bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Waste Ceramic Powder on the Properties of Alkali–Activated Slag and Fly Ash Pastes Exposed to High Temperature
Authors: Zhang Gui-Yu , Bae Sung-Chul , Lin Run-Sheng , Wang Xiao-Yong
Published: November 2021

This paper presents the effects of alkali-activated blast furnace slag and fly ash (AASF) paste added with waste ceramic powder (WCP) on mechanical properties, weight loss, mesoscopic cracks, reaction products, and microstructure when exposed to 300, 600, and 900 °C. Using waste ceramic powder to replace blast furnace slag and fly ash, the replacement rate was 0–20%. The samples cured at 45 °C for 28 days were heated to 300, 600, and 900 °C to determine the residual compressive strength and weight loss at the relevant temperature. We evaluated the deterioration of the paste at each temperature through mesoscopic images, ultrasonic pulse velocity (UPV), thermogravimetric analysis (TG), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), and with a scanning electron microscope (SEM). Relevant experimental results show that: (1) with the increase in waste ceramic powder content, the compressive strength of samples at various temperatures increased, and at 300 °C, the compressive strength of all the samples reached the highest value; (2) the residual weight increased with the increase in the content of the waste ceramic powder; (3) with a further increase in temperature, all the samples produced more mesoscopic cracks; (4) at each temperature, with the rise in waste ceramic powder content, the value of the ultrasonic pulse velocity increased; (5) the TG results showed that, as the content of waste ceramic powder increased, the formation of C-A-S-H gel and hydrotalcite decreased; (6) XRD and FTIR spectra showed that, at 900 °C, the use of waste ceramic powder reduced the formation of harmful crystalline phases; (7) the SEM image showed that, at 900 °C, as the content of waste ceramic powder increased, the compactness of the sample was improved. In summary, the addition of waste ceramic powder can improve the mechanical properties of the alkali-activated paste at high temperatures, reduce the occurrence of cracks, and make the microstructure denser.
Identification of Protein Biomarker Signatures for Acute Myeloid Leukemia (AML) Using Both Nontargeted and Targeted Approaches
Authors: Dowling Paul , Tierney Ciara , Dunphy Katie , Miettinen Juho J. , Heckman Caroline A. , Bazou Despina , O’Gorman Peter
Published: November 2021

Acute myeloid leukemia (AML) is characterized by an increasing number of clonal myeloid blast cells which are incapable of differentiating into mature leukocytes. AML risk stratification is based on genetic background, which also serves as a means to identify the optimal treatment of individual patients. However, constant refinements are needed, and the inclusion of significant measurements, based on the various omics approaches that are currently available to researchers/clinicians, have the potential to increase overall accuracy with respect to patient management. Using both nontargeted (label-free mass spectrometry) and targeted (multiplex immunoassays) proteomics, a range of proteins were found to be significantly changed in AML patients with different genetic backgrounds. The inclusion of validated proteomic biomarker panels could be an important factor in the prognostic classification of AML patients. The ability to measure both cellular and secreted analytes, at diagnosis and during the course of treatment, has advantages in identifying transforming biological mechanisms in patients, assisting important clinical management decisions.
Mass Spectrometric Identification of a Novel Factor XIIIa Cross-Linking Site in Fibrinogen
Authors: Semkova Mariya E. , Hsuan J. Justin
Published: November 2021

Transglutaminases are a class of enzymes that catalyze the formation of a protein:protein cross-link between a lysine and a glutamine residue. These cross-links play important roles in diverse biological processes. Analysis of cross-linking sites in target proteins is required to elucidate their molecular action on target protein function and the molecular specificity of different transglutaminase isozymes. Mass-spectrometry using settings designed for linear peptide analysis and software designed for the analysis of disulfide bridges and chemical cross-links have previously been employed to identify transglutaminase cross-linking sites in proteins. As no control peptide with which to assess and improve the mass spectrometric analysis of TG cross-linked proteins was available, we developed a method for the enzymatic synthesis of a well-defined transglutaminase cross-linked peptide pair that mimics a predicted tryptic digestion product of collagen I. We then used this model peptide to determine optimal score thresholds for correct peptide identification from y- and b-ion series of fragments produced by collision-induced dissociation. We employed these settings in an analysis of fibrinogen cross-linked by the transglutaminase Factor XIIIa. This approach resulted in identification of a novel cross-linked peptide in the gamma subunit. We discuss the difference in behavior of ions derived from different cross-linked peptide sequences and the consequent demand for a more tailored mass spectrometry approach for cross-linked peptide identification compared to that routinely used for linear peptide analysis.
Microscopic Dynamic Mechanism of Irreversible Thermodynamic Equilibration of Crystals
Authors: Zakharov Anatoly Yu. , Zakharov Maxim A.
Published: November 2021

