bluuebunny/crossref_metadata_2025 huggingface dataset:
Proteomic Analysis Identifies Markers of Exposure to Cadmium Sulphide Quantum Dots (CdS QDs)
Authors: Gallo Valentina , Srivastava Vaibhav , Bulone Vincent , Zappettini Andrea , Villani Marco , Marmiroli Nelson , Marmiroli Marta
Published: June 2020

The use of cadmium sulphide quantum dot (CdS QD)-enabled products has become increasingly widespread. The prospect of their release in the environment is raising concerns. Here we have used the yeast model Saccharomyces cerevisiae to determine the potential impact of CdS QD nanoparticles on living organisms. Proteomic analyses and cell viability assays performed after 9 h exposure revealed expression of proteins involved in oxidative stress and reduced lethality, respectively, whereas oxidative stress declined, and lethality increased after 24 h incubation in the presence of CdS QDs. Quantitative proteomics using the iTRAQ approach (isobaric tags for relative and absolute quantitation) revealed that key proteins involved in essential biological pathways were differentially regulated over the time course of the experiment. At 9 h, most of the glycolytic functions increased, and the abundance of the number of heat shock proteins increased. This contrasts with the situation at 24 h where glycolytic functions, some heat shock proteins as well as oxidative phosphorylation and ATP synthesis were down-regulated. It can be concluded from our data that cell exposure to CdS QDs provokes a metabolic shift from respiration to fermentation, comparable to the situation reported in some cancer cell lines.
Near-Infrared Rewritable, Non-Volatile Subwavelength Absorber Based on Chalcogenide Phase Change Materials
Authors: Zhang Jianfa , Zhang Yiqiong , Hong Qilin , Xu Wei , Zhu Zhihong , Yuan Xiaodong
Published: June 2020

Chalcogenide phase change materials enable the realization of novel, non-volatile, switchable electronic and photonic devices. In this paper, we propose a type of rewritable, non-volatile near infrared subwavelength absorber based on chalcogenide phase change materials. Our numerical simulations show that nearly perfect absorption more than 0.99 can be realized in the written state while the absorption of as-deposited or erased state is lower than 0.15 in the studied spectral range, leading to high contrast ratio of reflection more than 20 dB. Continuous tuning of the absorption spectra can be realized not only by varying the geometric parameters of the absorber but also by changing the crystallization ratio of the switched Ge 2 Sb 2 Te 5 (GST). The proposed device may find widespread applications in optical modulation, beam steering and so on.
Anti-Adipogenic Effect of Neferine in 3T3-L1 Cells and Primary White Adipocytes
Authors: Park Miey , Han Jinyoung , Lee Hae-Jeung
Published: June 2020

Neferine, an alkaloid component extracted from lotus seed embryos, is known for its anti-inflammatory, anticancer, and antioxidant properties. However, the anti-adipogenic activity of neferine has not been thoroughly investigated. In this study, neferine was found to inhibit lipid accumulation in a dose-dependent manner during the differentiation of 3T3-L1 cells without inducing cytotoxicity. Real-time polymerase chain reaction and immunoblot analysis revealed the downregulation in the expression of peroxisome proliferator activated receptor gamma (PPARγ), CCAAT/enhancer-binding protein alpha (C/EBPα), sterol regulatory element-binding protein-1c (SREBP-1c), and fatty acid synthase (FAS) and the upregulation in carnitine palmitoyltransferase-1 (CPT-1) and sirtuin 1 (SIRT1) levels following neferine treatment. Furthermore, neferine increased the phosphorylation of adenosine monophosphate-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC), which is an important regulator of fatty acid oxidation. Our result indicates that neferine attenuates adipogenesis and promotes lipid metabolism by activating AMPK-mediated signaling. Therefore, neferine may serve as a therapeutic candidate for obesity treatment.
Strategies to Improve Health Communication: Can Health Professionals Be Heroes?
Authors: Jenkins Eva L. , Ilicic Jasmina , Molenaar Annika , Chin Shinyi , McCaffrey Tracy A.
Published: June 2020

