bluuebunny/crossref_metadata_2025 huggingface dataset:
First-Principles Insight into Pd-Doped C3N Monolayer as a Promising Scavenger for NO, NO2 and SO2
Authors: Peng Ruochen , Zhou Qu , Zeng Wen
Published: May 2021

The adsorption and sensing behavior of three typical industrial toxic gases NO, NO2 and SO2 by the Pd modified C3N monolayer were studied in this work on the basic first principles theory. Meanwhile, the feasibility of using the Pd doped C3N monolayer (Pd-C3N) as a sensor and adsorbent for industrial toxic gases was discussed. First, the binding energies of two doping systems were compared when Pd was doped in the N-vacancy and C-vacancy sites of C3N to choose the more stable doping structure. The result shows that the doping system is more stable when Pd is doped in the N-vacancy site. Then, on the basis of the more stable doping model, the adsorption process of NO, NO2 and SO2 by the Pd-C3N monolayer was simulated. Observing the three gases adsorption systems, it can be found that the gas molecules are all deformed, the adsorption energy (Ead) and charge transfer (QT) of three adsorption systems are relatively large, especially in the NO2 adsorption system. This result suggests that the adsorption of the three gases on Pd-C3N belongs to chemisorption. The above conclusions can be further confirmed by subsequent deformable charge density (DCD) and density of state (DOS) analysis. Besides, through analyzing the band structure, the change in electrical conductivity of Pd-C3N after gas adsorption was studied, and the sensing mechanism of the resistive Pd-C3N toxic gas sensor was obtained. The favorable adsorption properties and sensing mechanism indicate that the toxic gas sensor and adsorbent prepared by Pd-C3N have great application potential. Our work may provide some guidance for the application of a new resistive sensor and gas adsorbent Pd-C3N in the field of toxic gas monitoring and adsorption.
Joint Effort towards Preventing Nutritional Deficiencies at the Extremes of Life during COVID-19
Authors: Spolidoro Giulia C. I. , Azzolino Domenico , Shamir Raanan , Cesari Matteo , Agostoni Carlo
Published: May 2021

The COVID-19 (Coronavirus disease 2019) pandemic is posing a threat to communities and healthcare systems worldwide. Malnutrition, in all its forms, may negatively impact the susceptibility and severity of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection in both children and older adults. Both undernutrition and obesity have been evoked as conditions associated with a higher susceptibility to the infection and poor prognosis. In turn, the COVID-19 infection may worsen the nutritional status through highly catabolic conditions, exposing individuals to the risk of malnutrition, muscle wasting, and nutritional deficiencies. Accordingly, the relationship between malnutrition and COVID-19 is likely to be bidirectional. Furthermore, the modification of nutritional behaviors and physical activity, required to limit the spread of the virus, are posing a challenge to health at both the extremes of life. Thus far, even the most advanced healthcare systems have failed to address the alarming consequences of malnutrition posed by this pandemic. If not properly addressed, we may run the risk that new and old generations will experience the consequences of COVID-19 related malnutrition.
Investigation of Chlorella pyrenoidosa Protein as a Source of Novel Angiotensin I-Converting Enzyme (ACE) and Dipeptidyl Peptidase-IV (DPP-IV) Inhibitory Peptides
Authors: Li Yuchen , Aiello Gilda , Fassi Enrico Mario Alessandro , Boschin Giovanna , Bartolomei Martina , Bollati Carlotta , Roda Gabriella , Arnoldi Anna , Grazioso Giovanni , Lammi Carmen
Published: May 2021

