bluuebunny/crossref_metadata_2025 huggingface dataset:
The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model
Authors: Rahman Azbina , Maggioni Viviana , Zhang Xinxuan , Houser Paul , Sauer Timothy , Mocko David M.
Published: January 2022

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.
TCD-Net: A Novel Deep Learning Framework for Fully Polarimetric Change Detection Using Transfer Learning
Authors: Habibollahi Rezvan , Seydi Seyd Teymoor , Hasanlou Mahdi , Mahdianpari Masoud
Published: January 2022

Due to anthropogenic and natural activities, the land surface continuously changes over time. The accurate and timely detection of changes is greatly important for environmental monitoring, resource management and planning activities. In this study, a novel deep learning-based change detection algorithm is proposed for bi-temporal polarimetric synthetic aperture radar (PolSAR) imagery using a transfer learning (TL) method. In particular, this method has been designed to automatically extract changes by applying three main steps as follows: (1) pre-processing, (2) parallel pseudo-label training sample generation based on a pre-trained model and fuzzy c-means (FCM) clustering algorithm, and (3) classification. Moreover, a new end-to-end three-channel deep neural network, called TCD-Net, has been introduced in this study. TCD-Net can learn more strong and abstract representations for the spatial information of a certain pixel. In addition, by adding an adaptive multi-scale shallow block and an adaptive multi-scale residual block to the TCD-Net architecture, this model with much lower parameters is sensitive to objects of various sizes. Experimental results on two Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) bi-temporal datasets demonstrated the effectiveness of the proposed algorithm compared to other well-known methods with an overall accuracy of 96.71% and a kappa coefficient of 0.82.
Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms
Authors: Elkhrachy Ismail
Published: January 2022

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote sensing images to extract features that are used as independent features by ML algorithms to estimate flood depths. Data of dependent variables were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, as applied to the New Cairo, Egypt, post-flash flood event from 24–26 April 2018. Gradient boosting regression (GBR), random forest regression (RFR), linear regression (LR), extreme gradient boosting regression (XGBR), multilayer perceptron neural network regression (MLPR), k-nearest neighbors regression (KNR), and support vector regression (SVR) were used to estimate floodwater depths; their outputs were compared and evaluated for accuracy using the root-mean-square error (RMSE). The RMSE accuracy for all ML algorithms was 0.18–0.22 m for depths less than 1 m (96% of all test data), indicating that ML models are relatively portable and capable of computing floodwater depths using remote sensing data as an input.
The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
Authors: Kim Jung-Rack , Lin Shih-Yuan , Oh Jong-Woo
Published: January 2022

Lava tubes, a major geomorphic element over volcanic terrain, have recently been highlighted as testbeds of the habitable environments and natural threats to unpredictable collapse. In our case study, we detected and monitored the risk of lava tube collapse on Jeju, an island off the Korean peninsula’s southern tip with more than 200 lava tubes, by conducting Interferometric Synthetic Aperture Radar (InSAR) time series analysis and a synthesized analysis of its outputs fused with spatial clues. We identified deformations up to 10 mm/year over InSAR Persistent Scatterers (PSs) obtained with Sentinel-1 time series processing in 3-year periods along with a specific geological unit. Using machine learning algorithms trained on time series deformations of samples along with clues from the spatial background, we classified candidates of potential lava tube networks primarily over coastal lava flows. What we detected in our analyses was validated via comparison with geophysical and ground surveys. Given that cavities in the lava tubes could pose serious risks, a detailed physical exploration and threat assessment of potential cave groups are required before the planned intensive construction of infrastructure on Jeju Island. We also recommend using the approach established in our study to detect undiscovered potential risks of collapse in the cavities, especially over lava tube networks, and to explore lava tubes on planetary surfaces using proposed terrestrial and planetary InSAR sensors.
Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study
Authors: Lan Hai , Stewart Kathleen , Sha Zongyao , Xie Yichun , Chang Shujuan
Published: January 2022

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.
Evaluating the Quality of Semantic Segmented 3D Point Clouds
Authors: Barnefske Eike , Sternberg Harald
Published: January 2022

Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.
Measuring Height Difference Using Two-Way Satellite Time and Frequency Transfer
Authors: Cheng Peng , Shen Wenbin , Sun Xiao , Cai Chenghui , Wu Kuangchao , Shen Ziyu
Published: January 2022

