bluuebunny/crossref_metadata_2025 huggingface dataset:
Geotourism Destinations Online Branding Co-Creation
Authors: Tiago Flávio , Correia Pedro , Briciu Victor-Alexandru , Borges-Tiago Teresa
Published: August 2021

The application of brand theory to destinations has grown in the last few decades, with the destination brand personality being a viable metaphor for creating and positioning destination brands. An often-overlooked tourism typology is geotourism; more specifically, volcanic tourism, since it can be a passive element of any tourism form or active nature tourism. However, its potential is relatively unexplored in terms of online branding. For this reason, the present study analyzes online brand image co-creation, using 300 websites related to three unique volcanic tourism destinations—Iceland, the Azores, and the Canary Islands. Three different types of sources (destination marketing organizations, commercial, and editorial websites) created these contents. The results demonstrate significant differences between the communication of the three destinations, with Iceland, where there is less aligned communication, most valuing geo elements in their communication, and the Azores, where all stakeholders communicate similar brand personality traits, displaying more aligned communication regarding brand personality. In the Canary Islands geotourism is less explored as a destination offer and is consequently less communicated. Acknowledging the different brand positioning and the parity and differentiation points among destinations with the same baseline offer—volcanic tourism—can be helpful for destination brand managers to reignite tourism and promote a unique tourism experience.
Influencing Mechanism of Green Human Resource Management and Corporate Social Responsibility on Organizational Sustainable Performance
Authors: Zhao Fuqiang , Kusi Manita , Chen Yun , Hu Wei , Ahmed Fawad , Sukamani Dinesh
Published: August 2021

Sustainable organizational achievement of firms is the dominant approach in today’s world, with an inclination on improving profitability, social condition, and a healthy environment. Based on ability, motivation, opportunity (AMO), and stakeholder theories, this study explored the green human resource management (GHRM) and corporate social responsibility (CSR) mechanism towards sustainable performance achievement. A structured questionnaire survey from 305 samples was appropriate for the quantitative study on this research. Moreover, widely used structural equation modeling and analysis of moment structures (AMOS) was used to analyze the proposed structural model of the study. Findings from the study added significance to all posited hypotheses and validated the study model. The current study added literature to the body of knowledge on green human resource management practices and corporate social responsibility in the pursuit of improved performance towards society, the environment, and sustainability of construction firms. For future studies, the inclusion of industries such as transportation-based industries and manufacturing companies that contribute to development should be considered for broad learning.
Spatiotemporal Differentiation of Land Surface Thermal Landscape in Yangtze River Delta Region, China
Authors: Wu Tong , Wang Lucang , Liu Haiyang
Published: August 2021

Advancements in the integrated development of the Yangtze River Delta are changing the structure and function of the surface thermal landscape and triggering a series of ecological and environmental problems. Therefore, examining the spatiotemporal differentiation characteristics and evolution laws of this land surface thermal landscape has great theoretical and practical significance in the context of optimizing functional zoning and realizing the harmonious development of the economy, society and nature. The paper uses the LST (land surface temperature) data retrieved by MODIS (MOD11A2) remote sensing satellites in 2007, 2010, 2013, 2016 and 2019 to extract a land surface thermal rating map of the Yangtze River Delta region, and to analyze the spatiotemporal differentiation in the land surface thermal landscape, combining of the land surface thermal landscape strip profile and thermal landscape pattern indices. The results show that the LST in the Yangtze River Delta region has increased in the past 12 years, the proportion of middle-, sub-high- and high-temperature zones increased by 33.42%, and the high-temperature zone has gradually extended into inland areas. The high-temperature zones in the areas surrounding core cities such as Shanghai, Nanjing, and Hangzhou have expanded. The corridor effect of thermal changes on the surface is obvious. The degree of aggregation in the lower-temperature areas has gradually decreased. The degree of aggregation in the higher-temperature regions has increased. The patch types of thermal landscape pattern increase, and the distribution of landscape area among various types tends to be even. this trend is most significant in optimized development region.
Sustainability of Ski Tourism in China: An Integrated Model of Skiing Tourists’ Willingness to Pay for Environmental Protection
Authors: Bai Zhijian , Zhang Yang
Published: August 2021

This study aimed to explore ski tourists’ willingness to pay for environmental protection for the sustainable development of a ski tourism destination as a valuable tourism market in China. The result of the structural model revealed that the integrative models of the Theory of Planned Behavior (TPB) and the Norm Activation Model (NAM) have the most significant explanatory power. The ski tourists’ willingness to pay can be enhanced by volitional, nonvolitional and altruistic factors. Additionally, the moderating and mediating roles of perceived authenticity on the integrative model have also been confirmed. This study is the first to provide a conceptual framework merging the TPB and NAM in the domain of environmental protection behavior in the Chinese ski tourism field for sustainable tourism development.
Preliminary Research into the Sustainable Responsibility of Teaware Design—A Fs/QCA Analysis of the Influence of the Smell and Taste of Tea through Visual and Tactile Perception
Authors: Yang Su-Chiu , Peng Li-Hsun
Published: August 2021

