bluuebunny/crossref_metadata_2025 huggingface dataset:
Natural Resources of Medicinal and Cosmetic Plants
Authors: Stebel Adam
Published: May 2022

This Special Issue (SI) of Plants is devoted to medicinal and cosmetic plants [...]
Influence of RAFT Agent on the Mechanism of Copolymerization of Polypropylene Glycol Maleinate with Acrylic Acid
Authors: Burkeev Meiram Zh. , Zhunissova Meruyert S. , Tazhbayev Yerkeblan M. , Fomin Vitaliy N. , Sarsenbekova Akmaral Zh. , Burkeyeva Gulsym K. , Kazhmuratova Akerke T. , Zhumagalieva Tolkyn S. , Zhakupbekova Elmira Zh. , Khamitova Tolkyn O.
Published: May 2022

Studies have shown the possibility of synthesizing new polymers based on polypropylene glycol maleate with acrylic acid in the presence of a RAFT agent (2-Cyano-2-propyl dodecyl trithiocarbonate CPDT). The effect of RAFT agent concentration on network density has been shown to be connected with product yield. Herein, the composition of the obtained copolymers was determined using FTIR spectrometry in combination with the chemometric method of partial least squares (or projection to latent structures). To investigate the synthesized hydrogels, the degrees of equilibrium swelling was studied. The resulting objects were characterized by infrared spectroscopy. The surface morphology of the polymers was studied and the pore sizes were estimated using scanning electron microscopy. The structure of the test samples was confirmed by NMR spectroscopy. The thermal stability of crosslinked polymers was determined using thermogravimetry.
Paclitaxel and Myrrh oil Combination Therapy for Enhancement of Cytotoxicity against Breast Cancer; QbD Approach
Authors: Shehata Tamer M. , Elsewedy Heba S.
Published: May 2022

Paclitaxel (PX), plant alkaloid, is a chemotherapeutic agent intended for treating a wide variety of cancers. The objective of the present study was to formulate and evaluate the anticancer activity of PX loaded into a nanocarrier, mainly PEGylated nanoemulsion (NE) fabricated with myrrh essential oil. Myrrh essential oil has been estimated previously to show respectable anticancer activity. Surface modification of the formulation with PEG-DSPE would help in avoiding phagocytosis and prolong the residence time in blood circulation. Various NE formulations were developed after operating (22) factorial design, characterized for their particle size, in vitro release, and hemolytic activity. The optimized formula was selected and compared to its naked counterpart in respect to several characterizations. Quantitative amount of protein absorbed on the formulation surfaces and in vitro release with and without serum incubation were evaluated. Ultimately, MTT assay was conducted to distinguish the anti-proliferative activity. PEGylated PX-NE showed particle size 170 nm, viscosity 2.91 cP, in vitro release 57.5%, and hemolysis 3.44%, which were suitable for intravenous administration. A lower amount of serum protein adsorbed on PEGylated PX-NE surface (16.57 µg/µmol) compared to naked counterpart (45.73 µg/µmol). In vitro release from PEGylated NE following serum incubation was not greatly affected (63.3%), in contrast to the naked counterpart (78.8%). Eventually, anti-proliferative effect was obtained for PEGylated PX-NE achieving IC50 38.66 µg/mL. The results obtained recommend PEGylated NE of myrrh essential oil as a candidate nanocarrier for passive targeting of PX.
What We Know about Research on Life Insurance Lapse: A Bibliometric Analysis
Authors: Shamsuddin Siti Nurasyikin , Ismail Noriszura , Roslan Nur Firyal
Published: May 2022

A lapsed policy is an insurance policy that has become inactive due to non-payment of premiums. The word “lapse” is an insurance topic that constantly evolves, proven by the recent increase in publications on this topic. The study explores the life insurance lapse decision through a comprehensive bibliometric analysis throughout the years, concentrating on publication trends; co-authorship networks among countries, authors, and scientific journals; and the field’s evolution. The research is based on the Scopus database. Ultimately, 178 documents were retrieved and analysed, demonstrating increased literature on insurance lapse from 1971 to 2021. The authors’ keyword co-occurrence network was also analysed for possible future directions of the field. Journals originating from the United Kingdom dominate the publication on life insurance lapsation. In contrast, an author from the United States is at the first rank in terms of the co-authorship network’s total link strength. The results may help researchers define the research objective and determine the aspects of the life insurance lapse for future research.
Temporal Clustering of the Causes of Death for Mortality Modelling
Authors: Bett Nicholas , Kasozi Juma , Ruturwa Daniel
Published: May 2022

Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling.
An AR Geo-Registration Algorithm for UAV TIR Video Streams Based on Dual-Antenna RTK-GPS
Authors: Ren Xiang , Sun Min , Zhang Xianfeng , Liu Lei , Wang Xiuyuan , Zhou Hang
Published: May 2022

In emergency response and disaster rescue, unmanned aerial vehicles (UAVs) onboard thermal infrared (TIR) sensors are an essential means of acquiring ground information in the nighttime working environment. To enable field personnel to make better decisions based on TIR video streams returned from a UAV, the geographic information enhancement of TIR video streams is required. At present, it is difficult for low-cost UAVs to carry high-precision attitude sensors and thus obtain high-precision camera attitude information to meet the enhanced processing requirements of UAV TIR video streams. To this end, this paper proposes an improved Kalman filter algorithm to improve the geographic registration (geo-registration) accuracy by fusing the positioning and heading data from the dual-antenna real-time kinematic global positioning system (RTK-GPS) with onboard internal measurement unit (IMU) data. This method can yield high-precision position and attitude data in real time based on low-cost UAV hardware, based on which high-precision geo-registration results can be obtained. The computational complexity can be reduced compared with video stream feature tracking algorithms. Furthermore, the problem of unstable matching due to the low resolution and texture level of TIR video streams can be avoided. The experimental results prove that the proposed method can reduce the registration error by 66.15%, and significantly improve the geo-registration accuracy of UAV TIR video streams. Thus, it can strongly support the popularization and practicality of the application of augmented reality (AR) technology to low-cost UAV platforms.
EfficientUNet+: A Building Extraction Method for Emergency Shelters Based on Deep Learning
Authors: You Di , Wang Shixin , Wang Futao , Zhou Yi , Wang Zhenqing , Wang Jingming , Xiong Yibing
Published: May 2022

Quickly and accurately extracting buildings from remote sensing images is essential for urban planning, change detection, and disaster management applications. In particular, extracting buildings that cannot be sheltered in emergency shelters can help establish and improve a city’s overall disaster prevention system. However, small building extraction often involves problems, such as integrity, missed and false detection, and blurred boundaries. In this study, EfficientUNet+, an improved building extraction method from remote sensing images based on the UNet model, is proposed. This method uses EfficientNet-b0 as the encoder and embeds the spatial and channel squeeze and excitation (scSE) in the decoder to realize forward correction of features and improve the accuracy and speed of model extraction. Next, for the problem of blurred boundaries, we propose a joint loss function of building boundary-weighted cross-entropy and Dice loss to enforce constraints on building boundaries. Finally, model pretraining is performed using the WHU aerial building dataset with a large amount of data. The transfer learning method is used to complete the high-precision extraction of buildings with few training samples in specific scenarios. We created a Google building image dataset of emergency shelters within the Fifth Ring Road of Beijing and conducted experiments to verify the effectiveness of the method in this study. The proposed method is compared with the state-of-the-art methods, namely, DeepLabv3+, PSPNet, ResUNet, and HRNet. The results show that the EfficientUNet+ method is superior in terms of Precision, Recall, F1-Score, and mean intersection over union (mIoU). The accuracy of the EfficientUNet+ method for each index is the highest, reaching 93.01%, 89.17%, 91.05%, and 90.97%, respectively. This indicates that the method proposed in this study can effectively extract buildings in emergency shelters and has an important reference value for guiding urban emergency evacuation.
The Collection of Hyperspectral Measurements on Snow and Ice Covers in Polar Regions (SISpec 2.0)
Authors: Salvatori Rosamaria , Salzano Roberto , Valt Mauro , Cerrato Riccardo , Ghergo Stefano
Published: May 2022

The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the observed surface cover. For snow and ice cover, we present an updated version of the Snow/Ice Spectral Archive (SISpec 2.0), which has been integrated into a web portal characterized by different functionalities. The adopted metadata scheme features basic geographic data, information about the acquisition setup, and parameters describing the different surface types. While the implementation of the IACS Classification of Seasonal Snow on the Ground is the core component for snow cover, ice cover is approached using different parameters associated with its surface roughness and location. The web portal is not only a visualization tool, but also supports interoperability functionalities, providing data in the NetCDF file format. The availability of these functionalities sets the foundation for sharing a novel platform with the community and is an interesting tool for calibrating and validating data and models.
BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection
Authors: Seydi Seyd Teymoor , Rastiveis Heidar , Kalantar Bahareh , Halin Alfian Abdul , Ueda Naonori
Published: May 2022

