bluuebunny/crossref_metadata_2025 huggingface dataset:
Dissatisfaction with Body Weight among Polish Adolescents Is Related to Unhealthy Dietary Behaviors
Authors: Wawrzyniak Agata , Myszkowska-Ryciak Joanna , Harton Anna , Lange Ewa , Laskowski Wacław , Hamulka Jadwiga , Gajewska Danuta
Published: August 2020

The aims of the study were to determine the socio-demographic factors that may affect body weight dissatisfaction and to analyze the relationship between eating habits and dissatisfaction with body weight among a national random sample of Polish adolescents aged 13–19 years. Data on gender, age, level of education, body weight status, screen time, body weight satisfaction and selected nutritional behaviors were collected using a questionnaire. Body mass status was assessed based on weight and height measurements. A total of 14,044 students from 207 schools participated in the study. A significant effect of gender, age, level of education, body weight status and screen time status on the participants’ dissatisfaction with the body weight was observed. The greater prevalence of body weight satisfaction was observed among boys, younger subjects, secondary school students, adolescents with normal body weight status and those with screen time up to 2 h. Whereas girls, older study participants (17–19 years old), overweight/obese adolescents and subjects with screen time over 4 h were more often dissatisfied with body weight. Furthermore, it has been shown that participants dissatisfied with their body weight less often met dietary recommendations. These findings can help dietitians, nutritionists and healthcare professionals to provide age-specific and gender-specific nutrition strategies to promote healthy lifestyle among school-going adolescents.
Headaches and Magnesium: Mechanisms, Bioavailability, Therapeutic Efficacy and Potential Advantage of Magnesium Pidolate
Authors: Maier Jeanette A. , Pickering Gisele , Giacomoni Elena , Cazzaniga Alessandra , Pellegrino Paolo
Published: August 2020

Magnesium deficiency may occur for several reasons, such as inadequate intake or increased gastrointestinal or renal loss. A large body of literature suggests a relationship between magnesium deficiency and mild and moderate tension-type headaches and migraines. A number of double-blind randomized placebo-controlled trials have shown that magnesium is efficacious in relieving headaches and have led to the recommendation of oral magnesium for headache relief in several national and international guidelines. Among several magnesium salts available to treat magnesium deficiency, magnesium pidolate may have high bioavailability and good penetration at the intracellular level. Here, we discuss the cellular and molecular effects of magnesium deficiency in the brain and the clinical evidence supporting the use of magnesium for the treatment of headaches and migraines.
Optimization and Evaluation of Poly(lactide-co-glycolide) Nanoparticles for Enhanced Cellular Uptake and Efficacy of Paclitaxel in the Treatment of Head and Neck Cancer
Authors: Haider Mohamed , Elsherbeny Amr , Jagal Jayalakshmi , Hubatová-Vacková Anna , Saad Ahmed Iman
Published: August 2020

The particle size (PS) and encapsulation efficiency (EE%) of drug-loaded nanoparticles (NPs) may inhibit their cellular uptake and lead to possible leakage of the drug into the systemic circulation at the tumor site. In this work, ultra-high paclitaxel-loaded poly(lactide-co-glycolide) NPs (PTX-PLGA-NPs) with ultra-small sizes were prepared and optimized by adopting the principles of quality by design (QbD) approach. The optimized PTX-PLGA-NPs showed ultra-small spherical particles of about 53 nm with EE% exceeding 90%, a relatively low polydispersity index (PDI) of 0.221, an effective surface charge of −10.1 mV, and a 10-fold increase in the in vitro drug release over 72 h relative to free drug. The cellular viability of pharynx carcinoma cells decreased by almost 50% in 24 h following treatment with optimized PTX-PLGA-NPs, compared to only 20% from the free drug. The intracellular uptake of PTX-PLGA-NPs was highly favored, and the antitumor activity of PTX was remarkably improved with a reduction in its half maximal inhibitory concentration (IC50), by almost 50% relative to free drug solution. These results suggest that the optimal critical formulation parameters, guided by QbD principles, could produce PLGA-NPs with remarkably high EE% and ultra-small PS, resulting in enhanced cellular uptake and efficacy of PTX.
Developments of Smart Drug-Delivery Systems Based on Magnetic Molecularly Imprinted Polymers for Targeted Cancer Therapy: A Short Review
Authors: Sanadgol Nasim , Wackerlig Judith
Published: August 2020

