bluuebunny/crossref_metadata_2025 huggingface dataset:
Catholics, Culture and the Renewal of Christian Humanism
Authors: Sullivan John
Published: May 2021

If Catholic educators are to equip students to engage with contemporary culture in a way that is credible and winsome, they need first, to be able to draw upon the living tradition of their faith appreciatively, critically and creatively, and second, to articulate a renewed form of Christian humanism. This paper addresses the second of these prerequisites by taking two steps towards the development of a Christian humanism for our times. First, I propose a rationale for the task of rethinking the case for Christian humanism as a resource for both cultural engagement and for educational practice. Second, I consider three potential sources and guides for becoming confident and competent in communicating this renewal of Christian humanism: Jacques Maritain, Romano Guardini and Pope Francis.
A Comparison of Wintertime Atmospheric Boundary Layer Heights Determined by Tethered Balloon Soundings and Lidar at the Site of SACOL
Authors: Zhang Min , Tian Pengfei , Zeng Huiyu , Wang Ligong , Liang Jiening , Cao Xianjie , Zhang Lei
Published: May 2021

High-precision and -resolution atmospheric boundary layer height (BLH) has received increasing attention in air pollution research in recent years. The low time resolution of sounding data is the main challenge to validate BLH retrieval from lidar observations. To resolve this issue, we conducted simultaneous tethered balloon sounding and lidar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) during winter 2019–2020. The BLHs derived from the tethered balloon sounding data were 170, 210, 393, 676, 423, and 190 m at 02:00, 08:00, 11:00, 14:00, 17:00, and 20:00 (Beijing time), respectively. The diurnal evolution of BLH was reasonably captured by lidar observation-based wavelet covariance transform and ideal profile fitting methods, which exhibited correlation coefficients of 0.91 and 0.89, respectively, with the BLHs determined from tethered balloon sounding data. The lidar results slightly overestimated the BLHs, though all results were acceptable when considering both the absolute and relative errors with respect to BLHs from the tethered balloon data. Our results revealed high-precision and -resolution diurnal variations in BLH at SACOL in Northwest China and suggest the importance of validating lidar-based BLHs using simultaneous sounding data.
Integration of Visible and Thermal Imagery with an Artificial Neural Network Approach for Robust Forecasting of Canopy Water Content in Rice
Authors: Elsherbiny Osama , Zhou Lei , Feng Lei , Qiu Zhengjun
Published: May 2021

A total of 120 rice plant samples were scanned by visible and thermal proximal sensing systems under different water stress levels to evaluate the canopy water content (CWC). The oven-drying method was employed for assessing the canopy’s water state. This CWC is of great importance for irrigation management decisions. The proposed framework is to integrate visible and thermal imaging data using an artificial neural network as a valuable promising implement for accurately estimating the water content of the plant. The RGB-based features included 20 color vegetation indices (VI) and 6 gray level co-occurrence matrix-based texture features (GLCMF). The thermal imaging features were two thermal indicators (T), namely normalized relative canopy temperature (NRCT) and the crop water stress index (CWSI), that were deliberated by plant temperatures. These features were applied with a back-propagation neural network (BPNN) for training the samples with minimal loss on a cross-validation set. Model behavior was affected by filtering high-level features and optimizing hyperparameters of the model. The results indicated that feature-based modeling from both visible and thermal images achieved better performance than features from the individual visible or thermal image. The supreme prediction variables were 21 features: 14VI, 5GLCMF, and 2T. The fusion of color–texture–thermal features greatly improved the precision of water content evaluation (99.40%). Its determination coefficient (R2 = 0.983) was the most satisfied with an RMSE of 0.599. Overall, the methodology of this work can support decision makers and water managers to take effective and timely actions and achieve agricultural water sustainability.
Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response
Authors: Pearse Grant D. , Watt Michael S. , Soewarto Julia , Tan Alan Y. S.
Published: May 2021

The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify Metrosideros excelsa (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen Austropuccinia psidii (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models.
Phenology Effects on Physically Based Estimation of Paddy Rice Canopy Traits from UAV Hyperspectral Imagery
Authors: Wang Li , Chen Shuisen , Peng Zhiping , Huang Jichuan , Wang Chongyang , Jiang Hao , Zheng Qiong , Li Dan
Published: May 2021

