bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
Authors: El Serafy Ghada Y.H. , Schaeffer Blake A. , Neely Merrie-Beth , Spinosa Anna , Odermatt Daniel , Weathers Kathleen C. , Baracchini Theo , Bouffard Damien , Carvalho Laurence , Conmy Robyn N. , Keukelaere Liesbeth De , Hunter Peter D. , Jamet Cédric , Joehnk Klaus D. , Johnston John M. , Knudby Anders , Minaudo Camille , Pahlevan Nima , Reusen Ils , Rose Kevin C. , Schalles John , Tzortziou Maria
Published: July 2021

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.
Unknown SAR Target Identification Method Based on Feature Extraction Network and KLD–RPA Joint Discrimination
Authors: Zeng Zhiqiang , Sun Jinping , Xu Congan , Wang Haiyang
Published: July 2021

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.
Through-Wall Multi-Subject Localization and Vital Signs Monitoring Using UWB MIMO Imaging Radar
Authors: Li Zhi , Jin Tian , Dai Yongpeng , Song Yongkun
Published: July 2021

Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) imaging radar and derive the relationship between radar images and vibrations caused by human cardiopulmonary movements. The derivation indicates that MIMO radar imaging with the stepped-frequency continuous-wave (SFCW) improves the signal-to-noise ratio (SNR) critically by the factor of radar channel number times frequency number compared with continuous-wave (CW) Doppler radars. We also apply the three-dimensional (3-D) higher-order cumulant (HOC) to locate multiple subjects and extract the phase sequence of the radar images as the vital signs signal. To monitor the cardiopulmonary activities, we further exploit the VMD algorithm with a proposed grouping criterion to adaptively separate the respiration and heartbeat patterns. A series of experiments have validated the localization and detection of multiple subjects behind a wall. The VMD algorithm is suitable for separating the weaker heartbeat pattern from the stronger respiration pattern by the grouping criterion. Moreover, the continuous monitoring of heart rate (HR) by the MIMO radar in real scenarios shows a strong consistency with the reference electrocardiogram (ECG).
The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications
Authors: Zhao Chuanpeng , Qin Cheng-Zhi
Published: July 2021

Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing studies have improved mangrove classifications by introducing time series images, constructing new indices sensitive to mangroves, and correcting classifications by empirical constraints and visual inspections. However, false positive misclassifications are still prevalent in current classification results before corrections, and the key reason for false positive misclassification in large-area mangrove classifications is unknown. To address this knowledge gap, a hypothesis that an inadequate classification scheme (i.e., the choice of categories) is the key reason for such false positive misclassification is proposed in this paper. To validate this hypothesis, new categories considering non-mangrove vegetation near water (i.e., within one pixel from water bodies) were introduced, which is inclined to be misclassified as mangroves, into a normally-used standard classification scheme, so as to form a new scheme. In controlled conditions, two experiments were conducted. The first experiment using the same total features to derive direct mangrove classification results in China for the year 2018 on the Google Earth Engine with the standard scheme and the new scheme respectively. The second experiment used the optimal features to balance the probability of a selected feature to be effective for the scheme. A comparison shows that the inclusion of the new categories reduced the false positive pixels with a rate of 71.3% in the first experiment, and a rate of 66.3% in the second experiment. Local characteristics of false positive pixels within 1 × 1 km cells, and direct classification results in two selected subset areas were also analyzed for quantitative and qualitative validation. All the validation results from the two experiments support the finding that the hypothesis is true. The validated hypothesis can be easily applied to other studies to alleviate the prevalence of false positive misclassifications.
SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images
Authors: Li Xiaolong , Zheng Hong , Han Chuanzhao , Zheng Wentao , Chen Hao , Jing Ying , Dong Kaihan
Published: July 2021

Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.
Social Network and Bibliometric Analysis of Unmanned Aerial Vehicle Remote Sensing Applications from 2010 to 2021
Authors: Wang Jingrui , Wang Shuqing , Zou Dongxiao , Chen Huimin , Zhong Run , Li Hanliang , Zhou Wei , Yan Kai
Published: July 2021

Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which “Environmental Science and Ecology” ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field.
Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery
Authors: Banerjee Bikram P. , Sharma Vikas , Spangenberg German , Kant Surya
Published: July 2021

Optimal crop emergence is an important trait in crop breeding for genotypic screening and for achieving potential growth and yield. Emergence is conventionally quantified manually by counting the sub-sections of field plots or scoring; these are less reliable, laborious and inefficient. Remote sensing technology is being increasingly used for high-throughput estimation of agronomic traits in field crops. This study developed a method for estimating wheat seedlings using multispectral images captured from an unmanned aerial vehicle. A machine learning regression (MLR) analysis was used by combining spectral and morphological information extracted from the multispectral images. The approach was tested on diverse wheat genotypes varying in seedling emergence. In this study, three supervised MLR models including regression trees, support vector regression and Gaussian process regression (GPR) were evaluated for estimating wheat seedling emergence. The GPR model was the most effective compared to the other methods, with R2 = 0.86, RMSE = 4.07 and MAE = 3.21 when correlated to the manual seedling count. In addition, imagery data collected at multiple flight altitudes and different wheat growth stages suggested that 10 m altitude and 20 days after sowing were desirable for optimal spatial resolution and image analysis. The method is deployable on larger field trials and other crops for effective and reliable seedling emergence estimates.
Impacts of Human Activities on the Variations in Terrestrial Water Storage of the Aral Sea Basin
Authors: Yang Xuewen , Wang Ninglian , Liang Qian , Chen An’an , Wu Yuwei
Published: July 2021

Assessing the impacts of human activities on the variations in terrestrial water storage (TWS) is essential for water resource management, particularly in regions like the Aral Sea Basin which suffers from severe water scarcity. In this study, the variations in TWS anomalies (TWSA) of the Aral Sea Basin during the period of April 2002 to June 2017 were analyzed using Gravity Recovery and Climate Experiment (GRACE) data and the Global Land Data Assimilation System (GLDAS) Noah model outputs. The impacts of human activities on TWS variations were further quantified through the variations in TWS components and the comparison of TWS obtained from GRACE and GLDAS. The results indicate that TWSA of the entire Aral Sea Basin derived from GRACE experienced a significant decreasing trend of 4.12 ± 1.79 mm/year (7.07 ± 3.07 km3/year) from 2002 to 2017. Trends in individual TWS components indicate that the reduction in TWS of the Aral Sea Basin was primarily attributed to surface water loss, followed by groundwater depletion, which account for ~53.16% and 11.65 ± 45.39 to 42.48 ± 54.61% of the total loss of TWS, respectively. Precipitation (P) and evapotranspiration (ET) both exhibited increasing trends, indicating that ET played a dominant role in TWS depletion from the perspective of water balance. The variations in ET and TWS induced by human activities contributed ~45.54% and ~75.24% to those in total ET and TWS of the Aral Sea Basin, respectively.
Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs
Authors: Rivera-Kempis Clariandys , Valera Leobardo , Sastre-Castillo Miguel A.
Published: July 2021

Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component analysis, ranking and classification of data using the Ward method, linear discriminant analysis, and Gaussian regression among others.
Internet Usage, Human Capital and CO2 Emissions: A Global Perspective
Authors: Wang Jing , Xu Yubing
Published: July 2021

