bluuebunny/crossref_metadata_2025 huggingface dataset:
Bio-Inspired Design of Bi/Tridirectionally Anisotropic Sliding Superhydrophobic Titanium Alloy Surfaces
Authors: Xu Jinkai , Hou Yonggang , Lian Zhongxu , Yu Zhanjiang , Wang Zuobin , Yu Huadong
Published: October 2020

Many biological surfaces with the multi-scale microstructure show obvious anisotropic wetting characteristics, which have many potential applications in microfluidic systems, biomedicine, and biological excitation systems. However, it is still a challenge to accurately prepare a metal microstructured surface with multidirectional anisotropy using a simple but effective method. In this paper, inspired by the microstructures of rice leaves and butterfly wings, wire electrical discharge machining was used to build dual-level (submillimeter/micrometer) periodic groove structures on the surface of titanium alloy, and then a nanometer structure was obtained after alkali-hydrothermal reaction, forming a three-level (submillimeter/micrometer/nanometer) structure. The surface shows the obvious difference of bidirectional superhydrophobic and tridirectional anisotropic sliding after modification, and the special wettability is easily adjusted by changing the spacing and angle of the inclined groove. In addition, the results indicate that the ability of water droplets to spread along parallel and perpendicular directions on the submillimeter groove structure and the different resistances generated by the inclined groove surface are the main reasons for the multi-anisotropic wettability. The research gives insights into the potential applications of metal materials with multidirectional anisotropic wetting properties.
Alteration, Reduction and Taste Loss: Main Causes and Potential Implications on Dietary Habits
Authors: Risso Davide , Drayna Dennis , Morini Gabriella
Published: October 2020

Our sense of taste arises from the sensory information generated after compounds in the oral cavity and oropharynx activate taste receptor cells situated on taste buds. This produces the perception of sweet, bitter, salty, sour, or umami stimuli, depending on the chemical nature of the tastant. Taste impairments (dysgeusia) are alterations of this normal gustatory functioning that may result in complete taste losses (ageusia), partial reductions (hypogeusia), or over-acuteness of the sense of taste (hypergeusia). Taste impairments are not life-threatening conditions, but they can cause sufficient discomfort and lead to appetite loss and changes in eating habits, with possible effects on health. Determinants of such alterations are multiple and consist of both genetic and environmental factors, including aging, exposure to chemicals, drugs, trauma, high alcohol consumption, cigarette smoking, poor oral health, malnutrition, and viral upper respiratory infections including influenza. Disturbances or loss of smell, taste, and chemesthesis have also emerged as predominant neurological symptoms of infection by the recent Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus strain 2 (SARS-CoV-2), as well as by previous both endemic and pandemic coronaviruses such as Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and SARS-CoV. This review is focused on the main causes of alteration, reduction, and loss of taste and their potential repercussion on dietary habits and health, with a special focus on the recently developed hypotheses regarding the mechanisms through which SARS-CoV-2 might alter taste perception.
Development of RisObIn.Com, a Screening Tool for Risk of Childhood Obesity in the Community
Authors: Moreira Ana Catarina , Almeida Oliveira Patrícia , Borrego Rute , Nogueira Telma , Ferreira Raquel , Virella Daniel
Published: October 2020

The prevalence of childhood overweight has increased considerably in the past three decades and there is evidence that childhood obesity can persist into adulthood. A simple tool to identify relevant risk factors may alert families and prevent overweight and obesity. This study aims to develop a pre-school screening tool to assess the risk of childhood obesity. Child anthropometric measurements and several risk factors for childhood obesity factors were obtained. The effect of the variables on the outcome of obesity (defined as increased anthropometry-estimated adiposity) was assessed by binary logistic regression analyses. The identified variables were submitted for expert panel validation and combined for the tool development. A total of 304 children were included. Eight items were included in the tool. A higher score of the tool indicates a greater risk for obesity in childhood with the cutoff point set at 0. The tool sensitivity for obesity was 95%, specificity was 74.4%, the positive predictive value was 37.3%, and negative predictive value was 98.9%. The Risk of childhood Obesity In the Community (RisObIn.Com) tool is proposed to be a comprehensive tool to identify children at high risk for late childhood obesity at admission to primary school. Further studies are needed to assess the performance of the tool.
A Cross-Sectional Survey of Salty Snack Consumption among Serbian Urban-Living Students and Their Contribution to Salt Intake
Authors: Timic Jasmina B. , Kotur-Stevuljevic Jelena , Boeing Heiner , Krajnovic Dušanka , Djordjevic Brizita , Sobajic Sladjana
Published: October 2020

