bluuebunny/crossref_metadata_2025 huggingface dataset:
Coupled and Coordinated Development of the Data-Driven Logistics Industry and Digital Economy: A Case Study of Anhui Province
Authors: Guo Yuxia , Ding Heping
Published: October 2022

The digital transformation of the logistics industry is the current trend of development. In order to promote the integrated development of the logistics industry (LI) and the digital economy (DE), we propose a data-driven method which can be used to measure, evaluate, and identify the coupled and coordinated development (CCD) of the LI and DE. On the basis of data collection, we use the entropy weight method to measure the comprehensive development level of the LI and DE. A coordination model is then used to evaluate their CCD level. Finally, an obstacle degree model (ODM) is used to identify the key factors inhibiting the coordinated development (CD) of the two. This method is then applied to gauge the integration development of the LI and DE in Anhui Province. The results show that energy consumption and the lack of logistics employees are the main obstacles to the development of the LI in Anhui Province. The main obstacles to the development of the DE are the low development level of the electronic communications equipment manufacturing industry and the limited digitization of enterprises. Accordingly, this study puts forward corresponding countermeasures and suggestions to provide decision support for the CCD of the LI and DE.
Removal of Dye (Methylene Blue) from Wastewater Using Bio-Char Derived from Agricultural Residues in Palestine: Performance and Isotherm Analysis
Authors: Sawalha Hassan , Bader Aseel , Sarsour Jinan , Al-Jabari Maher , Rene Eldon R.
Published: October 2022

The main aim of this study was to perform a parametric study for treating a model dye containing wastewater (i.e., methylene blue, MB) using locally available biomass wastes in Palestine as the adsorbent. Eight different types of biomasses were investigated in batch adsorption tests, including coffee grains, almond shells, pistachio shells, date pits, jute sticks, sunflower shells, peanut shells, and grapevine sticks. The experiments were conducted on three different phases of processing for these materials: as natural adsorbents, biochar, and activated carbon. The biochar was prepared by pyrolysis, while ZnCl2 was used to chemically activate the materials for obtaining activated carbon. The influences of pH, initial MB concentration, and adsorbent dosage on the adsorption capacity and kinetics were investigated for activated carbon obtained from sunflower shells. The results indicate that the adsorption efficiency of natural adsorbents and biochar is highly dependent on the biomass type. As a natural adsorbent, peanut hulls demonstrated the maximum efficiency (>95%) for removing MB, whereas date pits showed the lowest efficiency (20%). In terms of biochar, jute sticks provide the highest removal efficiency. After activation with ZnCl2, a considerable increase in their adsorption efficiency (>95%) was obtained for most of the adsorbents, with sunflower shells being the most efficient adsorbent. The results confirm the technical feasibility of the adsorption technology to treat dye containing wastewater using locally available biomass wastes.
Comparative Evaluation of Marking Effects of Two Fluorescent Chemicals, Alizarin Red S and Calcein, on Black Sea Bream (Acanthopagrus schlegelii)
Authors: Liu Yan , Guo Yunfeng , Liu Manting , Shan Binbin , Wang Liangming , Yu Wei , Yang Changping , Sun Dianrong
Published: October 2022

Two fluorescent dyes, alizarin red S (ARS) and calcein (CAL), were applied to evaluate the marking effects on the juveniles of Acanthopagrus schlegelii. The total mortality rates of the experimental groups were significantly lower (p < 0.05) than those of control groups, but no significant difference was detected between those of the two staining methods. The fluorescence microscopy observation results showed that the marking quality of ARS was better than that of CAL, with fin spines and fin rays being the best marking tissues. The optimal concentration for ARS and CAL was 200 mg/L and 350 mg/L, respectively. To ensure mark quality, the recommended dye grade was above 3, and the most suitable marking conditions were suggested to be fluorescence labeling with ARS dye at a concentration of 200 mg/L, with immersion for 24 h. The results will provide useful data information for future research on stock enhancement using the chemical marking method.
In Vivo Analysis of Intraoral Scanner Precision Using Open-Source 3D Software
Authors: Lo Giudice Roberto , Galletti Cosimo , Tribst João Paulo Mendes , Melenchón Laia Perez , Matarese Marco , Miniello Alessandra , Cucinotta Filippo , Salmeri Fabio
Published: October 2022