Cancer therapy is still a huge challenge, as especially chemotherapy shows several drawbacks like low specificity to tumor cells, rapid elimination of drugs, high toxicity and lack of aqueous solubility. The combination of molecular imprinting technology with magnetic nanoparticles provides a new class of smart hybrids, i.e., magnetic molecularly imprinted polymers (MMIPs) to overcome limitations in current cancer therapy. The application of these complexes is gaining more interest in therapy, due to their favorable properties, namely, the ability to be guided and to generate slight hyperthermia with an appropriate external magnetic field, alongside the high selectivity and loading capacity of imprinted polymers toward a template molecule. In cancer therapy, using the MMIPs as smart-drug-delivery robots can be a promising alternative to conventional direct administered chemotherapy, aiming to enhance drug accumulation/penetration into the tumors while fewer side effects on the other organs. Overview: In this review, we state the necessity of further studies to translate the anticancer drug-delivery systems into clinical applications with high efficiency. This work relates to the latest state of MMIPs as smart-drug-delivery systems aiming to be used in chemotherapy. The application of computational modeling toward selecting the optimum imprinting interaction partners is stated. The preparation methods employed in these works are summarized and their attainment in drug-loading capacity, release behavior and cytotoxicity toward cancer cells in the manner of in vitro and in vivo studies are stated. As an essential issue toward the development of a body-friendly system, the biocompatibility and toxicity of the developed drug-delivery systems are discussed. We conclude with the promising perspectives in this emerging field. Areas covered: Last ten years of publications (till June 2020) in magnetic molecularly imprinted polymeric nanoparticles for application as smart-drug-delivery systems in chemotherapy.
Pilot Study: Evaluating the Impact of Pharmacist Patient-Specific Medication Recommendations for Diabetes Mellitus Therapy to Family Medicine Residents
Authors: Masuda Camlyn , Randall Rachel , Ortiz Marina
Published: August 2020

Pharmacists have demonstrated effectiveness in managing diabetes mellitus (DM) and lowering hemoglobin A1C (A1C) through direct patient management. Often patients with diabetes and elevated A1C may not be able to come into the clinic for separate appointments with a pharmacist or for diabetes education classes. A novel way that pharmacists can assist in improving the control of patients’ diabetes and improve prescriber understanding and the use of medications for diabetes is by providing medication recommendations to medical residents prior to the patient’s appointment with the medical resident. The results of this pilot study indicate that the recommendations provided to family medicine residents and implemented at the patient’s office visit helped to lower A1C levels, although the population size was too small to show statistical significance. This pilot study’s results support performing a larger study to determine if the pharmacist’s recommendation not only improves patient care by lowering A1C levels but if it also helps improve medical resident’s understanding and use of medications for diabetes.
Genetic Structure of Invasive Baby’s Breath (Gypsophila paniculata L.) Populations in a Michigan Dune System
Authors: Leimbach-Maus Hailee B. , McCluskey Eric M. , Locher Alexandra , Parks Syndell R. , Partridge Charlyn G.
Published: August 2020