Building damage maps can be generated from either optical or Light Detection and Ranging (Lidar) datasets. In the wake of a disaster such as an earthquake, a timely and detailed map is a critical reference for disaster teams in order to plan and perform rescue and evacuation missions. Recent studies have shown that, instead of being used individually, optical and Lidar data can potentially be fused to obtain greater detail. In this study, we explore this fusion potential, which incorporates deep learning. The overall framework involves a novel End-to-End convolutional neural network (CNN) that performs building damage detection. Specifically, our building damage detection network (BDD-Net) utilizes three deep feature streams (through a multi-scale residual depth-wise convolution block) that are fused at different levels of the network. This is unlike other fusion networks that only perform fusion at the first and the last levels. The performance of BDD-Net is evaluated under three different phases, using optical and Lidar datasets for the 2010 Haiti Earthquake. The three main phases are: (1) data preprocessing and building footprint extraction based on building vector maps, (2) sample data preparation and data augmentation, and (3) model optimization and building damage map generation. The results of building damage detection in two scenarios show that fusing the optical and Lidar datasets significantly improves building damage map generation, with an overall accuracy (OA) greater than 88%.
Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification
Authors: Li Wenmei , Chen Huaihuai , Liu Qing , Liu Haiyan , Wang Yu , Gui Guan
Published: May 2022

Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction.
Surface Deformation of Expansive Soil at Ankang Airport, China, Revealed by InSAR Observations
Authors: Zhang Shuangcheng , Si Jinzhao , Niu Yufen , Zhu Wu , Fan Qianyou , Hu Xingqun , Zhang Changbo , An Peng , Ren Zhipeng , Li Zhenhong
Published: May 2022

Ankang Airport is constructed on an expansive soil-fill platform in Shaanxi Province, Central China. Since its completion in 2020, it has suffered surface deformation caused by the consolidation and settlement of the fill layer and instability of the expansive soil slope. Exploring the special deformation law of expansive soil regions by remote sensing and analyzing the deformation characteristics of airports in mountainous areas have always been key issues in related disaster research. Based on the intensity and phase observation data of 37 Sentinel-1 synthetic aperture radar images, this study obtained the spatio-temporal distribution of the deformation of Ankang Airport from May 2020 to October 2021. First, phase optimization was performed on the original interferograms. Second, the persistent scatterer synthetic aperture radar interferometry (PS-InSAR) method was applied to extract the surface deformation information of Ankang Airport, and the accuracy was evaluated. Finally, the singular spectrum analysis method was introduced to jointly analyze the deformation information obtained by the InSAR technology in combination with geological and climatic data. The results show that the excavation area of Ankang Airport was basically stable, the filling area had obvious surface and uneven deformation, and the expansive soil fill slope exhibits deformation characteristics strongly related to slope, rainfall, and fill depth. The deformation was mainly caused by consolidation and settlement, supplemented by the expansion and shrinkage deformation of the expansive soil.
Analysis of Environmental and Atmospheric Influences in the Use of SAR and Optical Imagery from Sentinel-1, Landsat-8, and Sentinel-2 in the Operational Monitoring of Reservoir Water Level
Authors: Souza Wendson de Oliveira , Reis Luis Gustavo de Moura , Ruiz-Armenteros Antonio Miguel , Veleda Doris , Ribeiro Neto Alfredo , Fragoso Jr. Carlos Ruberto , Cabral Jaime Joaquim da Silva Pereira , Montenegro Suzana Maria Gico Lima
Published: May 2022

In this work, we aim to evaluate the feasibility and operational limitations of using Sentinel-1 synthetic aperture radar (SAR) data to monitor water levels in the Poço da Cruz reservoir from September 2016–September 2020, in the semi-arid region of northeast Brazil. To segment water/non-water features, SAR backscattering thresholding was carried out via the graphical interpretation of backscatter coefficient histograms. In addition, surrounding environmental effects on SAR polarization thresholds were investigated by applying wavelet analysis, and the Landsat-8 and Sentinel-2 normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to compare and discuss the SAR results. The assessment of the observed and estimated water levels showed that (i) SAR accuracy was equivalent to that of NDWI/Landsat-8; (ii) optical image accuracy outperformed SAR image accuracy in inlet branches, where the complexity of water features is higher; and (iii) VV polarization outperformed VH polarization. The results confirm that SAR images can be suitable for operational reservoir monitoring, offering a similar accuracy to that of multispectral indices. SAR threshold variations were strongly correlated to the normalized difference vegetation index (NDVI), the soil moisture variations in the reservoir depletion zone, and the prior precipitation quantities, which can be used as a proxy to predict cross-polarization (VH) and co-polarization (VV) thresholds. Our findings may improve the accuracy of the algorithms designed to automate the extraction of water levels using SAR data, either in isolation or combined with multispectral images.
Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network
Authors: Guo Xiaoqiang , Liu Xinhua , Królczyk Grzegorz , Sulowicz Maciej , Glowacz Adam , Gardoni Paolo , Li Zhixiong
Published: May 2022

