bluuebunny/crossref_metadata_2025 huggingface dataset:
Mental Health of the General Population during the 2019 Coronavirus Disease (COVID-19) Pandemic: A Tale of Two Developing Countries
Authors: Wang Cuiyan , Fardin Mohammad A. , Shirazi Mahmoud , Pan Riyu , Wan Xiaoyang , Tan Yilin , Xu Linkang , McIntyre Roger S. , Tran Bach , Quek Travis T. C. , Husain Syeda F. , Wang Jiayun , Ho Cyrus , Ho Roger
Published: March 2021

Background: This study aimed to compare the severity of psychological impact, anxiety and depression between people from two developing countries, Iran and China, and to correlate mental health parameters with variables relating to the COVID-19 pandemic. Although China and Iran are developing countries based on the World Bank’s criteria, these two countries are different in access to resources and health care systems. We hypothesized that Iranians would show higher levels of depression, anxiety and stress as compared to Chinese. Methods: This study collected information related to the COVID-19 pandemic including physical health, precautionary measures and knowledge about the pandemic. We also used validated questionnaires such as the Impact of Event Scale-Revised (IES-R) and the Depression, Anxiety and Stress Scale (DASS-21) to assess the mental health status. Results: There were a total of 1411 respondents (550 from Iran; 861 from China). The mean IES-R scores of respondents from both countries were above the cut-off for post-traumatic stress disorder (PTSD) symptoms. Iranians had significantly higher levels of anxiety and depression (p < 0.01). Significantly more Iranians believed COVID-19 was transmitted via contact, practised hand hygiene, were unsatisfied with health information and expressed less confidence in their doctors, but were less likely to wear a facemask (p < 0.001). Significantly more Iranians received health information related to COVID-19 via television while Chinese preferred the Internet (p < 0.001). Conclusions: This cross-country study found that Iranians had significantly higher levels of anxiety and depression as compared to Chinese. The difference in reported measures between respondents from Iran and China were due to differences in access to healthcare services and governments’ responses to the pandemic.
Running the Numbers for the Path of Mantra: Distinguishing the Thirteenth Bhūmi in Fifteenth-Century Tibet
Authors: Dachille Rae
Published: March 2021

This article explores a Buddhist text in which numbers set the very stakes for liberation. In 1404, Ngor chen Kun dga’ bzang po (1382–1456), who was to become one of the most esteemed tantric commentators of the Tibetan Sakya tradition, composed his first polemical text, Dispelling Evil Misunderstandings of the Explanation of the Ground of Zung ‘jug Vajradhara. In this early work, Ngor chen grapples with the relationship between the path of perfections and of secret mantra as conduits to liberation. I illuminate the ways in which ritual, exegesis, and pedagogy converge in Ngor chen’s text to reveal larger implications for distinguishing the eleventh and thirteenth grounds (bhūmi) of Buddhahood in fifteenth-century Tibet. In concluding, I highlight the art of differentiation as a fundamental Tibetan scholastic enterprise and briefly engage Ngor chen’s acts of distinguishing sūtra and tantra in conversation with those of key Tibetan predecessors and contemporaries.
Spiritual Legitimacy in Contemporary Japan: A Case Study of the Power Spot Phenomenon and the Haruna Shrine, Gunma
Authors: Yasuda Shin
Published: March 2021

Since the 2000s, Japanese internet media as well as mass media, including magazines, television and newspapers, have promoted the concept of a “power spot” as part of the spirituality movement in the country. This emerging social environment for the power spot phenomenon has developed a new form of religiosity, which can be called “spiritual legitimacy,” according to the transformation of religious legitimacy embedded in Japanese society. This paper, therefore, examined the emergence of a new form of spiritual legitimacy utilizing a case study of the power spot phenomenon in the Haruna Shrine, Gunma Prefecture, in Japan. The development of the power spot phenomenon in the Haruna Shrine indicates that consumption of spiritual narratives has strongly promoted the construction of a social context of spiritual legitimacy, such as through shared images and symbols related to the narratives in the sacred site. As a result, this paper clarifies that this new form of spiritual legitimacy embodies stakeholders’ social consensus on spiritual narratives, which people have struggled to construct a social context for spiritual legitimacy to ensure hot authentication of their individual narratives and experiences.
Embodied Transcendence: The Buddha’s Body in the Pāli Nikāyas
Authors: Shulman Eviatar
Published: March 2021

