bluuebunny/crossref_metadata_2025 huggingface dataset:
Lunar Crater Detection on Digital Elevation Model: A Complete Workflow Using Deep Learning and Its Application
Authors: Lin Xuxin , Zhu Zhenwei , Yu Xiaoyuan , Ji Xiaoyu , Luo Tao , Xi Xiangyu , Zhu Menghua , Liang Yanyan
Published: January 2022

Impact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection algorithms (CDAs) have been carried out on the images from the remote sensing observations. Recently, CDAs via convolutional neural network (CNN) on digital elevation model (DEM) has been developed as it can combine the discrimination ability of CNN with the robust characteristic of the DEM data. However, most of the existing algorithms adopt a traditional two-stage detection pipeline including an edge segmentation and a template matching step. In this paper, we attempt to reduce the gap between the existing DEM-based CDAs and the advanced CNN methods for object detection, and propose a complete workflow including an end-to-end deep learning pipeline for lunar crater detection, in particular for craters smaller than 50 km in diameter. Based on the workflow, we benchmark nine representative CNN models involving three popular types of detection architectures. Moreover, we elaborate on the practical application of the proposed workflow, and provide an example method to demonstrate the performance advantage in terms of the precision (82.97%) and recall (79.39%). Furthermore, we develop a crater verification tool to manually validate the detection results, and the visualization results show that our detected craters are reasonable and can be used as a supplement to the existing hand-labeled datasets.
Towards a Contactless Stress Classification Using Thermal Imaging
Authors: Gioia Federica , Greco Alberto , Callara Alejandro Luis , Scilingo Enzo Pasquale
Published: January 2022

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner.
A Crack Propagation Method for Pipelines with Interacting Corrosion and Crack Defects
Authors: Xie Mingjiang , Wang Yifei , Xiong Weinan , Zhao Jianli , Pei Xianjun
Published: January 2022

Corrosion and crack defects often exist at the same time in pipelines. The interaction impact between these defects could potentially affect the growth of the fatigue crack. In this paper, a crack propagation method is proposed for pipelines with interacting corrosion and crack defects. The finite element models are built to obtain the Stress Intensity Factors (SIFs) for fatigue crack. SIF interaction impact ratio is introduced to describe the interaction effect of corrosion on fatigue crack. Two approaches based on extreme gradient boosting (XGBoost) are proposed in this paper to predict the SIF interaction impact ratio at the deepest point of the crack defect for pipelines with interacting corrosion and crack defects. Crack size, corrosion size and the axial distance between these two defects are the factors that have an impact on the growth of the fatigue crack, and so they are considered as the input of XGBoost models. Based on the synthetic samples from finite element modeling, it has been proved that the proposed approaches can effectively predict the SIF interaction impact ratio with relatively high accuracy. The crack propagation models are built based on the proposed XGBoost models, Paris’ law and corrosion growth model. Sensitivity analyses regarding corrosion initial depth and axial distance between defects are performed. The proposed method can support pipeline integrity management by linking the crack propagation model with corrosion size, crack size and the axial distance. The problem of how the interaction between corrosion and crack defects impacts crack defect growth is investigated.
Identifying and Monitoring the Daily Routine of Seniors Living at Home
Authors: Chifu Viorica Rozina , Pop Cristina Bianca , Demjen David , Socaci Radu , Todea Daniel , Antal Marcel , Cioara Tudor , Anghel Ionut , Antal Claudia
Published: January 2022

As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers’ interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.
Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs
Authors: Kasnesis Panagiotis , Doulgerakis Vasileios , Uzunidis Dimitris , Kogias Dimitris G. , Funcia Susana I. , González Marta B. , Giannousis Christos , Patrikakis Charalampos Z.
Published: January 2022

Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and auditory senses. In this work, we propose a deep-learning-assisted implementation incorporating a wearable device, a base station, a mobile application, and a cloud-based infrastructure that can first monitor in real-time the activity, the audio signals, and the location of a SaR dog, and second, recognize and alert the rescuing team whenever the SaR dog spots a victim. For this purpose, we employed deep Convolutional Neural Networks (CNN) both for the activity recognition and the sound classification, which are trained using data from inertial sensors, such as 3-axial accelerometer and gyroscope and from the wearable’s microphone, respectively. The developed deep learning models were deployed on the wearable device, while the overall proposed implementation was validated in two discrete search and rescue scenarios, managing to successfully spot the victim (i.e., obtained F1-score more than 99%) and inform the rescue team in real-time for both scenarios.
A Comprehensive Review of Internet of Things: Technology Stack, Middlewares, and Fog/Edge Computing Interface
Authors: Ali Omer , Ishak Mohamad Khairi , Bhatti Muhammad Kamran Liaquat , Khan Imran , Kim Ki-Il
Published: January 2022