Under the background of dealing with global warming, the widespread use of the internet provides a new idea for countries to develop a low-carbon economy at the right time. Based on the panel data of 70 countries from 1995–2018, this paper empirically analyzes the relationship between internet usage, human capital, and CO2 emissions under different levels of economic development by using system GMM and a threshold regression model. The results show that internet usage and human capital are essential drivers of low-carbon economy development, and human capital can inversely regulate the impact of internet usage on CO2 emissions. Internet usage can increase CO2 emissions when human capital is below the threshold value, and it can significantly inhibit CO2 emissions when human capital exceeds the threshold value. In other words, with the accumulation of human capital, the effect of internet usage on CO2 emissions has an inverted U-shaped nonlinear relationship. Furthermore, the empirical analysis of high-income and middle- and low-income countries indicates the hindrance effect of internet usage on CO2 emissions is more evident in high-income countries. For both the high-income and middle- and low-income countries, the relationship between internet usage and CO2 emissions generally shows an inverted “U-shaped” relationship, first rising and then falling as human capital accumulates.
Technical Sustainability of Cloud-Based Blockchain Integrated with Machine Learning for Supply Chain Management
Authors: Wong Simon , Yeung John-Kun-Woon , Lau Yui-Yip , So Joseph
Published: July 2021

Knowing the challenges of keeping and manipulating more and more immutable transaction records in a blockchain network of various supply chain parties and the opportunities of leveraging sophisticated analyses on the big data generated from these records, design of a robust blockchain architecture based on a cloud infrastructure is proposed. This paper presents this technical design with consideration of the technical sustainability in terms of scalability and big data processing and analytics. A case study was used to illustrate how the technical sustainability is achieved by applying the proposed technical design to the real-time detection of the maritime risk management. This case also illustrates how machine learning mechanism helps to reduce maritime risk by guiding a cargo ship to adjust to the planned or safe route from a detour to a danger zone. This paper also discusses the implications for further research direction.
“I Wanted a Profession That Makes a Difference”—An Online Survey of First-Year Students’ Study Choice Motives and Sustainability-Related Attributes
Authors: Oberrauch Anna , Mayr Helga , Nikitin Ivan , Bügler Tanja , Kosler Thorsten , Vollmer Christian
Published: July 2021

Higher education institutions are obligated to facilitate students in the development of sustainability competencies, which enable them to act as “change agents” in their future profession-specific environment. Therefore, students’ study motives, prior knowledge, attitudes, and experiences regarding sustainability should be considered when designing Education for Sustainable Development (ESD) programmes. The present study compares first-year students in teacher training with first-year students in other study programmes and explores their study choice motives as well as sustainability-related conceptions, engagement and self-efficacy beliefs using a semi-standardised online questionnaire. Results show that the choice of study is dominated by intrinsic factors and the relevance of extrinsic factors differs by degree programmes with lower extrinsic values for the teacher training students. Regarding sustainability, we find simple and often unelaborated concepts. Teacher training students show significantly higher scores than non-teacher training students regarding the sustainability-related behavioural domain and self-efficacy beliefs. In addition, a gender gap increasing with age and with lower sustainability scores for older males could be identified but only for teacher training students. In conclusion, the results show valuable starting points as well as challenges that should be considered when designing target-oriented learning processes in (inter)disciplinary sustainability courses at higher education institutions.
A Study on How the Five Senses Are Affected When Tourists Experience Towns with Forest Characteristics: An Empirical Analysis Based on the Data of Fujian, Guangdong and Sichuan in China
Authors: Shao Min , Lin Derong
Published: July 2021

Senses are the primary channel by which travelers enjoy tourism, and the sensory experience of tourists is very important to the quality of their tourism experience. In this study, 385 questionnaires were distributed to tourists visiting the first batch of characteristic towns classified by the China National Forestry and Grassland Administration as national towns with forest characteristics. SPSS software was used for regression analysis to test the differences in the impact of sensory experience on the experience quality and future loyalty of forest town tourists. The results show that: (1) vision makes the highest contribution to the quality of tourist experience; (2) olfactory experience makes the lowest contribution to the quality of tourist experience and has no significant impact on it; (3) auditory experience has a significant impact on the quality of tourism experience but has no significant impact on the loyalty index. The study further confirms that, when a single sensory experience of tourists is insufficient, satisfaction can be achieved through the other senses. It is of great significance for forest town managers to design a sensory landscape according to the combined components of a destination’s characteristics and the tourists’ physical identities.
Institutional Logics at Play in a Mobility-as-a-Service Ecosystem
Authors: Guyader Hugo , Nansubuga Brenda , Skill Karin
Published: July 2021