Radiation transform models such as PROSAIL are widely used for crop canopy reflectance simulation and biophysical parameter inversion. The PROSAIL model basically assumes that the canopy is turbid homogenous media with a bare soil background. However, the canopy structure changes when crop growth stages develop, which is more or less a departure from this assumption. In addition, a paddy rice field is inundated most of the time with flooded soil background. In this study, field-scale paddy rice leaf area index (LAI), leaf cholorphyll content (LCC), and canopy chlorophyll content (CCC) were retrieved from unmanned-aerial-vehicle-based hyperspectral images by the PROSAIL radiation transform model using a lookup table (LUT) strategy, with a special focus on the effects of growth-stage development and soil-background signature selection. Results show that involving flooded soil reflectance as background reflectance for PROSAIL could improve estimation accuracy. When using a LUT with the flooded soil reflectance signature (LUTflooded) the coefficients of determination (R2) between observed and estimation variables are 0.70, 0.11, and 0.79 for LAI, LCC, and CCC, respectively, for the entire growing season (from tillering to heading growth stages), and the corresponding mean absolute errors (MAEs) are 21.87%, 16.27%, and 12.52%. For LAI and LCC, high model bias mainly occurred in tillering growth stages. There is an obvious overestimation of LAI and underestimation of LCC for in the tillering growth stage. The estimation accuracy of CCC is relatively consistent from tillering to heading growth stages.
A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor
Authors: Lu Liang , Carrio Adrian , Sampedro Carlos , Campoy Pascual
Published: May 2021

Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integrated with a rapid-exploration random tree to construct a collision-avoidance system. The experimental results show that the proposed collision-avoidance algorithm has a robust performance at high flight speeds in challenging dynamic environments. The experimental results show that the proposed collision-avoidance algorithm can perform faster collision-avoidance maneuvers when compared to the state-of-art algorithms (the average computing time of the collision maneuver is 25.4 ms, while the minimum computing time is 10.4 ms). The average computing time is six times faster than one baseline algorithm. Additionally, fully autonomous flight experiments are also conducted for validating the presented collision-avoidance approach.
Debris Flow and Rockslide Analysis with Advanced Photogrammetry Techniques Based on High-Resolution RPAS Data. Ponte Formazza Case Study (NW Alps)
Authors: Notti Davide , Giordan Daniele , Cina Alberto , Manzino Ambrogio , Maschio Paolo , Bendea Iosif Horea
Published: May 2021

The use of a Remotely Piloted Aircraft System (RPAS) for the characterization and monitoring of landslides has been widely improved in the last decade. In particular, the use of this system is particularly effective for the study of areas prone to geohazards. Zones affected by landslides, such as rock slides and debris flows, are often quite critical in terms of accessibility due to unstable blocs that can strongly limit the direct access to the studied area. In this paper, we present the case study of Ponte Formazza in NW Italian Alps. In June 2019, a massive and complex debris flow re-mobilized about 300,000 m3 of a rockslide deposit that occurred in 2009. In this particular environment, we tested traditional, direct and mixed photogrammetric approaches using various configurations of Ground Control Points (GCPs) of the photogrammetric block and by calculating the relative errors. The minimum configuration of GCPs was established to reduce in situ measurements without degrading the accuracy of the cartographic products. The images of three RPAS campaigns (2017, 2018 and 2019), processed with a Structure from Motion (SfM) technique, allowed us to obtain very high-resolution orthophoto and digital surface models (DSMs) before and after the 2019 event. A few GCPs, geolocated with a Global Navigation Satellite System (GNSS), improved the orthophoto and DSM quality (Root Mean Squared Error RMSE 5 cm) even in the areas far from the drone deployment. The availability of high-resolution models has been fundamental for the identification of the volume changes. Furthermore, the 3D view supported and completed the geomorphological mapping of affected areas, particularly in the areas where the field survey is dangerous. The use of ancillary meteorological data and Sentinel-2 satellite images allows for a better definition of the kinematics and the predisposal and triggering factors of the 2019 debris flow.
Monitoring of Vegetation Disturbance around Protected Areas in Central Tanzania Using Landsat Time-Series Data
Authors: Komba Atupelye W. , Watanabe Teiji , Kaneko Masami , Chand Mohan Bahadur
Published: May 2021

Understanding vegetation disturbance around protected areas (PAs) is critical as it significantly affects the sustainable conservation of wildlife. However, there is a lack of analyses of consistent long-term data on vegetation disturbance. In this study, the LandTrendr algorithm and Google Earth Engine were used to access satellite data and explore the vegetation dynamics history across the Ruaha–Rungwa landscape, Tanzania. We characterized vegetation disturbance patterns and change attributes, including disturbance occurrence trends, rate, and severity, by using each pixel’s normalized burn ratio index time series. Between 2000 and 2019, 36% of the vegetation was significantly disturbed by anthropogenic activities. The results of this study show that the disturbance trends, severity, and patterns are highly variable and strongly depend on the management approaches implemented in the heterogeneous landscape: Ruaha National Park (RNP), Rungwa–Kizigo–Muhesi Game Reserves (RKMGR), and the surrounding zones. The disturbance rates and severity were pronounced and increased toward the edges of the western RKMGR. However, the disturbance in the areas surrounding the RNP was lower. The characterization of the vegetation disturbance over time provides spatial information that is necessary for policy makers, managers, and conservationists to understand the ongoing long-term changes in large PAs.
The CYGNSS Mission: On-Going Science Team Investigations
Authors: Carreno-Luengo Hugo , Crespo Juan A. , Akbar Ruzbeh , Bringer Alexandra , Warnock April , Morris Mary , Ruf Chris
Published: May 2021