Intraoral three-dimensional scanning techniques could be used to improve dental practice, leading to an improved overall quality of the prosthetic devices and improved comfort for the patient. An accurate and precise intraoral scanner allows proper diagnosis, follow-up evaluation, and prosthesis application. The aim of this research is to evaluate the precision of an intraoral scanners (Medit i500, Medit Corp., Seoul, Korea), using open-source software in the digital workflow. The precision was compared through repetitions of the scanning process of the upper dental arch, following superimpositions in the whole 3D arch area. It was possible to display colorimetric maps for qualitative comparison, and the deviations of the values were classified as clinically acceptable. Within the limitation of this study, the clinically acceptable in vivo frequency of points’ deviation, or the precision, was obtained in 98.8% ± 1.4%; therefore, the use of open-source software can be a viable option in the digital workflow, improving patient follow ups with the 3D model superimposition.
Applied Tests to Select the Most Suitable Fungal Strain for the Recovery of Critical Raw Materials from Electronic Waste Powder
Authors: Rosa Ester , Di Piazza Simone , Cecchi Grazia , Mazzoccoli Michela , Zerbini Micol , Cardinale Anna Maria , Zotti Mirca
Published: October 2022

Electrical and electronic wastes (WEEEs) are a potential source of raw materials. The main challenge for scientists is to set up a reliable and eco-friendly process to recycle raw materials and precious elements from WEEEs. Today, we know that fungi could play an active role in green technologies aimed at recycling valuable elements. The bioaccumulation mechanism and bioleaching activity of filamentous fungal species have already been exploited fruitfully in extraction processes. However, not all fungal strains possess the same characteristics, and it is crucial to choose the right strains to use. In this work, we show a method to assess the precious elements’ recovery efficiency from WEEE using fungal strains. A CAS agar screening test for siderophore detection was carried out with three strains. The following plate accumulation test performed on a medium added with 120 ppm of electronic waste powder highlighted the differences in accumulation capability, growth rate, and biomass production. Among the elements in tested waste, yttrium, copper, and palladium show the highest bioconcentration factor. The results confirm the biotechnological potential of fungi to recover valuable elements at the bench scale, highlighting the importance of effective screening tests to assess the most efficient strain for each kind of waste.
Adapting Christianity to Hakka Culture: The Basel Mission’s Activities among Indigenous People in China (1846–1931)
Authors: Li Lei
Published: October 2022

The Hakka are a branch of the Chinese Han people, who immigrated from central China to Kwangtung (Guangdong 广东) Province. They have their own cultural norms in terms of language, lineage, distribution of work roles and status of women. The trans-national Basel Mission was headquartered in the Swiss city of Basel, near the Swiss–German border. The Basel Mission was distinguished among the missions to China by its rural Hakka Christian community. This article sets out to illustrate how the Basel Mission supported and maintained the rural Hakka Christianity community by integrating Christianity with Hakka cultural precepts. Previous Christian historiographical research has generally chosen not to emphasize Hakka cultural beliefs and practices. Examining the activities of the Basel Mission from the perspective of the indigenous Hakka culture, this article aims to enhance our understanding of the cultural precepts of receptors to shape the global enterprise of missionary society.
“Let Us Have Our Libertie”: John Milton and Aemelia Lanyer Read Eve’s Fall
Authors: Ross Shaun
Published: October 2022

This essay compares two Renaissance poetic narratives that interpret the story of Eve’s fall in Genesis: Aemelia Lanyer’s Salve Deus Rex Judaeorum and John Milton’s Paradise Lost. It explores how each uses Eve’s fall to ground a radical call to liberation from oppressive hierarchical structures. In Milton’s case, these oppressive structures are political and ecclesial, while in Lanyer’s case they are the hierarchies of gender. It goes on to argue that there is a chiasmic relationship between these two narrative exhortations to liberty. In Paradise Lost, Milton’s endorses a political autonomy for the male subject that not only retains, but actively depends on the subordination of women to men’s domestic and spiritual rule within marriage. In Salve Deus, Lanyer utterly rejects the idea that women can only relate to God in such a mediated fashion. Yet, because of her precarious position as a commoner, and as a woman writing about religious matters, she depends on a more traditional appeal to her social superiors. The essay concludes with a consideration of Lanyer’s and Milton’s position within the evolution of modernity and what philosopher Charles Taylor calls a “direct-access society.”
The Essence of My Coaching Is to Serve: Monty Williams, Faith, and Relationality
Authors: Seth Pyar J.
Published: October 2022

Oftentimes, an athletic coach is tasked with establishing a player–coach relationship that is built on trust, commitment, accountability, hard work, and a belief in process. More recently, however, head coach of the Phoenix Suns, Monty Williams, has garnered considerable public attention for adding faith into that equation. Though faith is primarily considered a theological outlook and expression of spiritual value, it has extended beyond religiosity into his coaching praxis and pedagogy. In the paper, I look to add the voice of Monty Williams to the rich cohort of Black people assembled by Carey Latimore in Unshakable Faith: African American Stories of Redemption, Hope, and Community, a text principally concerned with illuminating the diversity in thought and expression of faith. Additionally, I draw on theories from Black Studies, post-colonial studies, and the sociology of sport to interrogate a particular discursive formulation advanced by Williams—“[…] the essence of my coaching is to serve”. I explore the nature of a faith-based coaching philosophy in the game of basketball and how the notion of coaching as service expresses a dynamic, complex set of religious histories, but also embodies a form of relationality centered on the following question: What does it mean to navigate sociopolitical life and death in community?
Evolution of the Sacrificial Ritual to the South Sea God in Song China
Authors: Wang Yuanlin , Ye Aiyun
Published: October 2022