This study investigated the behavior of urban-living students related to the salty snacks consumption, and their contribution to salt daily intake. A cross-sectional survey on 1313 urban-living students (16–25 years, 61.4% university students and 38.6% high school students) used a pre-verified questionnaire created specifically for the study. The logistic regression analysis was performed to investigate the factors influencing snack consumption. The results of salt content and the snack consumption frequency were used to evaluate snack contribution to salt intake. All subjects consumed salty snacks, on average several times per week, more often at home and slightly more during periods of intensive studying, with 42% of the participants reporting to consume two or more packages per snacking occasion. Most of the participants consumed such products between main meals, but 10% of them took snacks immediately after the main meal. More high-school students than university students were in the “high snack group” (p < 0.05). The most frequently consumed salty snacks were those with the highest content of salt. Salt intake from snack products for a majority of participants ranged between 0.4 and 1 g/day. The research revealed younger age, home environment and significant contribution to salt intake as critical points in salty snack consumption among urban-living students important for the better understanding of their dietary habits.
Negative Affectivity and Emotion Dysregulation as Mediators between ADHD and Disordered Eating: A Systematic Review
Authors: El Archi Sarah , Cortese Samuele , Ballon Nicolas , Réveillère Christian , De Luca Arnaud , Barrault Servane , Brunault Paul
Published: October 2020

Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with disordered eating, especially addictive-like eating behavior (i.e., binge eating, food addiction, loss of control overeating). The exact mechanisms underlying this association are unclear. ADHD and addictive-like eating behavior are both associated with negative affectivity and emotion dysregulation, which we hypothesized are mediators of this relationship. The purpose of this systematic review was to review the evidence related to this hypothesis from studies assessing the relationship between childhood or adulthood ADHD symptomatology, negative affectivity, emotion dysregulation and addictive-like eating behavior. The systematic review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) recommendations. The literature search was conducted in PubMed and PsycINFO (publication date: January 2015 to August 2020; date of search: 2 September 2020). Out of 403 potentially relevant articles, 41 were retained; 38 publications reported that ADHD and disordered eating or addictive-like eating behavior were significantly associated, including 8 articles that suggested a mediator role of negative affectivity or emotion dysregulation. Sixteen publications reported that the association between ADHD symptomatology and disordered eating or addictive-like eating behavior differed according to gender, eating behavior and ADHD symptoms (hyperactivity, impulsivity and inattention). We discuss the practical implications of these findings and directions future research.
Seroprevalence and Molecular Characterization of Coxiella burnetii in Cattle in the Republic of Korea
Authors: Hwang Sunwoo , Cho Hyung-Chul , Shin Seung-Uk , Kim Ha-Young , Park Yu-Jin , Jang Dong-Hoon , Kim Eun-Mi , Kim Jong Wan , Park Jinho , Choi Kyoung-Seong
Published: October 2020

This study was conducted to determine the prevalence of Coxiella burnetii in cattle and how that prevalence is influenced by cattle breed and growth type. A total of 491 cattle [cattle breed: 216 dairy cattle and 275 beef cattle; growth type: indoor housed (n = 294) and grazing (n = 197)] were used. The presence of C. burnetii DNA and antibodies was detected from blood and serum samples using polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The overall prevalence of C. burnetii was: 10.8% (95% CI: 8.0–13.5%) using PCR and 8.8% (95% CI: 6.3–11.3%) using ELISA. The prevalence of C. burnetii was significantly higher in beef cattle than in dairy cattle using both PCR (13.5% vs. 7.4%; P = 0.032) and ELISA (14.5% vs. 1.4%; P = 0.000), respectively. Comparison by growth type revealed that C. burnetii infection was significantly higher in grazing cattle than in housed cattle when using both PCR (24.9% vs. 1.4%; P = 0.000) and ELISA (21.3% vs. 0.3%; P = 0.000). Beef cattle were at a significantly higher risk of contracting C. burnetii compared with dairy cattle (odds ratio = 3.20, 95% CI: 1.80–5.67; P = 0.000). The risk of contracting C. burnetii in grazing cattle was increased by 32.57-fold (95% CI: 12.84–82.61; P = 0.000) compared with indoor housed cattle. The phylogenetic analysis based on the IS1111 gene revealed that our sequences grouped with human, tick, goat, and cattle isolates/strains found in several countries. C. burnetii sequences circulating in the Republic of Korea exhibit genetic variations. Thus, grazing is a high risk factor for the prevalence and transmission of C. burnetii.
Mānuka Oil—A Review of Antimicrobial and Other Medicinal Properties
Authors: Mathew Cynthia , Tesfaye Wubshet , Rasmussen Phil , Peterson Gregory M , Bartholomaeus Andrew , Sharma Manab , Thomas Jackson
Published: October 2020