The Internet of Things (IoT) is an extensive network of heterogeneous devices that provides an array of innovative applications and services. IoT networks enable the integration of data and services to seamlessly interconnect the cyber and physical systems. However, the heterogeneity of devices, underlying technologies and lack of standardization pose critical challenges in this domain. On account of these challenges, this research article aims to provide a comprehensive overview of the enabling technologies and standards that build up the IoT technology stack. First, a layered architecture approach is presented where the state-of-the-art research and open challenges are discussed at every layer. Next, this research article focuses on the role of middleware platforms in IoT application development and integration. Furthermore, this article addresses the open challenges and provides comprehensive steps towards IoT stack optimization. Finally, the interfacing of Fog/Edge Networks to IoT technology stack is thoroughly investigated by discussing the current research and open challenges in this domain. The main scope of this study is to provide a comprehensive review into IoT technology (the horizontal fabric), the associated middleware and networks required to build future proof applications (the vertical markets).
Computer Vision-Based Classification of Flow Regime and Vapor Quality in Vertical Two-Phase Flow
Authors: Kadish Shai , Schmid David , Son Jarryd , Boje Edward
Published: January 2022

This paper presents a method to classify flow regime and vapor quality in vertical two-phase (vapor-liquid) flow, using a video of the flow as the input; this represents the first high-performing and entirely camera image-based method for the classification of a vertical flow regime (which is effective across a wide range of regimes) and the first image-based tool for estimating vapor quality. The approach makes use of computer vision techniques and deep learning to train a convolutional neural network (CNN), which is used for individual frame classification and image feature extraction, and a deep long short-term memory (LSTM) network, used to capture temporal information present in a sequence of image feature sets and to make a final vapor quality or flow regime classification. This novel architecture for two-phase flow studies achieves accurate flow regime and vapor quality classifications in a practical application to two-phase CO2 flow in vertical tubes, based on offline data and an online prototype implementation, developed as a proof of concept for the use of these models within a feedback control loop. The use of automatically selected image features, produced by a CNN architecture in three distinct tasks comprising flow-image classification, flow-regime classification, and vapor quality prediction, confirms that these features are robust and useful, and offer a viable alternative to manually extracting image features for image-based flow studies. The successful application of the LSTM network reveals the significance of temporal information for image-based studies of two-phase flow.
Automating the Calibration of Visible Light Positioning Systems
Authors: Amsters Robin , Ruberto Simone , Demeester Eric , Stevens Nobby , Slaets Peter
Published: January 2022

Visible light positioning is one of the most popular technologies used for indoor positioning research. Like many other technologies, a calibration procedure is required before the system can be used. More specifically, the location and identity of each light source need to be determined. These parameters are often measured manually, which can be a labour-intensive and error-prone process. Previous work proposed the use of a mobile robot for data collection. However, this robot still needed to be steered by a human operator. In this work, we significantly improve the efficiency of calibration by proposing two novel methods that allow the robot to autonomously collect the required calibration data. In postprocessing, the necessary system parameters can be calculated from these data. The first novel method will be referred to as semi-autonomous calibration, and requires some prior knowledge of the LED locations and a map of the environment. The second, fully-autonomous calibration procedure requires no prior knowledge. Simulation results show that the two novel methods are both more accurate than manual steering. Fully autonomous calibration requires approximately the same amount of time to complete, whereas semi-autonomous calibration is significantly faster.
Mechanically Reconfigurable Waveguide Filter Based on Glide Symmetry at Millimetre-Wave Bands
Authors: Tamayo-Dominguez Adrian , Fernández-González José-Manuel , Quevedo-Teruel Oscar
Published: January 2022

This paper presents the design and fabrication of a mechanically reconfigurable filter at W band based on the concept of glide symmetry. The tunability is achieved by breaking and regenerating the glide symmetry. The filters are made of two glide-symmetric pieces that can be displaced in a certain direction, and therefore, break the symmetry. The high filtering capacity of these designs is demonstrated by simulation and measurement and can also be adjusted mechanically. The transmission level in the manufactured filter varies from a value between −1 and −2 dB when the filter is in the glide symmetry position to values close to −40 dB in the stop-band when it is in the broken symmetry position. The transmission band obtained in the symmetrical mode is around 20%, but, after breaking the symmetry, it is split into two passbands of 6.5% and 11% separated by a stop-band of 6%. The position, bandwidth, filtering level and filter roll-off can be adjusted for both modes of operation by appropriately selecting the unit cell design parameters and the number of unit cells.
Global Consensus of High-Order Discrete-Time Multi-Agent Systems with Communication Delay and Saturation Constraint
Authors: Tan Chong , Cui Yin , Li Yanjiang
Published: January 2022