Most studies concerned with sustainable design issues focus on product design to change user behavior, increase the product lifespan, reduce energy waste, or employ the user experience to influence the behavior of other users. Rarely do they discuss how to design products that meet the real needs of consumers and reduce design waste and excessive consumption. Teaware designers and producers have invisibly created a considerable carbon footprint with regard to nonrenewable clay and energy waste due to excessive production. Therefore, this research uses visual and tactile research into the Chinese drinking cup to integrate user experience and the designer’s thinking and methods to ensure the sustainable value of the design and industry. This research uses experimental methods to collect and analyze the data with a fuzzy set qualitative comparative analysis (fs/QCA). The research found that the visual, tactile, and sensory perceptions of general consumers and tea professionals have different influencing factors on the taste system. This research provides evidence that the size of the tea-drinking container and the thickness of the cup’s rim will affect the perception of the tea’s taste and smell. This research provides new thinking for the design of Chinese tea-drinking utensils. It could solve social problems and dilemmas through design and contribute to the sustainable development of the design.
Fashion Rental: Smart Business or Ethical Folly?
Authors: Gyde Ciara , McNeill Lisa S.
Published: August 2021

The research presented here illustrates the spectrum of fashion rental PSS firms and business strategies within the New Zealand fashion rental market. The evidence collected suggests that there is a market for fashion rental; however, this market is underdeveloped in regard to its potential as a benefit exchange medium that encourages alternative consumption practice. This study finds that there is, indeed, enormous potential in PSSs as a means to divert fashion-conscious consumers away from ownership behaviours; however, the current systems fall short of this goal. This study offers a taxonomy to create and develop fashion rental PSSs that achieve central aims of circular economy fashion systems, enhancing the collective, social aspects of access, value-sharing and continuing development of mutual gain within the system. It is anticipated that this taxonomy could be further refined and extended through research in other countries, including those with more established, larger fashion rental organisations. Further, there is potential for action research approaches to the design and analysis of alternative fashion rental PSSs.
The Influence of Tourists’ Experience on Destination Loyalty: A Case Study of Hue City, Vietnam
Authors: Hung Vo Viet , Dey Sandeep Kumar , Vaculcikova Zuzana , Anh Le Trieu Hoang
Published: August 2021

This research focuses on the impact of the tourist experience on the relationship among destination image, tourist motivation, and tourist satisfaction on destination loyalty by examining the theoretical and empirical evidence on the causal relationship between constructs. A research model was proposed, in which nine hypotheses were developed, and the empirical data were collected from Hue city, which is a major tourist destination in Vietnam. A total of 204 questionnaires were returned, and the data were analyzed using PLS-SEM. The moderating effect of the tourist experience on the impact of the destination image, tourist motivation, and tourist satisfaction on destination loyalty is analyzed in this article. The proposed conceptual model was tested, and the results reveal a significant relationship between the two constructs to destination loyalty (destination image and tourist satisfaction). Furthermore, the findings support the proposed destination loyalty model: destination image, tourist satisfaction directly influenced destination loyalty, and tourist experience play an important role as a moderator in the relationship between tourist motivation and tourist loyalty.
Thai Non-Life Insurance Companies’ Resilience and the Historic 2011 Floods: Some Recommendations for Greater Sustainability
Authors: Terdpaopong Kanitsorn , Rickards Robert C.
Published: August 2021

The severe flooding occurring in parts of Thailand in 2011 constituted the fifth most costly catastrophe worldwide during the past 31 years. Many businesses suffered either directly or indirectly. A sharp downturn in the country’s economy resulted, with Thai non-life insurance companies’ annual losses totaling USD 4.1 bn. Focusing first on changes in their key performance indicators (KPIs) as evidence of their financial resilience, this study analyses data for 58 companies from 2008–2010 (years prior to the flooding), 2011 (the flood year), and 2012–2014 (the immediate post-flood years). Descriptive and inferential statistics depict differences in firm characteristics and key performance indicators between these periods. The findings show that: (1) not surprisingly, the floods had a major impact on Thai non-life insurance companies’ finances; and (2) even after two years, they still had not recovered fully. Then, employing Data Envelopment Analysis (DEA), the study assesses the relative efficiency of 58 Thai non-life insurance companies in using their assets to generate operating profit. The evidence indicates that: (1) larger insurance companies are more efficient than smaller ones in this regard; and (2) almost all the entities examined performed less efficiently during the post-flood years than in earlier periods. These results serve as the basis for recommendations to Thai non-life insurance companies, government policymakers, and future researchers. Although Thai non-life insurance companies survived the challenges they faced during the study period, implementation of the measures recommended here likely would boost their technical efficiency and financial resilience, thereby facilitating their ability to operate more sustainably in the long run.
Collaborative Learning Experiences in a Changing Environment: Innovative Educational Approaches in Architecture
Authors: Antonini Ernesto , Gaspari Jacopo , Visconti Cristina
Published: August 2021