Chlorella pyrenoidosa (C. pyrenoidosa) is a microalgae species with a remarkably high protein content that may potentially become a source of hypotensive and hypoglycemic peptides. In this study, C. pyrenoidosa proteins were extracted and hydrolyzed overnight with pepsin and trypsin with final degrees of hydrolysis of 18.7% and 35.5%, respectively. By LC-MS/MS, 47 valid peptides were identified in the peptic hydrolysate (CP) and 66 in the tryptic one (CT). At the concentration of 1.0 mg/mL, CP and CT hydrolysates inhibit in vitro the angiotensin-converting enzyme (ACE) activity by 84.2 ± 0.37% and 78.6 ± 1.7%, respectively, whereas, tested at cellular level at the concentration of 5.0 mg/mL, they reduce the ACE activity by 61.5 ± 7.7% and 69.9 ± 0.8%, respectively. At the concentration of 5.0 mg/mL, they decrease in vitro the DPP-IV activity by 63.7% and 69.6% and in Caco-2 cells by 38.4% and 42.5%, respectively. Short peptides (≤10 amino acids) were selected for investigating the potential interaction with ACE and DPP-IV by using molecular modeling approaches and four peptides were predicted to block both enzymes. Finally, the stability of these peptides was investigated against gastrointestinal digestion.
A Nosocomial Outbreak of Invasive Listeriosis in An Italian Hospital: Epidemiological and Genomic Features
Authors: Russini Valeria , Spaziante Martina , Zottola Tiziana , Fermani Anna Giovanna , Di Giampietro Gina , Blanco Giovanni , Fabietti Paolo , Marrone Riccardo , Parisella Roberta , Parrocchia Sergio , Bossù Teresa , Bilei Stefano , De Marchis Maria Laura
Published: May 2021

Listeria monocytogenes (L. monocytogenes) is a widespread opportunistic pathogen that causes the listeriosis foodborne disease. This bacterium has become a common contaminant of handled food, and a relevant public health issue. Here we describe a nosocomial outbreak of listeriosis caused by an ST451 strain of L. monocytogenes involving three cancer and one immunocompromised patients hospitalized in different units from the same hospital during September and October 2020. The epidemiological investigation was conducted using traditional microbiological methodology combined with a whole genome sequencing approach. The source of contamination was identified in the kitchen hospital, where a meat slicer used to prepare patients’ meals was tested positive to the same sequence type (ST) of L. monocytogenes. This is the first report of an outbreak of listeriosis caused by ST451 in Italy.
Genetic and Epigenetic Alterations of CDH1 Regulatory Regions in Hereditary and Sporadic Gastric Cancer
Authors: Tedaldi Gianluca , Molinari Chiara , São José Celina , Barbosa-Matos Rita , André Ana , Danesi Rita , Arcangeli Valentina , Ravegnani Mila , Saragoni Luca , Morgagni Paolo , Rebuzzi Francesca , Canale Matteo , Pignatta Sara , Ferracci Elisa , Martinelli Giovanni , Ranzani Guglielmina Nadia , Oliveira Carla , Calistri Daniele , Ulivi Paola
Published: May 2021

E-cadherin is a key player in gastric cancer (GC) and germline alterations of CDH1, its encoding gene, are responsible for Hereditary Diffuse Gastric Cancer (HDGC) syndrome. This study aimed at elucidating the role of genetic variants and DNA methylation of CDH1 promoter and enhancers in the regulation of gene expression. For this purpose, we analyzed genetic variants of the CDH1 gene through Next-Generation Sequencing (NGS) in a series of GC cell lines (NCI-N87, KATO-III, SNU-1, SNU-5, GK2, AKG, KKP) and the corresponding CDH1 expression levels. By bisulfite genomic sequencing, we analyzed the methylation status of CDH1 regulatory regions in 8 GC cell lines, in a series of 13 sporadic GC tissues and in a group of 20 HDGC CDH1-negative patients and 6 healthy controls. The NGS analysis on CDH1 coding and regulatory regions detected genetic alterations in 3 out of 5 GC cell lines lacking functional E-cadherin. CDH1 regulatory regions showed different methylation patterns in patients and controls, GC cell lines and GC tissues, expressing different E-cadherin levels. Our results showed that alterations in terms of genetic variants and DNA methylation patterns of both promoter and enhancers are associated with CDH1 expression levels and have a role in its regulation.
Tambjamines and Prodiginines: Biocidal Activity against Trypanosoma cruzi
Authors: Herráez Rocío , Quesada Roberto , Dahdah Norma , Viñas Miguel , Vinuesa Teresa
Published: May 2021