The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.
Optimizing Calibration for a Capacitance-Based Void Fraction Sensor with Asymmetric Electrodes under Horizontal Flow in a Smoothed Circular Macro-Tube
Authors: Kim Moojoong , Komeda Kanta , Jeong Jongsoo , Oinuma Mizuki , Sato Tetsuya , Saito Kiyoshi
Published: May 2022

In this study, a technique that uses a capacitance sensor with an asymmetric electrode to measure the void fraction of a refrigerant was developed. It is known that the void fraction and flow pattern affect the measured capacitance. Therefore, the relationship between the void fraction and capacitance is not linear; hence, a calibration method for obtaining accurate measurements is necessary. A calibration method was designed in this study based on repeated capacitance measurements and the bimodal temporal distribution to calibrate the atypical and repetitive flow patterns of slug flow and its transition to the intermittent flow regime. The calibration method also considers the weighted-average relation for the gradual transition of the intermittent to annular flow pattern according to the change from low to high quality. The proposed method was experimentally analyzed under the conditions of R32 refrigerant, a tube inner diameter of 7.1 mm, saturation temperature of 25 °C, mass flux of 100–400 kg m−2 s−1, and vapor quality of 0.025–0.900, and it was validated using a quick-closing valve (QCV) system under identical conditions. A relative error of 2.99% was obtained for the entire system, indicating good agreement between the proposed and QCV-based methods.
Resource Scheduling and Energy Consumption Optimization Based on Lyapunov Optimization in Fog Computing
Authors: Huang Chenbin , Wang Hui , Zeng Lingguo , Li Ting
Published: May 2022

Delay-sensitive tasks account for an increasing proportion of all tasks on the Internet of Things (IoT). How to solve such problems has become a hot research topic. Delay-sensitive tasks scenarios include intelligent vehicles, unmanned aerial vehicles, industrial IoT, intelligent transportation, etc. More and more scenarios have delay requirements for tasks and simply reducing the delay of tasks is not enough. However, speeding up the processing speed of a task means increasing energy consumption, so we try to find a way to complete tasks on time with the lowest energy consumption. Hence, we propose a heuristic particle swarm optimization (PSO) algorithm based on a Lyapunov framework (LPSO). Since task duration and queue stability are guaranteed, a balance is achieved between the computational energy consumption of the IoT nodes, the transmission energy consumption and the fog node computing energy consumption, so that tasks can be completed with minimum energy consumption. Compared with the original PSO algorithm and the greedy algorithm, the performance of our LPSO algorithm is significantly improved.
Prioritizing the Potential Smartification Measures by Using an Integrated Decision Support System with Sustainable Development Goals (a Case Study in Southern Italy)
Authors: Guido Giuseppe , Haghshenas Sina Shaffiee , Haghshenas Sami Shaffiee , Vitale Alessandro , Gallelli Vincenzo , Astarita Vittorio
Published: May 2022

With the increasing population of cities, expanding roads as one of the essential urban infrastructures is a necessary task; therefore, adverse effects such as increased fuel consumption, pollution, noise, and road accidents are inevitable. One of the most efficient ways to mitigate congestion-related adverse effects is to introduce effective intelligent transportation systems (ITS), using advanced technologies and mobile communication protocols to make roads smarter and reduce negative impacts such as improvement in fuel consumption and pollution, and reduction of road accidents, which leads to improving quality of life. Smart roads might play a growing role in the improved safety of road transportation networks. This study aims to evaluate and rank the potential smartification measures for the road network in Calabria, in southern Italy, with sustainable development goals. For this purpose, some potential smartification measures were selected. Experts in the field were consulted using an advanced procedure: four criteria were considered for evaluating these smartification measures. The Integrated fuzzy decision support system (FDSS), namely the fuzzy Delphi analytic hierarchy process (FDAHP) with the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) were used for evaluating and ranking the potential smartification measures. The results demonstrated that the repetition of signals in the vehicle has the highest rank, and photovoltaic systems spread along the road axis has the lowest rank to use as smartification measures in the roads of the case study.
Unsettling the Hegemony of ‘Western’ Thinking: Critical Reflection on My Journey to Understanding Campesino-a-Campesino Pedagogy
Authors: Kerr Roseann
Published: May 2022

