bluuebunny/crossref_metadata_2025 huggingface dataset:
Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier
Authors: Shrestha Binita , Stephen Haroon , Ahmad Sajjad
Published: August 2021

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.
Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming
Authors: Li Kang , Jiu Bo , Liu Hongwei , Pu Wenqiang
Published: August 2021

To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer.
Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery
Authors: Liao Mingjie , Zhang Rui , Lv Jichao , Yu Bin , Pang Jiatai , Li Ran , Xiang Wei , Tao Wei
Published: August 2021

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.
Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data
Authors: Gao Rui , Park Jisun , Hu Xiaohang , Yang Seungjun , Cho Kyungeun
Published: August 2021

Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection.
Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model
Authors: Du Muyun , Gao Jidong , Zhang Guifu , Wang Yunheng , Heiselman Pamela L. , Cui Chunguang
Published: August 2021

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.
A Semi-Linear Elliptic Model for a Circular Membrane MEMS Device Considering the Effect of the Fringing Field
Authors: Versaci Mario , Jannelli Alessandra , Morabito Francesco Carlo , Angiulli Giovanni
Published: August 2021

In this study, an accurate analytic semi-linear elliptic differential model for a circular membrane MEMS device, which considers the effect of the fringing field on the membrane curvature recovering, is presented. A novel algebraic condition, related to the membrane electromechanical properties, able to govern the uniqueness of the solution, is also demonstrated. Numerical results for the membrane profile, obtained by using the Shooting techniques, the Keller–Box scheme, and the III/IV Stage Lobatto IIIa formulas, have been carried out, and their performances have been compared. The convergence conditions, and the possible presence of ghost solutions, have been evaluated and discussed. Finally, a practical criterion for choosing the membrane material as a function of the MEMS specific application is presented.
Convolutional Neural Networks for Challenges in Automated Nuclide Identification
Authors: Turner Anthony , Wheldon Carl , Wheldon Tzany , Gilbert Mark , Packer Lee , Burns Jonathan , Freer Martin
Published: August 2021

Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID.
Star Sensor Denoising Algorithm Based on Edge Protection
Authors: Lu Kaili , Liu Enhai , Zhao Rujin , Zhang Hui , Tian Hong
Published: August 2021

Single-pixel noise commonly appearing in a star sensor can cause an unexpected error in centroid extraction. To overcome this problem, this paper proposes a star image denoising algorithm, named Improved Gaussian Side Window Filtering (IGSWF). Firstly, the IGSWF algorithm uses four special triangular Gaussian subtemplates for edge protection. Secondly, it exploits a reconstruction function based on the characteristic of stars and noise. The proposed IGSWF algorithm was successfully verified through simulations and evaluated in a star sensor. The experimental results indicated that the IGSWF algorithm performed better in preserving the shape of stars and eliminating the single-pixel noise and the centroid estimation error (CEE) value after using the IGSWF algorithm was eight times smaller than the original value, six times smaller than that after traditional window filtering, and three times smaller than that after the side window filtering.
Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network
Authors: Al-Hiyali Mohammed Isam , Yahya Norashikin , Faye Ibrahima , Hussein Ahmed Faeq
Published: August 2021

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks
Authors: Cho Hyung-Ju
Published: August 2021

Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.
A SSTDR Methodology, Implementations, and Challenges
Authors: Kingston Samuel , Benoit Evan , Edun Ayobami S. , Elyasichamazkoti Farhad , E. Sweeney Dawn , Harley Joel B. , Kuhn Paul K. , Furse Cynthia M.
Published: August 2021

Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.
Dynamic Restaurants Quality Mapping Using Online User Reviews
Authors: Grimaldi Didier , Collins Carly , Acosta Sebastian Garcia
Published: August 2021

Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe food practices, and overall cleanliness. The purpose of our study is to analyze the use of online-generated reviews (OGRs) as a tool to complement official restaurant inspection procedures. Our case study-based approach, with the help of a Python-based scraping library, consists of collecting OGR data from TripAdvisor and comparing them to extant restaurants’ health inspection reports. Our findings reveal that a correlation does exist between OGRs and national health system scorings. In other words, OGRs were found to provide valid indicators of restaurant quality based on inspection ratings and can thus contribute to the prevention of foodborne illness among citizens in real time. The originality of the paper resides in the use of big data and social network data as a an easily accessible, zero-cost, and complementary tool in disease prevention systems. Incorporated in restaurant management dashboards, it will aid in determining what action plans are necessary to improve quality and customer experience on the premises.
Can the New Energy Vehicle Pilot Policy Achieve Green Innovation and Emission Reduction?—A Difference-in-Differences Analysis on the Evaluation of China’s New Energy Fiscal Subsidy Policy
Authors: Wang Susheng , Chen Gang , Huang Dawei
Published: August 2021