Communicating evidence-based nutrition messages to the public is challenging and is often in conflict with popular opinions, particularly from social media influencers (SMIs). In order to increase engagement with nutrition professionals (NPs) on social media, we aimed to explore young adults’ perceptions of the authenticity and trustworthiness of SMIs and NPs Instagram posts. A cross-sectional questionnaire was administered to students (n = 149) from an Australian University. Participants viewed a real-life Instagram profile and one post from both a NP and a SMI. Main outcomes were post authenticity and trustworthiness, and emotional message appeals measured on five-point Likert scales. Regression models were developed to assess whose post (the NP or SMI) was perceived to be more authentic and trustworthy. Participants were young adults (median age (25th, 75th percentiles): 20 (19,21)), with approximately half identifying as female. A high heroic message appeal (+1SD above mean) significantly increased the perceived authenticity of the NPs post only (p = 0.01). Post authenticity enhanced post trustworthiness, but only when a heroic message appeal was used by the NP. When appropriate, NPs should convey positive emotions such as bravery and success to enhance the authenticity and trustworthiness of their posts.
Diet Quality and Upper Gastrointestinal Cancers Risk: A Meta-Analysis and Critical Assessment of Evidence Quality
Authors: Moazzen Sara , van der Sloot Kimberley W. J. , Vonk Roel J. , de Bock Geertruida H. , Alizadeh Behrooz Z.
Published: June 2020

We aimed to assess the effect of a high-quality diet on the risk of upper gastrointestinal cancer and to evaluate the overall quality of our findings by searching PubMed, EMBASE, Web of Science, Cochrane, and the references of related articles to February 2020. Two reviewers independently retrieved the data and performed the quality assessments. We defined the highest-quality diet as that with the lowest Diet Inflammatory Index category and the highest Mediterranean Diet Score category. Overall odds ratios and 95% confidence intervals were estimated for upper gastrointestinal cancer risk comparing the highest- versus lowest-diet quality. A random-effects meta-analysis was then applied with Review Manager, and the quality of the overall findings was evaluated with the Grading of Recommendations Assessment, Development, and Evaluation approach. The highest-quality diets were significantly associated with reduced risk of upper gastrointestinal cancers, achieving odds ratios of 0.59 (95% confidence interval: 0.48–0.72) for the Diet Inflammatory Index, pooling the findings from nine studies, and 0.72 (95% confidence interval: 0.61–0.88) for the Mediterranean Diet Score, pooling the findings from 11 studies. We observed a minimum of 69% heterogeneity in the pooled results. The pooled results were graded as low quality of evidence. Although it may be possible to offer evidence-based general dietary advice for the prevention of upper gastrointestinal cancers, the evidence is currently of insufficient quality to develop dietary recommendations.
Role of Minerals and Trace Elements in Diabetes and Insulin Resistance
Authors: Dubey Pallavi , Thakur Vikram , Chattopadhyay Munmun
Published: June 2020

Minerals and trace elements are micronutrients that are essential to the human body but present only in traceable amounts. Nonetheless, they exhibit well-defined biochemical functions. Deficiencies in these micronutrients are related to widespread human health problems. This review article is focused on some of these minerals and trace element deficiencies and their consequences in diabetes and insulin resistance. The levels of trace elements vary considerably among different populations, contingent on the composition of the diet. In several Asian countries, large proportions of the population are affected by a number of micronutrient deficiencies. Local differences in selenium, zinc, copper, iron, chromium and iodine in the diet occur in both developed and developing countries, largely due to malnutrition and dependence on indigenous nutrition. These overall deficiencies and, in a few cases, excess of essential trace elements may lead to imbalances in glucose homeostasis and insulin resistance. The most extensive problems affecting one billion people or more worldwide are associated with inadequate supply of a number of minerals and trace elements including iodine, selenium, zinc, calcium, chromium, cobalt, iron, boron and magnesium. This review comprises various randomized controlled trials, cohort and case-controlled studies, and observational and laboratory-based studies with substantial outcomes of micronutrient deficiencies on diabetes and insulin resistance in diverse racial inhabitants from parts of Asia, Africa, and North America. Changes in these micronutrient levels in the serum and urine of subjects may indicate the trajectory toward metabolic changes, oxidative stress and provide disease-relevant information.
Participants’ Experiences of the 2018–2019 Government Shutdown and Subsequent Supplemental Nutrition Assistance Program (SNAP) Benefit Disruption Can Inform Future Policy
Authors: Gosliner Wendi , Chen Wei-Ting , Johnson Cathryn , Esparza Elsa Michelle , Price Natalie , Hecht Ken , Ritchie Lorrene
Published: June 2020