According to general relativity theory (GRT), the clock at a position with lower geopotential ticks slower than an identical one at a position with higher geopotential. Here, we provide a geopotential comparison using a non-transportable hydrogen clock and a transportable hydrogen clock for altitude transmission based on the two-way satellite time and frequency transfer (TWSTFT) technique. First, we set one hydrogen clock on the fifth floor and another hydrogen clock on the ground floor, with their height difference of 22.8 m measured by tape, and compared the time difference between these two clocks by TWSTFT for 13 days. Then, we set both clocks on the ground floor and compared the time difference between the two clocks for seven days for zero-baseline calibration (synchronization). Based on the measured time difference between the two clocks at different floors, we obtained the height difference 28.0 ± 5.4 m, which coincides well with the tape-measured result. This experiment provides a method of height propagation using precise clocks based on the TWSTFT technique.
The Coastal Imaging Research Network (CIRN)
Authors: Palmsten Margaret L. , Brodie Katherine L.
Published: January 2022

The Coastal Imaging Research Network (CIRN) is an international group of researchers who exploit signatures of phenomena in imagery of coastal, estuarine, and riverine environments. CIRN participants develop and implement new coastal imaging methodologies. The research objective of the group is to use imagery to gain a better fundamental understanding of the processes shaping those environments. Coastal imaging data may also be used to derive inputs for model boundary and initial conditions through assimilation, to validate models, and to make management decisions. CIRN was officially formed in 2016 to provide an integrative, multi-institutional group to collaborate on remotely sensed data techniques. As of 2021, the network is a collaboration between researchers from approximately 16 countries and includes investigators from universities, government laboratories and agencies, non-profits, and private companies. CIRN has a strong emphasis on education, exemplified by hosting annual “boot camps” to teach photogrammetry fundamentals and toolboxes from the CIRN code repository, as well as hosting an annual meeting for its members to present coastal imaging research. In this review article, we provide context for the development of CIRN as well as describe the goals and accomplishments of the CIRN community. We highlight components of CIRN’s resources for researchers worldwide including an open-source GitHub repository and coding boot camps. Finally, we provide CIRN’s perspective on the future of coastal imaging.
Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network
Authors: Sahoo Jaya Prakash , Prakash Allam Jaya , Pławiak Paweł , Samantray Saunak
Published: January 2022

Hand gesture recognition is one of the most effective modes of interaction between humans and computers due to being highly flexible and user-friendly. A real-time hand gesture recognition system should aim to develop a user-independent interface with high recognition performance. Nowadays, convolutional neural networks (CNNs) show high recognition rates in image classification problems. Due to the unavailability of large labeled image samples in static hand gesture images, it is a challenging task to train deep CNN networks such as AlexNet, VGG-16 and ResNet from scratch. Therefore, inspired by CNN performance, an end-to-end fine-tuning method of a pre-trained CNN model with score-level fusion technique is proposed here to recognize hand gestures in a dataset with a low number of gesture images. The effectiveness of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and regular CV tests on two benchmark datasets. A real-time American sign language (ASL) recognition system is developed and tested using the proposed technique.
Embedded UV Sensors in CMOS SOI Technology
Authors: Yampolsky Michael , Pikhay Evgeny , Roizin Yakov
Published: January 2022

We report on ultraviolet (UV) sensors employing high voltage PIN lateral photodiode strings integrated into the production RF SOI (silicon on isolator) CMOS platform. The sensors were optimized for applications that require measurements of short wavelength ultraviolet (UVC) radiation under strong visible and near-infrared lights, such as UV used for sterilization purposes, e.g., COVID-19 disinfection. Responsivity above 0.1 A/W in the UVC range was achieved, and improved blindness to visible and infrared (IR) light demonstrated by implementing back-end dielectric layers transparent to the UV, in combination with differential sensing circuits with polysilicon UV filters. Degradation of the developed sensors under short wavelength UV was investigated and design and operation regimes allowing decreased degradation were discussed. Compared with other embedded solutions, the current design is implemented in a mass-production CMOS SOI technology, without additional masks, and has high sensitivity in UVC.
Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis
Authors: Wang Lin , Meng Zuqiang
Published: January 2022