Mānuka oil is an essential oil derived from Leptospermum scoparium, a plant that has been used by the indigenous populations of New Zealand and Australia for centuries. Both the extracted oil and its individual components have been associated with various medicinal properties. Given the rise in resistance to conventional antibiotics, natural products have been targeted for the development of antimicrobials with novel mechanism of action. This review aimed to collate available evidence on the antimicrobial, anti-parasitic and anti-inflammatory activities of mānuka oil and its components. A comprehensive literature search of was conducted using PubMed and Embase (via Scopus) targeting articles from database inception until June 2020. Chemical structures and IUPAC names were sourced from PubChem. Unpublished information from grey literature databases, Google search, targeted websites and Google Patents were also included. The present review found extensive in vitro data supporting the antimicrobial effects of mānuka oil warrants further clinical studies to establish its therapeutic potential. Clinical evidence on its efficacy, safety and dosing guidelines are necessary for its implementation for medical purposes. Further work on regulation, standardization and characterization of the medicinal properties of mānuka oil is required for establishing consistent efficacy of the product.
Examining the Characteristics and Applications of Mesenchymal, Induced Pluripotent, and Embryonic Stem Cells for Tissue Engineering Approaches across the Germ Layers
Authors: Priester Caitlin , MacDonald Amber , Dhar Madhu , Bow Austin
Published: October 2020

The field of regenerative medicine utilizes a wide array of technologies and techniques for repairing and restoring function to damaged tissues. Among these, stem cells offer one of the most potent and promising biological tools to facilitate such goals. Implementation of mesenchymal stem cells (MSCs), induced pluripotent stem cells (iPSCs), and embryonic stem cells (ESCs) offer varying advantages based on availability and efficacy in the target tissue. The focus of this review is to discuss characteristics of these three subset stem cell populations and examine their utility in tissue engineering. In particular, the development of therapeutics that utilize cell-based approaches, divided by germinal layer to further assess research targeting specific tissues of the mesoderm, ectoderm, and endoderm. The combinatorial application of MSCs, iPSCs, and ESCs with natural and synthetic scaffold technologies can enhance the reparative capacity and survival of implanted cells. Continued efforts to generate more standardized approaches for these cells may provide improved study-to-study variations on implementation, thereby increasing the clinical translatability of cell-based therapeutics. Coupling clinically translatable research with commercially oriented methods offers the potential to drastically advance medical treatments for multiple diseases and injuries, improving the quality of life for many individuals.
PRN Medicines Optimization and Nurse Education
Authors: Vaismoradi Mojtaba , Jordan Sue , Vizcaya-Moreno Flores , Friedl Ingrid , Glarcher Manela
Published: October 2020

Medicines management is a high-risk and error prone process in healthcare settings, where nurses play an important role to preserve patient safety. In order to create a safe healthcare environment, nurses should recognize challenges that they face in this process, understand factors leading to medication errors, identify errors and systematically address them to prevent their future occurrence. “Pro re nata” (PRN, as needed) medicine administration is a relatively neglected area of medicines management in nursing practice, yet has a high potential for medication errors. Currently, the international literature indicates a lack of knowledge of both the competencies required for PRN medicines management and the optimum educational strategies to prepare students for PRN medicines management. To address this deficiency in the literature, the authors have presented a discussion on nurses’ roles in medication safety and the significance and purpose of PRN medications, and suggest a model for preparing nursing students in safe PRN medicines management. The discussion takes into account patient participation and nurse competencies required to safeguard PRN medication practice, providing a background for further research on how to improve the safety of PRN medicines management in clinical practice.
Visual Quality Assessment of Urban Scenes with the Contemplative Landscape Model: Evidence from a Compact City Downtown Core
Authors: Yanru Hou , Masoudi Mahyar , Chadala Agnieszka , Olszewska-Guizzo Agnieszka
Published: October 2020