The aim of this work was to explore new therapeutic options against Chagas disease by the in vitro analysis of the biocidal activities of several tambjamine and prodiginine derivatives, against the Trypanosoma cruzi CLB strain (DTU TcVI). The compounds were initially screened against epimastigotes. The five more active compounds were assayed in intracellular forms. The tambjamine MM3 and both synthetic and natural prodigiosins displayed the highest trypanocidal profiles, with IC50 values of 4.52, 0.46, and 0.54 µM for epimastigotes and 1.9, 0.57, and 0.1 µM for trypomastigotes/amastigotes, respectively. Moreover, the combination treatment of these molecules with benznidazole showed no synergism. Finally, oxygen consumption inhibition determinations performed using high-resolution respirometry, revealed a potent effect of prodigiosin on parasite respiration (73% of inhibition at ½ IC50), suggesting that its mode of action involves the mitochondria. Moreover, its promising selectivity index (50) pointed out an interesting trypanocidal potential and highlighted the value of prodigiosin as a new candidate to fight Chagas disease.
So Much for Glucosinolates: A Generalist Does Survive and Develop on Brassicas, but at What Cost?
Authors: Jeschke Verena , Zalucki Jacinta M. , Raguschke Bettina , Gershenzon Jonathan , Heckel David G. , Zalucki Myron P. , Vassão Daniel G.
Published: May 2021

While plants produce complex cocktails of chemical defences with different targets and efficacies, the biochemical effects of phytotoxin ingestion are often poorly understood. Here, we examine the physiological and metabolic effects of the ingestion of glucosinolates (GSLs), the frontline chemical defenses of brassicas (crucifers), on the generalist herbivore Helicoverpa armigera. We focus on kale and cabbage, two crops with similar foliar GSL concentrations but strikingly different GSL compositions. We observed that larval growth and development were well correlated with the nutritional properties of the insect diets, with low protein contents appearing to exacerbate the negative effects of GSLs on growth, pupation and adult eclosion, parameters that were all delayed upon exposure to GSLs. The different GSLs were metabolized similarly by the insect, indicating that the costs of detoxification via conjugation to glutathione (GSH) were similar on the two plant diets. Nevertheless, larval GSH contents, as well as some major nutritional markers (larval protein, free amino acids, and fat), were differentially affected by the different GSL profiles in the two crops. Therefore, the interplay between GSL and the nitrogen/sulfur nutritional availability of different brassicas strongly influences the effectiveness of these chemical defenses against this generalist herbivore.
Fabrication of Magnetic Catalyst Fe3O4-SiO2-V3 and Its Application on Lignin Extraction from Corncob in Deep Eutectic Solvent
Authors: Yuan Maonan , Wang Zhen , Liu Yu , Yang Guihua
Published: May 2021

Fe3O4-SiO2-V3 was prepared by deposited H6PMo9V3O40 on Fe3O4-SiO2 and employed as a catalyst to extract lignin from corncob in deep eutectic solvent (choline chloride/lactic acid = 1/10). Batch experiments were conducted in an autoclave under the conditions of 500 kPa, 90–130 °C and 15 h, while the dosage of the catalyst was set as a variable. Results indicated that the catalyst could effectively improve the qualities of the lignin, while the characteristics of the lignin showed prominent changes with the participation of the catalyst: the extraction rate increased from 71.65% to 98.13%, the purity was improved from 85.62% to 97.09%, and both the number average molecular weight and the weight average molecular weight also decreased significantly. Besides, the molecular distribution of the lignin achieved from the CC-LA-Fe-Si-V3 reaction system was found to be more highly concentrated (Polydispersity index = 1.746). Results from 2D NMR HSQC analysis indicated that lignin fractions achieved from the CC-LA-Fe-Si-V3 system showed distinct destruction involving C2-H2 in guaiacyl units (G), C5-H5 in guaiacyl units (G), and the Cγ-Hγ in γ-hydroxylated β-O-4′ substructures, but little changes in the Cγ-Hγ in phenylcoumaran substructures.
Recovery of Gelatin from Bovine Skin with the Aid of Pepsin and Its Effects on the Characteristics of the Extracted Gelatin
Authors: Ahmad Tanbir , Ismail Amin , Ahmad Siti Aqlima , Abdul Khalil Khalilah , Awad Elmutaz Atta , Akhtar Muhammad Tayyab , Sazili Awis Qurni
Published: May 2021