Previous studies on the Nanhaishen Temple 南海神廟 (Temple of the South Sea God) in Guangzhou in the Song dynasty focus mainly on its state sacrificial ritual and local temple fairs, without fully discussing the differences of the sacrificial ritual between the Southern and Northern Song dynasties or the changes of the sacrificial ritual in Lingnan after the Song dynasty. This paper aims to illuminate the following five points. First, after the reunification of the Northern Song dynasty, the sacrificial ritual to the South Sea God in Guangzhou was advanced. Second, when the South Sea God and his temple were conferred with the holy titles for the fourth time, the god’s role to bless local stability was further manifested, which means the imperial power gradually permeated into the Lingnan culture. Third, the blessing of the South Sea God was more prominent than ever before because of its geographical location in the southeast of the state during the Southern Song dynasty, and thus the Nanhaishen Temple Fair was the largest of its kind in Lingnan. Fourth, the stele inscription of Liuhou Zhi Ji 六侯之記 (Records of the Six Lords) shows that local people attempted to incorporate their folk beliefs into the canonized sacrifice to the South Sea God, and thus many religious spots were built in other places in Lingnan as detached palaces (ligong 離宮) of the god who was generally endorsed by the local officialdom. Fifth, the sacrifice to the South Sea God in Guangzhou in the Song dynasty had a far-reaching influence, as the god was worshipped by the later generations in the temples which also accommodated the worship of Buddhism and Daoism. In summary, the lengthy process for the South Sea God to evolve from a national god to a local patron is the result of the country’s long-term implementation of the ritual system as far as the ritual culture is concerned.
Posthuman Priests: Exploring the ‘New Visibility of Religion’ in Robotic Re(-)presentations of Religious Rituals
Authors: Midson Scott
Published: October 2022

In 2017, visitors to a church in Wittenberg were given the opportunity to receive blessings from a robot. The so-called ‘robot priest’, named ‘BlessU-2’, poses a lot of questions and possibilities. One of these is about the ways that robots might mediate and represent religious teachings, beliefs, and experiences, which is the focus of this paper. Taking Hoelzl and Ward’s ‘new visibility of religion’ hypothesis, the paper asks about what kind of visibility BlessU-2—and similar robots in religious contexts—represents: is it whimsical; novel; authentic; secular? By locating the robot in historical and theological frameworks, the nuances of what it represents and how it might be seen to mediate religiosity in some way are revealed and discussed.
Video Gaming Faith: Playing Out Theologies of Religions
Authors: Jones Gregory D.
Published: October 2022

Modern religious plurality invites religious and non-religious people to navigate four interreligious dialogical problems: (1) the inability to fully articulate faith, (2) the lack of persuasive religious language, (3) the reality of violence among the religions, and (4) the liquescent “truth” of modern times. How can plurality be framed for people whose sense of relationality is shaped by their participation in virtual worlds? One answer emerges in this autoethnographic consideration of how video gaming “plays out” fresh understandings of the interreligious encounter and relationality. Adopting a Christian perspective, the first section summarizes the major theologies of religions. These theologies correspond with video-game experiences of interreligious cooperation and contest found in playing out the enrichment and diminishment of (1) Christian spirit in Spiritual Warfare (NES), (2) human connection in Final Fantasy VI (Super NES), (3) sense of salvation in Final Fantasy X (PS2), and (4) symbiotic sacredness in Journey (iOS). These play experiences clarify a concept of expansive relationality among religions that is termed shared contest. The conclusion advances a contestant theology of religions; God removes every obstacle to including all in the company of God’s people, and God provides a playground of cooperation and contest for each religious tradition.
The Water–Energy–Food Nexus: An Analysis of Food Sustainability in Ecuador
Authors: Páez Carlos Francisco Terneus , Salazar Oswaldo Viteri
Published: October 2022

In Latin America and the Caribbean, the interrelationships among water, energy, and food are complex, partly due to their development models, which are intensive in their use of these resources. This research aims to recognize and quantify the use of water and energy in food in Ecuador by identifying the main interrelationships, together with their causes and current impacts. Regarding methodology, this research uses sectoral data available at the national level and international databases to obtain cross indexes. The following indicators were sought: percentage of energy demanded by food in different stages, extraction of water for agricultural use, food energy productivity, virtual water, and embodied energy. As a result, it was estimated that food consumes 50% of imported energy and cocoa stands out with 7.6 km3 of water footprint; the products with the highest energy consumption are shrimp with 2090 KBOE and fish with 1459 KBOE. Fishing and aquaculture products present with a significantly high consumption level. Nearly all the virtual water and embodied energy are exported in banana, cocoa, and shrimp. Exporting groups make extensive use of the country’s water and energy resources.
A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting
Authors: Redzwan Norkhairunnisa , Ramli Rozita
Published: October 2022