The dynamics of free and forced vibrations of a chain of particles are investigated in a harmonic model taking into account the retardation of interactions between atoms. It is found that the retardation of interactions between particles leads to the non-existence of stationary free vibrations of the crystal lattice. It is shown that in the case of a stable lattice, forced vibrations, regardless of the initial conditions, pass into a stationary regime. A non-statistical dynamic mechanism of the irreversible thermodynamic equilibration is proposed.
Christian Social Services in China: Growths and Limitation
Authors: Gao Jianguo
Published: November 2021

This paper discusses the types, faith attributes, development characteristics, and limitations of Christian social organizations in China. China’s religious social service organizations mainly include four types of organizations: associations, private non-enterprise units, foundations, and venues for religious activities. From the perspective of faith attributes, they can be divided into faith-permeated organizations, faith-centered organizations, faith-affiliated organizations, faith-background organizations, and faith–secular partnerships. Since the reform and opening up in 1978, China’s Christian social services have been in a period of organizational reconstruction. The main development characteristics are as follows: (a) social services have begun to take on multiple forms; (b) institutional services have transferred their original focus on those with disabilities to the elderly, and the services have been gradually extended from the low-income populations to other target groups; and (c) the basic functions of social services are still in the recovery stage, and the organizational system and service scale are not complete and sufficient. The multiple limitations of Christian social services involve (a) the social development stage and institutional background; (b) government policy text and implementation; and (c) maturity of its own organizational structure and service ability. There is still a long way to go to achieve the basic goals of reconstruction and development.
Why Open Theism Is Natural and Classical Theism Is Not
Authors: Launonen Lari , Mullins R. T.
Published: November 2021

The cognitive science of religion (CSR) indicates that belief in supernatural agents, or “gods”, is underpinned by maturationally natural cognitive biases and systems (Natural Religion). It is unclear, however, whether theism is natural. Does the god concept that our cognitive biases and systems give rise to approximate theism? In other words, is Natural Religion “theism-tracking”? As Christian theologians have different views of what God is like, we argue that the answer depends partly on one’s model of God. We discuss two models: classical theism and open theism. We argue that classical theism is far from being natural. The classical divine attributes are very hard to comprehend. Moreover, people naturally conceptualize God as a special sort of person, but the classical God strongly deviates from our cognitive expectations about persons. Open theism is much more natural. However, recent findings in CSR challenge the suggestion that Natural Religion tracks open theism. The possibility that we are “born idolaters” rather than “born believers” might undermine the Christian doctrine of general revelation and attempts to make CSR compatible with theology.
Aesthetics of Evil in Middle Ages: Beasts as Symbol of the Devil
Authors: Piñero Moral Ricardo
Published: November 2021