Coastal sand dunes are dynamic ecosystems with elevated levels of disturbance and are highly susceptible to plant invasions. One invasive plant that is of concern to the Great Lakes system is Gypsophila paniculata L. (perennial baby’s breath). The presence of G. paniculata negatively impacts native species and has the potential to alter ecosystem dynamics. Our research goals were to (1) estimate the genetic structure of invasive G. paniculata along the Michigan dune system and (2) identify landscape features that influence gene flow in this area. We analyzed 12 populations at 14 nuclear and two chloroplast microsatellite loci. We found strong genetic structure among populations (global FST = 0.228), and pairwise comparisons among all populations yielded significant FST values. Results from clustering analysis via STRUCTURE and discriminant analysis of principal components (DAPC) suggest two main genetic clusters that are separated by the Leelanau Peninsula, and this is supported by the distribution of chloroplast haplotypes. Land cover and topography better explained pairwise genetic distances than geographic distance alone, suggesting that these factors influence the genetic distribution of populations within the dunes system. Together, these data aid in our understanding of how invasive populations move through the dune landscape, providing valuable information for managing the spread of this species.
Improved Energy Storage Performance of All-Organic Composite Dielectric via Constructing Sandwich Structure
Authors: Feng Mengjia , Zhang Tiandong , Song Chunhui , Zhang Changhai , Zhang Yue , Feng Yu , Chi Qingguo , Chen Qingguo , Lei Qingquan
Published: August 2020

Improving the energy storage density of dielectrics without sacrificing charge-discharge energy storage efficiency and reliability is crucial to the performance improvement of modern electrical and electronic systems, but traditional methods of doping high-dielectric ceramics cannot achieve high energy storage densities without sacrificing reliability and storage efficiency. Here, an all-organic energy storage dielectric composed of ferroelectric and linear polymer with a sandwich structure is proposed and successfully prepared by the electrostatic spinning method. Additionally, the effect of the ferroelectric/linear volume ratio on the dielectric properties, breakdown, and energy storage is systematically studied. The results show that the structure has good energy storage characteristics with a high energy storage density (9.7 J/cm3) and a high energy storage efficiency (78%). In addition, the energy storage density of the composite dielectric under high energy storage efficiency (90%) is effectively improved (25%). This result provides theoretical analysis and experience for the preparation of multilayer energy storage dielectrics which will promote the development and application of energy storage dielectrics.
Bionanocomposite Films Containing Halloysite Nanotubes and Natural Antioxidants with Enhanced Performance and Durability as Promising Materials for Cultural Heritage Protection
Authors: Infurna Giulia , Cavallaro Giuseppe , Lazzara Giuseppe , Milioto Stefana , Dintcheva Nadka Tzankova
Published: August 2020

In the last decade, the interest toward the formulation of polymer films for cultural heritage protection continuously grew, and these films must be imperatively transparent, removable, and should not react/interact with surface of the artworks. In this research, bionanocomposite films, based on chitosan (Ch) and pectin (P) and containing naturally occurring fillers and antioxidants, were formulated by solvent casting methods and were accurately characterized. The natural halloysite nanotubes (HNT) have a two-fold role, specifically, physical compatibilizer and antioxidant carrier. Therefore, the theoretical solubility between Ch and P was estimated considering Hoy’s method for solubility of polymers, while the optimum ratio between biopolymer constituents was assessed by ζ-potential measurements. The transparency, wettability, and mechanical behavior of Ch:P films, also in presence of HNT without and with antioxidants, were investigated. The beneficial effects of natural antioxidants, such as vanillic acid (VA) and quercetin (Q), on Ch:P/HNT durability were found.
Synthesis of MOF525/PEDOT Composites as Microelectrodes for Electrochemical Sensing of Dopamine
Authors: Chen Season S. , Han Po-Chun , Kuok Wai-Kei , Lu Jian-Yu , Gu Yesong , Ahamad Tansir , Alshehri Saad M. , Ayalew Hailemichael , Yu Hsiao-hua , Wu Kevin C.-W.
Published: August 2020