In 2012, the National Aeronautics and Space Administration (NASA) selected the CYclone Global Navigation Satellite System (CYGNSS) mission coordinated by the University of Michigan (UM) as a low-cost and high-science Earth Venture Mission. The CYGNSS mission was originally proposed for ocean surface wind speed estimation over Tropical Cyclones (TCs) using Earth-reflected Global Positioning System (GPS) signals, as signals of opportunity. The orbital configuration of each CYGNSS satellite is a circular Low Earth Orbit (LEO) with an altitude ~520 km and an inclination angle of ~35°. Each single Delay Doppler Mapping Instrument (DDMI) aboard the eight CYGNSS microsatellites collects forward scattered signals along four specular directions (incidence angle of the incident wave equals incidence angle of the reflected wave) corresponding to four different transmitting GPS spacecrafts, simultaneously. As such, CYGNSS allows one to sample the Earth’s surface along 32 tracks simultaneously, within a wide range of the satellites’ elevation angles over tropical latitudes. Following the Earth Science Division 2020 Senior Review, NASA announced recently it is extending the CYGNSS mission through 30 September 2023. The extended CYGNSS mission phase is focused on both ocean and land surface scientific investigations. In addition to ocean surface wind speed estimation, CYGNSS has also shown a significant ability to retrieve several geophysical parameters over land surfaces, such as Soil Moisture Content (SMC), Above Ground Biomass (AGB), and surface water extent. The on-going science team investigations are presented in this article.
High-Sensitivity Dual-Probe Detection of Urinary miR-141 in Cancer Patients via a Modified Screen-Printed Carbon Electrode-Based Electrochemical Biosensor
Authors: Leung Wai-Hung , Pang Chi-Chia , Pang Sow-Neng , Weng Sheng-Xiang , Lin Yu-Lun , Chiou Yueh-Er , Pang See-Tong , Weng Wen-Hui
Published: May 2021

The screening and diagnosis of cancer are hallmarks of medicine in the aging population. Recently, microRNAs have shown potential for use as biomarkers, which could advance the field of diagnostics. The presence of miRNA-141 in the serum has been well described in several malignancies. However, the invasive approach used for sampling represents the major limitation for its practical application and, hence, its notable absence as a method for screening the general population. In light of this, we aimed to develop a high-sensitivity microRNA (miR) biosensor for application in the diagnosis of all miR-141-associated cancers, such as colorectal cancer (CRC) and breast cancer (BC). The novelty lies in our dual-probe design, which is reliant on the hybridization of the fluorescein isothiocyanate (FITC) targeting probe onto an existing sample of urinary miR-141 in the first step, followed by complementary binding with a biotinylated probe that has been coated on a modified screen-printed carbon electrode (SPCE). The hybridization of the probe and sensor produces signals via the catalytic reduction of H2O2 at HRP-modified SPCEs in the presence of H2O, which was measured by either cyclic voltammetry or chronoamperometry (CA) currents. In our study, the detection and expression of miR-141 in a cohort of colorectal cancer (n = 6) and breast cancer (n = 4) samples showed that its levels were significantly higher than in a healthy cohort (n = 9) (p < 0.004). Moreover, our miR sensor demonstrated high stability, reliability, and sensitivity (p < 0.0001). This work hopefully provides new information for the detection and monitoring of de novo and existing cancers.
Study of the Hydrodynamic Unsteady Flow Inside a Centrifugal Fan and Its Downstream Pipe Using Detached Eddy Simulation
Authors: Cai Jian-Cheng , Chen Hao-Jie , Brazhenko Volodymyr , Gu Yi-Hong
Published: May 2021