Since the very origin of art, human beings have faced the challenge of the representation of Evil. Within the medieval Christian context, we may find many beings which have attempted to convey the power of the devil. Demonic beings, terrifying beasts, fallen angels or even Satan himself can be frequently found and appear in many forms. They can be seen in chapitols, stained glass windows, codices … Our aim is to evaluate different creatures, animals and monstruous hybrids, which represent the efficient presence of the devil. We base our evaluation on some bestiaries, natural history books and encyclopedias from the XII and the XIII century, like the Bestiaire from Philippe de Thaon, Pierre de Beauvais, Guillaume le Clerc, or the so-called Cambridge Bestiary as well as the one from Oxford, the Livres dou Tresor from Brunetto Latini, the Liber monstrorum de diversis generibus, L’image du Monde from Gossuin, the Bestiario moralizzato di Gubbio, and of course, the Physiologus. Natural beings acquire a supernatural dimension in bestiaries and in natural history books. We will present the reader with a satanic bestiary, a short selection of these evil-related beings. In this, we will distinguish between those beasts representing evil through their ability to deceive and those which are able to generate not only fear, but also death.
Potential Temporal and Spatial Trends of Oceanographic Conditions with the Bloom of Ulva Prolifera in the West of the Southern Yellow Sea
Authors: Pan Yufeng , Ding Dong , Li Guangxue , Liu Xue , Liang Jun , Wang Xiangdong , Liu Shidong , Shi Jinghao
Published: November 2021

Based on National Oceanic and Atmospheric Administration/Advanced Very High-Resolution Radiometer (NOAA/AVHRR) remote sensing and Cross-Calibrated Multi-Platform (CCMP) wind field data from 2007 to 2019, oceanographic conditions are analysed, respectively, in the Source Area (SA) and Typical Bloom Area (TBA) of Ulva prolifera (U. prolifera) in the west of the Southern Yellow Sea (SYS) using Sea Surface Temperature (SST), Suspended Sediment Concentration (SSC) and Wind Speed over the years. The results indicate that the annual maximum SST Difference (SSTD) between U. prolifera SA and TBA is strongly consistent with the intensity of U. prolifera, and a high SST Warming Rate (WR) from May to July may constrain the U. prolifera blooms. The Taiwan Warm Current (TWC), crossing Yangtze River Estuary northward from March to April, leads to SST increasing in the SA and becomes a key trigger for the growth of U. prolifera in the early period. The amount of U. prolifera may decrease in the early period because of the lower light intensity with high SSC and turbidity in SA. The summer monsoon is one of determinants for the spread of U. prolifera, and the distribution of U. prolifera reaches its highest point with a higher mean wind speed in the TBA.
3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples
Authors: Feng Yuchao , Zheng Jianwei , Qin Mengjie , Bai Cong , Zhang Jinglin
Published: November 2021

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well known that 2D convolution suffers from the absent consideration of spectral information, while 3D convolution requires a huge amount of computational cost. In addition, the cost of labeling and the limitation of computing resources make it urgent to improve the generalization performance of the model with scarcely labeled samples. To relieve these issues, we design an end-to-end 3D octave and 2D vanilla mixed CNN, namely Oct-MCNN-HS, based on the typical 3D-2D mixed CNN (MCNN). It is worth mentioning that two feature fusion operations are deliberately constructed to climb the top of the discriminative features and practical performance. That is, 2D vanilla convolution merges the feature maps generated by 3D octave convolutions along the channel direction, and homology shifting aggregates the information of the pixels locating at the same spatial position. Extensive experiments are conducted on four publicly available HSI datasets to evaluate the effectiveness and robustness of our model, and the results verify the superiority of Oct-MCNN-HS both in efficacy and efficiency.
Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
Authors: Dey Subhadip , Bhogapurapu Narayanarao , Homayouni Saeid , Bhattacharya Avik , McNairn Heather
Published: November 2021

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter, θxP that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using θxP and the scattering entropy, HxP. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised HxP/θxP clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications.
Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics
Authors: Li Yuanrui , Zhou Qichao , Zhang Yun , Li Jingyi , Shi Kun
Published: November 2021

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.
Color Index of Transformer Oil: A Low-Cost Measurement Approach Using Ultraviolet-Blue Laser
Authors: Hasnul Hadi Muhamad Haziq , Ker Pin Jern , Lee Hui Jing , Leong Yang Sing , Hannan Mahammad A. , Jamaludin Md. Zaini , Mahdi Mohd Adzir
Published: November 2021