Dopamine (DA) is an important neurotransmitter responsible for the functions and activities of multiple systems in human. Electrochemical detection of DA has the advantages of fast analysis and cost-effectiveness, while a regular electrode probe is restricted to laboratory use because the probe size is too large to be suitable for an in vivo or in vitro analysis. In this study, we have developed porphyrin-based metal organic framework (MOF525) and poly(3,4-ethylenedioxythiophene) (PEDOT)-based composites to modify microelectrode for DA detection. Two types of PEDOT monomers with different functional groups were investigated in this study. By varying the monomer ratios, electrolyte concentrations, and electropolymerization temperature, it was found that the PEDOT monomer containing carboxylic group facilitated the formation of regular morphology during the electropolymerization process. The uniform morphology of the PEDOT promoted the electron transmission efficiency in the same direction, while the MOF525 provided a large reactive surface area for electrocatalysis of DA. Thus, the MOF525/PEDOT composite improved the sensitivity-to-noise ratio of DA signaling, where the sensitivity reached 11 nA/μM in a good linear range of 4–100 µM. In addition, porphyrin-based MOF could also increase the selectivity to DA against other common clinical interferences, such as ascorbic acid and uric acid. The as-synthesized microelectrode modified with MOF525/PEDOT in this study exhibited great potential in real time analysis.
Enzyme-Crosslinked Electrospun Fibrous Gelatin Hydrogel for Potential Soft Tissue Engineering
Authors: Nie Kexin , Han Shanshan , Yang Jianmin , Sun Qingqing , Wang Xiaofeng , Li Xiaomeng , Li Qian
Published: August 2020

Soft tissue engineering has been seeking ways to mimic the natural extracellular microenvironment that allows cells to migrate and proliferate to regenerate new tissue. Therefore, the reconstruction of soft tissue requires a scaffold possessing the extracellular matrix (ECM)-mimicking fibrous structure and elastic property, which affect the cell functions and tissue regeneration. Herein, an effective method for fabricating nanofibrous hydrogel for soft tissue engineering is demonstrated using gelatin–hydroxyphenylpropionic acid (Gel–HPA) by electrospinning and enzymatic crosslinking. Gel–HPA fibrous hydrogel was prepared by crosslinking the electrospun fibers in ethanol-water solution with an optimized concentration of horseradish peroxidase (HRP) and H2O2. The prepared fibrous hydrogel held the soft and elastic mechanical property of hydrogels and the three-dimensional (3D) fibrous structure of electrospun fibers. It was proven that the hydrogel scaffolds were biocompatible, improving the cellular adhesion, spreading, and proliferation. Moreover, the fibrous hydrogel showed rapid biodegradability and promoted angiogenesis in vivo. Overall, this study represents a novel biomimetic approach to generate Gel–HPA fibrous hydrogel scaffolds which have excellent potential in soft tissue regeneration applications.
Lastingly Colored Polylactide Synthesized by Dye-Initiated Polymerization
Authors: Jędrzkiewicz Dawid , Kowalczyk Sebastian , Plichta Andrzej , Ejfler Jolanta
Published: August 2020

An efficient synthesis strategy of a well-defined polylactide–dye conjugate in a controlled fashion is presented. The introduction of coloring species as end groups of polylactide (PLA) has been performed by using new homoleptic aminophenolate magnesium or zinc coordination compounds. The molecular structure of metal complexes has been determined in solution by NMR spectroscopy, and in the solid state by X-ray analysis. Lastingly colored polymers were obtained with 2-[4-(Nitrophenylazo)-N-ethylphenylamino]ethanol (Disperse Red 1) and 2-[4-(2-Chloro-4-nitrophenylazo)-N-ethylphenylamino]ethanol (Disperse Red 13) at very high lactide conversions, based on MALDI-ToF measurement, and the macromolecules were nearly fully chain end dye-functionalized. Based on 1H NMR, the DPn of conjugates was in the range of 10–300, which was consistent with the reaction setup. Various methods of gel-permeation chromatography (GPC) analysis were applied, and they demonstrated that the number-average molar mass (Mn) values (polystyrene (PS) standards) were a bit higher than calculated, the molar mass distribution index (ƉM) values were moderate to high, the TDA (triple detection array) system was inappropriate for analysis, measurements with PDA (photo diode array) detection at 470 nm gave nearly the same molar mass distributions such as the refractometer, and the relative absorbance of conjugates at 470 nm increased linearly versus (DPn)−1. The presented approach connects the gap between the current strategy of obtaining colored polymer fibers and the design of tailor-made initiators with eco polyesters designed for the targeted applications.
Development of Environmentally Friendly Atom Transfer Radical Polymerization
Authors: Yuan Ming , Cui Xuetao , Zhu Wenxian , Tang Huadong
Published: August 2020