The sense of uncertainty and fragility due to the effects and magnitude of global challenges we are facing (from the circumstances of the pandemic to the impacts of climate change) requires—much more than in the past—the capacity to generate a visionary and forefront design approach in the young generation, with an aim to stimulate their reaction attitude rather than providing consolidated tools from past conditions that no longer exist or will rapidly evolve. Within this general framework, we have investigated the effectiveness and impacts of experienced-based methods of learning and innovative educational tools in architecture that are aimed at shaping expertise that addresses the aspects of environment and climate change in the context of socio-cultural dynamics, real potentialities and constraints, and their transdisciplinary trajectories. We analyzed five international pioneering teaching experiences that provided the opportunity to understand the outcomes of collaborative and experiential learning processes by which the educational activities leverage dialogue between diverse communities (including academia, citizens, policymakers, and practitioners). The study outcomes show that shifting the pedagogical paradigm towards experience-based models can improve the awareness of future practitioners for the climate implications of architectural design, implement their analysis and project skills, and trigger processes of knowledge transfer and co-production at the community level. Experience-based models also allow them to better address the societal and cultural issues involved in decision making.
Research on the Spatial Pattern and Influence Mechanism of Industrial Transformation and Development of Traditional Villages
Authors: Lin Mingshui , Jian Jingsong , Yu Hu , Zeng Yanfang , Lin Menglung
Published: August 2021

Industrial transformation has been regarded as an important measure to promote traditional village revitalization. Research on the spatial pattern and influence mechanism of industrial transformation in traditional villages is urgently needed. In this context, this study takes the 211 national traditional villages in Fujian Province of China as research objects and uses GIS spatial analysis and geographical detectors to analyze the spatial pattern and influence mechanism of industrial transformation in traditional villages. The results show that: (1) the scale of traditional village industry presents the characteristics of wavy growth. High- and medium-density cluster areas were identified. (2) Traditional villages can be categorized into three types, not transformed, to be transformed and transformed villages. These three stages of transformation have different features of industry development and different dominant industries. (3) The core factors affecting the industrial transformation of traditional villages show obvious differences at different transformation stages and spatial differences in coastal and inland areas. Therefore, policies and measures should be customized to local conditions to improve the development quality of traditional villages and promote the industrial transformation and upgrading of traditional villages. This study improves the research on the transformation and development mechanism of traditional village industry from the perspective of industrial revitalization in theory and provides experience and models as reference for the revitalization of traditional village industry in practice.
Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment
Authors: Mahoto Naeem Ahmed , Shaikh Asadullah , Al Reshan Mana Saleh , Memon Muhammad Ali , Sulaiman Adel
Published: August 2021

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.
Where Is the Age of Digitalization Heading? The Meaning, Characteristics, and Implications of Contemporary Digital Transformation
Authors: Kim Seunghyun , Choi Byungchul , Lew Yong Kyu
Published: August 2021

Digital transformation is perceived not only as a change in certain technology but also as a large transition that will ultimately change our lives for the better. Industry convergence, the key to digital transformation, entails, for firms, both various opportunities for innovation and the crisis of falling behind. Therefore, from the perspective of firms, it is critical to examine how digital transformation affects their industries and products as well as how they perceive and respond to digital transformation. This is ultimately a matter of how firms survive and maintain sustainable growth in this great upheaval of digital transformation. Based on the understanding of the concept of digital transformation, this study explores how high-growth firms perceive various aspects of digital transformation. The findings show that digital transformation involves a change of firms based on constant innovation, not simply the acceptance of technology, and that there is a large digital divide that depends on the firm size and industry type. Based on the above, this study derives implications in terms of the innovation activities of firms to ensure that digital transformation does not serve as a handicap and barrier for firms.
Identification and Machine Learning Prediction of Nonlinear Behavior in a Robotic Arm System
Authors: Wang Cheng-Chi , Zhu Yong-Quan
Published: August 2021