In the face of rapid urbanization and the growing burden of mental health disease, there is a need to design cities with consideration for human mental health and well-being. There is an emerging body of evidence on the importance of everyday environmental exposures regarding the mental health of city inhabitants. For example, contemplative landscapes, through a series of neuroscience experiments, were shown to trigger improved mood and restoration of attention. While the Contemplative Landscape Model (CLM) for scoring landscape views was applied to single images, its suitability was never tested for walking paths and areas with a diversity of viewpoints. This study aims to fill this gap using the high-density downtown of Singapore, also known as a “City in a Garden” for its advanced urban greening strategies, as a case study. In this study, 68 360° photos were taken along four popular walking paths every 20 m. A photo set of 204 items was created by extracting three view angles from each photo. Each of them was independently scored by three experts and average CLM scores for each view and path were obtained. The results were then fed into an open-source Quantum Geographic Information System (QGIS) for visualization. Cohen’s kappa agreement between experts’ scores was computed. The outcomes were mapped to facilitate the identification of the most contemplative viewpoints and paths. Moreover, specific contemplative landscape patterns have been distinguished and assessed allowing the recommendation of design strategies to improve the quality of viewpoints and paths. The inter-rater agreement reached substantial to perfect values. CLM is a reliable and suitable tool that enables the fine-grained assessment and improvement of the visual quality of the urban living environments with consideration of the mental health and well-being of urbanites. It can be used at a larger scale owing to 360° photos taken from the pedestrian’s point of view. Utilizing spatially explicit maps in QGIS platforms enables a wider range of visualizations and allows for spatial patterns to be revealed that otherwise would have remained hidden. Our findings demonstrate the usefulness of our semi-automated method. Furthermore, given the high inter-rater agreement observed, we suggest that there is potential in developing fully automated methods.
Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation
Authors: Guerriero Luigi , Di Martire Diego , Calcaterra Domenico , Francioni Mirko
Published: October 2020

An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed providing a specific procedure and related MATLAB™ code to derive displacement field maps using digital image correlation of successive Google Earth images. The suitability of the procedure and the potential of such images are demonstrated here through their application to two relevant case histories, namely the Slumgullion landslide in Colorado and the Miage debris-covered glacier in Italy. Result validation suggests the effectiveness of the proposed procedure in deriving Earth’s surface displacement data from Google Earth images.
Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
Authors: Kong Weiya , Liu Bo , Sui Xiaohong , Zhang Running , Sun Jinping
Published: October 2020

Imaging Radar Altimeter (IRA) is the current development tendency for ocean surface topography (OST) altimetry, which utilizes Synthetic Aperture Radar (SAR) and interferometry to improve the spatial resolution of OST to several kilometers or even better. Meanwhile, centimetric altimetry accuracy should be guaranteed for applications such as geostrophic currents or marine gravity anomaly inversion. However, the baseline length of IRA which determines the altimetric sensitivity is confined by the satellite platform, in consideration of baseline vibration and payload capability. Therefore, the baseline length from a single satellite can extend to only tens of meters, making it difficult to achieve centimetric accuracy. Referring to the successful experience from TerraSAR-X/TanDEM-X, satellite formation can easily extend the baseline length to hundreds or thousands of meters, depending on the helix orbit. Therefore, we propose the large baseline IRA (LB-IRA) from satellite formation for OST altimetry: the carrier frequency shift (CFS) is brought in to compensate for the severe baseline decorrelation, and the helix orbit is carefully selected to prevent severe time decorrelation from along-track baseline. The numerical results indicate that the LB-IRA, whose cross-track baseline ranges between 629~1000 m and along-tack baseline ranges between 0~40 m, can achieve ~1 cm relative accuracy at 1 km resolution.
An Automated Framework for Plant Detection Based on Deep Simulated Learning from Drone Imagery
Authors: Hosseiny Benyamin , Rastiveis Heidar , Homayouni Saeid
Published: October 2020

Traditional mapping and monitoring of agricultural fields are expensive, laborious, and may contain human errors. Technological advances in platforms and sensors, followed by artificial intelligence (AI) and deep learning (DL) breakthroughs in intelligent data processing, led to improving the remote sensing applications for precision agriculture (PA). Therefore, technological advances in platforms and sensors and intelligent data processing methods, such as machine learning and DL, and geospatial and remote sensing technologies, have improved the quality of agricultural land monitoring for PA needs. However, providing ground truth data for model training is a time-consuming and tedious task and may contain multiple human errors. This paper proposes an automated and fully unsupervised framework based on image processing and DL methods for plant detection in agricultural lands from very high-resolution drone remote sensing imagery. The proposed framework’s main idea is to automatically generate an unlimited amount of simulated training data from the input image. This capability is advantageous for DL methods and can solve their biggest drawback, i.e., requiring a considerable amount of training data. This framework’s core is based on the faster regional convolutional neural network (R-CNN) with the backbone of ResNet-101 for object detection. The proposed framework’s efficiency was evaluated by two different image sets from two cornfields, acquired by an RGB camera mounted on a drone. The results show that the proposed method leads to an average counting accuracy of 90.9%. Furthermore, based on the average Hausdorff distance (AHD), an average object detection localization error of 11 pixels was obtained. Additionally, by evaluating the object detection metrics, the resulting mean precision, recall, and F1 for plant detection were 0.868, 0.849, and 0.855, respectively, which seem to be promising for an unsupervised plant detection method.
Digital Elevation Model Quality Assessment Methods: A Critical Review
Authors: Polidori Laurent , El Hage Mhamad
Published: October 2020

