonotic diseases transmitted by R. sanguineus.
Study on the Creep Characteristics and Fractional Order Model of Granite Tunnel Excavation Unloading in a High Seepage Pressure Environment
Authors: Chen Xingzhou , Zhang Quan , Ding Xinchao , Chen Lili , Du Wei , Jiang Hai , Gong Sheng
Published: March 2023

The creep associated with unloading surrounding rock during the excavation of deep tunnels seriously affects the stability of the tunnel, and a high seepage pressure will aggravate the strength attenuation and structural deterioration of the surrounding rock. Based on the background of the excavation-induced unloading of the surrounding rock of a deeply buried granite tunnel with high seepage pressure, in this paper we carry out a triaxial unloading seepage creep test that considers the effects of both excavation disturbance and seepage pressure. We also analyze the mechanism of unloading and seepage pressure leading to sample failure and construct a fractional creep damage constitutive model that considers the unloading effect. The results include the following findings, firstly, seepage pressure will affect the creep deformation of rock for a long time, and the circumferential expansion of the granite creep process is more obvious than the axial expansion. Secondly, a high seepage pressure will reduce the rock bearing capacity. Under 0, 2 and 4 MPa seepage pressures, the long-term strength of the samples are 193.7 MPa, 177.5 MPa and 162.1 MPa, respectively. Thirdly, the rock damage factor increases with increasing seepage pressure, time and deviatoric stress. Finally, the rationality of a fractional-order model that considers the effect of unloading and seepage is verified by the test data. These research results may provide some reference for the stability analysis of surrounding rock during excavation in environments under high-stress and high-seepage-pressure.
Analysis of Performance Yield Parameters for Selected Polycrystalline Solar Panel Brands in South Africa
Authors: Olofin Tosin Waidi , Longe Omowunmi Mary , Jen Tien-Chien
Published: March 2023

Electricity access is an essential factor for any nation’s fast-growing economic and technological development. Therefore, to meet the fast-growing world population, the adoption of a mix of energy sources, including renewable energy, is one of the ways to address the paucity supply of energy worldwide. In this paper, the performance yields of five solar photovoltaic (PV) modules, named PV1, PV2, PV3, PV4, and PV5, from different manufacturers were analyzed and compared to their respective cost benefits for profitable customer’s choice. The study on the panels was conducted at the geographical locations of 25.7535° S latitude and 28.2079° E longitude, with an average perimeter of 525.6 m in Pretoria, South Africa. The panels were installed without shading under the same condition of solar irradiation. The power output of each module was collected three times a day for six months. The analysis showed that the power outputs or performances of the respective modules are majorly affected by their surface temperatures as indicated by the values of multiple regression correlation of 92.9%, 96.9%, 99.1%, 97.2%, and 77.5% between the respective modules’ power outputs and temperature. The study also showed a techno-economic evaluation method that helps to economically alleviate the cost of solar PVs and balance the choice of the PV panels according to their short-term performances.
An Empirical Study of Parents’ Participation Behavior in the Home-Based Online Learning of Primary School Students
Authors: Li Peng
Published: March 2023

Parental participation has an important and direct influence on the effect of home-based online learning of primary school students. Taking more than 500 guardians of primary school students in a number of prefecture-level cities in the Pearl River Delta as the questionnaire respondents, and applying the structural equation model method, this paper, based on the theory of planned behavior, explores the effects of various factors on parental participation in the home-based online learning of primary school students. The results show that: (1) the behavioral intention of parents to participate in the home-based online learning of primary school students has a significant influence on their actual participation behavior. (2) Behavioral attitudes, subjective norms, and perceived behavioral control have a significant influence on behavioral intention. To strengthen the actual effects of parents’ participation behavior, this paper puts forward a number of suggestions. These include enhancing home-school partnerships, achieving home-school co-education, and training parents with regard to the integration of daily Internet use and online learning for primary school students. This study supports the effective implementation of home-based online learning of primary school students and the joint effect of home-school co-education, specifically from the perspective of parents in the subsequent implementation of integrated online and offline teaching.
Macroscopic Mechanical Properties of Brittle Materials with a 3D Internal Crack Based on Particle Flow Simulations
Authors: Chang Suling , Wang Zaiquan , Cong Yu
Published: March 2023