Pepsin enzyme was used to pretreat the bovine skin at the rate of 5, 15, and 25 units of enzyme/g of skin to recover gelatin, and the recovered gelatins were referred to as Pe5, Pe15, and Pe25, respectively. The gelatin yield increased significantly (p < 0.05) from 18.17% for Pe5 to 24.67% for Pe25 as the level of pepsin increased, but the corresponding gel strength and viscosity decreased significantly (p < 0.05) from 215.49 to 56.06 g and 9.17 to 8.17 mPa·s for Pe5 and Pe25, respectively. β- and α1- and α2-chains were degraded entirely in all the gelatins samples as observed in protein pattern elaborated by gel electrophoresis. 1H nuclear magnetic resonance (1H NMR) analysis indicated the coiled structure of gelatin protein chains. The lowest amide III amplitude of Pe25 as found by Fourier transform infrared (FTIR) spectroscopy indicated that α-helix structure of protein chains were lost to more irregular coiled structure. Thus, it could be summarized that pepsin might be used at the lower level (5 units/g of wet skin) to extract gelatin from bovine skin with good functional properties and at higher level (15/25 units/g of wet skin) to obtain gelatin of industrial grade with high yield.
Trends in Managing Cardiac and Orthopaedic Device-Associated Infections by Using Therapeutic Biomaterials
Authors: Scialla Stefania , Martuscelli Giorgia , Nappi Francesco , Singh Sanjeet Singh Avtaar , Iervolino Adelaide , Larobina Domenico , Ambrosio Luigi , Raucci Maria Grazia
Published: May 2021

Over the years, there has been an increasing number of cardiac and orthopaedic implanted medical devices, which has caused an increased incidence of device-associated infections. The surfaces of these indwelling devices are preferred sites for the development of biofilms that are potentially lethal for patients. Device-related infections form a large proportion of hospital-acquired infections and have a bearing on both morbidity and mortality. Treatment of these infections is limited to the use of systemic antibiotics with invasive revision surgeries, which had implications on healthcare burdens. The purpose of this review is to describe the main causes that lead to the onset of infection, highlighting both the biological and clinical pathophysiology. Both passive and active surface treatments have been used in the field of biomaterials to reduce the impact of these infections. This includes the use of antimicrobial peptides and ionic liquids in the preventive treatment of antibiotic-resistant biofilms. Thus far, multiple in vivo studies have shown efficacious effects against the antibiotic-resistant biofilm. However, this has yet to materialize in clinical medicine.
Ammonium Release and Adsorption Characters of Polyurethane–Biochar Crosslinked Material as an Additive Filler in Stormwater Treatment
Authors: Wang Yuan , Meng Yike , Wang Chuanyue , Wang Bao
Published: May 2021