The last decade has brought the transport sector to the forefront of discussions on sustainability and digital innovations: practitioners, researchers, and regulators alike have witnessed the emergence of a wide diversity of shared mobility services. Based on a longitudinal case study of a regional Mobility-as-a-Service (MaaS) ecosystem in Sweden, constituted of a document analysis and 24 semi-structured interviews with 18 representatives from regional authorities, mobility service providers, and other stakeholders from the public and private sectors, this study examines the co-existing and competing institutional logics at play, identified as State logic, Market logic, Sustainability logic, Experimental logic, and Service logic. The analysis reveals that these institutional logics pertain to tensions in the collaboration within the ecosystem’s stakeholders in terms of: (1) finding a common vision and scope for MaaS, (2) establishing a sustainable business model, (3) triggering a behavioral change regarding car travel, (4) being able to find one’s role within the project and to consequently collaborate with other stakeholders, and (5) managing uncertainty through testing and experimenting innovative solutions, which ultimately yielded key learnings about MaaS and the shared mobility ecosystem and its stakeholders. These case study findings, based on an institutional logics framework, provide a novel perspective on emerging ecosystems, from which implications for MaaS developers and further research on shared mobility are drawn.
Research on High-Quality Development Efficiency and Total Factor Productivity of Regional Economies in China
Authors: Hua Xiangyu , Lv Haiping , Jin Xiangrong
Published: July 2021

Different from the developmental mode of western developed countries, China’s economy has changed from a stage of high-speed growth to a stage of high-quality development, where the people’s growing needs for better lives can be met, embodying this new concept of development. The aim of our study is to evaluate the high-quality development efficiency and total factor productivity (TFP) of regional economies in China, and to explore the characteristics of spatial-temporal pattern evolution and their influencing factors. By using the slacks-based measure of directional distance functions (SBM-DDF) model, based on the undesirable output perspective, the high-quality development efficiency and TFP of regional economies in China, from 2000 to 2018, are evaluated in this paper. The exploratory spatial data analysis (ESDA) and Tobit models are then used to identify the spatial-temporal correlation patterns and influencing factors of high-quality development efficiency and TFP. The key results show the following: (1) from 2001 to 2018, the greatest high-quality development efficiency and TFP belonged to China’s eastern region and the least to its central region. (2) U and inverted-U trend lines show that high-quality development efficiency has significant regional difference in the east–west direction, presenting a significant feature of spatial imbalance. (3) Government, urbanization rate, and marketization level play a positive role in their impact of TFP, whereas financial development, infrastructure, foreign direct investment, and capital labor ratio play a negative one.
Numerical Simulation of Homogeneous–Heterogeneous Reactions through a Hybrid Nanofluid Flowing over a Rotating Disc for Solar Heating Applications
Authors: Alam Mir Waqas , Hussain Syed Ghazanfar , Souayeh Basma , Khan Muhammad Shuaib , Farhan Mohd
Published: July 2021