The color of transformer oil can be one of the first indicators determining the quality of the transformer oil and the condition of the power transformer. The current method of determining the color index (CI) of transformer oil utilizes a color comparator based on the American Society for Testing and Materials (ASTM) D1500 standard, which requires a human observer, leading to human error and a limited number of samples tested per day. This paper reports on the utilization of ultra violet-blue laser at 405- and 450-nm wavelengths to measure the CI of transformer oil. In total, 20 transformer oil samples with CI ranging from 0.5 to 7.5 were measured at optical pathlengths of 10 and 1 mm. A linear regression model was developed to determine the color index of the transformer oil. The equation was validated and verified by measuring the output power of a new batch of transformer oil samples. Data obtained from the measurements were able to quantify the CI accurately with root-mean-square errors (RMSEs) of 0.2229 for 405 nm and 0.4129 for 450 nm. This approach shows the commercialization potential of a low-cost portable device that can be used on-site for the monitoring of power transformers.
Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
Authors: Chmurski Mateusz , Mauro Gianfranco , Santra Avik , Zubert Mariusz , Dagasan Gökberk
Published: November 2021

The increasing integration of technology in our daily lives demands the development of more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.
Duck Eggshell Crack Detection by Nondestructive Sonic Measurement and Analysis
Authors: Lai Chia-Chun , Li Cheng-Han , Huang Ko-Jung , Cheng Ching-Wei
Published: November 2021

Duck eggs are a good source of essential nutrients for the human body. However, transportation, processing, and handling can easily cause cracks in the eggshells. These cracks can lead to microbial contamination, reducing the shelf life and compromising food safety. In this study, a method for the nondestructive testing of cracks in duck eggshells was developed. First, the acoustic emission signals of intact and cracked eggshells were measured, and the most significant frequency features were selected to establish a calibration curve for cracked eggshells. Logistic regression using the frequency features was then adopted to predict intact and cracked eggshells. Then, we establish a set of optimal regression models and used independent samples for verification. The overall accuracy rates of the calibration and prediction models using five frequencies of bandwidth (1500, 5000, 6000, 8500, and 10,000 Hz) were 89.7% and 87.6%, respectively. Sound measurement enables a simple and quantitative method for duck egg crack detection and classification. This nondestructive and cost-effective method can be used for duck egg quality screening and can be integrated into duck egg processing machinery.
Highly Sensitive Voltammetric Determination of Acrylamide Based on Ibuprofen Capped Mercury Nanoparticles
Authors: Sirajuddin  , Tagar Zulfiqar Ali , Ul Haq Muhammad Anwar , Shah Muhammad Raza , Mujeeb-ur-Rehman  , Sherazi Syed Tufail Hussain , Barek Jiri , Kalhoro Muhammad Siddique
Published: November 2021

Highly stable, small-sized and evenly distributed solid mercury nanoparticles capped with ibuprofen (Ibu-HgNPs) were prepared via reduction with hydrazine and capped with ibuprofen as a stabilizing agent. Characterization of Ibu-HgNPs was carried out by UV-Vis spectrophotometry and transmission electron microscopy (TEM). The prepared Ibu-HgNPs were immobilized onto a glassy carbon electrode (GCE) and used for the first time as the sensing element for voltammetric determination of low concentrations of acrylamide (AA) in aqueous solutions. Various parameters such as the type of supporting electrolyte, voltammetric mode, frequency, deposition time, stirring rate and initial potential were optimized to obtain the highest peak current of AA. The sensor delivered the best results in combination with the square wave voltammetry (SWV) mode, with good repeatability (relative standard deviation (RSD) of 25 repetitions was 1.4% for 1000 ppb AA). The study further revealed that Ibu-HgNPs are strongly adhered to GCE and hence do not contaminate the environment even after several runs. The newly developed AA sensor provides linear calibration dependence in the range of 100–1300 ppb with an R2 value of 0.996 and limit of detection (LOD) of 8.5 ppb. Negligible interference was confirmed from several organic compounds, cations and anions. The developed sensor was successfully applied for AA determination in various types of environmental real water samples to prove its practical usefulness and applicability.
Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
Authors: Lipovac Adriana , Lipovac Vlatko , Hamza Mirza , Batoš Vedran
Published: November 2021