Digital elevation models (DEMs) are widely used in geoscience. The quality of a DEM is a primary requirement for many applications and is affected during the different processing steps, from the collection of elevations to the interpolation implemented for resampling, and it is locally influenced by the landcover and the terrain slope. The quality must meet the user’s requirements, which only make sense if the nominal terrain and the relevant resolution have been explicitly specified. The aim of this article is to review the main quality assessment methods, which may be separated into two approaches, namely, with or without reference data, called external and internal quality assessment, respectively. The errors and artifacts are described. The methods to detect and quantify them are reviewed and discussed. Different product levels are considered, i.e., from point cloud to grid surface model and to derived topographic features, as well as the case of global DEMs. Finally, the issue of DEM quality is considered from the producer and user perspectives.
Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery
Authors: Malinowski Radek , Lewiński Stanisław , Rybicki Marcin , Gromny Ewa , Jenerowicz Małgorzata , Krupiński Michał , Nowakowski Artur , Wojtkowski Cezary , Krupiński Marcin , Krätzschmar Elke , Schauer Peter
Published: October 2020

Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.
Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks
Authors: Skublewska-Paszkowska Maria , Powroznik Pawel , Lukasik Edyta
Published: October 2020

Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.
Dynamic Adaptive Cross-Chain Trading Mode for Multi-Microgrid Joint Operation
Authors: Wang Longze , Wu Jing , Yuan Rongfang , Zhang Delong , Liu Jinxin , Jiang Siyu , Zhang Yan , Li Meicheng
Published: October 2020

The emerging blockchain technology has injected new vitality into the energy market, especially the peer-to-peer power trading of microgrid systems. However, with the increase of energy blockchain projects, the difficulty of data communication and value islands between blockchain networks have become open issues. Thus, in this paper, we propose a dynamic adaptive cross-chain trading mode for multi-microgrid joint operation. The novelty is to design a proof of credit threshold consensus mechanism to achieve effective information verification. This consensus mechanism can ensure the adaptive consistency of cross-chain information without changing the existing blockchain architecture of each system. At the same time, we design a corresponding key management interoperability protocol based on RSA algorithm and Chinese remainder theorem, which can realize data transfer and information consensus for cross-chain transactions. The theoretical analysis verifies that the cross-chain communication information is effective and the system is able to protect against the attack of malicious nodes. Finally, a cross-chain simulation experiment is established to analyze the operation efficiency. The result shows that this cross-chain trading takes place within seconds, which basically meets the response requirements for multi-microgrid joint operation.
Lysenin Channels as Sensors for Ions and Molecules
Authors: Bogard Andrew , Abatchev Gamid , Hutchinson Zoe , Ward Jason , Finn Pangaea W. , McKinney Fulton , Fologea Daniel
Published: October 2020

Lysenin is a pore-forming protein extracted from the earthworm Eisenia fetida, which inserts large conductance pores in artificial and natural lipid membranes containing sphingomyelin. Its cytolytic and hemolytic activity is rather indicative of a pore-forming toxin; however, lysenin channels present intricate regulatory features manifested as a reduction in conductance upon exposure to multivalent ions. Lysenin pores also present a large unobstructed channel, which enables the translocation of analytes, such as short DNA and peptide molecules, driven by electrochemical gradients. These important features of lysenin channels provide opportunities for using them as sensors for a large variety of applications. In this respect, this literature review is focused on investigations aimed at the potential use of lysenin channels as analytical tools. The described explorations include interactions with multivalent inorganic and organic cations, analyses on the reversibility of such interactions, insights into the regulation mechanisms of lysenin channels, interactions with purines, stochastic sensing of peptides and DNA molecules, and evidence of molecular translocation. Lysenin channels present themselves as versatile sensing platforms that exploit either intrinsic regulatory features or the changes in ionic currents elicited when molecules thread the conducting pathway, which may be further developed into analytical tools of high specificity and sensitivity or exploited for other scientific biotechnological applications.
Low-Rank Matrix Recovery from Noise via an MDL Framework-Based Atomic Norm
Authors: Qin Anyong , Xian Lina , Yang Yongliang , Zhang Taiping , Tang Yuan Yan
Published: October 2020