Atom transfer radical polymerization (ATRP) is one of the most successful techniques for the preparation of well-defined polymers with controllable molecular weights, narrow molecular weight distributions, specific macromolecular architectures, and precisely designed functionalities. ATRP usually involves transition-metal complex as catalyst. As the most commonly used copper complex catalyst is usually biologically toxic and environmentally unsafe, considerable interest has been focused on iron complex, enzyme, and metal-free catalysts owing to their low toxicity, inexpensive cost, commercial availability and environmental friendliness. This review aims to provide a comprehensive understanding of iron catalyst used in normal, reverse, AGET, ICAR, GAMA, and SARA ATRP, enzyme as well as metal-free catalyst mediated ATRP in the point of view of catalytic activity, initiation efficiency, and polymerization controllability. The principle of ATRP and the development of iron ligand are briefly discussed. The recent development of enzyme-mediated ATRP, the latest research progress on metal-free ATRP, and the application of metal-free ATRP in interdisciplinary areas are highlighted in sections. The prospects and challenges of these three ATRP techniques are also described in the review.
Assessing the Influence of Tourism-Driven Activities on Environmental Variables on Hainan Island, China
Authors: Chu Lixia , Oloo Francis , Chen Bin , Xie Miaomiao , Blaschke Thomas
Published: August 2020

Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 °C for the 20 years and a maximum difference of 1.7 °C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.
Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships
Authors: Gbodjo Yawogan Jean Eudes , Ienco Dino , Leroux Louise , Interdonato Roberto , Gaetano Raffaele , Ndao Babacar
Published: August 2020

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently exploit multiple sources of information and leverage their complementarity, in order to obtain the most out of radar and optical data. In this work, we propose to deal with land cover mapping in an object-based image analysis (OBIA) setting via a deep learning framework designed to leverage the multi-source complementarity provided by radar and optical satellite image time series (SITS). The proposed architecture is based on an extension of Recurrent Neural Network (RNN) enriched via a modified attention mechanism capable to fit the specificity of SITS data. Our framework also integrates a pretraining strategy that allows to exploit specific domain knowledge, shaped as hierarchy over the set of land cover classes, to guide the model training. Thorough experimental evaluations, involving several competitive approaches were conducted on two study sites, namely the Reunion island and a part of the Senegalese groundnut basin. Classification results, 79% of global accuracy on the Reunion island and 90% on the Senegalese site, respectively, have demonstrated the suitability of the proposal.
A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference
Authors: Yan Ran , Bai Jianjun
Published: August 2020

The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.
A Semianalytic Monte Carlo Simulator for Spaceborne Oceanic Lidar: Framework and Preliminary Results
Authors: Liu Qun , Cui Xiaoyu , Jamet Cédric , Zhu Xiaolei , Mao Zhihua , Chen Peng , Bai Jian , Liu Dong
Published: August 2020

Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.
NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
Authors: Liu Zhong , Shie Chung-Lin , Li Angela , Meyer David
Published: August 2020

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.
Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data
Authors: Xu Jing-Xian , Ma Jun , Tang Ya-Nan , Wu Wei-Xiong , Shao Jin-Hua , Wu Wan-Ben , Wei Shu-Yun , Liu Yi-Fei , Wang Yuan-Chen , Guo Hai-Qiang
Published: August 2020

Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R2 = 0.96, RMSE = 1.27 kg m−2) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R2 = 0.97, RMSE = 1.33 kg m−2). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.
Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds
Authors: Li Hongbo , Jiang Dapeng , Cao Jun , Zhang Dongyan
Published: August 2020