Optical time-domain reflectometer (OTDR) enables simple identification and localization of a plethora of refractive and reflective events on a fiber link, including splices, connectors and breaks, and measuring insertion/return loss. Specifically, large enough OTDR dynamic range (DR) and thus high signal-to-noise-ratio (SNR) enable clear far-end visibility of longer fibers. We point out here that, under such conditions, the optical bit-error-rate (BER) floor is dominantly determined by reflective events that introduce significant return loss. This complements the OTDR legacy tests by appropriate optical BER floor estimation in the field. As high SNR implies inter-symbol interference as dominating error generating mechanism, we could apply the classical time-dispersion channel model for the optical BER floor determined by the root-mean-square (rms) delay spread of the actual fiber channel power-delay profile. However, as the high-SNR condition is not always fulfilled mostly due to insufficient DR, we propose here inserting a low-noise optical preamplifier as the OTDR front-end to reduce noise floor and amplify the backscattered signal. In order to verify the model for the exemplar test situation, we measured BER on the same fiber link to find very good matching between the measured BER floor values and the ones predicted from the OTDR trace.
Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
Authors: Fan Kui , Peng Peng , Zhou Hongping , Wang Lulu , Guo Zhongyi
Published: November 2021

Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.
Effects of Concurrent and Terminal Visual Feedback on Ankle Co-Contraction in Older Adults during Standing Balance
Authors: Vitali Rachel V. , Barone Vincent J. , Ferris Jamie , Stirling Leia A. , Sienko Kathleen H.
Published: November 2021

This preliminary investigation studied the effects of concurrent and terminal visual feedback during a standing balance task on ankle co-contraction, which was accomplished via surface electromyography of an agonist–antagonist muscle pair (medial gastrocnemius and tibialis anterior muscles). Two complementary mathematical definitions of co-contraction indices captured changes in ankle muscle recruitment and modulation strategies. Nineteen healthy older adults received both feedback types in a randomized order. Following an analysis of co-contraction index reliability as a function of surface electromyography normalization technique, linear mixed-effects regression analyses revealed participants learned or utilized different ankle co-contraction recruitment (i.e., relative muscle pair activity magnitudes) and modulation (i.e., absolute muscle pair activity magnitudes) strategies depending on feedback type and following the cessation of feedback use. Ankle co-contraction modulation increased when concurrent feedback was used and significantly decreased when concurrent feedback was removed. Ankle co-contraction recruitment and modulation did not significantly change when terminal feedback was used or when it was removed. Neither ankle co-contraction recruitment nor modulation was significantly different when concurrent feedback was used compared to when terminal feedback was used. The changes in ankle co-contraction recruitment and modulation were significantly different when concurrent feedback was removed as compared to when terminal feedback was removed. Finally, this study found a significant interaction between feedback type, removal of feedback, and order of use of feedback type. These results have implications for the design of balance training technologies using visual feedback.
MNNMs Integrated Control for UAV Autonomous Tracking Randomly Moving Target Based on Learning Method
Authors: Li Mingjun , Cai Zhihao , Zhao Jiang , Wang Yibo , Wang Yingxun , Lu Kelin
Published: November 2021

In this paper, we investigate the problem of unmanned aerial vehicles (UAVs) autonomous tracking moving target with only an airborne camera sensor. We proposed a novel integrated controller framework for this problem based on multi-neural-network modules (MNNMs). In this framework, two neural networks are designed for target perception and guidance control, respectively. The deep learning method and reinforcement learning method are applied to train the integrated controller. The training result demonstrates that the integrated controller can be trained more quickly and efficiently than the end-to-end controller trained by the deep reinforcement learning method. The flight tests with the integrated controller are implemented in simulated and realistic environments, the results show that the integrated controller trained in simulation can easily be transferred to the realistic environment and achieve the UAV tracking randomly moving target, which has a faster motion velocity. The integrated controller based on the MNNMs structure has a better performance on an autonomous tracking target than the control mode that combines with a perception network and a proportional integral derivative controller.
Building and Strengthening Community at the Margins of Society through Social Enterprise
Authors: Ottlewski Lydia
Published: November 2021