Whether the new energy vehicle pilot policy (NEVPP) can achieve green innovation and emission reduction is an important exploration for China to achieve green and sustainable development. This research aims to empirically investigate the impact, impact mechanism, and heterogeneity characteristics of the NEVPP on urban green innovation and emission reduction based on panel data from 281 cities in China from 2004 to 2017, using difference-in-differences (DID) methods and fixed effect (FE) models. The results show that the NEVPP significantly reduces the carbon dioxide emissions of the pilot cities but significantly inhibits the green innovation, and the results are robust to the placebo test, propensity score matching DID (PSM-DID) test, instrumental variable (IV) estimation, emissions trading system (ETS), and Carbon-ETS interference test, and change of the dependent variable. In addition, further studies have shown that the NEVPP’s emission reduction effects are mainly achieved by reducing energy consumption, promoting technological innovation, and adjusting industrial structure. Moreover, we found that the NEVPP performed better in the regions where the level of economic development is high, the local government has a good relationship with the market, and the level of non-state economic development is high. In general, our research results show that the NEVPP has achieved innovation and emission reduction policy effects in China, but it is also accompanied by an inhibitory effect on green technological innovation.
Analysing Clusters of Consumers Who Use Medicinal and Aromatic Plant Products
Authors: Cadar Roxana-Larisa , Amuza Antonio , Dumitras Diana Elena , Mihai Mihaela , Pocol Cristina Bianca
Published: August 2021

The trend towards health and environmental protection has led to an increase in the consumption of medicinal and aromatic plant (MAP) products. The market for MAP products is influenced by consumer behaviour and therefore the aim of this study was to identify consumer segments that use MAP products for phytotherapeutic, cosmetic and personal care purposes according to their perceptions, attitudes and consumption habits and possible correlations between consumption of MAP products and interest in a healthy lifestyle and sustainable behaviour. Data collection was performed by administering a questionnaire to Cluj-Napoca residents. The final sample consisted of two groups of MAP consumers: the first with 231 respondents who use these products for phytotherapeutic purposes, the second group—with 297 respondents who use them as cosmetic and personal care products. The collected data were statistically analysed using the k-means clustering model, resulting in two distinct clusters in both consumer groups: cluster 1, the “Curious”, and Cluster 2, the “Knowledgeable”. For the consumers of the second cluster, consumption was positively correlated with healthcare and environmental protection, while they exhibited a growing interest in environmentally certified MAP products. The results obtained contribute to a better understanding of the MAP products consumption behaviour while tackling the practical implications for stakeholders in the pharmaceutical and cosmetics industry through the development and innovation of products tailored to consumer needs.
Telluric and Climate-Related Risk Awareness, and Risk Mitigation Strategies in the Azores Archipelago: First Steps for Building Societal Resilience
Authors: Ivčević Ante , Rego Isabel Estrela , Gaspar Rui , Statzu Vania
Published: August 2021

Islands are often considered excellent socio-ecological laboratories for testing the rapidity of global change since they experience the climate effects of sea-level rise faster than other areas. The Azores are a Portuguese volcanic archipelago located on the junction of the three tectonic plates: the Eurasian, the African and the North American plates. São Miguel, the main island of the Azores archipelago, hosts three active volcanoes, but the last significant volcanic eruption was the Capelinhos volcano on the island of Faial in 1957. Hence, the Azores offers the opportunity to assess insular risk awareness, facing both telluric and climate-related hazards. The key research question emerges from their natural situation: how does the local population perceive the threat of the natural hazards that occur in Azores? Because risks are socially constructed and depend on the uniqueness of territories, risk mitigation strategies must focus on the individual experiences of local dwellers, as a relationship between risk awareness and such strategies may be expected. To analyze this relationship, a web-based survey with a questionnaire including these variables was administered to a sample of Azoreans. The study aimed to assess risk awareness of the Azorean population and find a relationship between this and reported mitigation strategies. The results gave a preliminary insight into Azorean risk awareness of natural hazards and showed a significant positive relationship between risk awareness-raising activities and reported mitigation strategies. This is relevant information for municipalities and regional governments of areas with similar risk exposures, showing that, although risk awareness alone is not enough for measures to be implemented, it may be an important motivational first step for this to occur.
How Can the Celebrity Endorsement Effect Help Consumer Engagement? A Case of Promoting Tourism Products through Live Streaming
Authors: Qiu Luyi , Chen Xiaohua , Lee Timothy J.
Published: August 2021