Lipid content is an important indicator of the edible and breeding value of Pinus koraiensis seeds. Difference in origin will affect the lipid content of the inner kernel, and neither can be judged by appearance or morphology. Traditional chemical methods are small-scale, time-consuming, labor-intensive, costly, and laboratory-dependent. In this study, near-infrared (NIR) spectroscopy combined with chemometrics was used to identify the origin and lipid content of P. koraiensis seeds. Principal component analysis (PCA), wavelet transformation (WT), Monte Carlo (MC), and uninformative variable elimination (UVE) methods were used to process spectral data and the prediction models were established with partial least-squares (PLS). Models were evaluated by R2 for calibration and prediction sets, root mean standard error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Two dimensions of input data produced a faster and more accurate PLS model. The accuracy of the calibration and prediction sets was 98.75% and 97.50%, respectively. When the Donoho Thresholding wavelet filter ‘bior4.4’ was selected, the WT–MC–UVE–PLS regression model had the best predictions. The R2 for the calibration and prediction sets was 0.9485 and 0.9369, and the RMSECV and RMSEP were 0.0098 and 0.0390, respectively. NIR technology combined with chemometric algorithms can be used to characterize P. koraiensis seeds.
A Joint Power, Delay and Rate Optimization Model for Secondary Users in Cognitive Radio Sensor Networks
Authors: Ssajjabbi Muwonge Bernard , Pei Tingrui , Sansa Otim Julianne , Mayambala Fred
Published: August 2020

To maximize the limited spectrum among primary users and cognitive Internet of Things (IoT) users as we save the limited power and energy resources available, there is a need to optimize network resources. Whereas it is quite complex to study the impact of transmission rate, transmission power or transmission delay alone, the complexity is aggravated by the simultaneous consideration of all these three variables jointly in addition to a channel selection variable, since it creates a non-convex problem. Our objective is to jointly optimize the three major variables; transmission power, rate and delay under constraints of Bit Error Rate (BER), interference and other channel limitations. We analyze how total power, rate and delay vary with packet size, network size, BER and interference. The resulting problem is solved using a branch-and-cut polyhedral approach. For simulation of results, we use MATLAB together with the state-of-the-art BARON software. It is observed that an increase in packet size generally leads to an increase in total rate, total power and total transmission delay. It is also observed that increasing the number of secondary users on the channel generally leads to an increased power, delay and rate.
Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network
Authors: Chu Ruobo , Schweitzer Patrick , Zhang Rencheng
Published: August 2020

Arc faults induced by residential low-voltage distribution network lines are still one of the main causes of residential fires. When a series arc fault occurs on the line, the value of the fault current in the circuit is limited by the load. Traditional circuit protection devices cannot detect series arcs and generate a trip signal to implement protection. This paper proposes a novel high-frequency coupling sensor for extracting the features of low-voltage series arc faults. This sensor is used to collect the high-frequency feature signals of various loads in series arc state and normal working state. The signal will be transformed into two-dimensional feature gray images according to the temporal-domain sequence. A neural network with a three-layer structure based on convolution neural network is designed, trained and tested using the various typical loads’ arc states and normal states data sets composed of these images. This detection method can simultaneously accurately identify series arc, as well as the load type. Seven different domestic appliances were selected for experimental verification, including a desktop computer, vacuum cleaner, induction cooker, fluorescent lamp, dimmer, heater and electric drill. Then, the stability and universality of the detection algorithm is also verified by using electronic load with adjustable power factor and peak factor. The experimental results show that the designed sensor has the advantages of simple structure and wide frequency response range. The detection algorithm comparison confirms that the classification accuracy of the seven domestic appliances’ work states in the fourteen categories could reach 98.36%. The adjustable load in the two categories could reach above 99%. The feasibility of hardware implementation based on FPGA of this method is also evaluated.
Design, Analysis and Simulation of a MEMS-Based Gyroscope with Differential Tunneling Magnetoresistance Sensing Structure
Authors: Li Cheng , Yang Bo , Guo Xin , Chen Xinru
Published: August 2020