This article reassesses the role of the body in advanced meditation as it is presented in the early Buddhist Pāli discourses, showing that certain theorizations of liberation held that it contained a marked corporeal element. The article also reflects upon the understanding of the Buddha’s body in this textual corpus, and demonstrates that for important strands of the early tradition, the Buddha’s liberation was thought to manifest in his body, so that liberation impacted his physical presence and the quality of his movement. There are also marked metaphysical dimensions to the Buddha’s body, so that its nature transcends the material. Common approaches that take liberation to be a purely psychological transformation thus ignore important aspects of the traditional understanding, which also directs us to think of a plurality of approaches to liberation.
A Machine Learning Approach for Micro-Credit Scoring
Authors: Ampountolas Apostolos , Nyarko Nde Titus , Date Paresh , Constantinescu Corina
Published: March 2021

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases.
Meridional Changes in Satellite Chlorophyll and Fluorescence in Optically-Complex Coastal Waters of Northern Patagonia
Authors: Vásquez Sebastián I. , de la Torre María Belén , Saldías Gonzalo S. , Montecinos Aldo
Published: March 2021

Northern Patagonia is one of the largest estuarine systems worldwide. It is characterized by complex geography, including islands, peninsulas, channels, and fjords. Here, the Inner Sea of Chiloé (ISC) is the largest estuarine system extending about 230 km in the meridional direction. Phytoplankton’s long-term dynamics and the main physical drivers of their variability are not well understood yet. Time-space fluctuations of Chlorophyll-a (Chl-a) and Chlorophyll fluorescence (nFLH) within the ISC and their association with meteorological and oceanographic processes were analyzed using high resolution (1000 m) satellite data (2003–2019). Our results revealed a meridional Chl-a and nFLH gradient along the ISC, with higher concentrations north of the Desertores islands where the topography promotes a semi-closed system with estuarine characteristics yearlong. Satellite Chl-a and nFLH were characterized by asynchronous seasonal cycles (nFLH peaks in fall) that differed from the southern ISC where the maximum Chl-a and nFLH occurs in spring-summer. The adjacent coastal ocean influences the southern ISC, and thus, the Chl-a and nFLH variability correlated well with the seasonal variation of meridional winds. The northern ISC was clearly influenced by river discharges, which can bias the Chl-a retrievals, decoupling the annual cycles of Chl-a and nFLH. In situ data from a buoy in Seno Reloncaví reaffirmed this bias in satellite Chl-a and a higher correlation with nFLH, by which the construction of a local Chl-a algorithm for northern Patagonia is essential.
Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction
Authors: Gyamfi-Ampadu Enoch , Gebreslasie Michael , Mendoza-Ponce Alma
Published: March 2021

Forests contribute significantly to terrestrial biodiversity conservation. Monitoring of tree species diversity is vital due to climate change factors. Remote sensing imagery is a means of data collection for predicting diversity of tree species. Since various sensors have different spectral and spatial resolutions, it is worth comparing them to ascertain which could influence the accuracy of prediction of tree species diversity. Hence, this study evaluated the influence of the spectral and spatial resolutions of PlanetScope, RapidEye, Sentinel 2 and Landsat 8 images in diversity prediction based on the Shannon diversity index (H′), Simpson diversity Index (D1) and Species richness (S). The Random Forest regression was applied for the prediction using the spectral bands of the sensors as variables. The Sentinel 2 was the best image, producing the highest coefficient of determination (R2) under both the Shannon Index (R2 = 0.926) and the Species richness (R2 = 0.923). Both the Sentinel and RapidEye produced comparable higher accuracy for the Simpson Index (R2 = 0.917 and R2 = 0.915, respectively). The PlanetScope was the second-accurate for the Species richness (R2 = 0.90), whiles the Landsat 8 was the least accurate for the three diversity indices. The outcomes of this study suggest that both the spectral and spatial resolutions influence prediction accuracies of satellite imagery.
Dynamics of Vibrio cholerae in a Typical Tropical Lake and Estuarine System: Potential of Remote Sensing for Risk Mapping
Authors: Anas Abdulaziz , Krishna Kiran , Vijayakumar Syamkumar , George Grinson , Menon Nandini , Kulk Gemma , Chekidhenkuzhiyil Jasmin , Ciambelli Angelo , Kuttiyilmemuriyil Vikraman Hridya , Tharakan Balu , Koovapurath Useph Abdul Jaleel , Goult Elizabeth , Vengalil Jithin , Platt Trevor , Sathyendranath Shubha
Published: March 2021