Although much research has been devoted to the study of the impact of celebrity endorsements on the effectiveness of advertising in the literature on consumer behavior, few scholars pay attention to applying this concept to explain consumer behavior in the tourism field. This research tested celebrities’ expertise, trustworthiness, and attractiveness as the antecedents of consumer trust and engagement in the tourism and hospitality context. Specifically, through the analysis of 406 valid responses collected by online questionnaire, this study makes the following conclusions. First, it provides a new perspective on the influence of celebrity effects by focusing on the live-streaming behavior of CEOs on China’s largest online travel agency (OTA) platform. Secondly, it confirms that the three dimensions of the celebrity effect (expertise, trustworthiness, and attractiveness) positively affect the three dimensions of consumer trust (ability, benevolence, and integrity). At the same time, consumer trust can also predict the three dimensions of consumer engagement (cognitive processing, affection, and activation). In addition, the three dimensions of consumer trust also play a positive mediating role between the relationship of celebrity effects and consumer engagement. That is, the celebrity effects have a positive and indirect influence on consumer engagement. Therefore, by suggesting that celebrity endorsements can create consumer trust and engagement in the OTA platform, this research expands the research framework of celebrity endorsement credibility theory.
An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target
Authors: Cui Xiwen , E Shaojun , Niu Dongxiao , Wang Dongyu , Li Mingyu
Published: August 2021

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.
Expectations of Production Companies Operating in Poland towards Suppliers with Regards to Implementation of the Sustainability Concept
Authors: Urbaniak Maciej , Tundys Blanka , Ankiel Magdalena
Published: August 2021

It can be observed that manufacturing companies (especially Original Equipment Manufacturers (OEMs)), by promoting the implementation of the sustainability concept among their suppliers, refer to the requirements included in international standards which constitute guidelines for the implementation of quality, environmental and safety management systems. The effective implementation of these guidelines of the standards are often subject to initial and periodic evaluation as well as self-evaluation by suppliers. It can be seen that OEMs not only require effective implementation of quality, environmental and security management systems, but also offer a supplier a development program to improve their processes and products. The aim of the article was to try to define the expectations of production companies towards their suppliers regarding the implementation of the sustainability concept. The study presents the results of empirical research conducted with the computer-assisted telephone interviewing (CATI) technique in medium and large manufacturing companies operating in Poland. The results of these studies indicate the important role of the requirements included in international management standards as the expectations of manufacturing companies towards suppliers. The surveyed manufacturing companies focus in particular on reducing the risk of noncompliance and limiting the negative environmental impact by suppliers on the processes and products.
Employee Environmental Capability and Its Relationship with Corporate Culture
Authors: Woo Eun-Jung , Kang Eungoo
Published: August 2021

Due to rapid industrial developments and the effects of the economic revolution such as high production rates and non-ecofriendly supply systems, environmental pollution has been observed in recent years and environmental issues are increasingly becoming a concern on the planet. For this reason, there is little doubt that business organizations have been forced more and more to implement green business strategies for stakeholders, facing the necessity to improve their employees’ environmental performance. Using 461 US employees in the environmental related industries, we found empirical evidence between employees’ green performance, organizational culture and adaptability capability. Finally, the present study suggests two main corporate elements for green policy makers in eco-friendly organizations that the alignment of firm environmental strategy with strategic human resources (HR) should include (1) an organizational culture that supports employee green practices and (2) employee adaptability competency that enables workers to respond to the evolving environmental challenges as main component of analysis.
Deblurring Turbulent Images via Maximizing L1 Regularization
Authors: Duan Lizhen , Sun Shuhan , Zhang Jianlin , Xu Zhiyong
Published: August 2021