Several materials, such as aluminum and copper, exhibit non-Newtonian rheological behaviors. Aluminum and copper nanoparticles are ideal for wiring power grids, including overhead power transmission lines and local power distribution lines, because they provide a better conductivity-to-weight ratio than bulk copper; they are also some of the most common materials used in electrical applications. Therefore, the current investigation inspected the flow characteristics of homogeneous–heterogeneous reactions in a hybrid nanofluid flowing over a rotating disc. The velocity slip condition was examined. The energy equation was developed by employing the first law of thermodynamics. Mixed convection thermal radiation and the convective condition effect were addressed. The dimensionless governing models were solved to give the best possible investigative solution using the fourth- and fifth-order Runge–Kutta–Felhberg numerical method. The effects of different influential variables on the velocity and temperature were scrutinized graphically. The effects of the variation of various sundry parameters on the friction factor and Nusselt numbers were also analyzed. The results revealed that the velocity gradient increased significantly for augmented values of the mixed convection parameter. Here, the velocity gradient increased more rapidly for a hybrid nanoliquid than for a nanofluid. The thermal distribution was enhanced due to a significantly increased radiation parameter.
Untapped Aspects of Innovation and Competition within a European Resilient Circular Economy. A Dual Comparative Study
Authors: Bucea-Manea-Țoniş Rocsana , Šević Aleksandar , Ilić Milena P. , Bucea-Manea-Țoniş Radu , Popović Šević Nevenka , Mihoreanu Larisa
Published: July 2021

The paper aims to develop—based on a particular dual comparative analysis that follows the current European concerns—the concepts of competitiveness and innovation as pillars uprighting companies’ resilience, creating ecoinnovative jobs and social inclusion. In their struggle to meet the Circular Economy principles and Green Deal objectives, the countries chosen for analyses—Romania and Serbia—have started implementing added-value blockchain concepts in their societies to thrive in the resilient European market and build empowered societies. According to the World Economic Forum Global Sources of Competitiveness, skills considered in our study refer to businesses’ versatility and societies’ innovation capability. Based on specific data provided by Eurostat, the results showed a correlation between the ecoinnovation index and R&D personnel by sector and helped design a regression model. Hence, we demonstrate that R&D creativity, once stimulated through innovative teaching, blooms, having positive effects at society and market levels as reflected in the ecoinnovation index. Furthermore, cluster analysis within E.U. innovation helped identify strengths and weaknesses, provided new grounds in applying innovation, and led to further recommendations.
Spatial Analysis as a Tool for Plant Population Conservation: A Case Study of Tamarix chinensis in the Yellow River Delta, China
Authors: Jiao Le , Zhang Yue , Sun Tao , Yang Wei , Shao Dongdong , Zhang Peng , Liu Qiang
Published: July 2021

Saltcedar (Tamarix chinensis) is undergoing population declination and fragmentation due to climate change and human disturbance. The existing restoration strategies usually focus on improving the environmental conditions based on the environment–saltcedar relationship, while they ignore the role of spatial autocorrelation resulting from biological interaction and ecological processes. This oversight limits the efficiency and sustainability of the restoration. Here, we explored the spatial pattern of the saltcedar population in the Yellow River Delta, China, and its relationship with environmental factors, incorporating spatial autocorrelation. The plant and soil parameters were extracted by an airborne LiDAR system integrated with fixed soil environment measurements. The environment–saltcedar relationship incorporating spatial autocorrelation was evaluated with different regression models. Results showed that saltcedars aggregated at small scales (2–6 m), resulting from intraspecific facilitation and wind dispersal of seeds, while intraspecific competition was responsible for the random distribution at large scales (>10 m). The long-distance dispersal of seeds through water explained the significant positive spatial autocorrelation of saltcedars at distances up to 125 m. Consequently, resulting from intraspecific facilitation and seed dispersal, aggregation distribution and positive spatial autocorrelation within the saltcedar population improved the adaptability of saltcedar to environmental stress and thereby reduced the impact of environmental factors on the abundance of saltcedar.
Has Digital Financial Inclusion Narrowed the Urban-Rural Income Gap: The Role of Entrepreneurship in China
Authors: Ji Xuanming , Wang Kun , Xu He , Li Muchen
Published: July 2021