This article aimed to study the global consensus problem of high-order multi-agent systems with a saturation constraint and communication delay. Among them, all agents are described by discrete-time systems. Firstly, in order to compensate for the communication delay, a networked predictive control method is adopted and a predictive-based control protocol is designed. Secondly, for the neutrally stable agent model, leaderless and leader-following situations are considered and it is proven that, under a fixed communication topology, adopting the prediction-based control protocol makes the multi-agent systems with saturation constraint and communication delay achieve a global consensus. Finally, the results are illustrated via numerical simulation.
Acknowledgment to Reviewers of Scientia Pharmaceutica in 2021
Authors:   Scientia Pharmaceutica Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Acknowledgment to Reviewers of Separations in 2021
Authors:   Separations Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Acknowledgment to Reviewers of Social Sciences in 2021
Authors:   Social Sciences Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Geophysical and Geochemical Characterization of Solidwaste Dumpsite: A Case Study of Chowa Gujar, Peshawar (Part of Indus Basin)
Authors: Butt Nabeel Afzal , Khan Muhammad Younis , Khattak Seema Anjum , Akhter Gulraiz , Ge Yonggang , Shah Muhammad Tahir , Farid Asam
Published: January 2022

Open and non-engineered dumping is a typical method for solid waste disposal in most cities of Pakistan. This practice of waste dumping poses a serious threat to the surrounding ecosystem and human population due to the release and transport of decomposed organic matter, i.e., leachate from dumpsite into the groundwater. The present study was conducted over a non-engineered and open dumpsite (Chowa Gujar), located in the outskirts of the highly populated city of Peshawar by using integrated geophysical techniques such as electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) and geochemical techniques. The main goal was to delineate the characterization and depth of buried waste, to map the subsurface extension of contaminant plumes towards agricultural land and groundwater table and the concentration of heavy metals (HMs) in dump and agricultural soil. Geophysical results showed that the thickness of buried waste (predominantly composed of domestic waste) was around 4 m and the leachate plumes have percolated to the adjacent agricultural land. A range of heavy metals (mg/kg) such as Cr (20.5–26.6), Cd (2.6–5.7), Pb (0.35–21.25), Ni (2.5–53.05), Cu (29–68.3), Zn (45.7–77), and Co (18.9–23.2) have been found in the agricultural land adjacent to the dumpsite. The findings demonstrated that combined use of ERT and GPR successfully characterize the buried waste and spread of pollutant plumes spatially and vertically from Chowa Gujar dumpsite. The anomalous geophysical signatures were confirmed by geochemical characterization. The movement of leachate plumes towards agricultural land and groundwater table and the concentration of HMs in soil show that Chowa Gujar dumpsite is a potential source of contamination not only to the surrounding population but also to the agricultural land, surface (Bara River), and subsurface water bodies. In the study region, there is an urgency to take remediation and mitigation measures to reduce the level of pollution created by the dumpsite.
Quantitative Study on the Life-Cycle Carbon Emissions of a Nearly Zero Energy Building in the Severe Cold Zones of China
Authors: Wang Yangyang , Yang Xinyan , Hou Qingying , Tao Jin , Dong Jiankai
Published: January 2022

This work aimed to quantitatively study the carbon emissions and carbon reduction potential of a nearly Zero Energy Building in a severe cold zone of China from the perspective of its life cycle. The methods were based on China’s “Standard for building carbon emission calculation” (GB/T51366-2019), which include the production, transportation, operation, and demolition stages. For the nearly Zero Energy Building, the total carbon emissions over its whole life were 789.43 kg CO2/m2, an 86.20% reduction compared to the 5719.68 kg CO2/m2 for a 65% energy-saving building. The carbon emission percentages of the production stage and operation stage for the building materials are 75.62% and 20.40%, respectively. To enhance the carbon reduction potential, a sensitivity analysis was conducted to explore the impact of the use of a wood structural system, recycled concrete replacement, and photovoltaic power generation. The use of a wood structure system can achieve zero or negative carbon emissions in the production stage. The sensitivity of the carbon emissions of the nearly zero energy building (nZEB) to the increment of photovoltaic power generation is much higher than that of the recycled concrete replacement rate, providing a basis for further exploration of the development of nZEBs into zero carbon buildings.
Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture
Authors: Storey Gary , Meng Qinggang , Li Baihua
Published: January 2022

Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.
An Integrated Approach for Evaluating Climate Change Risks: A Case Study in Suriname
Authors: Antich-Homar Helena , Hess Katharina , Solaun Kepa , Alleng Gerard , Flores Adrian
Published: January 2022

This paper combines long-term state-of-the-art climate projections and indices to provide detailed insights into the future climate of Suriname to facilitate comprehensive information of areas and sectors at high climate risk for political decision-making. The study analyses Suriname’s historical climate (1990–2014) and provides climate projections for three time horizons (2020–2044, 2045–2069, 2070–2094) and two emissions scenarios (intermediate/SSP2-4.5 and severe/SSP5-8.5). Coupled Model Intercomparison Project (CMIP6) modeling is used to analyze changes in sea level, temperature, precipitation, relative humidity, and winds. In addition, risk impact chains were produced for the country’s four most important socio-economic sectors: agriculture and fisheries, forestry, water, and infrastructure. Results show the temperature is expected to increase for all regions and timeframes, reaching warming up to 6 °C in the southern region in the long-term future (2070–2094). Projections point towards a reduction in precipitation in the southwest and coastal regions and a rise in mean sea level. Regarding risk, Paramaribo and Wanica face the highest climate risk. Coronie and Nickerie face the least climate risk. These regions remain the most and least vulnerable in both the SSP2-4.5 and SSP5-8.5 scenarios, but overall values of their risk indices increase substantially over time.
Research on Passengers’ Preference for High-Speed Railways (HSRs) and High-Speed Trains (HSTs)
Authors: Wu Di , Martín Juan Carlos
Published: January 2022

This paper aims to study passengers’ preference for High-Speed Railways (HSRs) and High-Speed Trains (HSTs) through a systematic literature review. The existing relevant literature was examined by using the Scopus application, and the Latent Dirichlet Allocation (LDA) method is applied to extract the topics of the selected documents. By comparing the contents of the relevant literature, the general overview of research in this field can be further understood, and the key factors that have been studied so far that affect passengers’ preference can be analyzed. As a green, safe, and sustainable transport mode, HSRs do not only play an essential role in serving passengers but could also be a novel option for freight transport as long as HSRs are able to adjust their technology to the requirements of the rapid growth of the modern logistics industry. The evaluation of passengers’ preference for HSRs/HSTs presents some gaps that need to be addressed in future studies: (1) the speed of the HST line ought to be introduced clearly; (2) more competing alternatives, such as maglev, autonomous vehicles, and other access–egress transport modes to terminals, such as subways and aero taxis, need to be included; (3) the analysis of passengers’ preferences needs to be extended to the new role that HSRs can play in the logistics industry as new cargo services have been developed with the participation of HSRs; and (4) some attributes related to environmental costs and carbon emissions for the transport modes, as well as segmentation variables that are proxies for different passengers’ attitudes towards climate change concerns, need to be included in the design of the choice experiment.
Strategic Turnaround in the Paper Industry: A New Model for the Procurement of Recycled Paper
Authors: Marković Golub V. , Mihić Marko M.
Published: January 2022

In recent decades, the paper industry has undergone many changes, making this industry more lucrative, “greener”, and thus more attractive. Today, recycled paper is a key raw material for paper production. However, this intense growth has also increased the number of market players, making competition more intense and dynamic and causing frequent turmoil. In such an environment, planning procurement and forecasting the price of recycled paper is a big challenge, even for highly experienced procurement managers. In addition, paper production itself is a dynamic process that requires all key grades of recycled paper to be available at all times. Accordingly, managing the optimal level of recycled paper stocks is also a difficult task faced by the procurement unit. The goal of this paper is to address these challenges by developing a new model for the sustainable procurement of recycled paper in the paper industry with the help of the principles of strategic management. Specifically, we aim to enhance our understanding of the factors driving the complexity of recycled paper procurement management. To this end, we conducted a comparative case study in four European companies. To build cases, we collected secondary data on the sampled companies as well as primary data from interviews with top executives at these companies. On the basis of the results from the comparative case study, we propose a new model for the procurement of recycled paper that helps increase the accuracy of forecasting price trends and by extension overall procurement performance. In a nutshell, this paper seeks to improve procurement processes by reducing the complexity of the enterprise procurement unit as well as by offering guidelines for the maintenance of optimal stock levels of recycled paper.
Agri-Food Industry Waste as Resource of Chemicals: The Role of Membrane Technology in Their Sustainable Recycling
Authors: Papaioannou Emmanouil H. , Mazzei Rosalinda , Bazzarelli Fabio , Piacentini Emma , Giannakopoulos Vasileios , Roberts Michael R. , Giorno Lidietta
Published: January 2022