This article theorizes how a social enterprise builds, strengthens, and legitimizes community among marginalized people. Prior work investigated social enterprises and community-led social enterprises, or social enterprises rooted in community culture. Missing are perspectives on the roles of social enterprises in community creation and support among marginalized individuals. This qualitative interpretive study draws on ethnographic and netnographic data collection of the social enterprise Familyship; marginalized entrepreneurs developed a social enterprise to address a particular social problem, thus helping other marginalized people to address their constraints and collectively legitimizing a new meaning of what family is and does as a community. The study finds five overarching themes—namely, informing, protecting, connecting, supporting, and normalizing—that characterize Familyship’s process of building, supporting, and legitimizing a community among marginalized individuals. I discuss these findings with regard to contributions to theory on social enterprise and institutional voids, as well as social enterprise and online communities.
Women Sustainable Entrepreneurship: Review and Research Agenda
Authors: Barrachina Fernández Mercedes , García-Centeno Maria del Carmen , Calderón Patier Carmen
Published: November 2021

In various regions of the world, there is great concern about existing gender differences, which could affect opportunities for economic growth, and how to mitigate them. Entrepreneurship is of great importance to the economy and in a global society, and it is a hot topic for interested public decision makers due to its growing importance in economic activity—as it creates jobs, increases competitiveness and modernizes the economy. Sustainability is also a critical topic when designing the future economy, and combining female entrepreneurship with sustainability results in a very interesting topic to be evaluated when pursuing sustainable development. This paper tries to shed light on the relationship between female entrepreneurship and sustainability by analyzing 28 different papers from the Web of Science (WoS) database. Its main conclusion supports the idea that awareness of women is relevant to sustainability when starting a new company. However, further research is required due to the novelty of the topic and also the existing gaps in knowledge.
Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses
Authors: González-Carrillo Cristian D. , Restrepo-Calle Felipe , Ramírez-Echeverry Jhon J. , González Fabio A.
Published: November 2021

Jupyter notebooks provide an interactive programming environment that allows writing code, text, equations, and multimedia resources. They are widely used as a teaching support tool in computer science and engineering courses. However, manual grading programming assignments in Jupyter notebooks is a challenging task, thus using an automatic grader becomes a must. This paper presents UNCode notebook auto-grader, that offers summative and formative feedback instantaneously. It provides instructors with an easy-to-use grader generator within the platform, without having to deploy a new server. Additionally, we report the experience of employing this tool in two artificial intelligence courses: Introduction to Intelligent Systems and Machine Learning. Several programming activities were carried out using the proposed tool. Analysis of students’ interactions with the tool and the students’ perceptions are presented. Results showed that the tool was widely used to evaluate their tasks, as a large number of submissions were performed. Students expressed positive opinions mostly, giving feedback about the auto-grader, highlighting the usefulness of the immediate feedback and the grading code, among other aspects that helped them to solve the activities. Results remarked on the importance of providing clear grading code and formative feedback to help the students to identify errors and correct them.
An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning
Authors: Chandran Praneeth , Asber Johnny , Thiery Florian , Odelius Johan , Rantatalo Matti
Published: November 2021

The rail fastening system forms an indispensable part of the rail tracks and needs to be periodically inspected to ensure safe, reliable and sustainable rail operations. Automated visual inspection has gained significant importance for fastener inspection in recent years. Position accuracy, robustness, and practical limitations due to the complex environment are some of the major concerns associated with this method. This study investigates the combined use of image processing and deep learning algorithms for detecting missing clamps within a rail fastening system. The images used for this study was acquired during field inspections carried out along the Borlänge-Avesta line in Sweden. The image processing techniques proposed in this study enabled the improvement of the fastener position and removal of redundant information from the fastener images. In addition, image augmentation was carried out to enhance the data set, ensure experimental reliability and replicate practical challenges associated with such visual inspection. Convolutional neural network and ResNet-50 algorithms are used for classification purposes, and both the algorithms achieved over 98% accuracy during training and validation and over 94% accuracy during the test stage. Both the algorithms also maintained a good balance between the precision and recall scores during the test stage. CNN and ResNet-50 algorithms were also tested to analyse their performances when the clamp areas were covered. CNN was able to accurately predict the fastener state up to 70% of clamp area occlusion, and ResNet-50 was able to achieve accurate predictions up to 75% of clamp area occlusion.
NEET Rural–Urban Ecosystems: The Role of Urban Social Innovation Diffusion in Supporting Sustainable Rural Pathways to Education, Employment, and Training
Authors: Erdogan Emre , Flynn Paul , Nasya Bahanur , Paabort Heidi , Lendzhova Vladislava
Published: November 2021