The federal government shutdown from 22 December 2018 to 25 January 2019 created an unprecedented disruption in Supplemental Nutrition Assistance Program (SNAP) benefits. We conducted a cross-sectional qualitative study to begin to capture how the disruption affected food security and wellbeing among a small sample of California SNAP participants. We collected data from 26 low-income adults in four focus groups in four diverse California counties. We found that participants routinely struggle to secure an adequate and healthy diet in the context of high costs of living, the shutdown and benefit disruption added to participants’ stress and uncertainty and exacerbated food insecurity, and it diminished some participants’ faith in government. Participants reported that, while having additional benefits in January felt like a relief from typical end-of-month deprivation, the subsequent extended gap between benefit distributions and a lack of clarity about future benefits caused cascading effects as participants later had to divert money from other expenses to buy food and faced added uncertainty about future economic stability. Additionally, the shutdown highlighted challenges related to the availability, timing, and tone of communications between participants and SNAP agencies. Participants recommended that SNAP adjust benefit and eligibility levels to better address costs of living, improve customer service, and avoid future disruptions.
Vitamin D Serum Levels in the UK Population, including a Mathematical Approach to Evaluate the Impact of Vitamin D Fortified Ready-to-Eat Breakfast Cereals: Application of the NDNS Database
Authors: Calame Wim , Street Laura , Hulshof Toine
Published: June 2020

Vitamin D status is relatively poor in the general population, potentially leading to various conditions. The present study evaluates the relationship between vitamin D status and intake in the UK population and the impact of vitamin D fortified ready-to-eat cereals (RTEC) on this status via data from the National Diet and Nutrition Survey (NDNS: 2008–2012). Four cohorts were addressed: ages 4–10 (n = 803), ages 11–18 (n = 884), ages 19–64 (n = 1655) and ages 65 and higher (n = 428). The impact of fortification by 4.2 μg vitamin D per 100 g of RTEC on vitamin D intake and status was mathematically modelled. Average vitamin D daily intake was age-dependent, ranging from ~2.6 (age range 4–18 years) to ~5.0 μg (older than 64 years). Average 25(OH)D concentration ranged from 43 to 51 nmol/L, the highest in children. The relationship between vitamin D intake and status followed an asymptotic curve with a predicted plateau concentration ranging from 52 in children to 83 nmol/L in elderly. The fortification model showed that serum concentrations increased with ~1.0 in children to ~6.5 nmol/L in the elderly. This study revealed that vitamin D intake in the UK population is low with 25(OH)D concentrations being suboptimal for general health. Fortification of breakfast cereals can contribute to improve overall vitamin D status.
Classical Swine Fever Virus Biology, Clinicopathology, Diagnosis, Vaccines and a Meta-Analysis of Prevalence: A Review from the Indian Perspective
Authors: Malik Yashpal Singh , Bhat Sudipta , Kumar O. R. Vinodh , Yadav Ajay Kumar , Sircar Shubhankar , Ansari Mohd Ikram , Sarma Dilip Kumar , Rajkhowa Tridib Kumar , Ghosh Souvik , Dhama Kuldeep
Published: June 2020

Classical swine fever (CSF) is an economically significant, multi-systemic, highly contagious viral disease of swine world over. The disease is notifiable to the World Organization for Animal Health (OIE) due to its enormous consequences on porcine health and the pig industry. In India, the pig population is 9.06 million and contributes around 1.7% of the total livestock population. The pig industry is not well organized and is mostly concentrated in the eastern and northeastern states of the country (~40% of the country’s population). Since the first suspected CSF outbreak in India during 1944, a large number of outbreaks have been reported across the country, and CSF has acquired an endemic status. As of date, there is a scarcity of comprehensive information on CSF from India. Therefore, in this review, we undertook a systematic review to compile and evaluate the prevalence and genetic diversity of the CSF virus situation in the porcine population from India, targeting particular virus genes sequence analysis, published reports on prevalence, pathology, and updates on indigenous diagnostics and vaccines. The CSF virus (CSFV) is genetically diverse, and at least three phylogenetic groups are circulating throughout the world. In India, though genotype 1.1 predominates, recently published reports point toward increasing evidence of co-circulation of sub-genotype 2.2 followed by 2.1. Sequence identities and phylogenetic analysis of Indian CSFV reveal high genetic divergence among circulating strains. In the meta-analysis random-effects model, the estimated overall CSF prevalence was 35.4%, encompassing data from both antigen and antibody tests, and region-wise sub-group analysis indicated variable incidence from 25% in the southern to nearly 40% in the central zone, eastern, and northeastern regions. A country-wide immunization approach, along with other control measures, has been implemented to reduce the disease incidence and eliminate the virus in time to come.
Multi-Step Ubiquitin Decoding Mechanism for Proteasomal Degradation
Authors: Tsuchiya Hikaru , Endo Akinori , Saeki Yasushi
Published: June 2020