Mortality improvements and life expectancies have been increasing in recent decades, leading to growing interest in understanding mortality risk and longevity risk. Studies of mortality forecasting are of interest among actuaries and demographers because mortality forecasting can quantify mortality and longevity risks. There is an abundance of literature on the topic of modelling and forecasting mortality, which often leads to confusion in determining a particular model to be adopted as a reliable tool. In this study, we conducted a bibliometric analysis with a focus on citation and co-citation analyses and co-occurrences of keywords to determine the most widely used stochastic mortality model. We found that the Lee–Carter model has remained one of the most relevant mortality models since its development in the 1990s. Furthermore, we also aimed to identify emerging topics and trends relating to mortality modelling and forecasting based on an analysis of authors’ keywords. This study contributes to the literature by providing a comprehensive overview and evolution of publications in stochastic mortality modelling and forecasting. Researchers can benefit from the present work in determining and exploring emerging trends and topics for future studies.
Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data
Authors: Xie Shengkun , Luo Rebecca , Li Yuanshun
Published: October 2022

The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules.
Kinematic Graph for Motion Planning of Robotic Manipulators
Authors: Corves Burkhard , Shahidi Amir
Published: October 2022

We introduce a kinematic graph in this article. A kinematic graph results from structuring the data obtained from the sampling method for sampling-based motion planning algorithms in robotics with the motivation to adapt the method to the positioning problem of robotic manipulators. The term kinematic graph emphasises the fact that any path computed by sampling-based motion planning algorithms using a kinematic graph is guaranteed to correspond to a feasible motion for the positioning of the robotic manipulator. We propose methods to combine the information from the configuration and task spaces of the robotic manipulators to cluster the samples. The kinematic graph is the result of this systematic clustering and a tremendous reduction in the size of the problem. Hence, using a kinematic graph, it is possible to effectively employ sampling-based motion planning algorithms for robotic manipulators, where the problem is defined in higher dimensions than those for which these algorithms were developed. Other barriers that hindered adequate utilisation of such algorithms for robotic manipulators with articulated arms, such as the non-injective surjection of the forward kinematic function, are also addressed in the structure of the kinematic graph.
Exploring Ephemeral Features with Ground-Penetrating Radar: An Approach to Roman Military Camps
Authors: Sánchez Jesús García , Costa-García José Manuel , Fonte João , González-Álvarez David
Published: October 2022

This paper addresses an experimental approach to the archaeological study of Roman camps in NW Iberia using ground-penetrating radar (henceforth GPR). The main goal is to explore the capabilities of GPR to extract datasets from ephemeral features, such as temporary camps or siege works, among others. This information aims to maximise the data available before excavation, orienting it to areas that could provide good results in terms of feature detection and contrast between soil matrix and archaeological deposits. This paper explores the potential of the GPR approach and volumetric data visualisation to improve our understanding of four ephemeral sites: Alto da Raia (Montalegre, Portugal–Calvos de Randín, Spain), Sueros de Cepeda (Villamejil, Spain), Los Andinales (Villsandino, Spain), and Villa María (Sasamón, Spain). Despite the focus of this paper, other survey techniques (namely LiDAR, aerial photography, and magnetometry) were used in combination with GPR. Further excavation of the sites provided ground truthing for all data remotely gathered.
Unifying Deep ConvNet and Semantic Edge Features for Loop Closure Detection
Authors: Jin Jie , Bai Jiale , Xu Yan , Huang Jiani
Published: October 2022

Loop closure detection is an important component of Simultaneous Localization and Mapping (SLAM). In this paper, a novel two-branch loop closure detection algorithm unifying deep Convolutional Neural Network (ConvNet) features and semantic edge features is proposed. In detail, we use one feature extraction module to extract both ConvNet and semantic edge features simultaneously. The deep ConvNet features are subjected to a Context Feature Enhancement (CFE) module in the global feature ranking branch to generate a representative global feature descriptor. Concurrently, to reduce the interference of dynamic features, the extracted semantic edge information of landmarks is encoded through the Vector of Locally Aggregated Descriptors (VLAD) framework in the semantic edge feature ranking branch to form semantic edge descriptors. Finally, semantic, visual, and geometric information is integrated by the similarity score fusion calculation. Extensive experiments on six public datasets show that the proposed approach can achieve competitive recall rates at 100% precision compared to other state-of-the-art methods.
Comparison of S5P/TROPOMI Inferred NO2 Surface Concentrations with In Situ Measurements over Central Europe
Authors: Pseftogkas Andreas , Koukouli Maria-Elissavet , Segers Arjo , Manders Astrid , Geffen Jos van , Balis Dimitris , Meleti Charikleia , Stavrakou Trissevgeni , Eskes Henk
Published: October 2022