Pre-existing cracks significantly influence the macro-mechanical properties of rock. The macro-mechanical properties and crack propagation process of brittle materials with a 3D internal crack were investigated with PFC3D simulation in this paper. To determine the micro-parameters, the influence of micro-parameters on the macro-mechanical properties and ultimate failure mode was discussed. SJM’s parameters had little influence on the macro-mechanical properties and ultimate failure mode. Peak axial stress was changed greatly by strength parameters and friction coefficient, and the macro-elastic modulus was influenced greatly by Young’s modulus and changed slightly with other parameters. The failure mode changed gradually with all micro-parameters except Young’s modulus, which had a strong but irregular impact on it. The peak stress was 138 MPa in the simulation of the sample with a 3D internal crack, which agreed well with the experimental result (137 MPa). The crack propagation process can be divided into three stages: 17% of total crack was generated in the initial stage; 76% of the total crack was propagated when main failure surface coalesced; finally, the failure surface expanded downwards and caused the sample to be destroyed. Cracks initially appeared near the end of the lower major axis of the internal crack, which was in agreement with experimental results. The results demonstrated that PFC3D is a reliable method to simulate the failure process of brittle materials with internal cracks.
Treatment and Valorization of Agro-Industrial Anaerobic Digestate Using Activated Carbon Followed by Spirulina platensis Cultivation
Authors: Sánchez-Quintero Ángela , Leca Marie-Ange , Bennici Simona , Limousy Lionel , Monlau Florian , Beigbeder Jean-Baptiste
Published: March 2023

The increased production of biogas through the anaerobic digestion (AD) process has raised several concerns regarding the management of liquid digestate, which can present some environmental risks if not properly handled. Among the different techniques to treat AD digestate, microalgae and cyanobacteria cultivation has emerged as a sustainable approach to valorizing digestate while producing valuable biomass for production of biofuels and high value bioproducts. However, the intrinsic parameters of the liquid digestate can strongly limit the microalgae or cyanobacteria growth as well as limit the uptake of residual nutrients. In this study, the detoxification potential of activated carbon (AC) was evaluated on agro-industrial liquid digestate prior to Spirulina platensis cultivation. Different doses of AC, ranging from 5 to 100 g/L, were tested during adsorption experiments in order to determine the adsorption capacity as well as the removal efficiency of several compounds. Experimental results showed the high reactivity of AC, especially towards phosphate (PO4-P), total phenol (TP) and chemical oxygen demand (COD). At a dosage of 50 g/L, the AC pretreatment successfully achieved 54.7%, 84.7% and 50.0% COD, TP and PO4-P removal, corresponding to adsorption capacity of 94.7 mgDCO/g, 17.9 mgTP/g and 8.7 mgPO4-P/g, respectively. Even if the AC pretreatment did not show significant effects on Spirulina platensis growth during toxicity assays, the AC adsorption step strongly participated in the digestate detoxification by removing hardly biodegradable molecules such as phenolic compounds.
Alternating Iterative Power-Flow Algorithm for Hybrid AC–DC Power Grids Incorporating LCCs and VSCs
Authors: Yu Dong , Gao Shan , Zhao Xin , Liu Yu , Wang Sicheng , Song Tiancheng E.
Published: March 2023

AC–DC power-flow calculation is the basis for studying HVDC systems. Since traditional iterative methods need many alternative iterations and have convergence problems, this paper proposes an alternating iterative power-flow algorithm for hybrid AC–DC power grids incorporating line-commutated converters (LCCs) and voltage source converters (VSCs). Firstly, the algorithm incorporates the converter interface model into the AC side, considering the influence of the DC side on the AC side, and establishes an AC-augmented Jacobian matrix model with LCC/VSC interface equation variables. Then, according to the type of converter, control mode, and DC grid control strategy, a DC grid power-flow calculation model under various control modes is established for realizing the power-flow decoupling calculation of AC–DC power grids incorporating LCCs and VSCs. The accuracy and effectiveness of the improved algorithm are evaluated using modified IEEE 57 bus AC–DC networks and the CIGRE B4 DC grid test system. The improved algorithm is applicable to various DC grid control modes and considers the reasonable adjustment of the DC grid variable constraints and operating modes.
Analysis of the Levelized Cost of Renewable Hydrogen in Austria
Authors: Povacz Leonhard , Bhandari Ramchandra
Published: March 2023