In the field of education for sustainability, there is a call to consider diverse livelihoods and world views beyond dominant anthropocentric, scientific, and ‘Western’ ways of understanding and living. For scholars and educators trained in ‘Western’ culture, this is complicated by how this dominant culture is infused in all our ways of thinking and being. This paper explores the authors’ journey to unsettle their ‘Western’ thinking through analysis of reflexive field notes taken during field research. Data is shared from the author’s doctoral study of Campesino-a-Campesino (CaC) as an anti-racist pedagogy. The paper tells a story of the unsettling of the author’s assumptions about research, race, development, and education prompted by field experiences and guided by critical educational ethnography. An interdisciplinary approach to analysis is used including scholars in critical race theory, TribalCrit, Indigenous education, decolonization theory, and post-development theory. Conclusions illuminate researcher reflexivity, understanding critical context, learning the history of research, and shifting which scholars are considered in the analysis as crucial in the process of decolonizing the study of anti-racist pedagogies for sustainability.
Environmental Effects of City–County Mergers in China: Strengthening Governance or Aggravating Pollution?
Authors: Cao Zhiying , Wang Liangjian , Zhang Yang
Published: May 2022

Green and high-quality development is the focus of China’s urban development strategy in the new era. The city–county merger policy has been one of several powerful tools used by the Chinese government to promote urbanization in recent decades, but whether and how it influences the environment has been rarely discussed. Using the multi-period difference-in-differences method and urban panel datasets, we investigated the environmental effects of the city–county merger policy in China from 2000–2016 and obtained the following results. First, the city–county mergers significantly reduce the environmental pollution of merged cities. The robustness tests support this conclusion. Second, the effects of city–county mergers on environmental pollution control decrease with the increase in geographical distance between the merged cities and counties; the smaller the differences in economic strength of merged cities and counties, the better the coordinated control of environmental pollution; the environmental governance effects of merged cities in the eastern region are lower than those in the central and western regions. Third, by intensifying the vertical management of urban environmental protection agencies, unified urban planning and fiscal centralization, the city–county mergers can strengthen the overall environmental governance capabilities of merged cities, reduce the negative effects of urbanization, and ultimately improve the environmental quality.
Relationship between Variations in the Accumulated Workload and the Change of Direction Ability in Elite Young Soccer Players
Authors: Nobari Hadi , Ramachandran Akhilesh Kumar , Moreira Mário , Khani Saeed , Khezri Davood , Mainer-Pardos Elena
Published: May 2022

Background: The main aim of this study was to evaluate the relationships between training workload (WL) parameters with variations in the change of direction (COD) in under-16 soccer players. Methods: Twenty-seven under-16 elite soccer players were daily monitored for their WL across 15 weeks during the competitive soccer season. Additionally, players were assessed two times for anthropometric measures (weight, height, sitting height and leg length), COD performance (modified 505 test) and maturity offset measured using the peak height velocity (PHV). Results: A correlational analysis was performed to determine the relationship between the variation in COD performance and accumulated WL parameters. Moreover, a regression analysis was executed to explain the variations in the percentage of COD performance considering the accumulated WL parameters and PHV of the season (r = 0.93; p ≤ 0.01) and training monotony during the early-season (r = 0.53; p ≤ 0.05). There were associations between the acute workload during the start of the season and the COD during the end of the season (r = 0.47; p ≤ 0.05). The multiple linear regression analysis showed that 55% of the variation in COD performance between the early and end of season could be explained by the acute or chronic WL, training monotony or strain and the PHV. Conclusions: This information might be useful for practitioners and coaches aiming to improve the COD performance in youth soccer players during an entire competitive season.
A Multimodal Data Analysis Approach to Social Media during Natural Disasters
Authors: Zhang Mengna , Huang Qisong , Liu Hua
Published: May 2022

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from multimodal data, then, the multimodal data is analyzed by bidirectional encoder representation from transformers (Bert) and visual geometry group 16 (Vgg-16). Text and image data are classified separately, resulting in real mining of topic information during disasters. This study uses Weibo data during the 2021 Henan heavy storm as the research object. Comparing the data with previous experiment results, this study proposes a model that can classify natural disaster topics more accurately. The accuracy of this study is 0.93. Compared with a topic-based event classification model KGE-MMSLDA, the accuracy of this study is improved by 12%. This study results in a real-time understanding of different themed natural disasters to help make informed decisions.
China’s Domestic Image and Media Use: A Case Study and Empirical Analysis of China’s Post-90s Generation
Authors: Zhu Boyan , Cheng Hong , Wanta Wayne , Zhu Liang
Published: May 2022