The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest. However, in many low-level vision problems, the exact target rank of the underlying structure and the particular locations and values of the sparse outliers are not known. Thus, the conventional methods cannot separate the low-rank and sparse components completely, especially in the case of gross outliers or deficient observations. Therefore, in this study, we employ the minimum description length (MDL) principle and atomic norm for low-rank matrix recovery to overcome these limitations. First, we employ the atomic norm to find all the candidate atoms of low-rank and sparse terms, and then we minimize the description length of the model in order to select the appropriate atoms of low-rank and the sparse matrices, respectively. Our experimental analyses show that the proposed approach can obtain a higher success rate than the state-of-the-art methods, even when the number of observations is limited or the corruption ratio is high. Experimental results utilizing synthetic data and real sensing applications (high dynamic range imaging, background modeling, removing noise and shadows) demonstrate the effectiveness, robustness and efficiency of the proposed method.
Implementation and Evaluation of Open-Source Hardware to Monitor Water Quality in Precision Aquaculture
Authors: Bórquez López Rafael Apolinar , Martinez Cordova Luis Rafael , Gil Nuñez Juan Carlos , Gonzalez Galaviz Jose Reyes , Ibarra Gamez Jose Cuauhtemoc , Casillas Hernandez Ramon
Published: October 2020

Precision aquaculture is a new field that allows farmers to have better control over aquaculture processes, facilitating decision-making and improving efficiency. The implementation and evaluation of a low-cost water-quality monitoring system based on open-source hardware, which is easy to rebuild for scientific applications, is reported in this paper. The proposed system measures temperature, dissolved oxygen, and pH, taking records and sending information through a wireless protocol (ZigBee) to a graphical user interface which can display information numerically and graphically, as well as simultaneously storing the information in a database. These variables are very important for aquaculture, as they have a direct influence on critical culture parameters such as growth and survival. Although it is a low-cost system, it offers good quality data and demonstrates efficiency for its use in precision aquaculture.
Atmospheric Temperature and CO2: Hen-or-Egg Causality?
Authors: Koutsoyiannis Demetris , Kundzewicz Zbigniew
Published: October 2020

It is common knowledge that increasing CO2 concentration plays a major role in enhancement of the greenhouse effect and contributes to global warming. The purpose of this study is to complement the conventional and established theory that increased CO2 concentration due to human emissions causes an increase of temperature, by considering the reverse causality. Since increased temperature causes an increase in CO2 concentration, the relationship of atmospheric CO2 and temperature may qualify as belonging to the category of “hen-or-egg” problems, where it is not always clear which of two interrelated events is the cause and which the effect. We examine the relationship of global temperature and atmospheric carbon dioxide concentration at the monthly time step, covering the time interval 1980–2019, in which reliable instrumental measurements are available. While both causality directions exist, the results of our study support the hypothesis that the dominant direction is T → CO2. Changes in CO2 follow changes in T by about six months on a monthly scale, or about one year on an annual scale. We attempt to interpret this mechanism by involving biochemical reactions, as at higher temperatures soil respiration, and hence CO2 emission, are increasing.
Scholarship Suppression: Theoretical Perspectives and Emerging Trends
Authors: Stevens Sean T. , Jussim Lee , Honeycutt Nathan
Published: October 2020

This paper explores the suppression of ideas within an academic scholarship by academics, either by self-suppression or because of the efforts of other academics. Legal, moral, and social issues distinguishing freedom of speech, freedom of inquiry, and academic freedom are reviewed. How these freedoms and protections can come into tension is then explored by an analysis of denunciation mobs that exercise their legal free speech rights to call for punishing scholars who express ideas they disapprove of and condemn. When successful, these efforts, which constitute legally protected speech, will suppress certain ideas. Real-world examples over the past five years of academics that have been sanctioned or terminated for scholarship targeted by a denunciation mob are then explored.
Codependency in the Relations of Couples of Imprisoned Women
Authors: Aristizábal Luz Adriana
Published: October 2020

Female criminal behavior has sparked the interest of many researchers who, from different perspectives, have tried to identify what are the factors that lead them to commit a crime. Studies indicate that female affective bonds change into a potentiator of crime behavior and/or withdrawal of it. The objective of this study was to find out if the couple’s bonds (previous or during the prison) were codependent, and to analyze the possible relation between the latter and female crime. This qualitative study used mixed tools in a sample of 27 women in the Bucaramanga prison (Colombia). The I-CO instrument was applied analyzing the four codependency factors: (1) denial mechanisms; (2) incomplete identity development; (3) emotional repression and (4) rescue orientation. The qualitative data obtained through the in-depth interview and focus groups were also analyzed, showing mainly three emerging categories: (1) I did it for him; (2) Although he doesn’t love me; and (3) I preferred to remain silent. The results suggested the difficulty of leaving violent relationships and the possible interaction between codependency, violent partner relationships and female crime. This research raises the need to strengthen the empowerment of women inside and outside the prison.
Perceptions of Multistakeholder Partnerships for the Sustainable Development Goals: A Case Study of Irish Non-State Actors
Authors: Banerjee Aparajita , Murphy Enda , Walsh Patrick Paul
Published: October 2020