Vibrio cholerae, the bacterium responsible for the disease cholera, is a naturally-occurring bacterium, commonly found in many natural tropical water bodies. In the context of the U.N. Sustainable Development Goals (SDG) targets on health (Goal 3), water quality (Goal 6), life under water (Goal 14), and clean water and sanitation (Goal 6), which aim to “ensure availability and sustainable management of water and sanitation for all”, we investigated the environmental reservoirs of V. cholerae in Vembanad Lake, the largest lake in Kerala (India), where cholera is endemic. The response of environmental reservoirs of V. cholerae to variability in essential climate variables may play a pivotal role in determining the quality of natural water resources, and whether they might be safe for human consumption or not. The hydrodynamics of Vembanad Lake, and the man-made barrier that divides the lake, resulted in spatial and temporal variability in salinity (1–32 psu) and temperature (23 to 36 °C). The higher ends of this salinity and temperature ranges fall outside the preferred growth conditions for V. cholerae reported in the literature. The bacteria were associated with filtered water as well as with phyto- and zooplankton in the lake. Their association with benthic organisms and sediments was poor to nil. The prevalence of high laminarinase and chitinase enzyme expression (more than 50 µgmL−1 min−1) among V. cholerae could underlie their high association with phyto- and zooplankton. Furthermore, the diversity in the phytoplankton community in the lake, with dominance of genera such as Skeletonema sp., Microcystis sp., Aulacoseira sp., and Anabaena sp., which changed with location and season, and associated changes in the zooplankton community, could also have affected the dynamics of the bacteria in the lake. The probability of presence or absence of V. cholerae could be expressed as a function of chlorophyll concentration in the water, which suggests that risk maps for the entire lake can be generated using satellite-derived chlorophyll data. In situ observations and satellite-based extrapolations suggest that the risks from environmental V. cholerae in the lake can be quite high (with probability in the range of 0.5 to 1) everywhere in the lake, but higher values are encountered more frequently in the southern part of the lake. Remote sensing has an important role to play in meeting SDG goals related to health, water quality and life under water, as demonstrated in this example related to cholera.
UAV-Based Estimate of Snow Cover Dynamics: Optimizing Semi-Arid Forest Structure for Snow Persistence
Authors: Belmonte Adam , Sankey Temuulen , Biederman Joel , Bradford John , Goetz Scott , Kolb Thomas
Published: March 2021

Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.
The Effect of Forest Mask Quality in the Wall-to-Wall Estimation of Growing Stock Volume
Authors: Vangi Elia , D’Amico Giovanni , Francini Saverio , Giannetti Francesca , Lasserre Bruno , Marchetti Marco , McRoberts Ronald E. , Chirici Gherardo
Published: March 2021

Information about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities. Two of the most common forest variables needed to support sustainable forest management practices are forest cover area and growing stock volume (GSV m3 ha−1). Nowadays, national forest inventories (NFI) are complemented by wall-to-wall maps of forest variables which rely on models and auxiliary data. The spatially explicit prediction of GSV is useful for small-scale estimation by aggregating individual pixel predictions in a model-assisted framework. Spatial knowledge of the area of forest land is an essential prerequisite. This information is contained in a forest mask (FM). The number of FMs is increasing exponentially thanks to the wide availability of free auxiliary data, creating doubts about which is best-suited for specific purposes such as forest area and GSV estimation. We compared five FMs available for the entire area of Italy to examine their effects on the estimation of GSV and to clarify which product is best-suited for this purpose. The FMs considered were a mosaic of local forest maps produced by the Italian regional forest authorities; the FM produced from the Copernicus Land Monitoring System; the JAXA global FM; the hybrid global FM produced by Schepaschencko et al., and the FM estimated from the Corine Land Cover 2006. We used the five FMs to mask out non-forest pixels from a national wall-to-wall GSV map constructed using inventory and remotely sensed data. The accuracies of the FMs were first evaluated against an independent dataset of 1,202,818 NFI plots using four accuracy metrics. For each of the five masked GSV maps, the pixel-level predictions for the masked GSV map were used to calculate national and regional-level model-assisted estimates. The masked GSV maps were compared with respect to the coefficient of correlation (ρ) between the estimates of GSV they produced (both in terms of mean and total of GSV predictions within the national and regional boundaries) and the official NFI estimates. At the national and regional levels, the model-assisted GSV estimates based on the GSV map masked by the FM constructed as a mosaic of local forest maps were closest to the official NFI estimates with ρ = 0.986 and ρ = 0.972, for total and mean GSV, respectively. We found a negative correlation between the accuracies of the FMs and the differences between the model-assisted GSV estimates and the NFI estimate, demonstrating that the choice of the FM plays an important role in GSV estimation when using the model-assisted estimator.
Applying Close Range Non-Destructive Techniques for the Detection of Conservation Problems in Rock-Carved Cultural Heritage Sites
Authors: Frodella William , Elashvili Mikheil , Spizzichino Daniele , Gigli Giovanni , Nadaraia Akaki , Kirkitadze Giorgi , Adikashvili Luka , Margottini Claudio , Antidze Nikoloz , Casagli Nicola
Published: March 2021