The 26S proteasome is a 2.5-MDa protease complex responsible for the selective and ATP-dependent degradation of ubiquitylated proteins in eukaryotic cells. Proteasome-mediated protein degradation accounts for ~70% of all cellular proteolysis under basal conditions, and thereby any dysfunction can lead to drastic changes in cell homeostasis. A major function of ubiquitylation is to target proteins for proteasomal degradation. Accompanied by deciphering the structural diversity of ubiquitin chains with eight linkages and chain lengths, the ubiquitin code for proteasomal degradation has been expanding beyond the best-characterized Lys48-linked ubiquitin chains. Whereas polyubiquitylated proteins can be directly recognized by the proteasome, in several cases, these proteins need to be extracted or segregated by the conserved ATPases associated with diverse cellular activities (AAA)-family ATPase p97/valosin-containing protein (VCP) complex and escorted to the proteasome by ubiquitin-like (UBL)–ubiquitin associated (UBA) proteins; these are called substrate-shuttling factors. Furthermore, proteasomes are highly mobile and are appropriately spatiotemporally regulated in response to different cellular environments and stresses. In this review, we highlight an emerging key link between p97, shuttling factors, and proteasome for efficient proteasomal degradation. We also present evidence that proteasome-containing nuclear foci form by liquid–liquid phase separation under acute hyperosmotic stress.
The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece
Authors: Tsiotas Dimitrios , Magafas Lykourgos
Published: June 2020

Within the context of Greece promising a success story in the fight against the disease, this paper proposes a novel method for studying the evolution of the Greek COVID-19 infection curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spread of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection curve tends towards saturation. In terms of Gaussian modeling, this successive compression of the COVID-19 infection curve into five parts implies that the pandemic in Greece is about to reach the second (decline) half of the bell-shaped distribution. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability of meeting maximum (infection) values in the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19.
Data Science Tools for Monitoring the Global Repository Eco-System and its Lines of Evolution
Authors: Summann Friedrich , Czerniak Andreas , Schirrwagen Jochen , Pieper Dirk
Published: June 2020

The global network of scholarly repositories for the publication and dissemination of scientific publications and related materials can already look back on a history of more than twenty years. During this period, there have been many developments in terms of technical optimization and the increase of content. It is crucial to observe and analyze this evolution in order to draw conclusions for the further development of repositories. The basis for such an analysis is data. The Open Archives Initiative (OAI) service provider Bielefeld Academic Search Engine (BASE) started indexing repositories in 2004 and has collected metadata also on repositories. This paper presents the main features of a planned repository monitoring system. Data have been collected since 2004 and includes basic repository metadata as well as publication metadata of a repository. This information allows an in-depth analysis of many indicators in different logical combinations. This paper outlines the systems approach and the integration of data science techniques. It describes the intended monitoring system and shows the first results.
Academization of Pious Learning: A Student’s Quest in Religious Education
Authors: Groeninck Mieke
Published: June 2020

This paper focuses on the move towards “academization” of Islamic religious education in private institutes belonging to the reform movement in Brussels. An attempt is made to think through this move in terms of the sacred knowledge concerned, and the alleged implications for teachers and students of Islam. Some of the crucial elements that go with this shift are the aspiration for “distantiation” in teaching and knowing aspects of internal diversity, as well as the aspired changes in the professor–student (instead of shaykh–disciple) relationship. By focusing on ethnographic examples, the aim is to contribute to our understanding of the importance of the internal debates instigated by an attempt towards academization, the search for coherence that goes with it, its repercussions on people’s daily life and personal sensibilities, as well as on Islamic expert authority formation.
The Female Line in the Bible. Ratzinger’s Deepening of the Church’s Understanding of Tradition and Mary
Authors: McKenna Mary Frances
Published: June 2020