The agri-food sector generates substantial quantities of waste material on farm and during the processing of these commodities, creating serious social and environmental problems. However, these wastes can be resources of raw material for the production of valuable chemicals with applications in various industrial sectors (e.g., food ingredients, nutraceuticals, bioderived fine chemicals, biofuels etc.). The recovery, purification and biotransformation of agri-food waste phytochemicals from this microbial spoilage-prone, complex agri-food waste material, requires appropriate fast pre-treatment and integration of various processes. This review provides a brief summary and discussion of the unique advantages and the importance of membrane technology in sustainable recycling of phytochemicals from some of the main agri-food sectors. Membrane-based pressure -driven processes present several advantages for the recovery of labile compounds from dilute streams. For example, they are clean technologies that can operate at low temperature (20–60 °C), have low energy requirements, there is no need for additional chemicals, can be quite automated and electrifiable, and have low space requirements. Based on their permselective properties based on size-, shape-, and charge-exclusion mechanisms, membrane-based separation processes have unpaired efficiency in fractionating biological components while presenting their properties. Pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF) and nanofiltration (NF), as well as other advanced membrane-based processes such as membrane bioreactors (MBR), membrane emulsification (ME) and membrane distillation (MD), are presented. The integration of various membrane technologies from the initial recovery of these phytochemicals (MF, UF, NF) to the final formulation (by ME) of commercial products is described. A good example of an extensively studied agri-food stream is the olive processing industry, where many different alternatives have been suggested for the recovery of biophenols and final product fabrication. Membrane process integration will deliver in the near future mature technologies for the efficient treatment of these streams in larger scales, with direct impact on the environmental protection and society (production of compounds with positive health effects, new job creation, etc.). It is expected that integration of these technologies will have substantial impact on future bio-based societies over forthcoming decades and change the way that these chemicals are currently produced, moving from petrochemical-based linear product fabrication to a sustainable circular product design based in agri-food waste biomass.
Investigating the Experiences, Beliefs, and Career Intentions of Historically Underrepresented Science and Engineering Undergraduates Engaged in an Academic and Internship Program
Authors: McAlexander Shana L. , McCance Katherine , Blanchard Margaret R. , Venditti Richard A.
Published: January 2022

Women and students of marginalized race/ethnicity continue to be underrepresented in many science and engineering fields, and access to special programs, mentors, and internships may influence awareness, intention, and persistence in STEM fields. This mixed-methods case study investigated the experiences, beliefs, and career intentions of thirteen undergraduate students from historically underrepresented groups in the United States as they engaged in a federally funded grant program, “Sustainable Futures”. The program consisted of online courses, workshops, and a summer internship, intended to increase awareness, interest, and diverse participation in bioeconomy-related industries. The expectancy-value theory of achievement motivation theoretical framework guided this investigation of students’ changes in beliefs about bioproducts, bioenergy, the bioeconomy, and their career intentions. Program courses helped students develop skills and knowledge and program internships inspired and reinforced their career directions. Following program activities, students expressed greater intention to pursue bioproduct/bioenergy-related careers and articulated their career intentions with greater specificity. This study provides insight into the viability of focused academic and professional development programs as a practical method to promote students’ awareness, beliefs, and intentions to participate in careers in a sustainable bioeconomy, particularly across diverse populations.
Entrepreneurship Experiences among Vietnamese Marriage Immigrant Women in Taiwan
Authors: Wu Ya-Ling
Published: January 2022