In Chinese sentiment analysis tasks, many existing methods tend to use recurrent neural networks (e.g., long short-term memory networks and gated recurrent units) and standard one-dimensional convolutional neural networks (1D-CNN) to extract features. This is because a recurrent neural network can deal with the order dependence of the data to a certain extent and the one-dimensional convolution can extract local features. Although these methods have good performance in sentiment analysis tasks, recurrent neural networks (RNNs) cannot be parallelized, resulting in time-inefficiency, and the standard 1D-CNN can only extract a single sample feature, with the result that the feature information cannot be fully utilized. To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis. Firstly, we no longer use word encoding technology but use character-based integer encoding to retain more fine-grained information. Besides, in character-level vectors, the interactive features of different elements are introduced to improve the dimensionality of feature vectors and supplement semantic information so that the input matches the model network. In order to ensure that more sentiment features are learned, group strategies are used to form several feature mapping groups, so the learning object is converted from the traditional single sample to the learning of the feature mapping group, so as to achieve the purpose of learning more features. Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales. The experimental results on the Chinese dataset show that our proposed method outperforms other baseline and state-of-the-art methods.
Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle
Authors: Cui Xue-Zhi , Feng Quan , Wang Shu-Zhi , Zhang Jian-Hua
Published: January 2022

To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential frames from monocular videos are used to train the model. The model is composed of two subnetworks—the depth estimation subnetwork and the pose estimation subnetwork. The former is a modified version of U-Net that reduces the number of bridges, while the latter takes EfficientNet-B0 as its backbone network to extract the features of sequential frames and predict the pose transformation relations between the frames. The self-supervised strategy is adopted during the training, which means the depth information labels of frames are not needed. Instead, the adjacent frames in the image sequence and the reprojection relation of the pose are used to train the model. Subnetworks’ outputs (depth map and pose relation) are used to reconstruct the input frame, then a self-supervised loss between the reconstructed input and the original input is calculated. Finally, the loss is employed to update the parameters of the two subnetworks through the backward pass. Several experiments are conducted to evaluate the model’s performance, and the results show that MonoDA has competitive accuracy over the KITTI raw dataset as well as our vineyard dataset. Besides, our method also possessed the advantage of non-sensitivity to color. On the computing platform of our UAV’s environment perceptual system NVIDIA JETSON TX2, the model could run at 18.92 FPS. To sum up, our approach provides an economical solution for depth estimation by using monocular cameras, which achieves a good trade-off between accuracy and speed and can be used as a novel auxiliary depth detection paradigm for UAVs.
Incomplete Region Estimation and Restoration of 3D Point Cloud Human Face Datasets
Authors: Uddin Kutub , Jeong Tae Hyun , Oh Byung Tae
Published: January 2022

Owing to imperfect scans, occlusions, low reflectance of the scanned surface, and packet loss, there may be several incomplete regions in the 3D point cloud dataset. These missing regions can degrade the performance of recognition, classification, segmentation, or upsampling methods in point cloud datasets. In this study, we propose a new approach to estimate the incomplete regions of 3D point cloud human face datasets using the masking method. First, we perform some preprocessing on the input point cloud, such as rotation in the left and right angles. Then, we project the preprocessed point cloud onto a 2D surface and generate masks. Finally, we interpolate the 2D projection and the mask to produce the estimated point cloud. We also designed a deep learning model to restore the estimated point cloud to improve its quality. We use chamfer distance (CD) and hausdorff distance (HD) to evaluate the proposed method on our own human face and large-scale facial model (LSFM) datasets. The proposed method achieves an average CD and HD results of 1.30 and 21.46 for our own and 1.35 and 9.08 for the LSFM datasets, respectively. The proposed method shows better results than the existing methods.
Measurement System for Unsupervised Standardized Assessments of Timed Up and Go Test and 5 Times Chair Rise Test in Community Settings—A Usability Study
Authors: Fudickar Sebastian , Pauls Alexander , Lau Sandra , Hellmers Sandra , Gebel Konstantin , Diekmann Rebecca , Bauer Jürgen M. , Hein Andreas , Koppelin Frauke
Published: January 2022