Atmospheric turbulence significantly degrades image quality. A blind image deblurring algorithm is needed, and a favorable image prior is the key to solving this problem. However, the general sparse priors support blurry images instead of explicit images, so the details of the restored images are lost. The recently developed priors are non-convex, resulting in complex and heuristic optimization. To handle these problems, we first propose a convex image prior; namely, maximizing L1 regularization (ML1). Benefiting from the symmetrybetween ML1 and L1 regularization, the ML1 supports clear images and preserves the image edges better. Then, a novel soft suppression strategy is designed for the deblurring algorithm to inhibit artifacts. A coarse-to-fine scheme and a non-blind algorithm are also constructed. For qualitative comparison, a turbulent blur dataset is built. Experiments on this dataset and real images demonstrate that the proposed method is superior to other state-of-the-art methods in blindly recovering turbulent images.
Symplectic-Structure-Preserving Uncertain Differential Equations
Authors: Yin Xiuling , Gao Xiulian , Liu Yanqin , Shen Yanfeng , Wang Jinchan
Published: August 2021

Uncertain differential equations are important mathematical models in uncertain environments. This paper investigates uncertain multi-dimensional and multiple-factor differential equations. First, the solvability of the equations is analyzed. The α-path distributions and expected values of solutions are given. Then, structure preserving uncertain differential equations, uncertain Hamiltonian systems driven by Liu processes, which possess a kind of uncertain symplectic structures, are presented. A symplectic scheme with six-order accuracy and a Yao-Chen algorithm are applied to design an algorithm to solve uncertain Hamiltonian systems. At last, numerical experiments are given to investigate four uncertain Hamiltonian systems, which highlight the efficiency of our algorithm.
The Scalar Mean Chance and Expected Value of Regular Bifuzzy Variables
Authors: Wang Guang , Shen Yixuan , Jiang Yujiao , Chen Jiahao
Published: August 2021

As a natural extension of the fuzzy variable, a bifuzzy variable is defined as a mapping from a credibility space to the collection of fuzzy variables, which is an appropriate tool to model the two-fold fuzzy phenomena. In order to enrich its theoretical foundation, this paper explores some important measures for regular bifuzzy variables, the most commonly used type of bifuzzy variables. Firstly, we introduce the regular bifuzzy variables’ mean chance measure and some properties, including self-duality and its calculation formulas. Furthermore, we also investigate the mean chance distribution for strictly monotone functions of regular bifuzzy variables based on the proposed operational law. Finally, we present the expected value operator as well as equivalent analytical formulas of the expected value of regular bifuzzy variables and their strictly monotone functions.
Using Azadirachtin to Transform Spodoptera frugiperda from Pest to Natural Enemy
Authors: Lin Sukun , Li Shengnan , Liu Zhenghui , Zhang Li , Wu Hao , Cheng Dongmei , Zhang Zhixiang
Published: August 2021

Spodoptera frugiperda and Rhopalosiphum maidis, as main pests, seriously harm the safety of maize. At present, chemical pesticides are mainly used to control these pests. However, due to residue and resistance problems, more green, environmentally benign, simple preventive control technology is needed. In this study, we reported the reason for the antifeedant activity of azadirachtin on S. frugiperda and proposed that S. frugiperda treated with azadirachtin would turn from pest into natural enemy. S. frugiperda showed an obvious antifeeding phenomenon to maize leaf treated with various azadirachtin concentrations (0.5~20 mg/L). It was found that maize leaf treated with 1 mg/L of azadirachtin has a stimulating effect on the antenna and sensillum basiconicum of S. frugiperda, and azadirachtin can affect the feeding behavior of S. frugiperda. Additionally, after treating maize leaves or maize leaves + R. maidis with 1 mg/L of azadirachtin, the predatory behavior of S. frugiperda changed from a preference for eating maize leaves to R. maidis. Moreover, the molting of R. maidis can promote the change of this predatory behavior. Our results, for the first time, propose that the combined control technology of azadirachtin insecticide and biological control could turn S. frugiperda from pest into natural enemy, which can effectively eliminate R. maidis and protect maize. This combined control technology provides a new way for pest management and has good ecological, environmental, and economic benefits.
Street Trees for Bicyclists, Pedestrians, and Vehicle Drivers: A Systematic Multimodal Review
Authors: Eisenman Theodore S. , Coleman Alicia F. , LaBombard Gregory
Published: August 2021