Austria is committed to the net-zero climate goal along with the European Union. This requires all sectors to be decarbonized. Hereby, hydrogen plays a vital role as stated in the national hydrogen strategy. A report commissioned by the Austrian government predicts a minimum hydrogen demand of 16 TWh per year in Austria in 2040. Besides hydrogen imports, domestic production can ensure supply. Hence, this study analyses the levelized cost of hydrogen for an off-grid production plant including a proton exchange membrane electrolyzer, wind power and solar photovoltaics in Austria. In the first step, the capacity factors of the renewable electricity sources are determined by conducting a geographic information system analysis. Secondly, the levelized cost of electricity for wind power and solarphotovoltaics plants in Austria is calculated. Thirdly, the most cost-efficient portfolio of wind power and solar photovoltaics plants is determined using electricity generation profiles with a 10-min granularity. The modelled system variants differ among location, capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. Finally, selected variables are tested for their sensitivities. With the applied model, the hydrogen production cost for decentralized production plants can be calculated for any specific location. The levelized cost of hydrogen estimates range from 3.08 EUR/kg to 13.12 EUR/kg of hydrogen, whereas it was found that the costs are most sensitive to the capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. The novelty of the paper stems from the model applied that calculates the levelized cost of renewable hydrogen in an off-grid hydrogen production system. The model finds a cost-efficient portfolio of directly coupled wind power and solar photovoltaics systems for 80 different variants in an Austria-specific context.
Global Analysis Regarding the Impact of Digital Transformation on Macroeconomic Outcomes
Authors: Tudose Mihaela Brindusa , Georgescu Amalia , Avasilcăi Silvia
Published: March 2023

In the context of the development of information technologies, the concerns about assessing the effects of digital transformation have increased. Although it is intuitively accepted that digital transformation has a favourable impact on macroeconomic variables (based on the interdependencies between micro- and macroeconomic performance), there is little scientific research providing evidence of this. Building on this identified research problem, this study aims to bridge the gap between theory and practice. After assessing the extent to which the world’s economies have responded to the need for digital transformation, an econometric analysis was conducted to quantify the impact of digital transformation on economic and social outcomes. To ensure the representativeness of the results, the econometric analysis was conducted on a sample of 46 countries selected according to the size of their gross national income per capita. The NRI (Network Readiness Index) and the sub-indices associated with the economic environment (future technologies, business, and economy) were used as independent variables. Gross domestic product (GDP) was used as a dependent variable. The results indicate that NRI has a positive and significant impact on GDP per capita. Analysis at the sub-indices level partially confirms this result and highlights that their contributions to the growth of macroeconomic performance may be different. The study results have practical utility as they provide clues on the structural efficiency of the benchmarks underpinning the digital transformation. To increase the positive impact on macroeconomic outcomes, policy-makers can propose and implement policies to facilitate access to those technologies that prove to be more effective.
Development of Sustainable Magnetic Biosorbent Using Aqueous Leaf Extract of Vallesia glabra for Methylene Blue Removal from Wastewater
Authors: Medina-Zazueta Luis , Miranda-Castro F. C. , Romo-Garcia F. , Martínez-Gil M. , Esparza-Ponce H. E. , Encinas-Basurto D. , Ibarra Jaime
Published: March 2023

Vallesia glabra (Vg) is a species that has been used in traditional medicine due to its secondary metabolites (alkaloids, saponins, flavonoids, phenols, and cardiac glucosides) for the treatment of measles, rheumatism, muscle aches, and eye inflammation. The biosynthesis of magnetite nanoparticles (Fe3O4 NPs) was carried out using an aqueous leaf extract of Vg and was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Afterward, the magnetic adsorbent was tested for its potential to remove cationic dye from aqueous solutions at different pH and adsorbent mass and its reusability after several adsorption/desorption cycles. The XRD pattern and SEM micrographs resulted in an average size of NPs of 12.2 nm. Regarding the removal of MB from an aqueous solution, the kinetic and isotherm adsorption behavior is governed by the pseudo-second-order model and a Langmuir isotherm which describes an ionic exchange and chemisorption process between the positive partial charges of MB and Vg compounds stabilizing Fe3O4 NPs following a thermodynamically favorable process. Therefore, the green synthesis of NPs from Vg leaf extract is considered a sustainable alternative to removing dyes from aqueous solutions.