The aim of this paper is to evaluate the surface concentration of nitrogen dioxide (NO2) inferred from the Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI) NO2 tropospheric column densities over Central Europe for two time periods, summer 2019 and winter 2019–2020. Simulations of the NO2 tropospheric vertical column densities and surface concentrations from the Long-Term Ozone Simulation–European Operational Smog (LOTOS-EUROS) chemical transport model are also applied in the methodology. More than two hundred in situ air quality monitoring stations, reporting to the European Environment Agency (EEA) air quality database, are used to carry out comparisons with the model simulations and the spaceborne inferred surface concentrations. Stations are separated into seven types (urban traffic, suburban traffic, urban background, suburban background, rural background, suburban industrial and rural industrial) in order to examine the strengths and shortcomings of the different air quality markers, namely the NO2 vertical column densities and NO2 surface concentrations. S5P/TROPOMI NO2 surface concentrations are inferred by multiplying the fraction of the satellite and model NO2 vertical column densities with the model surface concentrations. The estimated inferred TROPOMI NO2 surface concentrations are examined further with the altering of three influencing factors: the model vertical leveling scheme, the versions of the TROPOMI NO2 data and the air mass factors applied to the satellite and model NO2 vertical column densities. Overall, the inferred TROPOMI NO2 surface concentrations show a better correlation with the in situ measurements for both time periods and all station types, especially for the industrial stations (R > 0.6) in winter. The calculated correlation for background stations is moderate for both periods (R~0.5 in summer and R > 0.5 in winter), whereas for traffic stations it improves in the winter (from 0.20 to 0.50). After the implementation of the air mass factors from the local model, the bias is significantly reduced for most of the station types, especially in winter for the background stations, ranging from +0.49% for the urban background to +10.37% for the rural background stations. The mean relative bias in winter between the inferred S5P/TROPOMI NO2 surface concentrations and the ground-based measurements for industrial stations is about −15%, whereas for traffic urban stations it is approximately −25%. In summer, biases are generally higher for all station types, especially for the traffic stations (~−75%), ranging from −54% to −30% for the background and industrial stations.
Shift Pooling PSPNet: Rethinking PSPNet for Building Extraction in Remote Sensing Images from Entire Local Feature Pooling
Authors: Yuan Wei , Wang Jin , Xu Wenbo
Published: October 2022

Building extraction by deep learning from remote sensing images is currently a research hotspot. PSPNet is one of the classic semantic segmentation models and is currently adopted by many applications. Moreover, PSPNet can use not only CNN-based networks but also transformer-based networks as backbones; therefore, PSPNet also has high value in the transformer era. The core of PSPNet is the pyramid pooling module, which gives PSPNet the ability to capture the local features of different scales. However, the pyramid pooling module also has obvious shortcomings. The grid is fixed, and the pixels close to the edge of the grid cannot obtain the entire local features. To address this issue, an improved PSPNet network architecture named shift pooling PSPNet is proposed, which uses a module called shift pyramid pooling to replace the original pyramid pooling module, so that the pixels at the edge of the grid can also obtain the entire local features. Shift pooling is not only useful for PSPNet but also in any network that uses a fixed grid for downsampling to increase the receptive field and save computing, such as ResNet. A dense connection was adopted in decoding, and upsampling was gradually carried out. With two open datasets, the improved PSPNet, PSPNet, and some classic image segmentation models were used for comparative experiments. The results show that our method is the best according to the evaluation metrics, and the predicted image is closer to the label.
Spatiotemporal Prediction of Monthly Sea Subsurface Temperature Fields Using a 3D U-Net-Based Model
Authors: Sun Nengli , Zhou Zeming , Li Qian , Zhou Xuan
Published: October 2022