Multimodal Complete Streets have emerged as a prominent aspiration of urban planning to ensure safe access for all users of streets including pedestrians, bicyclists, motorists, and transit users. Concurrently, municipal leaders are pursuing ambitious tree planting initiatives. These co-arising trends are potentially good news, as trees are important elements of livable cities and Complete Streets. Yet, street trees may have different health and safety benefits and disbenefits for various circulation modes. To advance a multimodal approach to research and practice, we undertook a systematic literature review with goals to (1) identify the scholarly literature addressing links between street trees, human health, and safety for pedestrians, bicyclists, and vehicle drivers; (2) depict the principal disciplines, themes, and conceptual scope of this research; and (3) discuss the implications for urban planning and design practice and research. This review drew upon 13 scholarly databases and yielded 63 relevant articles spanning 15 countries, of which 49 constituted original research. The systematic analysis covers eight research categories. Findings show exponential growth in related scholarship over the past two decades, especially for pedestrians. Journals oriented toward interdisciplinary planning and public health and safety are leading this rise, and benefits far outweigh disbenefits. Yet, there are multimodal tensions especially as it relates to the role of street trees in relationship to drivers and pedestrians. Implications for research and practice are discussed, with an eye towards governance, design, and equity.
Designing Lentiviral Vectors for Gene Therapy of Genetic Diseases
Authors: Poletti Valentina , Mavilio Fulvio
Published: August 2021

Lentiviral vectors are the most frequently used tool to stably transfer and express genes in the context of gene therapy for monogenic diseases. The vast majority of clinical applications involves an ex vivo modality whereby lentiviral vectors are used to transduce autologous somatic cells, obtained from patients and re-delivered to patients after transduction. Examples are hematopoietic stem cells used in gene therapy for hematological or neurometabolic diseases or T cells for immunotherapy of cancer. We review the design and use of lentiviral vectors in gene therapy of monogenic diseases, with a focus on controlling gene expression by transcriptional or post-transcriptional mechanisms in the context of vectors that have already entered a clinical development phase.
Detection and Molecular Characterization of Two Gammaherpesviruses from Pantesco Breed Donkeys during an Outbreak of Mild Respiratory Disease
Authors: Mira Francesco , Canuti Marta , Di Bella Santina , Puleio Roberto , Lavazza Antonio , Lelli Davide , Vicari Domenico , Purpari Giuseppa , Cannella Vincenza , Chiaramonte Gabriele , Schirò Giorgia , Castronovo Calogero , Guercio Annalisa
Published: August 2021

Equid and asinine gammaherpesviruses (GHVs; genus Percavirus) are members of the Herpesviridae family. Though GHVs have been reported in horse populations, less studies are available on gammaherpesviral infections in donkeys. This study reports the co-infection with two GHVs in Pantesco breed donkeys, an endangered Italian donkey breed. Samples (n = 124) were collected on a breeding farm in Southern Italy from 40 donkeys, some of which were healthy or presented erosive tongue lesions and/or mild respiratory signs. Samples were analysed by using a set of nested PCRs targeting the DNA polymerase, glycoprotein B, and DNA-packaging protein genes, and sequence and phylogenetic analyses were performed. Twenty-nine donkeys (72.5%) tested positive, and the presence of Equid gammaherpesvirus 7 and asinine herpesvirus 5 was evidenced. In 11 animals, we found evidence for co-infection with viruses from the two species. Virions with herpesvirus-like morphology were observed by electron microscopic examination, and viruses were successfully isolated in RK-13-KY cell monolayers. The histological evaluation of tongue lesions revealed moderate lympho-granulocytic infiltrates and rare eosinophilic inclusions. The detection of GHVs in this endangered asinine breed suggests the need long-life monitoring within conservation programs and reinforces the need for further investigations of GHV’s pathogenetic role in asinine species.
Retrospective Enhanced Bat Lyssavirus Surveillance in Germany between 2018–2020
Authors: Klein Antonia , Calvelage Sten , Schlottau Kore , Hoffmann Bernd , Eggerbauer Elisa , Müller Thomas , Freuling Conrad M.
Published: August 2021