The combination of digital technology and finance has brought about a new development model for financial inclusion. What impact will it have on the current imbalance in the distribution of financial resources and the urban-rural income gap in China? To answer this question, this paper uses relevant data from 2014–2018 to study the impact of digital inclusive finance on the urban-rural income gap from the theory of financial exclusion, and analyzes the transmission of digital inclusive finance through alleviating financial exclusion, widening financing channels and helping residents with entrepreneurial spirit to start their own businesses, thus increasing jobs, raising the income of rural residents and reducing the urban-rural income gap. The conclusions are as follows: (1) digital inclusive finance can significantly converge the urban-rural income gap; (2) among the dimensions of digital inclusive finance, only the breadth of coverage can significantly reduce the urban-rural income gap, while the effects of depth of use and digitalization are not significant; (3) digital inclusive finance can alleviate the urban-rural income gap through the transmission mechanism of promoting residents’ entrepreneurship; (4) the worse the regional economic development and education, the better the effect of digital inclusive finance on the urban-rural income gap. This paper combines the above results to propose corresponding policy recommendations.
Financial Risk Management and Sustainability
Authors: García-Benau María Antonia , Gambetta Nicolás , Sierra-García Laura
Published: July 2021

In the last decades, the studies that analyze the links between corporate social responsibility and financial performance in developed countries show mixed and inconclusive results, so additional research is required [...]
Computational Fluid Dynamics Modelling of the Microclimate within the Boundary Layer of Leaves Leading to Improved Pest Control Management and Low-Input Greenhouse
Authors: Fatnassi Hicham , Boulard Thierry , Poncet Christine , Katsoulas Nikolaos , Bartzanas Thomas , Kacira Murat , Giday Habtamu , Lee In-Bok
Published: July 2021

This work aims at using the Computational Fluid Dynamic (CFD) approach to study the distributed microclimate in the leaf boundary layer of greenhouse crops. Understanding the interactions in this microclimate of this natural habitat of plant pests (i.e., boundary layer of leaves), is a prerequisite for their control through targeted climate management for sustainable greenhouse production. The temperature and humidity simulations, inside the greenhouse, were performed using CFD code which has been adapted to simulate the plant activity within each mesh in the crop canopy. The air temperature and air humidity profiles within the boundary layer of leaves were deduced from the local surrounding climate parameters, based on an analytical approach, encapsulated in a Used Defined Function (UDF), and dynamically linked to the CFD solver, a work that forms an innovative and original task. Thus, this model represents a new approach to investigate the microclimate in the boundary layer of leaves under greenhouses, which resolves the issue of the inaccessibility of this area by the conventionnel measurement tools. The findings clearly showed that (i) contrarily to what might be expected, the microclimate parameters within the boundary layer of leaves are different from the surrounding climate in the greenhouse. This is particularly visible during photoperiods when the plant’s transpiration activity is at its maximum and that (ii) the climatic parameters in the leaf boundary layer are more coupled with leaf surfaces than with those of greenhouse air. These results can help developing localized intervention strategies on the microclimate within boundary layer of plant leaves, leading to improved and sustainable pest control management. The developed climatic strategies will make it possible to optimize resources use efficiency.
Assessing Impact of Land Use Change on the Ecosystem Service Value in Yinchuan City from 1980 to 2018
Authors: Wang Bo , Yang Taibao
Published: July 2021

Accurate assessment and response analysis of land use and land cover change (LUCC), and ecosystem service values (ESV), are critical to regional ecological security and economic development. There is a lack of detailed reports on the impact of LUCC on the temporal and spatial evolution of ESV in Yinchuan City, which is inconsistent with the pilot urban design policy. This paper, using the LUCC data of Yinchuan City from 1980 to 2018, calculated the ESV, analyzed the temporal and spatial patterns of LUCC and ESV, and discussed the response of ESV to LUCC. The results show that, from 1980 to 2018, the building land increased significantly in Yinchuan City, as did the cultivated land. Meanwhile, grassland and bare land decreased, while forest and the water body remained stable. The spatial connectivity of the building land showed regular improvements, while the urban landscape developed in a regular and balanced direction. During the study period, the total ESV of Yinchuan City decreased by 0.75 × 109 yuan. This was due to the decrease in grasslands and the increase in building area. The supply, regulation and support of three types of services have a high correlation with different land types. The prosperity and progress of culture reduces the ESV value of cultivated land to some extent, while the change in land use type leads to the significant loss of ESV in Yinchuan City.
From Schnitzel to Sustainability: Shifting Values at Vienna’s Urban Farmers Markets
Authors: Klimek Milena , Bingen Jim , Freyer Bernhard , Paxton Rebecca
Published: July 2021