Comprehensive measurements are needed in older populations to detect physical changes, initiate prompt interventions, and prevent functional decline. While established instruments such as the Timed Up and Go (TUG) and 5 Times Chair Rise Test (5CRT) require trained clinicians to assess corresponding functional parameters, the unsupervised screening system (USS), developed in a two-stage participatory design process, has since been introduced to community-dwelling older adults. In a previous article, we investigated the USS’s measurement of the TUG and 5CRT in comparison to conventional stop-watch methods and found a high sensitivity with significant correlations and coefficients ranging from 0.73 to 0.89. This article reports insights into the design process and evaluates the usability of the USS interface. Our analysis showed high acceptance with qualitative and quantitative methods. From participant discussions, suggestions for improvement and functions for further development could be derived and discussed. The evaluated prototype offers a high potential for early detection of functional limitations in elderly people and should be tested with other target groups in other locations.
Application of a Platform for Gluten-Free Diet Evaluation and Dietary Advice: From Theory to Practice
Authors: Perez-Junkera Gesala , Vázquez-Polo Maialen , Eizagirre Francisco Javier , Benjumea Laura , Tutau Carlos , Esteban Blanca , Miranda Jonatan , Larretxi Idoia , Navarro Virginia , Churruca Itziar , Lasa Arrate
Published: January 2022

The present work aimed to analyze, through the GlutenFreeDiet digital platform, the evolution over one year of the nutritional status, dietary profile and symptoms present among celiac people on a gluten-free diet (GFD) while receiving individualized dietary advice. Twenty-seven adults and thirty-one celiac children/adolescents participated in the study. This was then followed up by three visits, at diagnosis, and after 3 and 12 months (vt0, vt3 and vt12). Participants filled out dietary and gastrointestinal symptoms questionnaires. All patients received written personalized dietary advice from dietitians who interpreted data from the platform. Results obtained indicated that participants consumed proteins and lipids in excess and carbohydrates in defect. Low intakes of cereals, fruit and vegetables and high meat intakes were observed. However, gluten-free product (GFP) consumption and that of ultra-processed foods was reduced after 1 year in adults. Symptoms decreased after vt3 but recurred in vt12. Changes in ultra-processed foods and GFP intake, but lack of changes in the rest of the parameters suggested that the platform support was not effective enough. Even though the platform represents a useful tool for monitoring celiac patients and giving dietary advice, modules that require more continuous attention and nutritional education of patients should be provided for interventions to be more effective.
Comprehensive Analysis of Microgrids Configurations and Topologies
Authors: Cabana-Jiménez Katherine , Candelo-Becerra John E. , Sousa Santos Vladimir
Published: January 2022

Microgrids have been proposed as a solution to the growing deterioration of traditional electrical power systems and the energy transition towards renewable sources. One of the most important aspects of the efficient operation of a microgrid is its topology, that is, how the components are connected. Some papers have studied microgrid topologies; however, these studies do not perform an exhaustive analysis of the types of topologies, their applications, characteristics, or technical advantages and disadvantages. The contribution of this paper is the integration of the most important functional properties of microgrid topologies in terms of reliability, efficiency, structure, costs, and control methods. The study analyzes 21 topologies divided into six classifications with their respective sub-classifications. The analysis was based on the characteristics of the current (AC or DC), the control mechanisms, the transition between the operating modes, and the operating costs. As a result of the evaluation, it was evidenced that SST-based completely isolated coupled AC topologies, completely isolated two-stage AC decoupled, and multiple microgrids show the best performances. In contrast, the use of two-stage and three-stage partially isolated AC decoupled topologies is not recommended because of their high operating cost and low efficiency and reliability.
A Path Model of University Dropout Predictors: The Role of Satisfaction, the Use of Self-Regulation Learning Strategies and Students’ Engagement
Authors: Bernardo Ana , Galve-González Celia , Núñez José , Almeida Leandro
Published: January 2022

University dropout is a phenomenon that is a concern in many countries all over the world. However, although there are studies in which the direct relationship of the personal and contextual variables is observed individually to predict dropout, there is little research to know whether any of these variables mediate each other in a more dynamic and complex model. Thus, the objective of this study was to analyze the extent to which the intention to drop out of university courses is predicted by (i) satisfaction and expectations with the course, (ii) engagement with the course, and (iii) by the use of Self-Regulated Learning (SRL) strategies. Eight hundred and seventy-seven students from two Spanish universities completed the CARE questionnaire. Path analyses were performed using Mplus 8.3. The data obtained indicate that the intention to drop out is directly and significantly explained by students´ engagement (in 17.8%) and indirectly explained by the use of SRL strategies through engagement. Changes in engagement and in the use of SRL strategies were seen to be associated with satisfaction. Finally, the effect of satisfaction and the use of SRL strategies explained a proportion of students’ engagement (53.6%). It is important for research or interventions focused on students’ intention to drop out to understand that there are multiple variables that both directly and indirectly influence those intentions.
The Carbon Inventory of the Reuse Phase’s Life Cycle: The Example of the Reconstruction of a Zero-Carbon Campus on an Unused Military Camp
Authors: Liu Hua-Yueh , Lee Hsi-Chieh
Published: January 2022