The ability to monitor and predict sea temperature is crucial for determining the likelihood that ocean-related events will occur. However, most studies have focused on predicting sea surface temperature, and less attention has been paid to predicting sea subsurface temperature (SSbT), which can reflect the thermal state of the entire ocean. In this study, we use a 3D U-Net model to predict the SSbT in the upper 400 m of the Pacific Ocean and its adjacent oceans for lead times of 12 months. Two reconstructed SSbT products are added to the training set to solve the problem of insufficient observation data. Experimental results indicate that this method can predict the ocean temperature more accurately than previous methods in most depth layers. The root mean square error and mean absolute error of the predicted SSbT fields for all lead times are within 0.5–0.7 °C and 0.3–0.45 °C, respectively, while the average correlation coefficient scores of the predicted SSbT profiles are above 0.96 for almost all lead times. In addition, a case study qualitatively demonstrates that the 3D U-Net model can predict realistic SSbT variations in the study area and, thus, facilitate understanding of future changes in the thermal state of the subsurface ocean.
Improving the Modeling of Sea Surface Currents in the Persian Gulf and the Oman Sea Using Data Assimilation of Satellite Altimetry and Hydrographic Observations
Authors: Pirooznia Mahmoud , Raoofian Naeeni Mehdi , Atabati Alireza , Tourian Mohammad J.
Published: October 2022

Sea surface currents are often modeled using numerical models without adequately addressing the issue of model calibration at the regional scale. The aim of this study is to calibrate the MIKE 21 numerical ocean model for the Persian Gulf and the Oman Sea to improve the sea surface currents obtained from the model. The calibration was performed through data assimilation of the model with altimetry and hydrographic observations using variational data assimilation, where the weights of the objective functions were defined based on the type of observations and optimized using metaheuristic optimization methods. According to the results, the calibration of the model generally led the model results closer to the observations. This was reflected in an improvement of about 0.09 m/s in the obtained sea surface currents. It also allowed for more accurate evaluations of model parameters, such as Smagorinsky and Manning coefficients. Moreover, the root mean square error values between the satellite altimetry observations at control stations and the assimilated model varied between 0.058 and 0.085 m. We further showed that the kinetic energy produced by sea surface currents could be used for generating electricity in the Oman Sea and near Jask harbor.
Power Pylon Reconstruction from Airborne LiDAR Data Based on Component Segmentation and Model Matching
Authors: Qiao Yiya , Xi Xiaohuan , Nie Sheng , Wang Pu , Guo Hao , Wang Cheng
Published: October 2022

In recent years, with the rapid growth of State Grid digitization, it has become necessary to perform three-dimensional (3D) reconstruction of power elements with high efficiency and precision to achieve full coverage when simulating important transmission lines. Limited by the performance of acquisition equipment and the environment, the actual scanned point cloud usually has problems such as noise interference and data loss, presenting a great challenge for 3D reconstruction. This study proposes a model-driven 3D reconstruction method based on Airborne LiDAR point cloud data. Firstly, power pylon redirection is realized based on the Principal Component Analysis (PCA) algorithm. Secondly, the vertical and horizontal distribution characteristics of the power pylon point cloud and the graphical characteristics of the overall two-dimensional (2D) orthographic projection are analyzed to determine segmentation positions and the key segmentation position of the power pylon. The 2D alpha shape algorithm is adopted to obtain the pylon body contour points, and then the pylon feature points are extracted and corrected. Based on feature points, the components of original pylon and model pylon are registered, and the distance between the original point cloud and the model point cloud is calculated at the same time. Finally, the model with the highest matching degree is regarded as the reconstructed model of the pylon. The main advantages of the proposed method include: (1) identifying the key segmentation position according to the graphical characteristics; (2) for some pylons with much missing data, the complete model can be accurately reconstructed. The average RMSE (Root-Mean-Square Error) of all power pylon components in this study was 15.4 cm. The experimental results reveal that the effects of power pylon structure segmentation and reconstruction are satisfactory, which provides method and model support for digital management and security analysis of transmission lines.
A Convolutional Neural Network for Large-Scale Greenhouse Extraction from Satellite Images Considering Spatial Features
Authors: Chen Zhengchao , Wu Zhaoming , Gao Jixi , Cai Mingyong , Yang Xuan , Chen Pan , Li Qingting
Published: October 2022

Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network’s ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction.
On the Sensitivity of a Ground-Based Tropospheric Lidar to Aitken Mode Particles in the Upper Troposphere
Authors: Silva Matheus T. , Guerrero-Rascado Juan Luis , Correia Alexandre L. , Gouveia Diego A. , Barbosa Henrique M. J.
Published: October 2022