A study of the relationships between the image of a country and media use is one of the most appropriate methods to gain knowledge on various stakeholders’ different perceptions of the country’s sustainability. Through an online survey of China’s post-90s generation, this paper first studies the respondents’ domestic image of China (including social, political, economic, and cultural images), second, their media use behaviors, and third, the relationships between their perceptions of China’s image and their behaviors. Based on the CFA model, with 16 items obtained from the survey data, the results of the empirical analysis indicated that China’s domestic image, as well as its political, economic, and cultural images, were generally neutral for the respondents, while they tended to disagree with the social image. Furthermore, neither traditional media use time nor new media use time of the respondents had any statistically significant influence on their perceptions of China’s image, where the latter was significantly more than the former. However, the type of media contact had a significant influence on their perceptions of political image and on their perception of some items concerning economic and cultural images.
Circular Economy in the Digital Age
Authors: Bressanelli Gianmarco , Adrodegari Federico , Pigosso Daniela C. A. , Parida Vinit
Published: May 2022

The Circular Economy has been pointed out by scholars and policymakers as a promising approach to decouple economic growth from resource consumption and waste generation [...]
Designation, Incentivisation and Farmer Participation—Exploring Options for Sustainable Rural Landscapes
Authors: McDonagh John
Published: May 2022

This paper explores how policies, management practices and farmer participation can help shape resilient and sustainable rural environments. The key elements of the paper draw together policy initiatives on land designation for conservation, action-based and results-based agri-environment programmes, and locally led and inclusive partnership models. In doing this, the paper explores ways to address environmental decline, while also allowing farmers to farm and for management practices to be developed that farmers can readily endorse. The paper draws from empirical evidence gathered from two case studies in West Ireland. These studies include in-depth interviews, consultations with key stakeholders and an exploration of policy and other documentation associated with the management of rural landscapes. What emerges from the discussion and the field evidence is that, while there can be discontent, even disillusionment with some practices, there are models of great promise evolving. In particular, the research identifies the importance of enabling a space in which a farmer’s knowledge and expertise have due prominence and where they are afforded recognised input in the schemes being developed and promoted. The conclusion of the paper suggests that, while impacts vary, it is clear that combining forces from top-down and bottom-up, allied to locally led decision-making input, provides the Special Areas of Conservation combination whereby landscapes can be both farmed and protected.
Carbon Soil Storage and Technologies to Increase Soil Carbon Stocks in the South American Savanna
Authors: Castellano Gabriel Ribeiro , Santos Landerlei Almeida , Menegário Amauri Antonio
Published: May 2022

The expansion of the agricultural frontiers that occurred in the last decades in the South American savanna (Cerrado), the second-largest biome in Brazil (covering an area of 204 million hectares), has accounted for a substantial portion of South America’s CO2 emissions. In this context, our research investigated the potential for soil carbon storage in the biome. The analysis of previous data (n = 197) shows a vertical distribution pattern of soil carbon stock: 26.17% for the upper 0–30 cm layer, 37.67% for the 30–100 cm layer, and 36.15% for the 100–200 cm layer. The total soil carbon storage for the biome is 13.5 ± 6.7 gigatons (n = 71) for the upper 0–30 cm layer, 30.5 ± 18.9 Gt (n = 64) for the 0–100 cm layer, and 47.8 ± 4.3 (n = 9) for the 0–200 cm layer. The results indicate that the soil carbon stock up to 1 m deep in the Cerrado ranges from 0.5% to 2.29% of the global soil organic carbon storage for this depth. Further research is necessary to investigate what happens at a depth of at least 2 m. The results also indicate that the soil under pasture lands constitutes the largest manageable pool for increasing soil carbon stocks via the restoration of degraded pastures.
A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning
Authors: Haque Rezaul , Islam Naimul , Islam Maidul , Ahsan Md Manjurul
Published: May 2022

Social networks are essential resources to obtain information about people’s opinions and feelings towards various issues as they share their views with their friends and family. Suicidal ideation detection via online social network analysis has emerged as an essential research topic with significant difficulties in the fields of NLP and psychology in recent years. With the proper exploitation of the information in social media, the complicated early symptoms of suicidal ideations can be discovered and hence, it can save many lives. This study offers a comparative analysis of multiple machine learning and deep learning models to identify suicidal thoughts from the social media platform Twitter. The principal purpose of our research is to achieve better model performance than prior research works to recognize early indications with high accuracy and avoid suicide attempts. We applied text pre-processing and feature extraction approaches such as CountVectorizer and word embedding, and trained several machine learning and deep learning models for such a goal. Experiments were conducted on a dataset of 49,178 instances retrieved from live tweets by 18 suicidal and non-suicidal keywords using Python Tweepy API. Our experimental findings reveal that the RF model can achieve the highest classification score among machine learning algorithms, with an accuracy of 93% and an F1 score of 0.92. However, training the deep learning classifiers with word embedding increases the performance of ML models, where the BiLSTM model reaches an accuracy of 93.6% and a 0.93 F1 score.
Generation of Synthetic-Pseudo MR Images from Real CT Images
Authors: Abu-Qasmieh Isam F. , Masad Ihssan S. , Al-Quran Hiam H. , Alawneh Khaled Z.
Published: May 2022