The additive fillers in bioretention facilities play a leading role in stormwater treatment to purify polluted runoff. At present, many traditional materials could not meet the requirements at the same time, including low ammonium leaching quantities, high water storage volume and strong ammonium adsorption. This study investigated a polymer material, polyurethane–biochar crosslinked material (PCB), to evaluate the feasibility of using it as an additive filler in stormwater treatment compared with its raw material hardwood biochar (HB), and two traditional fillers. Successive leaching and ammonium isothermal adsorption experiments were conducted in deionized water and artificial stormwater. PCB leached 4.98–5.31 μmol/g NH4-N, less than the leaching quantities of compost, the traditional filler. After polyurethane modification, ammonium adsorption of PCB was improved: at a typical ammonium concentration of 2 mg/L in stormwater, PCB could adsorb 43.6 mg/kg ammonium versus 34.6 mg/kg for HB. With the addition of PCB in sand column, the ammonium adsorption improved from 31.34 to 84.72%. To improve the performance of bioretention facilities, PCB is recommended to be added into filter layers in stormwater treatment, taking advantage of its high cation exchange capacity and spongy internal structure to minimize overland flooding and enhance removal of ammonium from stormwater.
Morphological and Chemical Analysis of Low-Density Polyethylene Crystallized on Carbon and Clay Nanofillers
Authors: Depan Dilip , Chirdon William , Khattab Ahmed
Published: May 2021

Interest in carbon and clay-based nanofillers has grown in recent years. The crystallization behavior of low-density polyethylene (LDPE) was studied using a variety of notable nanofillers used in engineering applications and prepared using a solution crystallization method. Carbon nanotubes (CNTs), graphene oxide nano-platelets, clay (montmorillonite), and modified clay (surface-modified with trimethyl stearyl ammonium) were used to induce heterogeneous crystallization of LDPE. The crystallized LDPE samples, imaged using scanning and transmission electron microscopy, revealed different microstructures for each nanohybrid system, indicating these various nanofillers induce LDPE lamellae ordering. The underlying interactions between polymer and nanofiller were investigated using FTIR spectroscopy. X-ray diffraction (XRD) was used to determine crystallinity. This work examines how the differences in morphology and chemical structure of the nanofillers induce changes in the nucleation and growth of polymer crystals. These results will provide guidance on functional design of nano-devices with controlled properties.
Racializing the Religious during the COVID-19 Pandemic
Authors: Boddie Stephanie C. , Park Jerry Z.
Published: May 2021

In this article, we propose more research attention to an important dimension of social life that bears considerably on the racial patterns of the Coronavirus Disease (COVID-19) pandemic: religion. Drawing from recent insights into the complex relationship between religious affiliation and other intersecting social identities (namely race, gender and class), we argue that understanding the racial inequities of COVID-19 requires consideration of the religious beliefs, participation and the collective resources of racial minorities. We suggest that religion can simultaneously offer a salve for vulnerable communities during this outbreak and can exacerbate the spread of the disease without solving the problem of insufficient access to care. We describe how religion helps and hurts during these turbulent times.
Estimating the Suitability for the Reintroduced Arabian Oryx (Oryx leucoryx, Pallas 1777) of Two Desert Environments by NIRS-Aided Fecal Chemistry
Authors: Landau Serge Yan , Isler Ido , Dvash Levana , Shalmon Benny , Arnon Amir , Saltz David
Published: May 2021

The re-introduction paradigm is that Arabian Oryx (Oryx leucoryx) herds adjust the size of their home ranges depending on the availability of vegetation, which is directly related to rainfall. In Israel, Arabian oryx were released in two hyper-arid sites: the Arava Valley and in the Paran wilderness, belonging to the Sudanese and the Saharo–Arabian biogeographic zones, respectively. While post-release survival was similar in both, reproductive success in the Paran wilderness reintroduction site was extremely low, resulting in an acute decline of the reintroduced population over time. The hypothesis that impaired nutrition might be associated with this finding was assessed with near-infrared spectroscopy (NIRS)-aided chemistry of monthly sampled fecal pellets, used as remote sensing evidence of ingested diets, throughout a year. Fecal nitrogen (FN), used as an estimate of nutritional status, was consistently higher in the Arava. Grass was never the sole or even a major dietary component. The dietary contribution of tannin-rich browse was high and steady all year-round in the Arava and increased steadily in Paran from winter to summer, corresponding to the period of availability of Acacia raddiana pods in both regions. The oryx in Paran had a home-range that was ten-fold, compared to the Arava, suggesting less feed availability. Acacia browsing may mitigate the effects of temporal variance in primary production. Under such conditions, oryx should be preferably released in areas that support significant acacia stands.
Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images
Authors: Rubel Oleksii , Lukin Vladimir , Rubel Andrii , Egiazarian Karen
Published: May 2021

Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented.
Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection
Authors: Chen Chen , Ma Hongxiang , Yao Guorun , Lv Ning , Yang Hua , Li Cong , Wan Shaohua
Published: May 2021

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.
Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program
Authors: Brooks Colin , Weinstein Charlotte , Poley Andrew , Grimm Amanda , Marion Nicholas , Bourgeau-Chavez Laura , Hansen Dana , Kowalski Kurt
Published: May 2021

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.
Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield
Authors: Dobreva Iliyana D. , Ruiz-Guzman Henry A. , Barrios-Perez Ilse , Adams Tyler , Teare Brody L. , Payton Paxton , Everett Mark E. , Burow Mark D. , Hays Dirk B.
Published: May 2021

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.
Seasonal Net Shortwave Radiation of Bare Arable Land in Poland and Israel According to Roughness and Atmospheric Irradiance
Authors: Cierniewski Jerzy , Roujean Jean-Louis , Jasiewicz Jarosław , Królewicz Sławomir
Published: May 2021

Tillage of arable fields, using for instance a smoothing harrow, may increase the magnitude of albedo of such soil surfaces depending on the location, the sun’s illumination and atmospheric components. As these soil surfaces absorb less shortwave radiation compared to plowed soils, the result is an atmospheric cooling and a positive effect on the Earth’s climate. This paper is the follow-on of a previous study aimed at quantifying the seasonal dynamics of net shortwave radiation reflected by bare air-dried arable land areas located in contrasting environments, i.e. Poland and Israel. Soil tillage includes a plow, a disk harrow, and a smoothing harrow. Previous work concentrated on the estimate of net shortwave radiation under clear-sky theoretical scenarios, whereas the present study deals with a realistic atmosphere throughout the year 2014. This latter is characterized by the observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG). The variations of the net shortwave radiation for the selected bare arable land areas were assessed in combining observations from Landsat 8 images and digital maps of land use and soil, plus model equations that calculate the diurnal variations of the broadband blue-sky albedo with roughness inclusive. The daily amount of net shortwave radiation for air-dried bare arable land in Poland and Israel for the time their spatial coverage is the largest was found to be about 40–50% and 10% lower, respectively, in cloudy-sky conditions compared to clear-sky conditions.
Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network
Authors: Lee Yooho , Jun Dongsan , Kim Byung-Gyu , Lee Hunjoo
Published: May 2021

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.
Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning
Authors: Mardini Mamoun T. , Bai Chen , Wanigatunga Amal A. , Saldana Santiago , Casanova Ramon , Manini Todd M.
Published: May 2021

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20–50 years), middle-aged (50–70 years], and older adults (70–89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20–89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost’s models were high for sedentary (0.955–0.973), locomotion (0.942–0.964) and lifestyle (0.913–0.949) activity types with no apparent difference across age groups. Low (0.919–0.947), light (0.813–0.828) and moderate (0.846–0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835–1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.
Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network
Authors: Bach Nhat Hoang , Vu Le Ha , Nguyen Van Duc
Published: May 2021

This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods.
The “Little MonSta” Deep-Sea Benthic, Precision Deployable, Multi-Sensor and Sampling Lander Array
Authors: Wheeler Andrew J. , Lim Aaron , Butschek Felix , O’Reilly Luke , Harris Kimberley , O’Driscoll Paddy
Published: May 2021