Airborne observations have shown high concentrations of ultrafine aerosols in the Amazon upper troposphere (UT), which are key for replenishing the planetary boundary layer (PBL) with cloud condensation nuclei that sustain the “green ocean” clouds. Given their climatic relevance, long-term observations are needed, but aircraft measurements are only available in short-term campaigns. Alternatively, continuous observations of the aerosol vertical structure could be performed by a lidar (acronym for “light detection and ranging”) system in long-term campaigns. Here we assess whether a ground-based tropospheric lidar system could detect these ultrafine UT aerosols. To this aim, we simulated the lidar signal of a real instrument and then varied the instrument’s efficiency and the UT-particle concentration to determine under which conditions the detection is possible. Optical properties were computed with a Mie code based on the size distributions and numerical concentration profiles measured by the aircraft, and on the refractive indexes inverted from AERONET measurements. The aerosol optical depth (AOD) was retrieved by inverting the elastic lidar signal, and a statistical test was applied to evaluate the detection of the UT-aerosol layer. Our results indicate that, for the instrument we simulated, a 55-fold increase in the signal-to-noise ratio (SNR) is required for a 100% detection rate. This could be achieved by simultaneously time averaging over 30 min and spatially averaging to vertical bin lengths of 375 m, or by modifying the hardware. We repeated the analysis for under- and overestimated aerosol lidar ratio (Laer), and found that possible systematic errors did not affect the detection rate. Further studies are necessary to assess whether such long-time averages are feasible in the Amazon region (given the very high cloud cover), and to design a hardware upgrade. Although simulations and analyses here were based on a particular instrument and for the presence of new organic particles in the Amazonian upper troposphere, our methodology and results are general and applicable to other instruments and sites.
Recent Seasonal Spatiotemporal Variations in Alpine Glacier Surface Elevation in the Pamir
Authors: Du Weibing , Zheng Yanchao , Li Yangyang , Bao Anming , Li Junli , Ma Dandan , Gao Xin , Pan Yaming , Wang Shuangting
Published: October 2022

Climate change can lead to seasonal surface elevation variations in alpine glaciers. This study first uses DEM (Digital Elevation Model) of Pamir glaciers to develop a denoising model for laser altimetry of ICESat-2 footprints, which reduces the standard deviation of the differences between ICESat-2 footprints and corresponding datum DEM from 13.9 to 3.6 m. Second, the study constructs a calibration processing model for solving the problem that laser footprints obtained at different times have inconsistent plane positions. We calculates plane position and elevation differences between the two laser footprints in the local area of 0.05 × 0.05° from 2018 to 2021. The elevations constructed by laser footprints shows a strong correlation with the datum elevation over the different periods, and effectively preserve the time-series variation information of glacier surface elevation (GSE). Based on these two models, the spatiotemporal variations of the surface elevation of the Pamir glaciers is established as a function of seasons. There are three main conclusions: (1) The GSE in the Pamir increased slightly from 2018 to 2021 at an average rate of +0.02 ± 0.01 m/year. The time series with elevation increase was located exactly on the glacial ablation zone, and the time series with elevation decrease occurred on the glacial accumulation zone. Both observations demonstrate the surge state of the glacier. (2) The Pamir eastern (Zone I) and northwestern (Zone III) regions had large glacier accumulation areas. GSE in these two regions has increased in recent years at yearly rates of +0.25 ± 0.13 and +0.06 ± 0.04 m/year, respectively. In contrast, the GSE of small glaciers in Zones II and IV has decreased at a yearly rate of −0.96 ± 0.37 and −0.24 ± 0.18 m/year, respectively. Climate was the primary factor influencing the increase in GSE in Zones I and III. The westerly circulation had been reinforced in recent years, and precipitation had increased dramatically at a rate of +0.99 mm/year in the northwestern section of the Pamir; this was the primary cause of the increase in GSE. (3) The increased precipitation and decreased temperature were both important factors causing an overall +0.02 ± 0.01 m/year variation of GSE in this region. The GSE in the four sub-regions showed different variation trends because of variations in temperature and precipitation. The external causes that affected the increase in GSE in the region included an average yearly temperature decrease at the rate of 0.54 ± 0.36 °C/year and a total yearly precipitation increase of 0.46 ± 0.29 mm/year in the study area from 2018 to 2021.
Review of Ship Collision Avoidance Guidance Algorithms Using Remote Sensing and Game Control
Authors: Lisowski Józef
Published: October 2022

This work provides a mathematical description of a process game for the safe driving of a ship that encounters other ships. State and control constraint variables, as well as a set of acceptable ship tactics, are taken into consideration. Multi-criteria optimization operations are developed as positional and matrix games, based on cooperative, non-cooperative, and classical (non-game) optimal steering control. Adequate algorithms for ship collision avoidance, relating to the above operations, are developed and verified through digital simulation of a real navigational situation using MATLAB/Simulink.
The Impact of Future Sea-Level Rise on Low-Lying Subsiding Coasts: A Case Study of Tavoliere Delle Puglie (Southern Italy)
Authors: Scardino Giovanni , Anzidei Marco , Petio Paolo , Serpelloni Enrico , De Santis Vincenzo , Rizzo Angela , Liso Serena Isabella , Zingaro Marina , Capolongo Domenico , Vecchio Antonio , Refice Alberto , Scicchitano Giovanni
Published: October 2022