This study aimed to generate synthetic MR images from real CT images. CT# mean and standard deviation of a moving window across every pixel in the reconstructed CT images were mapped to their corresponding tissue-mimicking types. Identification of the tissue enabled remapping it to its corresponding intrinsic parameters: T1, T2, and proton density (ρ). Lastly, synthetic weighted MR images of a selected slice were generated by simulating a spin-echo sequence using the intrinsic parameters and proper contrast parameters (TE and TR). Experiments were performed on a 3D multimodality abdominal phantom and on human knees at different TE and TR parameters to confirm the clinical effectiveness of the approach. Results demonstrated the validity of the approach of generating synthetic MR images at different weightings using only CT images and the three predefined mapping functions. The slope of the fitting line and percentage root-mean-square difference (PRD) between real and synthetic image vector representations were (0.73, 10%), (0.9, 18%), and (0.2, 8.7%) for T1-, T2-, and ρ-weighted images of the phantom, respectively. The slope and PRD for human knee images, on average, were 0.89% and 18.8%, respectively. The generated MR images provide valuable guidance for physicians with regard to deciding whether acquiring real MR images is crucial.
An Australian Approach to Managing Traumatic Abdominal Wall Hernias
Authors: Liesegang Amanda G. , Murphy Skyle J. , Theodore Jane E. , Webb Peita M. , Sethi Harsheet , Lockwood David S. R.
Published: May 2022

Traumatic abdominal wall hernias (TAWH) occur in less than one percent of trauma presentations. In the absence of clinical guidelines, management is centre-specific and largely dependent on surgeon experience. This study describes the management of TAWH in a high volume Australian trauma centre. A single centre retrospective cohort study was performed. Adults with TAWH as a result of blunt trauma, between January 2016 and July 2020, were included. Primary outcomes were the mechanism of injury, presentation, timing of repair, and operative technique used. A total of 16 patients (63% male; median age 36 years; median Injury Severity Score [ISS] 19.5) were identified. In total, 75% were the result of a motor vehicle accident. A total of 13 patients (81%) underwent repair. Eight patients received repair during emergency exploration undertaken for concurrent injuries. Three patients had a delayed repair during index admission, and two patients had an elective repair. Primary tissue repair was performed in seven patients. Mesh repair was used in six. Patients were followed for a median of 55.5 days. One patient had a recurrence of hernia following primary repair. This local series demonstrates that traumatic abdominal wall hernias may be successfully repaired during index admission using tissue or mesh techniques.
Dust Formation in the Wind of AGB Stars—The Effects of Mass, Metallicity and Gas-Dust Drift
Authors: Tosi Silvia , Dell’Agli Flavia , Huerta-Martinez Erendira , Ventura Paolo
Published: May 2022

Dust production in the wind of stars evolving through the asymptotic giant branch is investigated by using a stationary wind model, applied to results from stellar evolution modelling. Results regarding 1–8M⊙ stars of metallicities Z=0.014 (solar) and Z=2×10−3 are compared, to infer the role played by stellar mass and chemical composition on the dust formation process. We find a dichotomy in mass: stars of (initial) mass below ∼3M⊙ produce silicates and alumina dust before they become carbon stars, then carbonaceous dust; the higher mass counterparts produce only silicates and alumina dust, in quantities that scale with metallicity. The presence of drifts with average drift velocities ∼5 Km/s leads to higher dust formation rates owing to the higher growth rates of the dust grains of the different species. However, no significant changes are found in the overall optical depths, because the higher rate of dust formations favours a fast expansion of the wind, that prevents further significant production of dust. As far as oxygen-rich stars are concerned, the presence of drifts makes the main dust component to change from olivine to pyroxene. The release of the assumption that the number density of the seed particles is independent of the dust species considered affects dust formation in the wind of carbon stars: a factor 10 reduction in the density of the seeds of SiC leads to bigger sized SiC grains, and partly inhibits the formation of solid carbon, since the wind is accelerated and the densities in the carbon formation zone are smaller. No substantial differences are found in the winds of oxygen-rich stars.
An Online Experiment of NHS Information Framing on Mothers’ Vaccination Intention of Children against COVID-19
Authors: Van Hoecke Audrey L. , Sanders Jet G.
Published: May 2022