The detailed unsteady turbulent flow inside a centrifugal fan and its downstream pipe was studied using detached eddy simulation (DES) at three flowrates, namely, the best efficiency point (BEP), 0.75BEP, and 1.49BEP. Both the mean and fluctuating flow fields were analyzed on the basis of the root-mean-square value as the indication of fluctuating intensity. Results showed that the pressure fluctuation had the minimum value at BEP, but the velocity fluctuation increased with the flowrate. Most regions inside the centrifugal fan underwent large pressure fluctuation with the magnitude of about 10~20% of pref = 0.5 ρu22, where u2 is the blade velocity at the impeller outlet. The pressure fluctuation had a maximum value at the impeller side of the tongue tip rather than the stagnation point, and it decreased rapidly along the outlet pipe with magnitude about 1% of pref after distance of five pipe diameters. The spectra of hydrodynamic pressure showed conspicuous spikes at the blade passing frequency (BPF) in the volute but not in the downstream pipe. At the downstream pipe entrance, pressure fluctuation spectra agreed with experimental results, showing that hydrodynamic pressure fluctuations were dominant; however, the experimental data showed a much slower decreasing rate due to the acoustic fluctuations.
An Innovative and Interactive Teaching Model for Cultivating Talent’s Digital Literacy in Decision Making, Sustainability, and Computational Thinking
Authors: Yuan Yu-Hsi , Liu Chia-Hui , Kuang Szu-Sheng
Published: May 2021

In the modern era, talent cultivation plays an essential role in the transition process to sustainable development. The emerging direction of talent cultivation is intended to achieve global competence in computational thinking and digital literacy; however, there is still a gap in Taiwan. Scholars and educators have identified the contents of digital literacy, which include statistical concepts, data analysis skills, and critical thinking. To fill this gap, this study proposed an innovative educational teaching method that integrates data visualization techniques and critical thinking training to improve students’ decision-making literacy. An experiment on the curriculum with a one-group pre-test and post-test design was conducted for 79 participants. The data consisted of a geographical data map, visualized data diagrams, and a statistical test; the scores for critical thinking, academic self-confidence, and academic performance were separated into high, middle, and low achievement groups. For data analysis, the bootstrapping method (resampled 1000 times), paired sample t-test, and ANCOVA were applied for data analysis to compare the difference between each independent group. The results showed that the post-test scores (statistic test M = 9.59, infographic test M = 6.09, map test M = 5.02) were higher than the pre-test scores (statistic test M = 7.15, infographic test M = 3.46, map test M = 3.13). The most significant implication is the innovative teaching approach based on integrated data visualization and critical thinking. It plays a very significant role in enhancing decision-making abilities.
Risk Assessment and Sustainability of Wastewater Treatment Plant Operation
Authors: Tušer Irena , Oulehlová Alena
Published: May 2021

Water and water management, which also includes wastewater treatment plants (WWTPs), are considered essential elements critical infrastructure. A disruption of their operation can result in the discharge of wastewater into the environment without having been adequately treated. This can cause health problems, contamination of soil, groundwater and surface water, etc. In order to provide operational safety and sustainability of WWTP operation, it is necessary to establish protection against potential risk activation, together with risk minimization and enhanced preparedness to address the risks. The article deals with the application of risk management stages on the selected object of the municipal WWTP facility in the form of a case study. The risk identification phase consisted of the identification of assets and risks. The assets were grouped together due to the complexity of the WWTP operation. A combination of methods, a safety audit, a checklist and semi-structured interviews were used to identify the risks in three iterations. A risk register was created as a result of the risk identification phase. Subsequently, a risk analysis was carried out, in which the scenarios of the risk impact on the assets were examined, the index levels of probability and impacts were determined, and the risk estimate was performed. The Bowtie Analysis Method was used to illustrate the causes and consequences for one of the critical risks. The result of the study is the risk evaluation identified undesirable and unacceptable risks for which risk management methods were proposed
Household Food Waste from an International Perspective
Authors: Giordano Claudia , Franco Silvio
Published: May 2021

The food waste debate has flourished during the last years, leading to an impressive increase in the number of scientific publications [...]
Time to Get Emotional: Determinants of University Students’ Intention to Return to Rural Areas
Authors: Simões Francisco , Rocca Antonella , Rocha Rui , Mateus Carlos , Marta Elena , Tosun Jale
Published: May 2021

The social sustainability of rural areas is affected by the phenomenon of “brain drain” due to younger generations’ outward migration. Our study examines how structural and subjective factors determine the returning intentions of university students over time, before completion of their studies. We conducted a longitudinal, 3-wave survey between 2018 and 2020, involving 349 students (Mean age = 21.89; 63.04% women) and originating from a rural, remote region of Portugal. Using a Tobit panel model approach for data analysis, we found that participants whose mothers had a university degree, who expected higher income 3 years after studies completion, and who were more attached to the place where they were studying were less inclined to return to their native rural area. Conversely, those who were more attached to their rural origins were more likely to show an increased interest in returning over time. Our findings show that university students originating from rural areas and their returning intentions are affected by both structural and subjective factors, in a context of increasing individualisation of mobility intentions and decision making. Consequently, decision makers must start to include the sustained promotion of youths’ emotional bonds to rural areas as a vector of education policy packages in order to combat rural brain drain.
Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking
Authors: Luo Qiang , Zang Xiaodong , Cai Xu , Gong Huawei , Yuan Jie , Yang Junheng
Published: May 2021