Low-lying coastal zones are highly subject to coastal hazards as a result of sea-level rise enhanced by natural or anthropogenic land subsidence. A combined analysis using sea-level data and remote sensing techniques allows the estimation of the current rates of land subsidence and shoreline retreat, supporting the development of quantified relative sea-level projections and flood maps, which are appropriate for specific areas. This study focuses on the coastal plain of Tavoliere delle Puglie (Apulia, Southern Italy), facing the Adriatic Sea. In this area, land subsidence is mainly caused by long-term tectonic movements and sediment compaction driven by high anthropogenic pressure, such as groundwater exploitation and constructions of buildings. To assess the expected effects of relative sea-level rise for the next decades, we considered the following multidisciplinary source data: (i) sea-level-rise projections for different climatic scenarios, as reported in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, (ii) coastal topography from airborne and terrestrial LiDAR data, (iii) Vertical Land Movement (VLM) from the analysis of InSAR and GNSS data, and (iv) shoreline changes obtained from the analysis of orthophotos, historic maps, and satellite images. To assess the expected evolution of the coastal belt, the topographic data were corrected for VLM values, assuming that the rates of land subsidence will remain constant up to 2150. The sea-level-rise projections and expected flooded areas were estimated for the Shared Socioeconomic Pathways SSP1-2.6 and SSP5-8.5, corresponding to low and high greenhouse-gas concentrations, respectively. From our analysis, we estimate that in 2050, 2100, and 2150, up to 50.5 km2, 118.7 km2 and 147.7 km2 of the coast could be submerged, respectively, while beaches could retreat at rates of up to 5.8 m/yr. In this area, sea-level rise will be accelerated by natural and anthropogenic land subsidence at rates of up to −7.5 ± 1.7 mm/yr. Local infrastructure and residential areas are thus highly exposed to an increasing risk of severe inundation by storm surges and sea-level rise in the next decades.
On the Problem of the Sea Ice Detection by Orbital Microwave Doppler Radar at the Nadir Sounding
Authors: Karaev Vladimir , Titchenko Yury , Panfilova Maria , Ponur Kiril , Ryabkova Maria , Meshkov Eugeny , Kovaldov Dmitry
Published: October 2022

Orbital radars are used to monitor the state of the sea ice in the Arctic and Antarctic. The backscattering radar cross section (RCS) is used to determine the type of scattering surface. The power of the reflected signal depends on many factors, so the problem of separating sea ice and sea waves is not always unambiguous. Previous research has shown that microwave Doppler radar installed on aircrafts can be used to determine the boundary of sea ice. The width of the Doppler spectrum for wide or knife-like antenna beam depends on the statistical parameters of the reflecting surface, so sea ice and sea waves are easily separated. However, when installing a Doppler radar on a satellite, the spatial resolution becomes extremely low. In this research, we discuss the possibility of improving the spatial resolution by dividing the antenna footprint into elementary scattering cells. To do this, it is proposed to use the original incoherent synthesis procedure, which allows one to determine the dependence of the RCS on the incidence angle for an elementary scattering cell. Numerical modeling was performed and processing of model data confirmed that sea ice and sea waves are separated. The coefficient of kurtosis was used as a criterion in the algorithm. In addition, for sea waves, it is possible to determine the mean square slopes (mss) of large-scale waves, compared to the electromagnetic wavelength of sea waves along the sounding direction.
Clustering Arid Rangelands Based on NDVI Annual Patterns and Their Persistence
Authors: Sanz Ernesto , Sotoca Juan José Martín , Saa-Requejo Antonio , Díaz-Ambrona Carlos H. , Ruiz-Ramos Margarita , Rodríguez Alfredo , Tarquis Ana M.
Published: October 2022

Rangeland ecosystems comprise more than a third of the global land surface, sustaining essential ecosystem services and livelihoods. In Spain, Southeast Spain includes some of the driest regions; accordingly, rangelands from Murcia and Almeria provinces were selected for this study. We used time series metrics and the Hurst Exponent from rescale range and detrended fluctuation analysis to cluster different rangeland dynamics to classify temporally and spatially diverse rangelands. The metrics were only calculated for three time periods that showed significant NDVI changes: March to April, April to July, and September to December. Detrended fluctuation analysis was not previously employed to cluster vegetation. This study used it to improve rangeland classification. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The best clustering results were obtained when unsupervised random forest was used with the Hurst exponent calculated with detrended fluctuation analysis. We used the Silhouette Index to evaluate the clustering results and a spatial comparison with topographical data. Our results show that adding the Hurst exponent, calculated with detrended fluctuation analysis, provided a better classification when clustering NDVI time series, while classifications without the Hurst exponent or with the Hurst exponent calculated with the rescale range method showed lower silhouette values. Overall, this shows the importance of using detrending when calculating the Hurst exponent on vegetation time series, and its usefulness in studying rangeland dynamics for management and research.