In this study, the subject of investigation was the dynamic double pendulum crank mechanism used in a robotic arm. The arm is driven by a DC motor though the crank system and connected to a fixed side with a mount that includes a single spring and damping. Robotic arms are now widely used in industry, and the requirements for accuracy are stringent. There are many factors that can cause the induction of nonlinear or asymmetric behavior and even excite chaotic motion. In this study, bifurcation diagrams were used to analyze the dynamic response, including stable symmetric orbits and periodic and chaotic motions of the system under different damping and stiffness parameters. Behavior under different parameters was analyzed and verified by phase portraits, the maximum Lyapunov exponent, and Poincaré mapping. Firstly, to distinguish instability in the system, phase portraits and Poincaré maps were used for the identification of individual images, and the maximum Lyapunov exponents were used for prediction. GoogLeNet and ResNet-50 were used for image identification, and the results were compared using a convolutional neural network (CNN). This widens the convolutional layer and expands pooling to reduce network training time and thickening of the image; this deepens the network and strengthens performance. Secondly, the maximum Lyapunov exponent was used as the key index for the indication of chaos. Gaussian process regression (GPR) and the back propagation neural network (BPNN) were used with different amounts of data to quickly predict the maximum Lyapunov exponent under different parameters. The main finding of this study was that chaotic behavior occurs in the robotic arm system and can be more efficiently identified by ResNet-50 than by GoogLeNet; this was especially true for Poincaré map diagnosis. The results of GPR and BPNN model training on the three types of data show that GPR had a smaller error value, and the GPR-21 × 21 model was similar to the BPNN-51 × 51 model in terms of error and determination coefficient, showing that GPR prediction was better than that of BPNN. The results of this study allow the formation of a highly accurate prediction and identification model system for nonlinear and chaotic motion in robotic arms.
Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map
Authors: Ghosh Gopal , Kavita  , Anand Divya , Verma Sahil , Rawat Danda B. , Shafi Jana , Marszałek Zbigniew , Woźniak Marcin
Published: August 2021

Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.
The Interplay between Diradical Character and Stability in Organic Molecules
Authors: Petakova Vaska , Nedyalkova Miroslava , Stoycheva Joanna , Tadjer Alia , Romanova Julia
Published: August 2021

The number of scientific papers on the unique properties and the potential for various applications of compounds with a diradical character is growing constantly. The diradical character enhances and even engenders certain desired optical properties and its modulation is a modern molecular design strategy. Nowadays, molecules with a non-zero diradical character are regarded as promising materials for new-generation and highly efficient solar cells and photonics devices. What is the price, however, of the unique properties of open-shell compounds? Alongside all the benefits, the diradical character is usually associated with low stability and high reactivity—unwanted molecular qualities for practical purposes. Thus, from a fundamental and applied point of view, it is important to investigate the correlation between the diradical character and laboratory stability, which is the goal of the present paper. Here, we report a combined quantum–chemical study (conceptual DFT and spin-projected HF theory) and multivariate analysis of the diradical character of a series of o- and p-quinomethides, for the stability of which experimental data are available. Our results reveal that a compromise between the diradical character and laboratory stability of a molecule is feasible and that the relationship between these two quantities can be understood in the framework of Clar’s sextet theory.
Quantum Current Algebra Symmetry and Description of Boltzmann Type Kinetic Equations in Statistical Physics
Authors: Ivankiv Lev I. , Prykarpatsky Yarema A. , Samoilenko Valeriy H. , Prykarpatski Anatolij K.
Published: August 2021

We review a non-relativistic current algebra symmetry approach to constructing the Bogolubov generating functional of many-particle distribution functions and apply it to description of invariantly reduced Hamiltonian systems of the Boltzmann type kinetic equations, related to naturally imposed constraints on many-particle correlation functions. As an interesting example of deriving Vlasov type kinetic equations, we considered a quantum-mechanical model of spinless particles with delta-type interaction, having applications for describing so called Benney-type hydrodynamical praticle flows. We also review new results on a special class of dynamical systems of Boltzmann–Bogolubov and Boltzmann–Vlasov type on infinite dimensional functional manifolds modeling kinetic processes in many-particle media. Based on algebraic properties of the canonical quantum symmetry current algebra and its functional representations, we succeeded in dual analysis of the infinite Bogolubov hierarchy of many-particle distribution functions and their Hamiltonian structure. Moreover, we proposed a new approach to invariant reduction of the Bogolubov hierarchy on a suitably chosen correlation function constraint and deduction of the related modified Boltzmann–Bogolubov kinetic equations on a finite set of multi-particle distribution functions. There are also presented results of application of devised methods to describing kinetic properties of a many-particle system with an adsorbent surface, in particular, the corresponding kinetic equation for the occupation density distribution function is derived.
Evaluation of Thoracic Equivalent Multiport Circuits Using an Electrical Impedance Tomography Hardware Simulation Interface
Authors: Dimas Christos , Alimisis Vassilis , Georgakopoulos Ioannis , Voudoukis Nikolaos , Uzunoglu Nikolaos , Sotiriadis Paul P.
Published: August 2021