Lyssaviruses are the causative agents for rabies, a zoonotic and fatal disease. Bats are the ancestral reservoir host for lyssaviruses, and at least three different lyssaviruses have been found in bats from Germany. Across Europe, novel lyssaviruses were identified in bats recently and occasional spillover infections in other mammals and human cases highlight their public health relevance. Here, we report the results from an enhanced passive bat rabies surveillance that encompasses samples without human contact that would not be tested under routine conditions. To this end, 1236 bat brain samples obtained between 2018 and 2020 were screened for lyssaviruses via several RT-qPCR assays. European bat lyssavirus type 1 (EBLV-1) was dominant, with 15 positives exclusively found in serotine bats (Eptesicus serotinus) from northern Germany. Additionally, when an archived set of bat samples that had tested negative for rabies by the FAT were screened in the process of assay validation, four samples tested EBLV-1 positive, including two detected in Pipistrellus pipistrellus. Subsequent phylogenetic analysis of 17 full genomes assigned all except one of these viruses to the A1 cluster of the EBLV-1a sub-lineage. Furthermore, we report here another Bokeloh bat lyssavirus (BBLV) infection in a Natterer’s bat (Myotis nattereri) found in Lower Saxony, the tenth reported case of this novel bat lyssavirus.
Romanian Medical Students’ Attitude towards and Perceived Knowledge on COVID-19 Vaccination
Authors: Bălan Ana , Bejan Ioana , Bonciu Simona , Eni Cristina Elena , Ruță Simona
Published: August 2021

In Romania, the first phase of the COVID-19 vaccination campaign prioritized medical personnel, which included healthcare students. This study aimed to assess their knowledge, attitudes towards, and perception of COVID-19 vaccination. An anonymous, single-answer, 42-item online survey was conducted from 12 January until 3 March 2021, in the country’s largest University of Medicine and Pharmacy. Among the 1581 respondents (14.9% response rate), 88.5% were pro-vaccination, 7.8% were undecided, and 3.7% were vaccine resistant. The main reason for vaccine rejection was the perceived speed of vaccine development (strong agreement among the vaccine resistant, moderate agreement among the undecided, p < 0.001). Concern over long-term adverse reaction was present in only 11.5% of the respondents, significantly more frequent in the undecided and vaccine resistant. Perceived knowledge on the vaccines’ safety, efficacy, and technology correlated with a pro-vaccine attitude (p < 0.001). Most respondents had a positive stance towards vaccination in general, influencing their behaviour as future parents (99.3% of the pro-vaccination, 95.1% of the undecided, and 89.1% of the vaccine resistant will vaccinate their children, p < 0.001) and as medical professionals (99.7% of the pro-vaccination, 93.5% of those undecided, and 89.8% of the vaccine resistant would advise parents to vaccinate their children, p < 0.001). Healthcare students can thus serve as important vectors for scientifically sound information, influencing vaccine uptake in the community.
Heterologous ChAdOx1 nCoV-19/BNT162b2 Prime-Boost Vaccination Induces Strong Humoral Responses among Health Care Workers
Authors: Benning Louise , Töllner Maximilian , Hidmark Asa , Schaier Matthias , Nusshag Christian , Kälble Florian , Reichel Paula , Buylaert Mirabel , Grenz Julia , Ponath Gerald , Klein Katrin , Zeier Martin , Süsal Caner , Schnitzler Paul , Morath Christian , Speer Claudius
Published: August 2021