Lane-changing behavior is one of the most common driving behaviors while driving. Due to the complexity of its operation, vehicle collision accidents are prone to occur when changing lanes. Under the environment of vehicle networking, drivers can obtain more accurate traffic information in time, which can be of great help in terms of improving lane-changing safety. This paper analyzes the core factors that affect the safety of vehicles changing lanes, establishes the weight model of influencing factors of lane-changing behavior using the analytic hierarchy process (AHP), and obtains the calculation method of lane-changing behavior factors (LCBFs). Based on the fuzzy reasoning theory, the headway between the lane-changing vehicle and adjacent vehicles in the target lane was examined, and fuzzy logic lane-changing models were established for both situations (i.e., change to the left and change to the right lane). The fuzzy logic lane-changing models were tested via simulation experiments, and the test results showed that the models have a better warning effect on lane changing (LCBF = 1.5), with an accuracy of more than 90%. Thus, the established model in this paper can provide theoretical support for safety warnings when changing lanes and theoretical support for the sustainable development of transportation safety.
Fuel Cell Electric Vehicles (FCEV): Policy Advances to Enhance Commercial Success
Authors: Asif Usman , Schmidt Klaus
Published: May 2021

Many initiatives and policies attempt to make our air cleaner by reducing the carbon foot imprint on our planet. Most of the existing and planned initiatives have as their objectives the reduction of carbon dependency and the enhancement of newer or better technologies in the near future. However, numerous policies exist for electric vehicles (EVs), and only some policies address specific issues related to fuel cell electric vehicles (FCEV). The lack of a distinction between the policies for EVs and FCEVs provides obstacles for the advancement of FCEV-related technologies that may otherwise be successful and competitive in the attempt to create a cleaner planet. Unfortunately, the lack of this distinction is not always based on intellectual or scientific evidence. Therefore, governments may need to introduce clearer policy distinctions in order to directly address FCEV-related challenges that may not pertain to other EVs. Unfortunately, lobbyism continues to exist that supports the maintenance of the status quo as new technologies may threaten traditional, less sustainable approaches to provide opportunities for a better environment. This lobbyism has partially succeeded in hindering the advancement of new technologies, partially because the development of new technologies may reduce profit and business opportunities for traditionalists. However, these challenges are slowly overcome as the demand for cleaner air and lower carbon emissions has increased, and a stronger movement toward newer and cleaner technologies has gained momentum. This paper will look at policies that have been either implemented or are in the process of being implemented to address the challenge of overcoming traditional obstacles with respect to the automobile industry. The paper reviewed, synthesized, and discussed policies in the USA, Japan, and the European Union that helped implement new technologies with a focus on FCEVs for larger mass markets. These regions were the focus of this paper because of their particular challenges. South Korea and China were not included in this discussion as these countries already have equal or even more advanced policies and initiatives in place.
Drivers and Barriers Leading to a Successful Paradigm Shift toward Regenerative Neighborhoods
Authors: Haselsteiner Edeltraud , Rizvanolli Blerta Vula , Villoria Sáez Paola , Kontovourkis Odysseas
Published: May 2021

Regenerative sustainability is gaining great attention as an essential concept for a transformative process, a re-designed mindset shifting from the narrowed focus of considering particular aspects such as energy efficiency, renewable materials, or sustainable technology towards the creation of a self-regenerating social and ecological system. Apart from being a vision of the future, regenerative sustainability has already been implemented successfully in individual projects, plans, and extensive strategies. The goals of this research are (1) to set up the conceptual framework for regenerative sustainability principles in the built environment; (2) to investigate and identify the drivers and barriers faced during the implementation of regenerative principles in the built environment; and (3) to identify gaps in the paradigm shift towards regenerative districts and macro-level projects. A multi-stage methodology was implemented. First, an in-depth literature review was conducted aiming to understand regenerative sustainability state of the art and define the key principles. Then, quantitative data analysis was conducted aiming to identify drivers and barriers of regenerative implementation in buildings following by semi-structured interviews with the representatives of regenerative buildings or districts. The step-by-step methodology resulted in the identified drivers of applying the regenerative principles, which are available financial incentives; marketing and sales benefits; improved companies/investors market image and competitive market advantage; reduced building lifecycle costs/effective use of energy and resources; enhancement buildings’ users’ well-being; and receiving building certification. The main barriers identified were lack of knowledge and experience working with regenerative materials and technologies by employees, consultants, and construction companies and usage of the available tools that enable such constructions; overall stakeholders’ culture and their resistance to changing their mindset toward a regenerative approach; inadequacy of national and international standards and legislation to address regenerative policies; and increased construction cost and time and lack of financial incentives. Ultimately, during the broad examination of the case studies, regenerative qualities served as a valuable insight to understand barriers and drivers at neighborhood and macro levels.
Determinants of Learning Management Systems during COVID-19 Pandemic for Sustainable Education
Authors: Cavus Nadire , Mohammed Yakubu Bala , Yakubu Mohammed Nasiru
Published: May 2021