The United Nations 2030 Agenda emphasizes the importance of multistakeholder partnerships for achieving the 17 UN Sustainable Development Goals (SDGs). Indeed, Goal 17 includes a target for national governments to promote multistakeholder partnerships between state and non-state actors. In this paper, we explore how members of civil society organizations and the private sector perceive both the possibilities and challenges of multistakeholder partnerships evolving in Ireland for achieving the SDGs. The research uses data gathered during 2018 and includes documentary research, participant observations of stakeholder forums in Ireland and the United Nations, and semi-structured interviews to address related questions. The results demonstrate that numerous challenges exist for forming multistakeholder partnerships for the SDGs, including a fragmented understanding of the Goals. They also note previous examples of successful multistakeholder partnership models, the need for more leadership from government, and an overly goal-based focus on SDG implementation by organizations as major impediments to following a multistakeholder partnership approach in the country. These findings suggest that although Goal 17 identifies multistakeholder partnerships as essential for the SDGs, they are challenging to form and require concerted actions from all state and non-state actors for SDG implementation.
Understanding N2O Emissions in African Ecosystems: Assessments from a Semi-Arid Savanna Grassland in Senegal and Sub-Tropical Agricultural Fields in Kenya
Authors: Bigaignon Laurent , Delon Claire , Ndiaye Ousmane , Galy-Lacaux Corinne , Serça Dominique , Guérin Frédéric , Tallec Tiphaine , Merbold Lutz , Tagesson Torbern , Fensholt Rasmus , André Sylvain , Galliau Sylvain
Published: October 2020

This study is based on the analysis of field-measured nitrous oxide (N2O) emissions from a Sahelian semi-arid grassland site in Senegal (Dahra), tropical humid agricultural plots in Kenya (Mbita region) and simulations using a 1D model designed for semi arid ecosystems in Dahra. This study aims at improving present knowledge and inventories of N2O emissions from the African continent. N2O emissions were larger at the agricultural sites in the Mbita region (range: 0.0 ± 0.0 to 42.1 ± 10.7 ngN m−2 s−1) than at the Dahra site (range: 0.3 ± 0 to 7.4 ± 6.5 ngN m−2 s−1). Soil water and nitrate (NO3−) contents appeared to be the most important drivers of N2O emissions in Dahra at the seasonal scale in both regions. The seasonal pattern of modelled N2O emissions is well represented, though the model performed better during the rainy season than between the rainy and dry seasons. This study highlighted that the water-filled pore space threshold recognised as a trigger for N2O emissions should be reconsidered for semi-arid ecosystems. Based on both measurements and simulated results, an annual N2O budget was estimated for African savanna/grassland and agricultural land ranging between 0.17–0.26 and 1.15–1.20 TgN per year, respectively.
Exopolysaccharides Producing Bacteria for the Amelioration of Drought Stress in Wheat
Authors: Ilyas Noshin , Mumtaz Komal , Akhtar Nosheen , Yasmin Humaira , Sayyed R. Z. , Khan Wajiha , Enshasy Hesham A. El , Dailin Daniel J. , Elsayed Elsayed A. , Ali Zeshan
Published: October 2020

This research was designed to elucidate the role of exopolysaccharides (EPS) producing bacterial strains for the amelioration of drought stress in wheat. Bacterial strains were isolated from a farmer’s field in the arid region of Pakistan. Out of 24 isolated stains, two bacterial strains, Bacillus subtilis (Accession No. MT742976) and Azospirillum brasilense (Accession No. MT742977) were selected, based on their ability to produce EPS and withstand drought stress. Both bacterial strains produced a good amount of EPS and osmolytes and exhibited drought tolerance individually, however, a combination of these strains produced higher amounts of EPS (sugar 6976 µg/g, 731.5 µg/g protein, and 1.1 mg/g uronic acid) and osmolytes (proline 4.4 µg/mg and sugar 79 µg/mg) and significantly changed the level of stress-induced phytohormones (61%, 49% and 30% decrease in Indole Acetic Acid (IAA), Gibberellic Acid (GA), and Cytokinin (CK)) respectively under stress, but an increase of 27.3% in Abscisic acid (ABA) concentration was observed. When inoculated, the combination of these strains improved seed germination, seedling vigor index, and promptness index by 18.2%, 23.7%, and 61.5% respectively under osmotic stress (20% polyethylene glycol, PEG6000). They also promoted plant growth in a pot experiment with an increase of 42.9%, 29.8%, and 33.7% in shoot length, root length, and leaf area, respectively. Physiological attributes of plants were also improved by bacterial inoculation showing an increase of 39.8%, 61.5%, and 45% in chlorophyll a, chlorophyll b, and carotenoid content respectively, as compared to control. Inoculations of bacterial strains also increased the production of osmolytes such asproline, amino acid, sugar, and protein by 30%, 23%, 68%, and 21.7% respectively. Co-inoculation of these strains enhanced the production of antioxidant enzymes such as superoxide dismutase (SOD) by 35.1%, catalase (CAT) by 77.4%, and peroxidase (POD) by 40.7%. Findings of the present research demonstrated that EPS, osmolyte, stress hormones, and antioxidant enzyme-producing bacterial strains impart drought tolerance in wheat and improve its growth, morphological attributes, physiological parameters, osmolytes production, and increase antioxidant enzymes.
Revision of Sustainable Road Rating Systems: Selection of the Best Suited System for Hungarian Road Construction Using TOPSIS Method
Authors: Szpotowicz (née Nádasi) Réka , Tóth Csaba
Published: October 2020