Electrical impedance tomography is a low-cost, safe, and high temporal resolution medical imaging modality which finds extensive application in real-time thoracic impedance imaging. Thoracic impedance changes can reveal important information about the physiological condition of patients’ lungs. In this way, electrical impedance tomography can be a valuable tool for monitoring patients. However, this technique is very sensitive to measurement noise or possible minor signal errors, coming from either the hardware, the electrodes, or even particular biological signals. Thus, the design of a good performance electrical impedance tomography hardware setup which properly interacts with the tissue examined is both an essential and a challenging concept. In this paper, we adopt an extensive simulation approach, which combines the system’s analogue and digital hardware, along with equivalent circuits of 3D finite element models that represent thoracic cavities. Each thoracic finite element model is created in MATLAB based on existing CT images, while the tissues’ conductivity and permittivity values for a selected frequency are acquired from a database using Python. The model is transferred to a multiport RLC network, embedded in the system’s hardware which is simulated at LT SPICE. The voltage output data are transferred to MATLAB where the electrical impedance tomography signal sampling and digital processing is also simulated. Finally, image reconstructions are performed in MATLAB, using the EIDORS library tool and considering the signal noise levels and different electrode and signal sampling configurations (ADC bits, sampling frequency, number of taps).
Application of the Artificial Neural Network (ANN) Approach for Prediction of the Kinetic Parameters of Lignocellulosic Fibers
Authors: Ornaghi Heitor Luiz , Neves Roberta Motta , Monticeli Francisco M.
Published: August 2021

Lignocellulosic fibers are widely applied as reinforcement in polymer composites due to their properties. The thermal degradation behavior governs the maximum temperature at which the fiber can be applied without significant mass loss. It is possible to determine this temperature using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined by using Arrhenius-based equations, and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost and time. Methods to predict thermogravimetric curves can be very useful in the materials science field, and in this sense, mathematical approaches are powerful tools, if well employed. For this reason, in the present study, thermogravimetric curves from curaua fiber were obtained at four different heating rates (5, 10, 20 and 40 °C·min−1) and Vyazovkin kinetic parameters were obtained using free available software. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM) aiming to obtain curves at any heating rate between the minimum and maximum experimental heating rates. Finally, Vyazovkin kinetic parameters were tested again, with the new predicted curves at the heating rates of 7, 15, 30 and 50 °C·min−1. Similar values of the kinetic parameters were obtained compared to the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.
Acute Kidney Injury and Organ Dysfunction: What Is the Role of Uremic Toxins?
Authors: Lara-Prado Jesús Iván , Pazos-Pérez Fabiola , Méndez-Landa Carlos Enrique , Grajales-García Dulce Paola , Feria-Ramírez José Alfredo , Salazar-González Juan José , Cruz-Romero Mario , Treviño-Becerra Alejandro
Published: August 2021

Acute kidney injury (AKI), defined as an abrupt increase in serum creatinine, a reduced urinary output, or both, is experiencing considerable evolution in terms of our understanding of the pathophysiological mechanisms and its impact on other organs. Oxidative stress and reactive oxygen species (ROS) are main contributors to organ dysfunction in AKI, but they are not alone. The precise mechanisms behind multi-organ dysfunction are not yet fully accounted for. The building up of uremic toxins specific to AKI might be a plausible explanation for these disturbances. However, controversies have arisen around their effects in organs other than the kidney, because animal models usually depict AKI as a kidney-specific injury. Meanwhile, humans present AKI frequently in association with multi-organ failure (MOF). Until now, medium-molecular-weight molecules, such as inflammatory cytokines, have been proven to play a role in endothelial and epithelial injury, leading to increased permeability and capillary leakage, mainly in pulmonary and intestinal tissues.
Traces of Anisotropic Quasi-Regular Structure in the SDSS Data
Authors: Ryabinkov Andrei I. , Kaminker Alexander D.
Published: August 2021

The aim of this study is to search for quasi-periodical structures at moderate cosmological redshifts z ≲ 0.5. We mainly use the SDSS DR7 data on the luminous red galaxies (LRGs) with redshifts 0.16 ≤ z ≤ 0.47. At first, we analyze features (peaks) in the power spectra of radial (shell-like) distributions using separate angular sectors in the sky and calculate the power spectra within each sector. As a result, we found some signs of a large-scale anisotropic quasi-periodic structure detectable through 6 sectors out of a total of 144 sectors. These sectors are distinguished by large amplitudes of dominant peaks in their radial power spectra at wavenumbers k within a narrow interval of 0.05 < k < 0.07 h Mpc−1. Then, passing from a spherical coordinate system to a Cartesian one, we found a special direction such that the total distribution of LRG projections on it contains a significant (≳5σ) quasi-periodical component. We assume that we are dealing with a signature of a quasi-regular structure with a characteristic scale 116 ± 10 h−1 Mpc. Our assumption is confirmed by a preliminary analysis of the SDSS DR12 data.
Post-COVID-19 Arthritis and Sacroiliitis: Natural History with Longitudinal Magnetic Resonance Imaging Study in Two Cases and Review of the Literature
Authors: Colatutto Donatella , Sonaglia Arianna , Zabotti Alen , Cereser Lorenzo , Girometti Rossano , Quartuccio Luca
Published: August 2021