This paper presents an empirically grounded investigation of the values and practices of farmers markets (FM) in Vienna, Austria and their linkages to wider alternative food practices of ecological, social and economic sustainability. If the FMs are to play a vibrant role in the Viennese alternative food system, enhancing urban–rural connections and urban resilience, they must re–align their values to this system. A values-based conceptual framework is used to examine the structures and functions of six Viennese FMs and the alignment of values and practices among FM managers, farmers/vendors and consumers. Data from qualitative interviews, participant observation and dot surveys were collected at each FM. Value alignment is discovered as necessary to support and perpetuate alternative values. Governance is found to be significant for aligning values related to FM sustainability. Current structures and functions of Viennese FMs cannot be easily aligned with participant values and practices. As one of the first examinations of Viennese FMs, this work illustrates concrete challenges, priorities and emphasizes the role that governance and social organizing plays in successful markets as contributors towards sustainable urban food systems. Lessons learned can be applied to municipal FMs and other food system actors that face similar challenges.
Construction of Differentiated Periodic Freight Train Paths in Dense Mixed Traffic
Authors: Drábek Michal , Janoš Vít
Published: July 2021

Freight rail transport plays key role in the transition to sustainable development. However, on European mainlines, freight trains have to cope with busy passenger operation—mostly in the form of (integrated) periodic timetables. Freight trains are characterized with very diverse parameters, so scheduling pre-arranged periodic freight train paths (PFTPs) on the basis of one sample freight train does not meet the needs of most freight operators. This article introduces new detailed framework process for hierarchized construction of differentiated (segmented) pre-arranged PFTPs. The process considers fluctuations in demand for capacity from freight rail operators, so the quality of a freight train path in terms of number of stops is related with its construction priority. This way, the process enhances competitiveness and decreases energy consumption of freight railway, as a factor for sustainable development. Correctness of the framework process is tested on the example of the Prague—Dresden mainline, in the context of prospective (denser) model passenger timetable. Results show that above 70% of real freight trains from the available historical data can fit into the proposed PFTPs. As a conclusion, the authors recommend reduction of service of the least frequent stops of regional trains to reduce number of scheduled stops for freight trains.
An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification
Authors: Zeybek Sultan , Pham Duc Truong , Koç Ebubekir , Seçer Aydın
Published: July 2021

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs.
Enriched Multivalued Contractions with Applications to Differential Inclusions and Dynamic Programming
Authors: Abbas Mujahid , Anjum Rizwan , Berinde Vasile
Published: July 2021

The purpose of this paper is to introduce the class of enriched multivalued contraction mappings. Both single-valued and multivalued enriched contractions are defined by means of symmetric inequalities. Our main result extends and generalizes the recent result of Berinde and Păcurar (Approximating fixed points of enriched contractions in Banach spaces, Journal of Fixed Point Theory and Applications, 22 (2), 1–10, 2020). We also study a data dependence problem of the fixed point set and Ulam–Hyers stability of the fixed point problem for enriched multivalued contraction mappings. Applications of the results obtained to the problem of the existence of a solution of differential inclusions and dynamic programming are presented.
Sensitivity of KM3NeT to Violation of Equivalence Principle
Authors: Chianese Marco , Fiorillo Damiano F. G. , Mangano Gianpiero , Miele Gennaro , Morisi Stefano , Pisanti Ofelia
Published: July 2021