There are a number of sustainable and environmentally friendly techniques and methods currently available in the construction industry. To promote sustainable development, different rating and certificating systems that evaluate the level of sustainability during the development of infrastructure construction projects have been developed. The aim of the research presented in this paper was to examine the applicability of sustainability rating systems in Hungary and find the most suitable option. After a review of commonly used rating systems, i.e., Greenroads, GreenLITES, I-LAST, Envision, and INVEST, the most suitable existing rating system is selected with the help of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) mathematical decision analysis method. This was achieved by utilizing 12 categories of input data (weights) analyzed through TOPSIS. The input data (weights) of the TOPSIS method were determined by a small research group of industry experts and academic professionals based on Hungarian practices and methodology. As a result of the calculation, the study found that the Envision rating system satisfies the criteria best, closely followed by Greenroads.
Changes in Air Quality during the First-Level Response to the Covid-19 Pandemic in Shanghai Municipality, China
Authors: Bai Yang , Zhou Yi , Alatalo Juha M. , Hughes Alice C.
Published: October 2020

Ongoing rapid urban population growth world-wide has led to serious environmental problems that affect ecosystems and also lower the security and happiness of urban residents about their living environment. The most frequently reported negative impact is a deterioration in urban air quality. In this study, we performed a comprehensive assessment of the effects of the city lockdown policy in response to Covid-19 on air quality in Shanghai Municipality, China, and sought to identify a balance point between human activities and improving air quality. The first-level response (FLR) by Shanghai to control the spread of Covid-19 was to launch a lockdown, which remained in place from 24 January to 23 March, 2020. We compared airborne pollutant concentrations in different regions (downtown, suburbs) of Shanghai city in three periods (Pre-FLR, During-FLR, and Post-FLR) and in the corresponding periods in the previous year. The results showed that air quality improved significantly During-FLR compared with Pre-FLR, with the concentrations of PM2.5, PM10, SO2, NO2, and CO all decreasing significantly. The concentrations of all pollutants except O3 also decreased significantly compared with the same period in the previous year. There were also some differences in pollutant concentrations between the downtown region and the suburbs of Shanghai. However, we found that the concentrations of pollutants rebounded gradually when the restrictions on human activities ended after two months of lockdown. This study provides empirical evidence of the important effect of limiting human activities on air quality. For sustainable and clean future urban management in Shanghai and beyond, central government policy regulations requiring a low-carbon lifestyle and cleaner production in industries should be established.
Durability Characteristics of Concrete Mixture Based on Red Ceramic Waste Aggregate
Authors: Horňáková Marie , Lehner Petr , Le Tuan Duc , Konečný Petr , Katzer Jacek
Published: October 2020

The article aims to observe durability parameters of red ceramic waste aggregate concrete based on a measured chloride profile by the standardized NT Build 443 method, and to compare the results to the values measured by a test based on electrical resistivity measurements. The parameters related to the chloride ion diffusion are investigated on the new type of concrete designed in the previous project, which contains waste material–red ceramics fine aggregate, and artificial expanded clay coarse aggregate. Ceramic materials contribute to the highest percentage of the construction and demolition wastes and, in most cases, this type of waste is disposed of in landfills. Significant factors limiting the use of the studied material are the unavailability of standards, avoidance of risk, and lack of knowledge and experience in using ceramic wastes for construction purposes. The obtained results of the studied mixture are compared to a reference concrete in terms of mechanical properties and durability parameters. The calculated diffusion coefficient is a crucial input parameter for modeling of the degradation process and the prediction of concrete durability; therefore, proper identification is of interest in order to allow for a broader application of ceramic waste aggregate-based concrete. The research showed unproportionality of results measured by the two methods in the case of waste aggregate concrete (60% difference in comparison with reference concrete), therefore it was proved that the electrical resistivity measurements need correlation of the resulting diffusion coefficient for proper modeling.