Severe acute respiratory coronavirus-2 syndrome (SARS-CoV-2) is a well-known pandemic infectious disease caused by an RNA virus belonging to the coronaviridae family. The most important involvement during the acute phase of infection concerns the respiratory tract and may be fatal. However, COVID-19 may become a systemic disease with a wide spectrum of manifestations. Herein, we report the natural history of sacroiliac inflammatory involvement in two females who developed COVID-19 infection with mild flu-like symptoms. After the infection they reported inflammatory back pain, with magnetic resonance imaging (MRI) studies showing typical aspects of sacroiliitis. Symptoms improved with NSAIDs therapy over the following months while MRI remained positive. A literature review was performed on this emerging topic. To our knowledge, this is the first MRI longitudinal study of post-COVID-19 sacroiliitis with almost one year of follow-up. Predisposing factors for the development of articular involvement are unclear but a long-lasting persistence of the virus, demonstrated by nasopharyngeal swab, may enhance the probability of altering the immune system in a favourable background.
Hantavirus Replication Cycle—An Updated Structural Virology Perspective
Authors: Meier Kristina , Thorkelsson Sigurdur R. , Quemin Emmanuelle R. J. , Rosenthal Maria
Published: August 2021

Hantaviruses infect a wide range of hosts including insectivores and rodents and can also cause zoonotic infections in humans, which can lead to severe disease with possible fatal outcomes. Hantavirus outbreaks are usually linked to the population dynamics of the host animals and their habitats being in close proximity to humans, which is becoming increasingly important in a globalized world. Currently there is neither an approved vaccine nor a specific and effective antiviral treatment available for use in humans. Hantaviruses belong to the order Bunyavirales with a tri-segmented negative-sense RNA genome. They encode only five viral proteins and replicate and transcribe their genome in the cytoplasm of infected cells. However, many details of the viral amplification cycle are still unknown. In recent years, structural biology methods such as cryo-electron tomography, cryo-electron microscopy, and crystallography have contributed essentially to our understanding of virus entry by membrane fusion as well as genome encapsidation by the nucleoprotein. In this review, we provide an update on the hantavirus replication cycle with a special focus on structural virology aspects.
Temporal Dynamics of Influenza A(H5N1) Subtype before and after the Emergence of H5N8
Authors: Amer Fatma , Li Ruiyun , Rabie Neveen , El-Husseiny Mohamed H. , Yehia Nahed , Hagag Naglaa M. , Samy Mohamed , Selim Abdullah , Hassan Mohamed K. , Hassan Wafaa M. M. , Arafa Abdel-Sattar , Lundkvist Åke , Shahein Momtaz A. , Naguib Mahmoud M.
Published: August 2021

Highly pathogenic avian influenza (HPAI) viruses continue to circulate worldwide, causing numerous outbreaks among bird species and severe public health concerns. H5N1 and H5N8 are the two most fundamental HPAI subtypes detected in birds in the last two decades. The two viruses may compete with each other while sharing the same host population and, thus, suppress the spread of one of the viruses. In this study, we performed a statistical analysis to investigate the temporal correlation of the HPAI H5N1 and HPAI H5N8 subtypes using globally reported data in 2015–2020. This was joined with an in-depth analysis using data generated via our national surveillance program in Egypt. A total of 6412 outbreaks were reported worldwide during this period, with 39% (2529) as H5N1 and 61% (3883) as H5N8. In Egypt, 65% of positive cases were found in backyards, while only 12% were found in farms and 23% in live bird markets. Overall, our findings depict a trade-off between the number of positive H5N1 and H5N8 samples around early 2017, which is suggestive of the potential replacement between the two subtypes. Further research is still required to elucidate the underpinning mechanisms of this competitive dynamic. This, in turn, will implicate the design of effective strategies for disease control.
Virus Infection Variability by Single-Cell Profiling
Authors: Suomalainen Maarit , Greber Urs F.
Published: August 2021