This paper explores the female line in the Bible that Joseph Ratzinger identifies as running in parallel to, and being indispensable for, the male line in the Bible. This female line expands the understanding of Salvation History as described by Dei Verbum so that it runs not just from Adam through to Jesus, but also from Adam and Eve to Mary and Jesus, the final Adam. Ratzinger’s female line demonstrates that women are at the heart of God’s plan for humanity. I illustrate that this line is evident when Ratzinger’s method of biblical interpretation is applied to the women of Scripture. Its full potential comes into view through Ratzinger’s development of the Christian notion of person: Person as revealed by Jesus Christ is relatedness without reserve with God and is fully applicable to the human being through Christ. I argue that together, the male and female lines in the Bible form the human line in the Bible, in which the male line represents “the humanity”, every human being, while the female line represents the communal aspect of humanity. Moreover, I contend that Christianity’s notion of mother in relation to God (as Father, Son and Holy Spirit) should be understood through Mary’s response at the Annunciation. Mother in relation to God is to be understood through the Incarnation when Mary, as person, lived her life wholly in relation with and for God.
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
Authors: Hegarty-Craver Meghan , Polly Jason , O’Neil Margaret , Ujeneza Noel , Rineer James , Beach Robert H. , Lapidus Daniel , Temple Dorota S.
Published: June 2020

Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover.
Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model
Authors: Orellana-Alvear Johanna , Célleri Rolando , Rollenbeck Rütger , Muñoz Paul , Contreras Pablo , Bendix Jörg
Published: June 2020

Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.
Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)
Authors: Rosero-Montalvo Paul D. , López-Batista Vivian F. , Riascos Jaime A. , Peluffo-Ordóñez Diego H.
Published: June 2020

This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption.
Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point
Authors: Sánchez-Rodríguez David , Quintana-Suárez Miguel A. , Alonso-González Itziar , Ley-Bosch Carlos , Sánchez-Medina Javier J.
Published: June 2020

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.
Parametric Synthetic Aperture Radar Image Recovery for Multiple Linear Structures: An Image Domain Approach
Authors: Wen Yuhan , Wang Yan , Ding Zegang , Zeng Tao , Chen Xinliang
Published: June 2020

The linear structures in synthetic aperture radar (SAR) images can provide important geometric information regarding the illuminated objects. However, the linear structures of various objects sometimes disappear in traditional SAR images, which severely affects the application in automated scene analysis and information extraction techniques. This paper proposes a parametric SAR image recovery method for linear structures of extended targets. By extracting the spatial phase distribution feature in image domain, the proposed method is used to identify the endpoints among all of the scattered points in SAR images and reconstruct the disappeared linear structures based on the scattering model. The method can be generally divided into three procedures: endpoints and point targets classification, linear structures recognition, and linear structures reconstruction. In the first step, the endpoints of linear structures and point targets are classified by setting a threshold related to the spatial phase distribution feature. Afterwards, the linear structures recognition method is used to determine which two endpoints can be formed into a linear structure. Finally, the parametric scattering models are used to reconstruct the disappeared linear structures. Experiments are conducted on both computer simulations and the data that were acquired by microwave anechoic chamber experiment, tower crane radar experiment, and unmanned vehicle radar experiment in order to validate the effectiveness and robustness of the proposed method.
Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models
Authors: Cui Lijuan , Dou Zhiguo , Liu Zhijun , Zuo Xueyan , Lei Yinru , Li Jing , Zhao Xinsheng , Zhai Xiajie , Pan Xu , Li Wei
Published: June 2020

Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.
Satellite Remote Sensing for the Analysis of the Micia and Germisara Archaeological Sites
Authors: Dana Negula Iulia , Moise Cristian , Lazăr Andi Mihai , Rișcuța Nicolae Cătălin , Cristescu Cătălin , Dedulescu Andreea Luminița , Mihalache Cristina Elena , Badea Alexandru
Published: June 2020