Children under the age of 5, will likely all be offered vaccination against SARS-CoV-2 soon. Parental concerns over vaccination of children are long standing and could impede the success of a vaccination campaign. In the UK, a trusted source to inform vaccination choices is the NHS website. Here we used a randomized controlled experiment of framing effects in NHS information content for COVID-19 and flu with 550 mothers under the age of 5. We compared both vaccination offers following two commonly used frames in vaccination informational campaigns: alerting to the risks of no vaccination for the child itself vs. those in their community. We find that vaccination intention was twice as high when risks to the child are emphasized, relative to risks to the community. Exploratory analyses suggest that these effects may differ between white and non-white mothers. Whilst communication directed at adult vaccination against COVID-19 generally focuses on risks of infecting others, communication about vaccination of children may benefit from emphasizing risks to the children themselves. This pattern is in line with flu vaccination research from pre-COVID-19 times.
Health Beliefs and Socioeconomic Determinants of COVID-19 Booster Vaccine Acceptance: An Indonesian Cross-Sectional Study
Authors: Wirawan Gede Benny Setia , Harjana Ngakan Putu Anom , Nugrahani Nur Wulan , Januraga Pande Putu
Published: May 2022

Introduction: The threat of new SARS-CoV-2 variants indicates the need to implement COVID-19 vaccine booster programs. The aim of this study was to identify the level of booster acceptance and its determinants. Methods: A cross-sectional online survey was conducted in Jakarta and Bali, Indonesia. Booster acceptance was divided into three categories: non-acceptor, planned acceptor, and actual acceptor. The primary independent variables were health beliefs, media influence, and trust in authoritative sources. Other covariates included demographics, socioeconomic status, and COVID-19 history. A primary analysis was conducted through multinomial logistic regression. The effects of the hypothetical situations on booster acceptance were tested using the Wilcoxon signed-rank test. Results: The final analysis included 2674 respondents with a booster acceptance rate of 56.3% (41.2% planned acceptors, 15.1% actual acceptors). Health beliefs, social media influence, and trust in authoritative information sources were identified as determinants for planned and actual booster acceptance. Socioeconomic status indicators were also identified as determinants for actual booster acceptance. Booster acceptance was increased in hypothetical scenarios involving booster requirements for work, travel, and accessing public places. Conclusions: Booster acceptance was found to be lower than the predicted primary vaccine acceptance prior to its launch. The acceleration of booster coverage requires strategies that leverage health beliefs and focus on people with a lower socioeconomic status.
Prediction of Sediment Yield in a Data-Scarce River Catchment at the Sub-Basin Scale Using Gridded Precipitation Datasets
Authors: Ijaz Muhammad Asfand , Ashraf Muhammad , Hamid Shanawar , Niaz Yasir , Waqas Muhammad Mohsin , Tariq Muhammad Atiq Ur Rehman , Saifullah Muhammad , Bhatti Muhammad Tousif , Tahir Adnan Ahmad , Ikram Kamran , Shafeeque Muhammad , Ng Anne W. M.
Published: May 2022

Water-related soil erosion is a major environmental concern for catchments with barren topography in arid and semi-arid regions. With the growing interest in irrigation infrastructure development in arid regions, the current study investigates the runoff and sediment yield for the Gomal River catchment, Pakistan. Data from a precipitation gauge and gridded products (i.e., GPCC, CFSR, and TRMM) were used as input for the SWAT model to simulate runoff and sediment yield. TRMM shows a good agreement with the data of the precipitation gauge (≈1%) during the study period, i.e., 2004–2009. However, model simulations show that the GPCC data predicts runoff better than the other gridded precipitation datasets. Similarly, sediment yield predicted with the GPCC precipitation data was in good agreement with the computed one at the gauging site (only 3% overestimated) for the study period. Moreover, GPCC overestimated the sediment yield during some years despite the underestimation of flows from the catchment. The relationship of sediment yields predicted at the sub-basin level using the gauge and GPCC precipitation datasets revealed a good correlation (R2 = 0.65) and helped identify locations for precipitation gauging sites in the catchment area. The results at the sub-basin level showed that the sub-basin located downstream of the dam site contributes three (3) times more sediment yield (i.e., 4.1%) at the barrage than its corresponding area. The findings of the study show the potential usefulness of the GPCC precipitation data for the computation of sediment yield and its spatial distribution over data-scarce catchments. The computations of sediment yield at a spatial scale provide valuable information for deciding watershed management strategies at the sub-basin level.