Rock-carved cultural heritage sites are often developed in slopes formed by weak rocks, which due to their peculiar lithological, geotechnical, and morpho-structural features are characterized by excellent carvability, which at the same time makes them prone to weathering, deterioration, and slope instability issues. In this context the use of advanced close-range nondestructive techniques, such as Infrared Thermography (IRT) and Unmanned Aerial vehicle-based Digital Photogrammetry (UAV-DP) can be profitably used for the rapid detection of conservation issues (e.g., open fractures, unstable ledges-niches, water seepage and moisture) that can lead to slope instability phenomena. These techniques, when combined with traditional methods (e.g., field surveys, laboratory analysis), can provide fundamental data (such as 3D maps of the kinematic mechanisms) to implement a site-specific and interdisciplinary approach for the sustainable protection and conservation of such fragile cultural heritage sites. In this paper some examples of conservation problems in several rupestrian sites characterized by different geological contexts, from the mountainous regions of Georgia to the ancient city of Petra in Jordan, are presented, with the aim of evaluating the potential of the proposed integrated approach. The final aim is to provide conservators, practitioners, and local authorities with a useful, versatile, and low-cost methodology, to be profitably used in the protection and conservation strategies of rock-carved sites.
The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies
Authors: Pérez-Cruzado César , Kleinn Christoph , Magdon Paul , Álvarez-González Juan Gabriel , Magnussen Steen , Fehrmann Lutz , Nölke Nils
Published: March 2021

Forest biomass is currently among the most important and most researched target variables in forest monitoring. The common approach of observing individual tree biomass in forest inventory is to assign the total tree biomass to the dimensionless point of the tree position. However, the tree biomass, in particular in the crown, is horizontally distributed above the crown projection area. This horizontal distribution of individual tree biomass (HBD) has not attracted much attention—but if quantified, it can improve biomass estimation and help to better represent the spatial distribution of forest fuel. In this study, we derive a first empirical model of the branch HBD for individual trees of European beech (Fagus sylvatica L.). We destructively measured 23 beech trees to derive an empirical model for the branch HBD. We then applied Terrestrial Laser Scanning (TLS) to a subset of 17 trees to test a simple point cloud metric predicting the branch HBD. We observed similarities between a branch HBD and commonly applied taper functions, which inspired our HBD model formulations. The models performed well in representing the HBD both for the measured biomass, and the TLS-based metric. Our models may be used as first approximations to the HBD of individual trees—while our methodological approach may extend to trees of different sizes and species.
Analytical Approach to Sampling Estimation of Underwater Tunnels Using Mechanical Profiling Sonars
Authors: Machado Jorge Vitor Augusto , de Cerqueira Gava Pedro Daniel , Belchior de França Silva Juan Ramon , Mancilha Thais , Vieira Waldir , Adabo Geraldo José , Nascimento Cairo Lúcio
Published: March 2021

Hydroelectric power plants often make use of tunnels to redirect the flow of water to the plant power house. Such tunnels are often flooded and can span considerable distances. Periodical inspections of such tunnels are highly desirable since a tunnel collapse will be catastrophic, disrupting the power plant operation. In many cases, the use of Unmanned Underwater Vehicles (UUVs) equipped with mechanical profiling sonars is a suitable and affordable way to gather data to generate 3D mapping of flooded tunnels. In this paper, we study the resolution of 3D tunnel maps generated by one or more mechanical profiling sonars working in tandem, considering synchronization and occlusion problems. The article derives the analytical equations to estimate the sampling of the underwater tunnels using mechanical profiling sonars (scanning sonars). Experiments in a simulated environment using up to four sensors simultaneously are presented. We also report experimental results obtained by a UUV inside a large power plant tunnel, together with a first map of this environment using a single sonar sensor.
Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels
Authors: Stankoski Simon , Jordan Marko , Gjoreski Hristijan , Luštrek Mitja
Published: March 2021