The capabilities of satellite remote sensing technologies and their derived data for the analysis of archaeological sites have been demonstrated in a large variety of studies over the last decades. Likewise, the Earth Observation (EO) data contribute to the disaster management process through the provision of updated information for areas under investigation. In addition, long term studies may be performed for the in–depth analysis of the disaster–prone areas using archive satellite imagery and other cartographic materials. Hence, satellite remote sensing represents an essential tool for the study of hazards in cultural heritage sites and landscapes. Depending on the size of the archaeological sites and considering the fact that some parts of the site might be covered, the main concern regards the suitability of satellite data in terms of spatial and spectral resolution. Using a multi–temporal Sentinel–2 dataset between 2016 and 2019, the present study focuses on the hazard risk identification for the Micia and Germisara archaeological sites in Romania as they are endangered by industrialisation and major infrastructure works and soil erosion, respectively. Furthermore, the study includes a performance assessment of remote sensing vegetation indices for the detection of buried structures. The results clearly indicate that Sentinel–2 imagery proved to be fundamental in meeting the objectives of the study, particularly due to the extensive archaeological knowledge that was available for the cultural heritage sites. The main conclusion to be drawn is that satellite–derived products may be enhanced by integrating valuable archaeological context, especially when the resolution of satellite data is not ideally fitting the peculiarities (e.g., in terms of size, underground structures, type of coverage) of the investigated cultural heritage sites.
Performing Calibration of Transmittance by Single RGB-LED within the Visible Spectrum
Authors: Carreres-Prieto Daniel , García Juan T. , Cerdán-Cartagena Fernando , Suardiaz-Muro Juan
Published: June 2020

Spectrophotometry has proven to be an effective non-invasive technique for the characterization of the pollution load of sewer systems, enabling compliance with new environmental protection regulations. This type of equipment has costs and an energy consumption which make it difficult to place it inside a sewer network for real-time and massive monitoring. These shortcomings are mainly due to the use of incandescent lamps to generate the working spectrum as they often require the use of optical elements, such as diffraction gratings, to work. The search for viable alternatives to incandescent lamps is key to the development of portable equipment that is cheaper and with a lower consumption that can be used in different points of the sewer network. This research work achieved the following results in terms of the measured samples: First, the development a calibration procedure that enables the use of RGB-LED technology as a viable alternative to incandescent lamps, within the range of 510 to 645 nm, with high accuracy. Secondly, demonstration of a simple method to model the transmittance value of a specific wavelength without the need for optical elements, achieving a cost-effective equipment. Thirdly, it provides a simple method to obtain the transmittance based on the combination of RGB colors. Finally its viability is demonstrated for the spectral analysis of wastewater.
Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study
Authors: Soto-Ocampo César Ricardo , Mera José Manuel , Cano-Moreno Juan David , Garcia-Bernardo José Luis
Published: June 2020

Data acquisition is a crucial stage in the execution of condition monitoring (CM) of rotating machinery, by means of vibration analysis. However, the major challenge in the execution of this technique lies in the features of the recording equipment (accuracy, resolution, sampling frequency and number of channels) and the cost they represent. The present work proposes a low-cost data acquisition system, based on Raspberry-Pi, with a high sampling frequency capacity in the recording of up to three channels. To demonstrate the effectiveness of the proposed data acquisition system, a case study is presented in which the vibrations registered in a bearing are analyzed for four degrees of failure.
On Exploiting Millimeter-Wave Spectrum Trading in Countrywide Mobile Network Operators for High Spectral and Energy Efficiencies in 5G/6G Era
Authors: Saha Rony Kumer
Published: June 2020