Cell-to-cell variability of infection has long been known, yet it has remained one of the least understood phenomena in infection research. It impacts on disease onset and development, yet only recently underlying mechanisms have been studied in clonal cell cultures by single-virion immunofluorescence microscopy and flow cytometry. In this review, we showcase how single-cell RNA sequencing (scRNA-seq), single-molecule RNA-fluorescence in situ hybridization (FISH), and copper(I)-catalyzed azide-alkyne cycloaddition (click) with alkynyl-tagged viral genomes dissect infection variability in human and mouse cells. We show how the combined use of scRNA-FISH and click-chemistry reveals highly variable onsets of adenoviral gene expression, and how single live cell plaques reveal lytic and nonlytic adenovirus transmissions. The review highlights how scRNA-seq profiling and scRNA-FISH of coxsackie, influenza, dengue, zika, and herpes simplex virus infections uncover transcriptional variability, and how the host interferon response tunes influenza and sendai virus infections. We introduce the concept of “cell state” in infection variability, and conclude with advances by single-cell simultaneous measurements of chromatin accessibility and mRNA counts at high-throughput. Such technology will further dissect the sequence of events in virus infection and pathology, and better characterize the genetic and genomic stability of viruses, cell autonomous innate immune responses, and mechanisms of tissue injury.
COVID-19 Vaccine Hesitancy in Poland—Multifactorial Impact Trajectories
Authors: Sowa Paweł , Kiszkiel Łukasz , Laskowski Piotr Paweł , Alimowski Maciej , Szczerbiński Łukasz , Paniczko Marlena , Moniuszko-Malinowska Anna , Kamiński Karol
Published: August 2021

Since the declaration of the SARS-CoV-2 pandemic confirmed by World Health Organization, work on the development of vaccines has been stimulated. When vaccines are commonly available, a major problem is persistent vaccine hesitancy in many European countries. The main goal of our study was to understand the multidimensional factors inducing this phenomenon in Poland. Our study was carried out at the third wave’s peak of the pandemic, with record rates of daily cases and deaths associated with COVID-19. The results indicate that vaccine hesitancy/acceptability should always be considered in an interdisciplinary manner and according to identified factors where most negative attitudes could be altered. Our analyses included the assessment of a representative quota sample of adult Poles (N = 1000). The vaccine hesitancy in the studied group reached 49.2%. We performed stepwise logistic regression modeling to analyze variables set into six trajectories (groups) predicting the willingness to vaccinate. Apart from typical, socio-demographic and economic determinants, we identified the fear of vaccines’ side effects, beliefs in conspiracy theories and physical fitness. We were also able to establish the order of importance of factors used in a full model of all impact trajectories.
Cost–Benefit Analysis of a Mass Vaccination Strategy to Control Brucellosis in Sheep and Goats in Northern Iraq
Authors: Al Hamada Ali , Bruce Mieghan , Barnes Anne , Habib Ihab , D. Robertson Ian
Published: August 2021

Brucellosis is a major economic and production-limiting disease for livestock owners and the community in Iraq. A cost–benefit analysis was conducted to evaluate the impact of an expanded annual mass vaccination programme of sheep and goats that involves all female and male sheep and goats over the age of 3 months with Rev. 1 vaccine. The proposed expanded vaccination programme was compared to the current annual vaccination program, which involved only vaccinating female sheep and goats between the ages of 3 and 6 months of age with Rev. 1. The cost-benefit analysis model was developed utilizing data collected in Dohuk Governorate, northern Iraq. The seroprevalence in small ruminants (using Rose Bengal test and ELISA in series) was predicted to decrease from 9.22% to 0.73% after 20 years of implementing the proposed annual mass vaccination program. The net present value of the mass vaccination program was estimated to be US$ 10,564,828 (95% Confidence Interval (CI): −16,203,454 to 37,049,245), the benefit–cost ratio was estimated to be 4.25 (95% CI: −2.71 to 11.22), and the internal rate of return was 91.38% (95% CI:11.71 to 190.62%). The proposed vaccination strategy was predicted to decrease the overall financial loss caused by brucellosis from 1.75 to 0.55 US$ per adult female animal. The results of this economic analysis highlight the benefit of implementing an annual mass vaccination program of small ruminants with Rev. 1 vaccine to reduce the prevalence of brucellosis in northern Iraq.
Implementation of Mini-FLOTAC in Routine Diagnosis of Coccidia and Helminth Infections in Domestic and Exotic Birds
Authors: Lozano João , Almeida Cristina , Victório Ana Cláudia , Melo Pedro , Rodrigues João Paulo , Rinaldi Laura , Cringoli Giuseppe , Gomes Lídia , Oliveira Manuela , Paz-Silva Adolfo , Madeira de Carvalho Luís
Published: August 2021