Understanding people’s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist.
Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate
Authors: Jian Jia-Zheng , Ger Tzong-Rong , Lai Han-Hua , Ku Chi-Ming , Chen Chiung-An , Abu Patricia Angela R. , Chen Shih-Lun
Published: March 2021

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.
Review on Carbon Nanomaterials-Based Nano-Mass and Nano-Force Sensors by Theoretical Analysis of Vibration Behavior
Authors: Shi Jin-Xing , Lei Xiao-Wen , Natsuki Toshiaki
Published: March 2021

Carbon nanomaterials, such as carbon nanotubes (CNTs), graphene sheets (GSs), and carbyne, are an important new class of technological materials, and have been proposed as nano-mechanical sensors because of their extremely superior mechanical, thermal, and electrical performance. The present work reviews the recent studies of carbon nanomaterials-based nano-force and nano-mass sensors using mechanical analysis of vibration behavior. The mechanism of the two kinds of frequency-based nano sensors is firstly introduced with mathematical models and expressions. Afterward, the modeling perspective of carbon nanomaterials using continuum mechanical approaches as well as the determination of their material properties matching with their continuum models are concluded. Moreover, we summarize the representative works of CNTs/GSs/carbyne-based nano-mass and nano-force sensors and overview the technology for future challenges. It is hoped that the present review can provide an insight into the application of carbon nanomaterials-based nano-mechanical sensors. Showing remarkable results, carbon nanomaterials-based nano-mass and nano-force sensors perform with a much higher sensitivity than using other traditional materials as resonators, such as silicon and ZnO. Thus, more intensive investigations of carbon nanomaterials-based nano sensors are preferred and expected.
Kubernetes Cluster for Automating Software Production Environment
Authors: Poniszewska-Marańda Aneta , Czechowska Ewa
Published: March 2021

Microservices, Continuous Integration and Delivery, Docker, DevOps, Infrastructure as Code—these are the current trends and buzzwords in the technological world of 2020. A popular tool which can facilitate the deployment and maintenance of microservices is Kubernetes. Kubernetes is a platform for running containerized applications, for example microservices. There are two main questions which answer was important for us: how to deploy Kubernetes itself and how to ensure that the deployment fulfils the needs of a production environment. Our research concentrates on the analysis and evaluation of Kubernetes cluster as the software production environment. However, firstly it is necessary to determine and evaluate the requirements of production environment. The paper presents the determination and analysis of such requirements and their evaluation in the case of Kubernetes cluster. Next, the paper compares two methods of deploying a Kubernetes cluster: kops and eksctl. Both of the methods concern the AWS cloud, which was chosen mainly because of its wide popularity and the range of provided services. Besides the two chosen methods of deployment, there are many more, including the DIY method and deploying on-premises.
Development of the First Portuguese Radar Tracking Sensor for Space Debris
Authors: Pandeirada João , Bergano Miguel , Neves João , Marques Paulo , Barbosa Domingos , Coelho Bruno , Ribeiro Valério
Published: March 2021

Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with this issue. Ground-based radars can detect Earth orbiting debris down to a few square centimeters and therefore constitute a major building block of a space debris monitoring system. New radar sensors are required in Europe to enhance capabilities and availability of its small radar network capable of tracking and surveying space objects and to respond to the debris increase expected from the New Space economy activities. This article presents ATLAS, a new tracking radar system for debris detection located in Portugal. It starts by an extensive technical description of all the system components followed by a study that estimates its future performance. A section dedicated to waveform design is also presented, since the system allows the usage of several types of pulse modulation schemes such as LFM and phase coded modulations while enabling the development and testing of more advanced ones. By presenting an architecture that is highly modular with fully digital signal processing, ATLAS establishes a platform for fast and easy development, research, and innovation. The system follows the use of Commercial-Off-The-Shelf technologies and Open Systems which is unique among current radar systems.
A Viable Approach to Mitigating Irreproducibility
Authors: Trafimow David , Wang Tonghui , Wang Cong
Published: March 2021