The “Little MonSta” benthic lander array consists of 8 ROV-deployable (remotely operated vehicle) instrumented lander platforms for monitoring physical and chemical oceanographic properties and particle sampling developed as part of the MMMonKey_Pro program (mapping, modeling, and monitoring key processes and controls in cold-water coral habitats in submarine canyons). The Little MonStas offer flexible solutions to meet the need to monitor marine benthic environments during a historically unprecedented time of climate-driven oceanic change, develop an understanding of meso-scale benthic processes (natural and man-made), and to calibrate geological environmental archives. Equipped with acoustic Doppler current profilers (ADCPs), sediment traps, nylon settlement plates and homing beacons, the compact and upgradable lander platforms can be deployed by ROVs to precise locations in extreme terrains to a water depth of 3000 m. The array allows cluster-monitoring in heterogeneous environments or simultaneous monitoring over wider areas. A proof-of-concept case study was presented from the cold-water coral habitable zone in the upper Porcupine Bank Canyon, where the Little MonStas collected 868.8 h of current speed, direction, temperature, and benthic particulate flux records, as well as 192 particle samples subsequently analyzed for particular organic carbon (POC), lithic sediment, live foraminifera, and microplastics. The potential to upgrade the Little MonStas with additional sensors and acoustic releases offers greater and more flexible operational capabilities.
BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces
Authors: Márquez-Sánchez Sergio , Campero-Jurado Israel , Robles-Camarillo Daniel , Rodríguez Sara , Corchado-Rodríguez Juan M.
Published: May 2021

Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.
Real-Time Small Drones Detection Based on Pruned YOLOv4
Authors: Liu Hansen , Fan Kuangang , Ouyang Qinghua , Li Na
Published: May 2021

To address the threat of drones intruding into high-security areas, the real-time detection of drones is urgently required to protect these areas. There are two main difficulties in real-time detection of drones. One of them is that the drones move quickly, which leads to requiring faster detectors. Another problem is that small drones are difficult to detect. In this paper, firstly, we achieve high detection accuracy by evaluating three state-of-the-art object detection methods: RetinaNet, FCOS, YOLOv3 and YOLOv4. Then, to address the first problem, we prune the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Furthermore, to improve the accuracy of small drone detection, we implement a special augmentation for small object detection by copying and pasting small drones. Experimental results verify that compared to YOLOv4, our pruned-YOLOv4 model, with 0.8 channel prune rate and 24 layers prune, achieves 90.5% mAP and its processing speed is increased by 60.4%. Additionally, after small object augmentation, the precision and recall of the pruned-YOLOv4 almost increases by 22.8% and 12.7%, respectively. Experiment results verify that our pruned-YOLOv4 is an effective and accurate approach for drone detection.
Health Insurance Coverage before and after the Affordable Care Act in the USA
Authors: Patrick Jesse , Yang Philip Q.
Published: May 2021

The Affordable Care Act (ACA) is at the crossroads. It is important to evaluate the effectiveness of the ACA in order to make rational decisions about the ongoing healthcare reform, but existing research into its effect on health insurance status in the United States is insufficient and descriptive. Using data from the National Health Interview Surveys from 2009 to 2015, this study examines changes in health insurance status and its determinants before the ACA in 2009, during its partial implementation in 2010–2013, and after its full implementation in 2014 and 2015. The results of trend analysis indicate a significant increase in national health insurance rate from 82.2% in 2009 to 89.4% in 2015. Logistic regression analyses confirm the similar impact of age, gender, race, marital status, nativity, citizenship, education, and poverty on health insurance status before and after the ACA. Despite similar effects across years, controlling for other variables, youth aged 26 or below, the foreign-born, Asians, and other races had a greater probability of gaining health insurance after the ACA than before the ACA; however, the odds of obtaining health insurance for Hispanics and the impoverished rose slightly during the partial implementation of the ACA, but somewhat declined after the full implementation of the ACA starting in 2014. These findings should be taken into account by the U.S. Government in deciding the fate of the ACA.
A Bibliometric Study of Research Topics and Sustainability of Packaging in the Greater China Region
Authors: Wang Rui-Liang , Hsu Tzu-Fan , Hu Chen-Zhong
Published: May 2021