Common themes of EU social policy include: the promotion of employment; improved living and working conditions; the equal treatment of employees; adequate social protection; and capacity building of the European citizenship. However, it is often the case that rural dwellers and, more specifically, rural NEETs, experience higher levels of marginalisation than their urban counterparts. Such marginalisation is evidenced by their exclusion from decision-making, public life, community, and society. These issues are compounded by an underdeveloped rural infrastructure, problematic access to education, limited employment opportunities, and a lack of meaningful social interaction. This study, a cross-sectional analysis, assesses a number (n = 51) of social interventions under the Youth Guarantee Programme from a social innovation perspective and presents a characterisation of examples of best practice across different dimensions of social innovations. This paper presents an examination of the potential of sustainable rural–urban ecosystems that are focused on supporting the symbiotic social innovation diffusion methods which can help to establish and sustain rural–urban pathways to improved education, employment, and training.
Estimating the Economic Value of Improving the Asian Dust Aerosol Model in the Korean Household Sector: A Choice Experiment
Authors: Kim Hye-Min , Kim In-Gyum , Lim Byunghwan , Yoo Seung-Hoon
Published: November 2021

Asian dust (AD), a seasonal phenomenon in East Asia, refers to dust winds that blow from inland China and the Gobi Desert. Generally, AD occurs in the spring, but due to climate change and the desertification of AD sources, it has recently started occurring in every season except for midsummer. The occurrence of AD adversely affects human health and damages various industries. Accordingly, the Korean Meteorological Administration has provided an AD forecast service to minimize damage. The most essential part of providing the AD forecast service is the Asian Dust Aerosol Model (ADAM). This study tries to provide useful information for policymaking and research and development by deriving the economic value of ADAM improvement. To this end, a choice experiment (CE) was applied. The CE can derive the marginal value of or the marginal willingness to pay (MWTP) of improvement in attribute. All the MWTP estimates obtained in this study were statistically significant. The household MWTP estimates for a 1% improvement in ADAM accuracy and a one-level subdividing were obtained as USD 0.04 and USD 0.12 per household. This finding indicates that people have a greater preference for improving AD forecast segmentation than for improving ADAM accuracy.
Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR
Authors: Yuan Weipeng , Sun Hui , Chen Yu , Xia Xuechao
Published: November 2021

In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.
Neutral Sugar Content and Composition as a Sensitive Indicator of Fire Severity in the Andisols of an Araucaria–Nothofagus Forest in Southern Chile
Authors: Rivas Yessica , Retamal-Salgado Jorge , Knicker Heike , Matus Francisco , Rivera Diego
Published: November 2021

Wildfire induces soil alterations that have a long-term impact on soil organic matter (SOM) quality. We postulated that after different fire severities, the neutral sugars in soils can be used as an indicator of soil organic matter quality after fire. The aim of this study was to determine the contribution of neutral sugar, bulk and occluded particulate organic matter (oPOM) affected by wildfire, at different soil depths in an Araucaria–Nothofagus Forest, four years post-after fire. The concentration and composition of the neutral sugars in the soils clearly comprised the major fraction in the unburned soil. Medium- and high-severity fires caused a drastic reduction in soil sugars in the bulk soil as well as in the oPOM fractions. The 13C-CPMAS NMR spectroscopy analysis revealed a high contribution of recalcitrant carbon to the decomposition such as aryl–C and aryl–O derived from charred material, whereas the abundance of O–alkyl C and alkyl C functional groups were decreased. The neutral sugars (Galactose+Mannose/Xilose+Arabinose) revealed a major microbial origin in fire affected areas as the ratio was >2. Therefore. Therefore, we suggest that the neutral sugar content of soil should be used for monitoring both short- and long-term changes in SOM altered by fires.
Environmental Performances of a Cubic Modular Steel Structure: A Solution for a Sustainable Development in the Construction Sector
Authors: Maxineasa Sebastian George , Isopescu Dorina Nicolina , Baciu Ioana-Roxana , Lupu Marius Lucian
Published: November 2021