In a recent article, Trafimow suggested the usefulness of imagining an ideal universe where the only difference between original and replication experiments is the operation of randomness. This contrasts with replication in the real universe where systematicity, as well as randomness, creates differences between original and replication experiments. Although Trafimow showed (a) that the probability of replication in the ideal universe places an upper bound on the probability of replication in the real universe, and (b) how to calculate the probability of replication in the ideal universe, the conception is afflicted with an important practical problem. Too many participants are needed to render the approach palatable to most researchers. The present aim is to address this problem. Embracing skewness is an important part of the solution.
Investigating the Causal Relationships among Carbon Emissions, Economic Growth, and Life Expectancy in Turkey: Evidence from Time and Frequency Domain Causality Techniques
Authors: Rjoub Husam , Odugbesan Jamiu Adetola , Adebayo Tomiwa Sunday , Wong Wing-Keung
Published: March 2021

It is not a gainsaying that challenges to both healthy living and the environment are the result of deteriorating environmental quality with the attendant effect on environmental sustainability. To provide a solution to the issue, our study uses long time-series data from 1960 to 2018, and employs an overlapping generational model, the Bayer–Hanck cointegration test, wavelet coherence, Fourier Toda–Yamamoto, and Breitung–Candelon frequency-domain spectral causality tests to investigate the causal relationships among carbon emissions (CO2), economic growth (GDP), and life expectancy (LE) in Turkey. Different from the literature, we find a positive co-movement between life expectancy and CO2 and a positive correlation between LE and GDP at different scales; CO2 has a causal relationship with LE and a bidirectional causal relationship between LE and GDP, as well as short, medium, and long-run causal relationships with LE; GDP has medium and long-run causal relationships with LE, and LE has short, medium, and long-run causal relationships with GDP. Our findings guide policymakers on their policy decision-making that will address the energy consumption, environmental degradation, human health, environmental hazards, and allocation to science and technology in Turkey with the aim of ensuring overall sustainable development.
Effects of Exogenous Microbial Agents on Soil Nutrient and Microbial Community Composition in Greenhouse-Derived Vegetable Straw Composts
Authors: Yun Cuixia , Yan Changrong , Xue Yinghao , Xu Zhiyu , Jin Tuo , Liu Qin
Published: March 2021

Vegetable waste causes resource waste and environmental pollution, giving rise to the spread of harmful organisms and causing disease in normal vegetable cultivation. Random distribution of vegetable waste can increase the risk of non-point agricultural pollution and explore the feasibility of its resource utilization. This study was designed to evaluate the effects of different doses of exogenous microbial agents on soil microbial communities after in situ composting of cucumber straw on plots with biodegradable mulch films. The cucumber straw and chicken manure were used as the base materials, and the next generation sequencing was used to analyze changes in the microbiome following composting. The results demonstrate that the addition of exogenous microbial agents had prolonged the high-temperature duration, reduced the total organic carbon (TOC) content, and accelerated the decline in the C/N ratio, ensuring compost maturity and effectively shortening the composting time. The predominant bacterial phyla of the four treatment groups were Proteobacteria and Firmicutes; while among fungal phyla, these treatments decreased the relative abundance of Ascomycota. The treatment of 300 t/ha microbial agents significantly increased the richness and diversity of both the bacterial and fungal communities. Redundancy analysis suggested that soil total nitrogen (TN) content had a significant effect on the bacterial community, while TN content, pH, and temperature influenced the fungal community in these samples. Collectively, the treatment of 300 t/ha exogenous microbial agents improved the quality of composting and promoted microbiome diversity.
The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange
Authors: Jiang Zhuhua , Gupta Rangan , Subramaniam Sowmya , Yoon Seong-Min
Published: March 2021

We investigated the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we used dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative influence on the equity returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative impacts on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the weather effect of the abnormal temperature on the stock returns is greater in severe bearish market. Whereas the effect of the air pollution on the stock returns is greater in the bullish market. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analyzing these effects in a very volatile emerging market such as the Shenzhen stock market.
A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications
Authors: Chou Li-Wei , Shen Jun-Hong , Lin Hui-Ting , Yang Yi-Tung , Hu Wen-Pin
Published: March 2021