Since the 1990s, Taiwan has experienced growing numbers of commercially arranged marriages between Vietnamese women and socioeconomically disadvantaged Taiwanese men. Most Vietnamese marriage immigrant women proactively engage in the labor market due to the heavy financial burden of their Taiwanese and natal families. Employing a sociocultural and post-structural feminist approach, this study draws from life-story interviews of 13 married Vietnamese women to investigate the entrepreneurship experiences among Vietnamese marriage immigrant women in Taiwan. These women are pushed and pulled towards creating demanding micro-entrepreneurships based on their self-employed socialization, thereby fulfilling family obligations and achieving career goals. Targeting their host market, these women operate their businesses using Taiwanese customer networks and their institutionalized learning and sustainable resilience while negotiating self-identity. Running entrepreneurships empowers these women, facilitating their self-identity, social integration, family position within the boundaries of gender, family expectations, and business while they struggle with unexpected challenges. Clearly, these individuals and their significant others, homeland culture and socialization, and their life experiences and positions in Taiwan shape these immigrant women’s businesses and their sense of meaning. This study extends the feminist perspective of this topic, focusing on the sustainable agency and sense of competence of female marriage immigrants.
GIS-Based Spatial Correlation Analysis: Sustainable Development and Two Generations of Demographic Changes
Authors: Yaakub Nur Faziera , Masron Tarmiji , Marzuki Azizan , Soda Ryoji
Published: January 2022

Population growth is a global issue that contributes to the changes in the distribution and concentration of population. Population growth affects the sustainable development of an area from both a social and spatial point of view. To relate the global problem to a local issue, this research investigates one of the Malaysian government policies addressed as the New Economic Policy (NEP) because the policy may be linked to long-term spatial demographic changes in Peninsular Malaysia, particularly in the distribution of people. Back in 1970, the policy was implemented after an unwanted incident on 13 May 1969. Its goals were to eradicate poverty regardless of race and to restructure society by eliminating the identification of race with economic functions. To measure the successfulness of the policy, two indicators that were derived from the goals are the long-term spatial changes of both racial and occupational segregation. The magnitude for both segregations was calculated using the Entropy Index (H). The values were then carried forward to evaluate the relationship between these two variables. The final analysis was conducted using the Local Bivariate Relationships application of a Geographic Information System (GIS) tool. The outputs then reflect the two sustainable goals that are, (i) reduced inequalities, and (ii) sustainable cities and communities in Peninsular Malaysia.
Trend Analysis of Global Disaster Education Research Based on Scientific Knowledge Graphs
Authors: Zhang Min , Wang Juanle
Published: January 2022

Disaster reduction and prevention have become urgent issues worldwide. Disaster education is an effective way to deal with frequent global disaster risks, carry out disaster prevention and relief measures in a timely manner, and reduce disaster losses. Based on the Web of Science database, using bibliometrics and network analysis methods based on scientific knowledge graphs, we conducted a visual analysis of global disaster education research trends from the perspectives of national cooperation spatial distribution, research hotspot mining, hybrid network analysis, and institutional cooperation spatial distribution of disaster education. The following conclusions were drawn. (1) The spatial distribution of disaster education research is uneven: it is clustered in Europe, evenly distributed in Asia and Africa, and scattered in North America and Oceania. Moreover, the United States in North America, China and Japan in Asia, and Australia in Oceania have the largest number of articles. (2) The field of disaster education focuses mainly on the themes of education, disaster nursing, disaster risk and reduction, disaster awareness, and earthquakes. The general trend of research hotspots is disaster risk >> disaster preparedness >> disaster nurse >> disaster awareness >> disaster risk and reduction, realizing the great transformation from disaster rescue to disaster preparedness and then to disaster prevention awareness. (3) A hybrid network of keywords and countries revealed the research focus of various countries in the field of disaster education, and a hybrid network of keywords and categories showed that the research on disaster education primarily focuses on the disciplines of environment, nursing, geography, geology, atmosphere, ecology, and psychology. On this basis, the breadth and depth of the disaster education system should be further improved. (4) The spatial layout of disaster education research institutions showed a clustered distribution of research institutions in North America and Europe, even distribution in some regions in Asia, and sporadic distribution in Africa and Oceania. In-depth cooperation among institutions should be strengthened, the degree of attention paid to disaster education should be increased, and external cooperation should be actively carried out to improve the level of disaster education, particularly in Africa and Asia.
Greenwashing and Bluewashing in Black Friday-Related Sustainable Fashion Marketing on Instagram
Authors: Sailer Astrid , Wilfing Harald , Straus Eva
Published: January 2022