Quemoy University has taken over Cemetery 2 region after it was released by the military. It became the school’s other campus, with plans to change the site from an unused military camp to a sustainable campus. The finished project will include a carbon inventory of the buildings and landscape of the original camp, and overall campus planning and design. Incorporating the concept of applying GIS, the planning and survey data mentioned above will be used as the basis for the future research design and implementation. Aerial maps will be combined with cadastral and topographic maps to establish a basic evaluation resource map: a site plan map, data point map, building carbon footprint map, and route carbon footprint map. The main carbon hotspot of each building’s life cycle total carbon footprint is 549,293.14 kgCO2e/30 yr. Through putting solar panel systems on the buildings’ rooftops and the 30 year landscape carbon inventory principle of no disturbance as the carbon offset, the unused military campus will become a zero-carbon campus. The maps above will act as carbon diagnostics for future campus operation carbon footprint analysis and provide the current situation of the campus’s environmental sustainability and future visual scenario simulations, helping decision makers to build a sustainable campus environment strategy.
Assessing the State of ICZM in an Island Tourist Destination—Applying SESs and Ostrom’s Collective Action Principles: A View from Coastal Communities
Authors: Arefipour Tahereh , Alipour Habib , Safaeimanesh Farzad
Published: January 2022

This paper aims to investigate the state of integrated coastal zone management (ICZM), which is justified as a strategy for managing coastal resources with respect to increasing pressures from tourism, farming, climate change, urbanization, population growth, etc. In the case of island states, the impact of tourism and second-home development is paramount. The use of coastal areas as commons and ICZM as a governance strategy have been established for a long time; however, the implementation of ICZM has remained a challenge due to the forces of global mass tourism and unsustainable resource use in island states. This study focused on views of the coastal communities in North Cyprus, who are in constant interaction with coastal ecosystems for their livelihood. For the analytical purpose of the study, 251 survey questionnaires were administered to eight communities along the coastal areas. Data analysis was conducted using descriptive statistical analysis with a post hoc test. Socio-ecological systems (SES) and Ostrom’s collective action principles guided the study as the main theoretical frameworks. The study revealed that the ICZM strategy has been neglected and coastal communities are not invited to be involved in any form of ICZM. Furthermore, the study revealed the tourism development has been the major activity of the Anthropocene in coastal areas without a proactive coastal development strategy that is supposed to consider the vulnerability of coastal ecosystems. Practical and theoretical implications are also discussed.
Assessment of Quality Indicators of Pressed Biofuel Produced from Coarse Herbaceous Plants and Determination of the Influence of Moisture on the Properties of Pellets
Authors: Jasinskas Algirdas , Kleiza Vytautas , Streikus Dionizas , Domeika Rolandas , Vaiciukevičius Edvardas , Gramauskas Gvidas , Valentin Marvin T.
Published: January 2022

Three coarse herbaceous energy plants—such as Miscanthus (Miscanthus sinensis), sida (Sida hermaphrodita Rusby) and cup plant (Silphium perfoliatum L.)—were grown and investigated in the experimental fields of Vytautas Magnus University Agriculture Academy, and the technical means of plant processing and utilization for solid biofuel were investigated. The physical–mechanical properties and quality indicators (moisture content, biometrical properties, density, and resistance to compression) of coarse stem herbaceous plants milled and compressed into 6 mm diameter pellets were investigated. The moisture content of the tested pellets was sufficiently low and ranged from 8.7% to 9.6%. The highest density was that of sida pellets (1072.3 ± 43.4 kg m−3 DM), and the lowest density was that of Miscanthus pellets (713.5 ± 67.1 kg m−3 DM). In order to evaluate the influence of moisture content on the properties of biofuel pressed into pellets, the density and the destructive compressive force of the different-moisture pellets were investigated and their change in the range of 5–15% pellet moisture content was evaluated. Criterion k was calculated to determine the effect of moisture on the pellet quality indicators (density, destructive compressive force, and lower heating value), and the following results were obtained: the highest influence of moisture on density was observed in sida (k = 34.280), on destructive compressive force in Miscanthus (k = 14.5), and on the lower heating value, also in Miscanthus (k = 0.198). After a comprehensive investigation and evaluation of these properties, an empirical model suitable for practical use was developed and prepared. Emissions of harmful gases, such as carbon monoxide, carbon dioxide, and nitrogen oxides, were determined when various coarse stem herbaceous energy plants were burned. The determined emissions of harmful gases into the environment did not exceed the permissible values.
Research Hotspots and Evolution Trends of Carbon Neutrality—Visual Analysis of Bibliometrics Based on CiteSpace
Authors: Wang Duomin , Huangfu Yubin , Dong Zuoji , Dong Yiqi
Published: January 2022