The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, we investigate the potential applications of insoles with fewer sensing spots and the consequent reduction in the amount of data acquired from both feet. The main purpose is to discuss the influence of the layout of these pressure sensing points of the insole design on the center of pressure (COP) calculation. The insole used in this study has 89 pressure sensing spots, and we used data from 36, 29, 20, and 11 sensing points in simplified calculation types. Among these four simplified calculation types, Type 1 exhibited the best accuracy of the COP calculation, and Type 4 obtained the worst results. Type 2 and Type 3 exhibited inferior accuracy of the COP calculation, but they still sufficed for applications that did not require high accuracy. Aside from the factor of the number of sensing spots used in the calculation, we also demonstrated that the location of selected sensors could influence the accuracy of COP calculation in the analyses by using the different combinations of metatarsal areas and other areas (heel, central, lateral toes, and hallux). The results of this research could be a reference for making a simplified form of pressure sensing Internet-of-Health Things (IoHT) insole with a reduced product cost.
Influence of Service Valuation and Package Cost on Market Segmentation: The Case of Online Demand for Spanish and Andorra Ski Resorts
Authors: Barreal Jesús , Ferrer-Rosell Berta , Cristobal-Fransi Eduard , Jannes Gil
Published: March 2021

Ski resorts are important tourist resources in mountain areas. They have a high impact on the environment but also on the development of the territory. Corporate social responsibility management plays an important role in influencing consumer purchasing behavior. This research seeks to understand the behavior of ski tourists and to classify them. Approximately 50,000 online purchases of tourist packages to ski resorts in Spain and Andorra are analyzed in order to describe the different segments of demand in these resorts through a latent class model. The tourists’ age and previous experience, the type of accommodation, and the season—among other variables—are considered to clarify the different classes. Six different segments were found. Some relevant results for the online ski-package market are highlighted, such as the influence of skier’s expenditure on service valuation. Managerial implications, limitations of this study and recommendations for future research are also discussed.
Organizational Safety Climate Factor Model in the Urban Rail Transport Industry through CFA Analysis
Authors: Derahim Norfadillah , Arifin Kadir , Wan Isa Wan Mohammad Zaidi , Khairil Muhammad , Mahfudz Mahfudz , Ciyo Muhammad Basir , Ali Muhammad Nur , Lampe Ilyas , Samad Muhammad Ahsan
Published: March 2021

Occupational Safety and Health (OSH) issues in the urban rail transport industry need to be given full attention due to the factors of the instability of declining occupational accident rate, increasing number of passengers each year, and the pressure of technological development; in addition, the day-to-day operations also involve the public and various interested communities. Organization is one of the factors that influence worker safety and health status. This study aimed to propose a factor model of the organizational safety climate towards a better safety and health status for Malaysian urban rail industry. This quantitative study used a questionnaire randomly distributed to Malaysian rail system workers. A total of 441 workers in the operation and maintenance division were involved in this study. Confirmatory factor analysis (CFA) using IBM SEM-AMOS was conducted to determine the reliability and validity of the observed variables and the latent variables. This study proved that all four dimensions identified as safety communication, safety training, safety support system, and safety value represents the organizational safety climate. Following the analysis, an organizational safety climate model is successfully developed. This factor model aims to be used in the context of rail management studies to measure the safety climate of their organization, thereby improving the safety level of the workers within the organization.
Technological Adaption and Open Innovation in SMEs: An Strategic Assessment for Women-Owned SMEs Sustainability in Bangladesh
Authors: Meng Lingyan , Qamruzzaman Md , Adow Anass Hamad Elneel
Published: March 2021

Technological adaption and innovative activities foster small and medium enterprises (SMEs) growth by allowing production and process diversifications. Furthermore, open innovation practices, especially SMEs, rely on several firms’ specific attributes, and their impact varies accordingly. This study’s motivation is to explore the impact of technological adaptation and open innovation on SMEs run by women entrepreneurs and the challenges encountered in implementing open innovation. A sample of 580 questionnaires was sent to target SMEs, following the stratified random sampling technique, of which a complete 375 responses were duly received. The open innovation has been measured through eight innovative practices, reflecting the exploration and exploitation of technology in SMEs. This study found that women-owned enterprises were involved in many open innovation policies during the last five years. The result of this study indicated that there are no significant differences between manufacturing and industry regarding open innovation practices. Still, women-owned enterprises are more impressively engaged in open innovation practices. The research also identified that women-owned SMEs follow open innovation, mainly for market-related intentions, to compete with competitors and meet customers’ demands. The study contributes to the theoretical and practical implications. Further, the study is helpful for SMEs, researchers, practitioners, and decision-makers.
Planning and Preparation for Cruising Infrastructure: Cuba as a Case Study
Authors: Nyberg John E. , Pe’eri Shachak , Slocum Susan L. , Rice Matthew , Komwa Maction , Sun Donglian
Published: March 2021