The symmetry of the theory of relativity under diffeomorphisms strongly depends on the equivalence principle. Violation of Equivalence Principle (VEP) can be tested by looking for deviations from the standard framework of neutrino oscillations. In recent works, it has been shown that strong constraints on the VEP parameter space can be placed by means of the atmospheric neutrinos observed by the IceCube neutrino telescope. In this paper, we focus on the KM3NeT neutrino telescope and perform a forecast analysis to assess its capacity to probe VEP. Most importantly, we examine the crucial role played by systematic uncertainties affecting the neutrino observations. We find that KM3NeT will constrain VEP parameters times the local gravitational potential at the level of 10−27. Due to the systematic-dominated regime, independent analyses from different neutrino telescopes are fundamental for robustly testing the equivalence principle.
Exact Solutions to the Navier–Stokes Equations with Couple Stresses
Authors: Baranovskii Evgenii S. , Burmasheva Natalya V. , Prosviryakov Evgenii Yu.
Published: July 2021

This article discusses the possibility of using the Lin–Sidorov–Aristov class of exact solutions and its modifications to describe the flows of a fluid with microstructure (with couple stresses). The presence of couple shear stresses is a consequence of taking into account the rotational degrees of freedom for an elementary volume of a micropolar liquid. Thus, the Cauchy stress tensor is not symmetric. The article presents exact solutions for describing unidirectional (layered), shear and three-dimensional flows of a micropolar viscous incompressible fluid. New statements of boundary value problems are formulated to describe generalized classical Couette, Stokes and Poiseuille flows. These flows are created by non-uniform shear stresses and velocities. A study of isobaric shear flows of a micropolar viscous incompressible fluid is presented. Isobaric shear flows are described by an overdetermined system of nonlinear partial differential equations (system of Navier–Stokes equations and incompressibility equation). A condition for the solvability of the overdetermined system of equations is provided. A class of nontrivial solutions of an overdetermined system of partial differential equations for describing isobaric fluid flows is constructed. The exact solutions announced in this article are described by polynomials with respect to two coordinates. The coefficients of the polynomials depend on the third coordinate and time.
Bothrops Jararaca Snake Venom Modulates Key Cancer-Related Proteins in Breast Tumor Cell Lines
Authors: Kisaki Carolina Yukiko , Arcos Stephanie Santos Suehiro , Montoni Fabio , da Silva Santos Wellington , Calacina Hamida Macêdo , Lima Ismael Feitosa , Cajado-Carvalho Daniela , Ferro Emer Suavinho , Nishiyama-Jr Milton Yutaka , Iwai Leo Kei
Published: July 2021

Cancer is characterized by the development of abnormal cells that divide in an uncontrolled way and may spread into other tissues where they may infiltrate and destroy normal body tissue. Several previous reports have described biochemical anti-tumorigenic properties of crude snake venom or its components, including their capability of inhibiting cell proliferation and promoting cell death. However, to the best of our knowledge, there is no work describing cancer cell proteomic changes following treatment with snake venoms. In this work we describe the quantitative changes in proteomics of MCF7 and MDA-MB-231 breast tumor cell lines following treatment with Bothrops jararaca snake venom, as well as the functional implications of the proteomic changes. Cell lines were treated with sub-toxic doses at either 0.63 μg/mL (low) or 2.5 μg/mL (high) of B. jararaca venom for 24 h, conditions that cause no cell death per se. Proteomics analysis was conducted on a nano-scale liquid chromatography coupled on-line with mass spectrometry (nLC-MS/MS). More than 1000 proteins were identified and evaluated from each cell line treated with either the low or high dose of the snake venom. Protein profiling upon venom treatment showed differential expression of several proteins related to cancer cell metabolism, immune response, and inflammation. Among the identified proteins we highlight histone H3, SNX3, HEL-S-156an, MTCH2, RPS, MCC2, IGF2BP1, and GSTM3. These data suggest that sub-toxic doses of B. jararaca venom have potential to modulate cancer-development related protein targets in cancer cells. This work illustrates a novel biochemical strategy to identify therapeutic targets against cancer cell growth and survival.