The production of building materials is a significant component of the impact the construction sector has on the natural environment. Steel is among the most utilized materials, having various applications specific to the built environment. Therefore, understanding the impact of this structural material represents an important step in achieving global sustainable development. The paper aims to analyze the effects of different steel structural elements on the Earth’s ecosystem with respect to concerns over sustainability. In order to reach this goal, the authors have analyzed a dwelling steel structure based on cubic modules with high structural modularity. In addition, the study looks at the influence of an over the floor reinforced concrete slab in order to gain an overall view regarding environmental performances. The impact on the natural environment has been analyzed by considering the cradle-to gate with options Life Cycle Assessment study. The paper provides up-to-date knowledge on the environmental performances of the analyzed structure, presenting encouraging conclusions for construction sector specialists with respect to the use of steel as a material that can represent a solution in the current global effort to minimize the environmental burdens imposed by the construction sector.
Using Mobile Food Delivery Applications during the COVID-19 Pandemic: Applying the Theory of Planned Behavior to Examine Continuance Behavior
Authors: Tran Van Dat
Published: November 2021

COVID-19 has opened up a significant market for food delivery services in Vietnam. Based on the theory of planned behavior, subjective norms and social isolation were hypothesized to have a positive impact on behavior intention and continuance behavior. The study also aimed to determine the positive impacts of perceived food safety, food delivery, and behavioral intention on continuance behavior, while the perception of food safety and food delivery were also proposed to have direct impacts on behavioral intention. Structured equation modeling was used to evaluate data obtained from 299 respondents who were experienced in using delivery apps for ordering food. As a result, this study contributes to prior findings regarding the positive effects of the subject norm—social isolation, food delivery hygiene, perceived food safety—on behavioral intention to use mobile apps. Additionally, customers’ continued usage behavior was found to be also influenced by behavioral intention, perceived food safety, and food delivery hygiene. It was concluded that these findings have significant theoretical and practical implications. This study adds to the existing debate on consumer behavior in the context of online food delivery in Vietnam and sheds light on the elements that could be used to forecast people’s willingness to buy food through food delivery apps, and for how long they would continue to use them.
Combined Impacts of Climate and Land Use Changes on Long-Term Streamflow in the Upper Halda Basin, Bangladesh
Authors: Raihan Farzana , Ondrasek Gabrijel , Islam Mohammad Shahidul , Maina Joseph M. , Beaumont Linda J.
Published: November 2021

In Bangladesh, rapid population growth and associated land-use changes are escalating water scarcity issues, which will be further exacerbated under ongoing climate change. As such, predicting the consequences of climate and land-use change on freshwater supplies is critical for the sustainable management of water resources. In this study, a Soil and Water Assessment Tool (SWAT) associated with a Land Cover Model (LCM) were used to simulate long-term stream flows in the Halda Basin, Bangladesh, under baseline and future climate and land-use change scenarios. In addition, the separate and combined impacts of both types of change on long-term streamflow projections were assessed. Results indicate that by the 2060s, the maximum temperature of the Halda Basin may rise by 1.6 °C in comparison to the baseline 1986–2005 period, while minimum temperature will also increase, albeit at a lower rate than maximum temperature. Precipitation during the dry season is expected to increase, although it may decline in the monsoon period. Simulations show that these changes in climate are likely to increase future streamflow in the Halda catchment, with monthly streamflow influenced mainly by the variability in precipitation. The LCM projected decreases in grassland along with cultivated land at the expense of artificial areas. Combined, future climate and land-use changes are projected to increase annual streamflow, with climate change likely to be a greater driver of altered streamflow than land-use changes. Our results should guide environmental management authorities in more sustainable and strategic water resource planning under global climate change.