Climate change is one of the most urgent challenges facing the world. All countries should take joint actions to achieve the goal of carbon neutrality, which include controlling global warming to within a 1.5 °C temperature rise, to mitigate the extreme harm caused by climate change. However, ways in which to achieve economically and environmentally sustainable carbon neutrality are yet to be established. Carbon neutrality appears frequently in international policy and the scientific literature, but there is little detailed literature. It is necessary to conduct an in-depth analysis of the development context of its research. This paper analyzed the literature on carbon neutrality using bibliometric methods. A total of 1383 research papers were collected from the “Web of Science core database” from 1995 to 2021. Descriptive statistical analysis and keyword co-occurrence and literature co-citation network analyses were utilized to sort the research hotspots, and the detected bursts, the top 30 keywords in terms of word frequency, and 12 clusters were selected. It was found that the existing carbon neutrality research literature mainly focuses on carbon neutrality energy transformation, carbon neutrality technology development, carbon neutrality effect evaluation, and carbon neutrality industry examples. The analysis process involved comprehensively reading the key articles and considering the co-citation, burstiness, centrality, and other indicators under clustering; the carbon neutrality research was then divided into three stages, and evolving themes were observed. Based on the burst detection, this paper holds that with the energy structure transformation, energy consumption assessment and carbon neutrality schemes of various industries, carbon dioxide capture technology, and biogas resource utilization, urban carbon neutrality policy will become a research hotspot in the future. This paper helps to provide a reference for scholars’ theoretical research and has important reference value for policymakers to formulate relevant policy measures. It is helpful for enterprises to make strategic decisions and determine the direction of technology, for R&D and investment, and it is of considerable significance to promote the research of carbon neutrality technology.
The Impact of Energy Tax on Carbon Emission Mitigation: An Integrated Analysis Using CGE and SDA
Authors: Li Hong , Wang Jing , Wang Shuai
Published: January 2022

By originally integrating the structural decomposition analysis (SDA) into a computable general equilibrium (CGE) model, this paper simulates and analyzes the impact and mechanism of energy taxes on carbon emissions. Changes in carbon dioxide emissions, energy consumption structure, and other macroeconomic variables are investigated under different pre-set scenarios. The conclusion shows that the imposition of an ad valorem energy tax will indeed impact the production and consumption of enterprises. A higher tax rate leads to more pronounced reductions in carbon dioxide emissions. The carbon intensity effect is the dominant factor driving national carbon emissions and carbon emission intensity decline. Although the production structure effect and final demand effect play a role, their influences are relatively weak. While levying energy taxes, subsidies for personal income tax or corporate production tax can achieve double dividends. The progress of energy utilization technology is capable of increasing unit energy output and easing the negative impact of energy tax collection, and the gross national product may rise rather than fall. Under this circumstance, the production structure effect will play a greater role because the total demand coefficients of various industries for energy industry products will further decline. Only by levying energy taxes on coal and oil, exempting energy taxes on natural gas, or using energy tax revenue to subsidize investment in the natural gas industry can the government optimize the energy consumption structure. Subsidies will boost final demand for the natural gas mining and processing industry and increase the consumption share of natural gas, a cleaner energy source than coal and oil, which is critical in the current energy transition process.
Social Networks, Social Responsibility, and Sustainable Development of Chinese Corporations in Africa
Authors: Yang Xiaoting , Yang Jie , An Ning
Published: January 2022