Growing awareness of the fashion industry’s negative impact on people and the environment has led to considerable growth of the sustainable fashion market. At the same time, Black Friday purchases increase annually as the sales event develops into a global phenomenon. As sustainable fashion brands are choosing to participate in the event, many communicate their offers via the social media platform Instagram. To gain a competitive advantage and maintain their sustainable corporate images, some brands use greenwashing and/or bluewashing strategies. The first part of this study explores which strategies were employed in Instagram content posted by sustainable brands, using quantitative and qualitative content analysis. We propose a research-based model of nine greenwashing/bluewashing strategies. The second part of the study examines predictive factors for consumer evaluations of Black Friday ads by sustainable brands, using an online survey and a stepwise multiple regression analysis. Findings show that consumers’ critical attitude towards Black Friday and high ad skepticism predict positive evaluations while sustainable purchase behavior predicts negative evaluations. These insights suggest that ‘sustainable’ Black Friday campaigns may appeal to consumers who show a general concern for the environment and issues of social sustainability, but not to those who exhibit actual sustainable behavior.
Acknowledgment to Reviewers of Surgeries in 2021
Authors:   Surgeries Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Acknowledgment to Reviewers of Tourism and Hospitality in 2021
Authors:   Tourism and Hospitality Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Acknowledgment to Reviewers of Tropical Medicine and Infectious Disease in 2021
Authors:   TropicalMed Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Warm Dark Matter in Simulations
Authors: Paduroiu Sinziana
Published: January 2022

In recent years, warm dark matter models have been studied as a viable alternative to the cold dark matter models. The warm dark matter particle properties are expected to imprint distinct signatures on the structure formation at both large and small scales and there have been many attempts to study these properties with numerical simulations. In this paper, we review and update on warm dark matter simulation studies from the past two decades and their most significant results: structure formation mechanisms, halos evolution, sizes and distribution, and internal structure properties. We discuss the theoretical assumptions and the limitations of the methods employed. In this context, several controversial claims are scrutinized in the attempt to clarify these confusing and sometimes even contradictory conclusions in the numerical simulation literature. We address the circumstances in which a promising keV dark matter candidate should be properly treated in the simulations.
On the Origin of Persistent Radio and X-ray Emission from Brown Dwarf TVLM 513-46546
Authors: Stepanov Alexander , Zaitsev Valery
Published: January 2022

We study the origin of unusually persistent microwave and X-ray radiation from the ultracool dwarf TVLM 513-46546. It is shown that the source of ≈1 keV X-ray emission is not the entire corona of the brown dwarf, but a population of several hundreds of coronal magnetic loops, with 10 MK plasma heated upon dissipation of the electric current generated by the photospheric convection. Unlike models, which assume a large-scale magnetic structure of the microwave source, our model suggests that the microwave radiation comes from hundreds of magnetic loops quasi-uniformly distributed over the dwarf’s surface. We propose a long-term operating mechanism of acceleration of electrons generating gyrosynchrotron radio emission caused by oscillations of electric current in the magnetic loops as an equivalent RLC circuit. The second population of magnetic loops—the sources of microwave radiation, is at the same time a source of softer (≈0.2 keV) X-ray emission.
A Novel Approach to β-Decay: PANDORA, a New Experimental Setup for Future In-Plasma Measurements
Authors: Mascali David , Santonocito Domenico , Amaducci Simone , Andò Lucio , Antonuccio Vincenzo , Biri Sándor , Bonanno Alfio , Bonanno Vincenza Piera , Briefi Stefan , Busso Maurizio , Celona Luigi , Cosentino Luigi , Cristallo Sergio , Cuffiani Marco , De Angelis Costantino , De Angelis Giacomo , De Salvador Davide , Di Donato Loreto , Ducret Jean-Eric , Eshkevar Vakili Aref , Fantz Ursel , Galatà Alessio , Gallo Carmelo Sebastiano , Gammino Santo , Isernia Tommaso , Koivisto Hannu , Kratz Karl-Ludwig , Kronholm Risto , La Cognata Marco , Leoni Silvia , Locatelli Andrea , Maggiore Mario , Maimone Fabio , Malferrari Luciana , Mancini Giorgio , Maunoury Laurent , Mauro Giorgio Sebastiano , Mazzaglia Maria , Mengoni Alberto , Miraglia Andrea , Mishra Bharat , Musumeci Mario , Napoli Daniel Ricardo , Naselli Eugenia , Odorici Fabrizio , Palladino Libero , Palmisano Giuseppe , Pavone Santi , Pennisi Salvatore , Perego Albino , Pidatella Angelo , Rácz Richard , Reitano Riccardo , Rifuggiato Danilo , Rinaldi Matteo , Russo Antonio Domenico , Russo Filippo , Schillaci Gaetano , Selleri Stefano , Simonucci Stefano , Sorbello Gino , Spartà Roberta , Taioli Simone , Tinschert Klaus , Torrisi Giuseppe , Trifirò Antonio , Tsikata Sedina , Tumino Aurora , Vescovi Diego , Vincetti Luca
Published: January 2022