Despite limited data on safety and immunogenicity, heterologous prime-boost vaccination is currently recommended for individuals with ChAdOx1 nCoV-19 prime immunization in certain age groups. In this prospective, single-center study we included 166 health care workers from Heidelberg University Hospital who received either heterologous ChAdOx1 nCoV-19/BNT162b2, homologous BNT162b2 or homologous ChAdOx1 nCoV-19 vaccination between December 2020 and May 2021. We measured anti-S1 IgG, SARS-CoV-2 specific neutralizing antibodies, and antibodies against different SARS-CoV-2 fragments 0–3 days before and 19–21 days after boost vaccination. Before boost, 55/70 (79%) ChAdOx1 nCoV-19-primed compared with 44/45 (98%) BNT162b2-primed individuals showed positive anti-S1 IgG with a median (IQR) anti-S1 IgG index of 1.95 (1.05–2.99) compared to 9.38 (6.26–17.12). SARS-CoV-2 neutralizing antibodies exceeded the threshold in 24/70 (34%) of ChAdOx1 nCoV-19-primed and 43/45 (96%) of BNT162b2-primed individuals. After boosting dose, median (IQR) anti-S1 IgG index in heterologous ChAdOx1 nCoV-19/BNT162b2 vaccinees was 116.2 (61.84–170), compared to 13.09 (7.03–29.02) in homologous ChAdOx1 nCoV-19 and 145.5 (100–291.1) in homologous BNT162b2 vaccinees. All boosted vaccinees exceeded the threshold for neutralization, irrespective of their vaccination scheme. Vaccination was well-tolerated overall. We show that heterologous ChAdOx1 nCoV-19/BNT162b2 vaccination is safe and induces a strong and broad humoral response in healthy individuals.
Assessment of Future Climate Change Impact on an Agricultural Reservoir in South Korea
Authors: Lee Jaenam , Shin Hyungjin
Published: August 2021

Drought has been frequently occurring in South Korea due to climate change. Analyzing the water supply capacity of the water resource system provides essential information for water resource management. This study evaluates the future water supply capacity of the Gwanghye (GH) agricultural reservoir based on the representative concentration pathways 4.5 and 8.5 climate change scenarios. We performed a reservoir simulation by reflecting the full water level of the reservoir before and after reservoir heightening. Climate change is expected to decrease the GH reservoir’s future available water resources due to the overall reduction in the reservoir’s runoff. After the reservoir-heightening project, an overall improvement was observed in the stability of the future irrigation water supply. Moreover, the remaining water after the supply of the irrigation water could supply 0.6–7.2 × 103 m3 of daily instream water. Thus, flexible reservoir operations are necessary according to climate change scenarios and the reservoir operation period. The use of climate change information should be expanded to establish reasonable water management policies for future climate change scenarios.
Study on the Shape of the Aerator of High-Head Discharge Tunnel with Mild Bottom Slope
Authors: Zheng Xueyu , Zhang Luchen , Wu Shiqiang , Song Kai
Published: August 2021

Due to the high flow velocity and easy cavitation of high-head drainage tunnels, it is usually necessary to set up aeration facilities. In particular, when the bottom slope of the tunnel is mild, the aeration facilities often have problems such as difficulty with air intake, short cavity, and serious water accumulation, which aggravate the risk of cavitation damage. In this paper, based on the Rumei hydropower station and the Gushui hydropower station, a method combining theoretical analysis and model testing is used to solve the connection problem between the aeration facility and the 3% mild bottom slope of a tunnel body, and the aeration facility shape of “lifting ridge + flat (mild) slope + steep slope” is put forward. The research shows that the steep slope section can smoothly connect the water flow over the cantilever, reduce the jet impact angle, prevent the water from backtracking, and produce a long and stable cavity in the flat (mild) slope section. The aeration concentration along the bottom of the tunnel is higher than 3% at 140 m over the top of the dam. The aeration effect of this type is better, and it can provide effective long-distance protection for a drainage tunnel with high head and a mild bottom slope.
Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs
Authors: Syah Rahmad , Heidary Alireza , Rajabi Hossein , Elveny Marischa , Shayesteh Ali , Ramdan Dadan , Davarpanah Afshin
Published: August 2021

Nowadays, water savings on industrial plants have become a significant concern for various plants and sections. It is vitally essential to propose applicable and efficient techniques to retreat produced water from onshore and offshore production units. This paper aimed to implement the PFF (Photo Fenton Flotation) method to optimize the water treatment procedure, as it is a two-stage separation technique. The measurements were recorded for the HF (hydraulic fracturing) and CEOR (chemically enhanced oil recovery) methods separately to compare the results appropriately. To assure the efficiency of this method, we first recorded the measurements for five sequential days. As a result, the total volume of 2372.5 MM m3/year of water can be saved in the HF process during the PFF treatment procedure, and only 20% of this required fresh water should be provided from other resources. On the other hand, the total volume of 7482.5 MM m3/year of water can be saved in CEOR processes during the PFF treatment procedure, and only 38% of this required fresh water should be provided from other resources. Therefore, the total water volume of 9855 MM m3 can be saved each year, indicating the efficiency of this method in supplying and saving the water volume during the production operations from oilfield units.