In this paper, we propose a dynamic exclusive-use spectrum access (DESA) method to improve the overall licensed millimeter-wave (mmWave) spectrum utilization of all mobile network operators (MNOs) in a country. By exploiting secondary spectrum trading, the proposed DESA method shares partly and exclusively the licensed mmWave spectrum of one MNO to another in a dynamic and on-demand basis for a certain agreement term. We formulate the proposed DESA method for an arbitrary number of MNOs in a country. We then present an iterative algorithm to find the optimal amount of shared spectrum for each MNO, which is updated at each agreement term. We derive average capacity, spectral efficiency, energy efficiency, and cost efficiency performance metrics for all MNOs countrywide and present extensive numerical and simulation results and analyses for an example scenario of a country with four MNOs each assigned statically with an equal amount of 28-GHz mmWave spectrum. By applying DESA, we show that MNOs with a lack of minimum licensed spectra to serve their data traffic can lease at the cost of payment of the required additional spectra from other MNOs having unused or under-utilized licensed spectra. Moreover, it is shown that the overall countrywide average capacity, spectral efficiency, energy efficiency, and cost efficiency can be improved, respectively, by 25%, 25%, 17.5%, and 20%. Furthermore, we show that, by applying DESA to all MNOs countrywide, the expected spectral efficiency and energy efficiency requirements for sixth-generation (6G) mobile systems can be achieved by reusing the same mmWave spectrum to 20% fewer buildings of small cells. Finally, using the statistics of subscribers of all MNOs, we present a case study for fifth-generation (5G) networks to demonstrate the application of the proposed DESA method to an arbitrary country of four MNOs.
GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Chan Wensong , Tian Zhiqiang , Wu Yang
Published: June 2020

Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connections between joints, which contain discriminative information for different actions. In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action. In addition, to filter out the useless and redundant information in the temporal dimension, we propose a simple yet effective operation named gated temporal convolution. These two major novelties ensure the superiority of our proposed method, as demonstrated on three large-scale public datasets: NTU-RGB + D, Kinetics, and NTU-RGB + D 120, and also shown in the detailed ablation studies.
Spectral Characteristics and Sensor Ability of a New 1,8-Naphthalimide and Its Copolymer with Styrene
Authors: Staneva Desislava , Angelova Silvia , Grabchev Ivo
Published: June 2020

In this study, a novel 6-(allylamino)-2-(2-(dimethylamino)ethyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (NI3) was synthesized and characterized. Its copolymer with styrene was also obtained. The photophysical characteristics of NI3 were investigated in organic solvents and the results were compared with those of its structural analogue, 2-allyl-6-((2-(dimethylamino)ethyl)amino)-1H-benzo[de]isoquinoline-1,3(2H)-dione (NI4). The influences of the pH in the medium and different metal ions on the fluorescent intensity of monomers and polymers were also investigated. Computational tools (DFT and TDDFT calculations) were employed when studying the structure and properties of the 1,8-naphthalimide-based chromophores. Although the position of the N,N-dimethylaminoethylamine receptor fragment did not significantly impact proton detection, it was still important for detecting metal ion sensor ability, especially for monomeric 1,8-naphthalimide structures and their copolymers with styrene.
Congestion Control and Traffic Differentiation for Heterogeneous 6TiSCH Networks in IIoT
Authors: Farag Hossam , Österberg Patrik , Gidlund Mikael
Published: June 2020

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.
Wireless Sensor Networks Fault-Tolerance Based on Graph Domination with Parallel Scatter Search
Authors: Hedar Abdel-Rahman , Abdulaziz Shada N. , Mabrouk Emad , El-Sayed Gamal A.
Published: June 2020

In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel by transmitting control messages causing “broadcast storm problem”. Thus, inspired by the physical backbone in wired networks, a Virtual Backbone (VB) in wireless sensor/ad hoc networks can help achieve efficient broadcasting. A well-known and well-researched approach for constructing virtual backbone is solving the Connected Dominating Set (CDS) problem. Furthermore, minimizing the size of the CDS is a significant research issue. We propose a new parallel scatter search algorithm with elite and featured cores for constructing a wireless sensor/ad hoc network virtual backbones based on finding minimum connected dominating sets of wireless nodes. Also, we addressed the problem of VB node/nodes failure by either deploying a previously computed VBs provided by the main pSSEF algorithm that does not contain the failed node/nodes, or by using our proposed FT-pSSEF algorithm repairing the broken VBs. Finally, as nodes in a VB incur extra load of communication and computation, this leads to faster power consumption compared to other nodes in the network. Consequently, we propose the virtual backbone scheduling algorithm SC-pSSEF which aims to find multiple VBs using the VBs provided by the pSSEF algorithm and switch between them periodically to prolong the network life time.
Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification
Authors: Pinto Gisela , Carvalho João M. , Barros Filipa , Soares Sandra C. , Pinho Armando J. , Brás Susana
Published: June 2020

Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples’ emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.