The cruise line industry (CLI) provides an opportunity to rapidly improve national (and regional) economies in destinations. However, lack of planning and proper preparation by destination authorities and the cruise industry can have significant impacts on the local community, commerce, and environment. This paper identifies and quantifies near-shore challenges between the national authorities and the CLI that include port facility preparedness and the potential stresses on local infrastructure. These key parameters used to quantify the impact of the CLI on established destinations can potentially become part of the analysis, negotiation, and communication between all parties involved (government, business, and tourists) as part of the contribution to ensure sustainable CLI destinations. The Port of Havana, Cuba was selected as the study site and was compared against similar cruise line destinations around the Meso-American region (Philipsburg, Sint Maarten, Belize City, Belize, and Progreso, Mexico). The Port of Havana’s natural infrastructure and the carriage available for the incoming ship (i.e., nautical charting) appear to be adequate for welcoming increased cruise ship traffic. The main concerns are the potential toll on local resources including the impact on port traffic and the local infrastructure required to support tourists once they depart the ship.
Through the Lens of Telecoupling and Metacoupling: New Perspectives for Global Sustainability
Authors: Hovis Ciara L. , Dou Yue , Herzberger Anna , Liu Jianguo
Published: March 2021

Human and natural systems are more interconnected across distances than ever before [...]
Assessing the Difference of Emotional Intelligence among Visually Impaired, Blind Cricketers, and Non-Cricketers
Authors: Hussain Sarmad , Ali Asif , Choi Young Gin , Hwang Jinsoo
Published: March 2021

(1) Background: Emotional intelligence plays a critical role in one’s socialization and psychological adjustment. The current study is an attempt to obtain a comparative analysis of the emotional intelligence among the cricketers that are blind or suffer from visual impairment. The major objective of the study is to measure the emotional intelligence of visually impaired athletes along with the influence of their physical activities and demographic variables. (2) Methods: The employed survey method, with a descriptive research design, was based on a selective purpose sample of 220, which included a mean age of 28.38 years and an age range from 18 to 40 years, visually impaired cricketers, and non-cricketers from Punjab. The selective individuals completely provided their demographic information by filling out two forms, which included the short-form trait emotional intelligence questionnaire (TEIQue-SF) and the international physical activity questionnaire (IPAQ-SF). (3) Results: There is a significant relationship with emotional intelligence among the cricketers that are blind or suffer from visual impairment. Furthermore, the demographic variables had a significant impact on emotional intelligence (EI). Based on these findings, this study suggests administering a longitudinal study of visually impaired athletes in future research. In addition, different statistical populations of visually impaired athletes can be studied in order to compare their emotional intelligence with other psychological variables.
Profiling Visitors to Romanian Ecotourism Destinations
Authors: Constantin Cristinel Petrișor , Papuc-Damașcan Vasile , Blumer Andrei , Albu Ruxandra-Gabriela , Suciu Titus , Candrea Adina Nicoleta , Ispas Ana
Published: March 2021

Visitors’ increasing interest in nature-based and cultural tourism, especially in the context of ecotourism destinations, has generated a heated debate in tourism literature. Some authors consider that ecotourism should be approached through a niche strategy rather than through mass marketing. Therefore, identifying the main characteristics of visitors to ecotourism destinations is very important in setting management and marketing strategies adapted to their specific needs. The present paper aims to identify the profile of visitors to Romanian ecotourism destinations, considering the ecotourism potential of this country and the scarcity of empirical studies on these types of destinations. To reach this aim, a survey was conducted in four Romanian ecotourism destinations. By computing the data collected from a sample of 1157 visitors, four visitor segments have been identified based on a single characteristic (visit purpose). Crosstabulation and Chi-square analysis were used in order to identify the profiles of these segments. The results reveal that most of respondents are nature travellers, followed by culture travellers. The findings may be used by ecotourism destination managers in order to target specific market segments and establish proper management and marketing strategies.