Research has shown that effective and efficient learning management systems (LMS) were the main reasons for sustainable education in developed nations during COVID-19 pandemic. However, due to slow take-up of LMS many schools in developing countries, especially Africa were completely shut down due to COVID-19 pandemic. To fill this gap, 4 AI-based models; Support Vector Machine (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT) were developed for prediction of LMS determinants. Nonlinear sensitivity analysis was employed to select the key parameters of the LMS determinants data obtained from 1244 schools’ students. Five statistical indices were used to validate the models. The performance results of the four developed AI models discovered facilitating conditions, attitude towards LMS, perceived enjoyment, users’ satisfaction, perceived usefulness, and ease of use to be the most significant factors that affect educational sustainability in Nigeria during COVID-19. Further, single model’s performance results comparison proved that SVM has the highest prediction ability compared to GPR, ANN, and BRT due to its robustness in handling data uncertainties. The study results identified the factors responsible for total schools’ closure during COVID-19. Future studies should examine the application of other linear and other nonlinear AI techniques.
Upshots of Intrinsic Traits on Social Entrepreneurship Intentions among Young Business Graduates: An Investigation through Moderated-Mediation Model
Authors: Sana Hameed Asghar , Alkhalaf Salem , Zulfiqar Salman , Al-Rahmi Waleed Mugahed , Al-Adwan Ahmad Samed , AlSoud Anas Ratib
Published: May 2021

Social entrepreneurship has recently become a much-desired area of research for academia, practices, and policymaking. Natural or cognitive personal thoughtfulness like loving-kindness meditation (LKM) and compassion trigger individual intentions towards the social entrepreneurial venture. In this process of individual social entrepreneurial intention personality trait plays a very vital role, such as entrepreneurship resilience. For this study, a purposive sampling technique was incorporated and data was collected from 631 business and management sciences students. Data is analyzed by SPSS 23 and for the hypothesis testing, we used the bootstrap analysis of Hayes PROCESS v3.5. This study depicts that LKM has a positive significant impact on compassion and no significant impact on social entrepreneurship intentions while resilience strengthens the direct relationship of compassion with social entrepreneurship and the indirect relationship of LKM with social entrepreneurship via compassion. This study contributes to solving the economic and social problems over the globe especially by boosting the LKM and resilience traits so that the young graduate commence social entrepreneurship. This study helps the academician and policymakers to adopt strategies through which they can encourage youth to indulge in social entrepreneurial ventures solve the social problem and decrease unemployment.
Growing the Beautiful Anthropocene: Ethics of Care in East European Food Gardens
Authors: Sovová Lucie , Jehlička Petr , Daněk Petr
Published: May 2021

This study contributes to research proposing the ethics of care framework as a way of imagining a food system that cares for Others. We expand this exploration to the everyday practice of home gardening and the related social relationships and material flows. This area complements current scholarship, which mostly focuses on food-related care as a form of activism driven by intentionality and knowledge about the effects of consumption choices. Combining a survey of a representative sample of the population and an in-depth qualitative study, our paper highlights the importance of inconspicuous but materially significant food self-provisioning and sharing practices as caring behaviors that do not rely on educational campaigns but draw on the desire to produce healthy food for human Others. Home grown food is distributed in the generalized reciprocity mode within wide food-sharing networks. The desire to produce healthy food further translates into the adoption of caring methods of cultivation that benefit non-human Others involved in the garden ecosystems.
Conserving the Paraguay-Paraná Fluvial Corridor in the XXI Century: Conflicts, Threats, and Challenges
Authors: Baigún Claudio Rafael Mariano , Minotti Priscilla Gail
Published: May 2021

The Paraguay-Paraná river system represents a unique, free-flowing corridor that extends about 3500 km southwards from the Pantanal to the Rio de la Plata estuary, crossing four countries. The absence of fragmentation along the main channels and its still well-connected floodplains have preserved longitudinal and lateral functional and structural connectivity, defining critical ecological gradients for the biota and species life cycles. The lack of dams represents a noticeable feature that benefits migratory species supporting small-scale fisheries providing food security and the conservation of other ecosystem services with associated livelihoods. The ecological integrity of this corridor could be severely affected by the potential expansion of the Hidrovía Paraguay-Paraná for improving commercial trade, the foreseen installation of new dams in the Paraná basin, in addition to other current impacts and threats. Conservation of the corridor for societal benefits, involving the sustainable development of activities associated with the use of the fluvial territory, requires maintaining the natural ecological process that sustains livelihoods and biodiversity. This calls for innovative strategies encompassing water governance process, social and environmental information related to expected impacts, a better understanding of synergies between processes, and foremost an interdisciplinary approach to design and apply integrative and multi-scale management policies.
First Arrival Picking on Microseismic Signals Based on K-Means with a ReliefF Algorithm
Authors: Li Yijia , Wang Zhengfang , Wang Jing , Sui Qingmei , Li Shufan , Wang Hanpeng , Cao Zhiguo
Published: May 2021