Mini-FLOTAC (MF) has recently been proposed for the fecal quantification of gastrointestinal (GI) parasites in birds due to its higher sensitivity and precision in comparison with the McMaster method. The current research aimed to test the use of MF in routine diagnosis of coccidia and helminth infections in several domestic and exotic bird collections in Portugal. Between July 2020 and April 2021, a total of 142 fecal samples from organic layers, peacocks and ratites were collected in four Portuguese bird collections and processed using MF and fecal cultures to identify and calculate GI parasite shedding and prevalence. The McMaster method was also used to compare the shedding levels obtained for both quantitative techniques. MF’s relative sensitivity and specificity were also assessed, using McMaster as the reference technique. The implementation of MF resulted in an average Eimeria spp. shedding higher in peacocks from bird collection 2 (502 OPG), followed by peacocks from collection 1 (107 OPG) and organic layers (24 OPG) and peacocks from collection 3 (9 OPG). Peacocks were also positive for Capillaria spp., Trichostrongylus tenuis and Strongyloides pavonis, whereas ostriches and emus were infected by L. douglassii. The MF protocol for exotic animals and the McMaster method did not differ significantly for each parasitic agent and bird species, and MF achieved relative sensitivities and specificities higher than 70% for Galliform Eimeria spp., peacock helminths and ratites’ L. douglassii infections. Higher L. douglassii EPG values were identified using the MF protocol for exotic species (2 g of feces/38 mL of sucrose solution), followed by McMaster 2/28, MF 5/45 and MF 2/18. The use of MF allowed for obtaining different intestinal parasitic populations in several bird species and locations, and MF 2/38 is globally proposed as the most suitable protocol for bird fecal samples as an alternative to the McMaster method in the diagnosis of avian intestinal parasitic infections.
A Collaborative Sensing System for Farmland Water Conservancy Project Maintenance through Integrating Satellite, Aerial, and Ground Observations
Authors: Chao Zhenhua , Fang Xuan , Na Jiaming , Che Mingliang
Published: August 2021

More and more attention has been paid to farmland water conservancy project (FWCP) maintenance in China, which can reallocate water resources in a more rational and efficient manner. Compared with the traditional survey such as field survey, FWCP maintenance can be improved efficiently with geospatial technology. To improve the level of FWCP maintenance in China, a collaborative sensing system framework by integrating satellite, aerial, and ground remote sensing is put forward. The structure of the system framework includes three sections, namely the data acquisition, the operational work, and the application and service. Through the construction and operation of such collaborative sensing system, it will break through the limitation of any single remote sensing platform and provide all-around and real-time information on FWCP. The collaborative monitoring schemes for the designed FWCP maintenance can engage ditch riders to maintain more effectively, which will enable them to communicate more specifically with smallholders in the process of irrigation. Only when ditch riders and farmers are fully involved, irrigation efficiency will be improved. Furthermore, the collaborative sensing system needs feasible standards for multi-source remote sensing data processing and intelligent information extraction such as data fusion, data assimilation, and data mining. In a way, this will promote the application of remote sensing in the field of agricultural irrigation and water saving. On the whole, it will be helpful to improve the traditional maintenance problems and is also the guarantee for establishing a long-term scientific management mechanism of FWCP maintenance in developing countries, especially in China.
Ecohydraulics Modelling and Simulation
Authors: Žagar Dušan
Published: August 2021

Ecohydraulics, the interdiscipline of ecology and hydraulics, has been rapidly developing and receiving ever-growing attention both in hydraulic research efforts and in environmentally oriented professional and lay communities in recent years [...]
Páramo Lakes of Colombia: An Overview of Their Geographical Distribution and Physicochemical Characteristics
Authors: Zapata Angela , Rivera-Rondón Carlos A. , Valoyes Daivan , Muñoz-López Claudia L. , Mejía-Rocha Marino , Catalan Jordi
Published: August 2021

The páramo lakes, Colombia, situated in the neotropical region, are of great value both as natural heritage and as water source to the most populated areas of the Andes, but are threatened by expanding agriculture, livestock, mining, and landscape fragmentation. Nonetheless, a general assessment of the lakes’ distribution and biogeochemical characteristics was lacking. We made a complete inventory of the Colombian páramo lakes and characterized their morphometry and water chemistry based on a survey of 51 lakes in the Eastern Cordillera. There are 3250 lakes distributed across 28 páramo complexes in Colombia, mainly located between 3600 and 4400 m a.s.l. The lakes are usually small (<10 ha) and shallow (<10 m). Most of them are slightly acidic (average pH~6), with high nutrient (total phosphorus ~0.6 mg/L, total nitrogen ~1 mg/L) and total organic carbon (~6 mg/L) concentrations and low oxygen (~3.5 mg/L) at the bottom. Water chemistry varies according to two main independent gradients related to watershed bedrock geology and trophic state. Global change pressures may be challenging the preservation of these unique ecosystems. Increasing the protection of more lake watersheds should reduce these potential impacts by mitigating negative synergies with local pressures.