Theoretical predictions as well as experiments performed at storage rings have shown that the lifetimes of β-radionuclides can change significantly as a function of the ionization state. In this paper we describe an innovative approach, based on the use of a compact plasma trap to emulate selected stellar-like conditions. It has been proposed within the PANDORA project (Plasmas for Astrophysics, Nuclear Decay Observation and Radiation for Archaeometry) with the aim to measure, for the first time in plasma, nuclear β-decay rates of radionuclides involved in nuclear-astrophysics processes. To achieve this task, a compact magnetic plasma trap has been designed to reach the needed plasma densities, temperatures, and charge-states distributions. A multi-diagnostic setup will monitor, on-line, the plasma parameters, which will be correlated with the decay rate of the radionuclides. The latter will be measured through the detection of the γ-rays emitted by the excited daughter nuclei following the β-decay. An array of 14 HPGe detectors placed around the trap will be used to detect the emitted γ-rays. For the first experimental campaign three isotopes, 176Lu, 134Cs, and 94Nb, were selected as possible physics cases. The newly designed plasma trap will also represent a tool of choice to measure the plasma opacities in a broad spectrum of plasma conditions, experimentally poorly known but that have a great impact on the energy transport and spectroscopic observations of many astrophysical objects. Status and perspectives of the project will be highlighted in the paper.
Viromes of Freshwater Fish with Lacustrine and Diadromous Life Histories Differ in Composition
Authors: Perry Benjamin J. , Darestani Mitra Mohamadi , Ara Motia Gulshan , Hoste Amélie , Jandt Jennifer M. , Dutoit Ludovic , Holmes Edward C. , Ingram Travis , Geoghegan Jemma L.
Published: January 2022

Viruses that infect fish are understudied, yet they provide important evolutionary context to the viruses that infect terrestrial vertebrates. We surveyed gill tissue meta-transcriptomes collected from two species of native freshwater fish from Aotearoa New Zealand—Retropinna retropinna and Gobiomorphus cotidianus. A total of 64 fish were used for gill tissue meta-transcriptomic sequencing, from populations with contrasting life histories—landlocked (i.e., lacustrine) and diadromous—on the South Island and Chatham Islands. We observed that both viral richness and taxonomic diversity were significantly associated with life history and host species, with lacustrine R. retropinna characterised by higher viral alpha diversity than diadromous R. retropinna. Additionally, we observed transcripts of fish viruses from 12 vertebrate host-associated virus families, and phylogenetically placed eight novel RNA viruses and three novel DNA viruses in the Astroviridae, Paramyxoviridae, Orthomyxoviridae, Rhabdoviridae, Totiviridae, Poxviridae, Alloherpesviridae, and Adintoviridae in their evolutionary contexts. These results represent an important survey of the viruses that infect two widespread native fish species in New Zealand, and provide insight useful for future fish virus surveys.
Induction of Hepatitis E Virus Anti-ORF3 Antibodies from Systemic Administration of a Muscle-Specific Adeno-Associated Virus (AAV) Vector
Authors: Maurer Lars , El Andari Jihad , Rapti Kleopatra , Spreyer Laura , Steinmann Eike , Grimm Dirk , Dao Thi Viet Loan
Published: January 2022

The hepatitis E virus (HEV) is a major global health problem, leading to large outbreaks in the developing world and chronic infections in the developed world. HEV is a non-enveloped virus, which circulates in the blood in a quasi-enveloped form. The quasi-envelope protects HEV particles from neutralising anti-capsid antibodies in the serum; however, most vaccine approaches are designed to induce an immune response against the HEV capsid. In this study, we explored systemic in vivo administration of a novel synthetic and myotropic Adeno-associated virus vector (AAVMYO3) to express the small HEV phosphoprotein ORF3 (found on quasi-enveloped HEV) in the musculature of mice, resulting in the robust and dose-dependent formation of anti-ORF3 antibodies. Neutralisation assays using the serum of ORF3 AAV-transduced mice showed a modest inhibitory effect on the infection of quasi-enveloped HEV in vivo, comparable to previously characterised anti-ORF3 antibodies used as a control. The novel AAVMYO3 capsid used in this study can serve as a versatile platform for the continued development of vector-based vaccines against HEV and other infectious agents, which could complement traditional vaccines akin to the current positive experience with SARS-CoV-2.