Based on the questionnaire data and the qualitative data obtained in two field surveys in Zimbabwe in 2018 and 2019, this study examined local people’s attitudes towards Chinese corporations. Focusing on locals’ impressions of Chinese corporations and their willingness to support the sustainable development of Chinese corporations as dependent variables, and using demographic statistics, social networks, and social responsibility as independent variables, we applied multiple linear regression analysis to explore the effect of Chinese corporations’ social networks and social responsibility on their sustainability. The findings showed that social networks overall help to enhance locals’ impressions of Chinese corporations, but social networks have no positive impact on local communities’ willingness to support more Chinese corporations. In contrast, corporate social responsibility clearly has a much greater impact on the capability of the corporation to gain support from local communities for its sustainable development. This study speaks to the need for a scholarly investigation of China–Africa links as a global hotspot issue and provides a reference value for Chinese or other corporations for how to handle the relationship with local communities.
Irrational Carbon Emission Transfers in Supply Chains under Environmental Regulation: Identification and Optimization
Authors: Sun Licheng , Fang Sui
Published: January 2022

Irrational transfer of carbon emissions in the supply chain refers to the phenomenon that after the transfer of carbon emissions occurs, the profits of any party in the supply chain are reduced compared to before the transfer. Identifying and optimizing irrational transfers of carbon emissions in supply chains under environmental regulation are the bases for establishing green supply chains. By constructing a manufacturer-led Steinberg model, we obtained identification intervals for such transfers, then analyzed the influences of the changes in various coefficients. Finally, we designed a carbon emission transfer cost-sharing contract to obtain optimized intervals for shifts from irrational to rational transfers and used a Nash bargaining model to obtain the optimal share rates within the intervals. The results indicated irrational transfer intervals existed in supply chains. When a supplier has a low ability to receive transfers, the range of the irrational transfer intervals increases as the supplier’s capacity coefficient for receiving carbon emission transfers, the transfer investment cost coefficient, the emission reduction investment cost coefficient, and the consumer’s low-carbon awareness intensity increase. Otherwise, the range decreases as these coefficients increase when the supplier’s ability to receive transfers has a large coefficient. In this range, a cost-sharing contract can effectively shift the transfers from irrational to rational and an optimal cost-sharing ratio can help the transfers reach the optimal level, which is beneficial in terms of constructing a green supply chain.
A Regularized Generalized Popov’s Method to Solve the Hierarchical Variational Inequality Problem with Generalized Lipschitzian Mappings
Authors: Wang Yuanheng , Gao Yidan , Jiang Bingnan
Published: January 2022

In this article, we introduce a new inertial multi-step regularized generalized Popov’s extra-gradient method to solve the hierarchical variational inequality problem (HVIP). We extend the previous Lipschitzian and strongly monotone mapping to a hemicontinuous, generalized Lipschitzian and strongly monotone mapping. We also obtain a strong convergence theorem about the new Popov’s algorithm. Furthermore, we utilize some numerical experiments to highlight the feasibility and effectiveness of our method.
Co-Exposure with an Invasive Seaweed Exudate Increases Toxicity of Polyamide Microplastics in the Marine Mussel Mytilus galloprovincialis
Authors: Rodrigues Filipa , Vieira Hugo , Campos Diana , Pires Sílvia , Rodrigues Andreia , Silva Ana , Soares Amadeu , Oliveira Jacinta , Bordalo Maria
Published: January 2022

Plastic pollution and invasive species are recognised as pervasive threats to marine biodiversity. However, despite the extensive on-going research on microplastics’ effects in the biota, knowledge on their combination with additional stressors is still limited. This study investigates the effects of polyamide microplastics (PA-MPs, 1 mg/L), alone and in combination with the toxic exudate from the invasive red seaweed Asparagopsis armata (2%), after a 96 h exposure, in the mussel Mytilus galloprovincialis. Biochemical responses associated with oxidative stress and damage, neurotoxicity, and energy metabolism were evaluated in different tissues (gills, digestive gland, and muscle). Byssus production and PA-MP accumulation were also assessed. Results demonstrated that PA-MPs accumulated the most in the digestive gland of mussels under PA-MP and exudate co-exposure. Furthermore, the combination of stressors also resulted in oxidative damage at the protein level in the gills as well as in a significant reduction in byssus production. Metabolic capacity increased in both PA-MP treatments, consequently affecting the energy balance in mussels under combined stress. Overall, results show a potential increase of PA-MPs toxicity in the presence of A. armata exudate, highlighting the importance of assessing the impact of microplastics in realistic scenarios, specifically in combination with co-occurring stressors, such as invasive species.
Exposure of Malaysian Children to Air Pollutants over the School Day
Authors: Ezani Eliani , Brimblecombe Peter
Published: January 2022