The consumption and sustainability of packaging are significant concerns. With development of the division of labor during economic globalization, packaging issues are viewed differently in different regions. This study focused on the Greater China region, which has recently experienced rapid economic growth. Bibliometric analyses were performed using data from two Chinese academic databases, CNKI-CAJD of Mainland China and CEPS of Taiwan; 24,790 journal papers on packaging published between 1989 and 2019 were investigated using keyword co-occurrence analyses and cluster maps. The history of research on packaging in Greater China is summarized and the research areas are categorized into five common subjects: design and consumption assessment, marketing and image packaging, food packaging, special item packaging, and structure and materials. Among these subjects, CNKI-CAJD mostly focuses on industrial applications in packaging institutions and industry, division of labor in the packaging industry, and foreign trade commodity packaging, while CEPS focuses on diversified aspects of green design and education in packaging and environmental protection and packaging development. The results map out Chinese research on packaging and can serve as a reference for applying environmental sustainability in the packaging field.
Fostering Workplace Innovation through CSR and Authentic Leadership: Evidence from SME Sector
Authors: Gao Wei , Wang Lin , Yan Jingdong , Wu Yanxiong , Musse Sadik Yusuf
Published: May 2021

The cut-throat situation of competitiveness in almost every business sector, followed by globalization, shortened product life cycles, and rapid technological changes have raised the importance of innovation to overrun the rivals. Scholars have established that appropriate leadership style is a key enabler for organizational success. However, it is not clear in existing literature how the concept of authentic leadership is related to innovative work behavior (IWB). Likewise, the role of corporate social responsibility (CSR) to induce IWB is also vague in current literature. Thus, the basic purpose of the current study was to test the relationship of CSR and IWB with the mediating effect of authentic leadership. The proposed model was tested in the small and medium-sized enterprises (SMEs) sector of China. The data were collected through a questionnaire that was distributed among different respondents of the current survey. The data were obtained from a dyad of supervisor and subordinate serving in different SMEs in Wuhan city of China. The study used the structural equation modeling (SEM) technique to validate different hypotheses. The empirical results confirm that CSR positively relates to IWB while authentic leadership partially mediates this relationship. The findings of the current survey will be helpful for policymakers to recognize employees as a source of innovation through CSR and authentic leadership.
How Can Information Technology Use Improve Construction Labor Productivity? An Empirical Analysis from China
Authors: Lu Hao , Zhang Qin , Cui Qinghong , Luo Yuanyuan , Pishdad-Bozorgi Pardis , Hu Xiancun
Published: May 2021

Labor productivity is a significant indicator to measure the sustainable development potential and competitiveness of the construction industry. Under the background of the integration of global construction industry and information and communication technology (ICT), the pursuit of the growth of construction labor productivity (CLP) requires deepened understanding of how these technological advancements characterized by ICT take effect in the change of CLP as well as what the key factors are that led to the variation of CLP at this stage. The paper aims to investigate the effect of ICT progress on CLP and examine the key factors influencing CPL. Based on the data of 31 regions from the China Construction Industry Statistical Yearbook and the Local Statistical Yearbook during the period 2000–2018, this study proposed new methodology (Cobb–Douglas production function, growth rate model, and Malmquist Data Envelopment Analysis) for measuring the technology progress contribution and identified the key factors affecting the change of CLP. The analysis results illustrate that the information technology progress has a significant contribution to CLP growth, but the contribution rate is decreasing with the growing degree of development of the regional construction industry. Three main factors affecting the further improvement of CLP have been identified: human resources, research and development (R&D) investment, and ICT level. The findings can provide the decision-making reference and the general methodology for the local and international industry practitioners to improve the labor productivity performance of the construction sector.
Evaluation and Prediction of PM10 and PM2.5 from Road Source Emissions in Kuala Lumpur City Centre
Authors: Azhari Azliyana , Halim Nor Diana Abdul , Mohtar Anis Asma Ahmad , Aiyub Kadaruddin , Latif Mohd Talib , Ketzel Matthias
Published: May 2021

Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.