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.
Direct Esthetic Composite Restorations in Anterior Teeth: Managing Symmetry Strategies
Authors: Paolone Gaetano , Scolavino Salvatore , Gherlone Enrico , Spagnuolo Gianrico
Published: May 2021

A novel procedure for symmetric and consistent layer thickness management in esthetic direct restoration of anterior teeth is presented. For the purpose of obtaining a satisfactory final outcome of an esthetic direct restoration, it is crucial to standardize either margin preparation design and dentin and enamel layer thickness. Leaving too much space for the final translucent layer may lead in fact to “low value—gray” restorations if not correctly managed. The most common tool used to check layer thickness is the sagittal silicone index, which is reliable but involves planning; therefore, it requires two stages appointments. In this clinical case, a novel procedure is used to prepare, to model and to check thickness of composite shades in a single appointment, thus providing a symmetric esthetic outcome. A healthy 21-year-old woman referred to our dental office for the esthetic rehabilitation of both maxillary central incisors. The correct composite bilateral and symmetric layer thickness management provided a predictable esthetic outcome of the restorations. The main objective of this case presentation is to describe a novel technique that is able to save chair-time and dental laboratory costs during direct restorations in anterior teeth.
Network Analyzing by the Aid of Orbit Polynomial
Authors: Ghorbani Modjtaba , Dehmer Matthias
Published: May 2021

This article aims to be a further contribution to the research on structural complexity networks. Here, we emphasize measures to determine symmetry. The so-called “orbit polynomial” is defined by OG(x)=∑iaixi, where ai is the number of orbits of size i. Furthermore, the graph polynomial 1−OG(x) has a unique positive root in the interval (0,1), which can be considered as a relevant measure of the symmetry of a graph. In the present paper, we studied some properties of the orbit polynomial with respect to the stabilizer elements of each vertex. Furthermore, we constructed graphs with a small number of orbits and characterized some classes of graphs in terms of calculating their orbit polynomials. We studied the symmetry structure of well-known real-world networks in terms of the orbit polynomial.
Study of Parameters in the Genetic Algorithm for the Attack on Block Ciphers
Authors: Tito-Corrioso Osmani , Borges-Trenard Miguel Angel , Borges-Quintana Mijail , Rojas Omar , Sosa-Gómez Guillermo
Published: May 2021

In recent years, the use of Genetic Algorithms (GAs) in symmetric cryptography, in particular in the cryptanalysis of block ciphers, has increased. In this work, the study of certain parameters that intervene in GAs was carried out, such as the time it takes to execute a certain number of iterations, so that a number of generations to be carried out in an available time can be estimated. Accordingly, the size of the set of individuals that constitute admissible solutions for GAs can be chosen. On the other hand, several fitness functions were introduced, and which ones led to better results was analyzed. The experiments were performed with the block ciphers AES(t), for t∈{3,4,7}.
Time-Conjugation in a Unified Quantum Theory for Hermitian and Non-Hermitian Electronic Systems under Time-Reversal Symmetry
Authors: Weinhold F.
Published: May 2021

We propose a reformulation of the mathematical formalism of many-electron quantum theory that rests entirely on the physical properties of the electronic system under investigation, rather than conventional mathematical assumption of Hermitian operators in Hilbert space. The formalism is based on a modified dot-product that replaces the familiar complex-conjugation in Hilbert space ℌ (fixed for all physical systems) by time-conjugation in T-space (as generated by the specific spin, magnetic field, or other explicit t-dependence of the system Hamiltonian ℋ of interest), yielding different spatial structure for different systems. The usual Hermitian requirement for physical operators is thereby generalized to a self-t-adjoint (“t-reversible”) character, leading to correspondingly generalized theorems of virial and hypervirial type. The T-space reformulation preserves the real values of measurable properties and the Born-probabilistic interpretations of state functions that underlie the present quantum theory of measurement, while also properly distinguishing “temporal” behavior of internal decay (tunneling-type) phenomena from that of applied fields with parametric t-dependence on an external clock. The t-product represents a further generalization of the “c-product” that was previously found useful in complex coordinate-rotation studies of autoionizing resonances.
Botulinum Toxin Type-A (Botulax®) Treatment in Patients with Intractable Chronic Occipital Neuralgia: A Pilot Study
Authors: Kim Hyeyun , Jang Bongjin , Kim Seong-Taek
Published: May 2021