Children are sensitive to air pollution and spend long hours in and around their schools, so the school day has an important impact on their overall exposure. This study of Kuala Lumpur, Selangor and its surroundings assesses exposure to PM2.5 and NO2, from travel, play and study over a typical school day. Most Malaysian children in urban areas are driven to school, so they probably experience peak NO2 concentrations in the drop-off and pick-up zones. Cyclists are likely to receive the greatest school travel exposure during their commute, but typically, the largest cumulative exposure occurs in classrooms through the long school day. Indoor concentrations tend to be high, as classrooms are well ventilated with ambient air. Exposure to PM2.5 is relatively evenly spread across Selangor, but NO2 exposure tends to be higher in areas with a high population density and heavy traffic. Despite this, ambient PM2.5 may be more critical and exceed guidelines as it is a particular problem during periods of widespread biomass burning. A thoughtful adjustment to school approach roads, design of playgrounds and building layout and maintenance may help minimise exposure.
Identification and Characterization of a New Type of Holin-Endolysin Lysis Cassette in Acidovorax oryzae Phage AP1
Authors: Zhang Muchen , Wang Yanli , Chen Jie , Hong Xianxian , Xu Xinyan , Wu Zhifeng , Ahmed Temoor , Loh Belinda , Leptihn Sebastian , Hassan Sabry , Hassan Mohamed M. , Sun Guochang , Li Bin
Published: January 2022

Phages utilize lysis systems to allow the release of newly assembled viral particles that kill the bacterial host. This is also the case for phage AP1, which infects the rice pathogen Acidovorax oryzae. However, how lysis occurs on a molecular level is currently unknown. We performed in silico bioinformatics analyses, which indicated that the lysis cassette contains a holin (HolAP) and endolysin (LysAP), which are encoded by two adjacent genes. Recombinant expression of LysAP caused Escherichia coli lysis, while HolAP arrested growth. Co-expression of both proteins resulted in enhanced lysis activity compared to the individual proteins alone. Interestingly, LysAP contains a C-terminal region transmembrane domain, which is different from most known endolysins where a N-terminal hydrophobic region is found, with the potential to insert into the membrane. We show that the C-terminal transmembrane domain is crucial for protein localization and bacterial lysis in phage AP1. Our study characterizes the new phage lysis cassette and the mechanism to induce cell disruption, giving new insight in the understanding of phage life cycles.
Presence of Recombinant Bat Coronavirus GCCDC1 in Cambodian Bats
Authors: Zhu Feng , Duong Veasna , Lim Xiao Fang , Hul Vibol , Chawla Tanu , Keatts Lucy , Goldstein Tracey , Hassanin Alexandre , Tu Vuong Tan , Buchy Philippe , Sessions October M. , Wang Lin-Fa , Dussart Philippe , Anderson Danielle E.
Published: January 2022

Bats have been recognized as an exceptional viral reservoir, especially for coronaviruses. At least three bat zoonotic coronaviruses (SARS-CoV, MERS-CoV and SARS-CoV-2) have been shown to cause severe diseases in humans and it is expected more will emerge. One of the major features of CoVs is that they are all highly prone to recombination. An extreme example is the insertion of the P10 gene from reoviruses in the bat CoV GCCDC1, first discovered in Rousettus leschenaultii bats in China. Here, we report the detection of GCCDC1 in four different bat species (Eonycteris spelaea, Cynopterus sphinx, Rhinolophus shameli and Rousettus sp.) in Cambodia. This finding demonstrates a much broader geographic and bat species range for this virus and indicates common cross-species transmission. Interestingly, one of the bat samples showed a co-infection with an Alpha CoV most closely related to RsYN14, a virus recently discovered in the same genus (Rhinolophus) of bat in Yunnan, China, 2020. Taken together, our latest findings highlight the need to conduct active surveillance in bats to assess the risk of emerging CoVs, especially in Southeast Asia.