Intractable chronic occipital neuralgia (ON) is an uncommon type of headache often experienced by patients in outpatient neurological clinics. Among patients unresponsive to oral neuralgia medications, needling or injections with several drugs were suggested alternatives for treating chronic ON. This study aimed to determine the effectiveness and safety of botulinum toxin type-A (BTX-A) injection treatments, where eight patients with unilateral chronic ON received BTX-A injections at the pain sites. The pain relief effect was observed 2 weeks after receiving the injections, gradually showing improvements up to 12 weeks after injection. There were no adverse events or changes from baseline in serologic studies and vital signs in any of the participants. The treatment’s pain-relieving effects were confirmed through regular, 12-week follow-ups, confirming the safety and effectiveness of BTX-A on chronic ON and suggesting that this method is an effective, novel alternative option for chronic ON treatment.
Acute Toxicity of Gambierone and Quantitative Analysis of Gambierones Produced by Cohabitating Benthic Dinoflagellates
Authors: Murray J. Sam , Finch Sarah C. , Puddick Jonathan , Rhodes Lesley L. , Harwood D. Tim , van Ginkel Roel , Prinsep Michèle R.
Published: May 2021

Understanding the toxicity and production rates of the various secondary metabolites produced by Gambierdiscus and cohabitating benthic dinoflagellates is essential to unravelling the complexities associated with ciguatera poisoning. In the present study, a sulphated cyclic polyether, gambierone, was purified from Gambierdiscus cheloniae CAWD232 and its acute toxicity was determined using intraperitoneal injection into mice. It was shown to be of low toxicity with an LD50 of 2.4 mg/kg, 9600 times less toxic than the commonly implicated Pacific ciguatoxin-1B, indicating it is unlikely to play a role in ciguatera poisoning. In addition, the production of gambierone and 44-methylgambierone was assessed from 20 isolates of ten Gambierdiscus, two Coolia and two Fukuyoa species using quantitative liquid chromatography–tandem mass spectrometry. Gambierone was produced by seven Gambierdiscus species, ranging from 1 to 87 pg/cell, and one species from each of the genera Coolia and Fukuyoa, ranging from 2 to 17 pg/cell. The production of 44-methylgambierone ranged from 5 to 270 pg/cell and was ubiquitous to all Gambierdiscus species tested, as well as both species of Coolia and Fukuyoa. The relative production ratio of these two secondary metabolites revealed that only two species produced more gambierone, G. carpenteri CAWD237 and G. cheloniae CAWD232. This represents the first report of gambierone acute toxicity and production by these cohabitating benthic dinoflagellate species. While these results demonstrate that gambierones are unlikely to pose a risk to human health, further research is required to understand if they bioaccumulate in the marine food web.
An Innovative Synbiotic Formulation Decreases Free Serum Indoxyl Sulfate, Small Intestine Permeability and Ameliorates Gastrointestinal Symptoms in a Randomized Pilot Trial in Stage IIIb-IV CKD Patients
Authors: Cosola Carmela , Rocchetti Maria Teresa , di Bari Ighli , Acquaviva Paola Maria , Maranzano Valentina , Corciulo Simone , Di Ciaula Agostino , Di Palo Domenica Maria , La Forgia Flavia Maria , Fontana Sergio , De Angelis Maria , Portincasa Piero , Gesualdo Loreto
Published: May 2021

Proteolytic dysbiosis of the gut microbiota has been recognized as both a typical feature of chronic kidney disease (CKD) and a risk factor for its progression. Blood accumulation of gut-derived uremic toxins (UTs) like indoxyl sulfate (IS) and p-cresyl sulfate (PCS), intestinal permeability and constipation are typical features accompanying CKD progression and triggering chronic inflammation. In order to verify the efficacy of the innovative synbiotic formulation NATUREN G® in modulating the levels of circulating UTs, intestinal permeability and gastrointestinal symptoms, we set up a randomized, single-blind, placebo-controlled, pilot trial in stage IIIb-IV CKD patients and in healthy controls. Two-month administration of the synbiotic resulted in a decrease of free IS, as compared with the placebo-treated arm, only in the CKD group. The other UTs did not significantly change, although different trends in time (increase in the placebo arm and decrease in the synbiotic arm) were observed. Moreover, after supplementation, reduction of small intestinal permeability and amelioration of abdominal pain and constipation syndromes were observed only in the CKD group. The obtained results suggest the specificity of action of NATUREN G® in CKD and justify further validation in a wider study population.