The design, analysis, and simulation of a new Micro-electromechanical System (MEMS) gyroscope based on differential tunneling magnetoresistance sensing are presented in this paper. The device is driven by electrostatic force, whereas the Coriolis displacements are transferred to intensity variations of magnetic fields, further detected by the Tunneling Magnetoresistance units. The magnetic fields are generated by a pair of two-layer planar multi-turn copper coils that are coated on the backs of the inner masses. Together with the dual-mass structure of proposed tuning fork gyroscope, a two-stage differential detection is formed, thereby enabling rejection of mechanical and magnetic common-mode errors concurrently. The overall conception is described followed by detailed analyses of proposed micro-gyroscope and rectangle coil. Subsequently, the FEM simulations are implemented to determine the mechanical and magnetic characteristics of the device separately. The results demonstrate that the micro-gyroscope has a mechanical sensitivity of 1.754 nm/°/s, and the micro-coil has a maximum sensitivity of 41.38 mOe/µm. When the detection height of Tunneling Magnetoresistance unit is set as 60 µm, the proposed device exhibits a voltage-angular velocity sensitivity of 0.131 mV/°/s with a noise floor of 7.713 × 10−6°/s/Hz in the absence of any external amplification.
An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar
Authors: Cao Lin , Zhang Xinyi , Wang Tao , Du Kangning , Fu Chong
Published: August 2020

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.
New Strategy for Improving the Accuracy of Aircraft Positioning Based on GPS SPP Solution
Authors: Krasuski Kamil , Ciećko Adam , Bakuła Mieczysław , Wierzbicki Damian
Published: August 2020

The paper describes and presents a new calculation strategy for the determination of the aircraft’s resultant position using the GPS (Global Positioning System) SPP (Single Point Positioning) code method. The paper developed a concept of using the weighted average model with the use of measuring weights to improve the quality of determination of the coordinates and accuracy of GPS SPP positioning. In this research, measurement weights were used as a function of the number of GPS satellites being tracked, and geometric PDOP (Position Dilution of Precision) coefficient. The calculations were made using navigation data recorded by two independent GPS receivers: Thales Mobile Mapper and Topcon HiPerPro. On the basis of the obtained results, it was found that the RMS (Root Mean Square) accuracy of positioning for XYZ geocentric coordinates was better than 1.2% to 33.7% for the weighted average method compared to a single GPS SPP solution. The proposed approach is therefore of practical application in air navigation to improve the quality of aircraft positioning.
Estimating Throwing Speed in Handball Using a Wearable Device
Authors: Skejø Sebastian D. , Bencke Jesper , Møller Merete , Sørensen Henrik
Published: August 2020

Throwing speed is likely a key determinant of shoulder-specific load. However, it is difficult to estimate the speed of throws in handball in field-based settings with many players due to limitations in current technology. Therefore, the purpose of this study was to develop a novel method to estimate throwing speed in handball using a low-cost accelerometer-based device. Nineteen experienced handball players each performed 25 throws of varying types while we measured the acceleration of the wrist using the accelerometer and the throwing speed using 3D motion capture. Using cross-validation, we developed four prediction models using combinations of the logarithm of the peak total acceleration, sex and throwing type as the predictor and the throwing speed as the outcome. We found that all models were well-calibrated (mean calibration of all models: 0.0 m/s, calibration slope of all models: 1.00) and precise (R2 = 0.71–0.86, mean absolute error = 1.30–1.82 m/s). We conclude that the developed method provides practitioners and researchers with a feasible and cheap method to estimate throwing speed in handball from segments of wrist acceleration signals containing only a single throw.
Estimation of Ultrasound Echogenicity Map from B-Mode Images Using Convolutional Neural Network
Authors: Shen Che-Chou , Yang Jui-En
Published: August 2020

In ultrasound B-mode imaging, speckle noises decrease the accuracy of estimation of tissue echogenicity of imaged targets from the amplitude of the echo signals. In addition, since the granular size of the speckle pattern is affected by the point spread function (PSF) of the imaging system, the resolution of B-mode image remains limited, and the boundaries of tissue structures often become blurred. This study proposed a convolutional neural network (CNN) to remove speckle noises together with improvement of image spatial resolution to reconstruct ultrasound tissue echogenicity map. The CNN model is trained using in silico simulation dataset and tested with experimentally acquired images. Results indicate that the proposed CNN method can effectively eliminate the speckle noises in the background of the B-mode images while retaining the contours and edges of the tissue structures. The contrast and the contrast-to-noise ratio of the reconstructed echogenicity map increased from 0.22/2.72 to 0.33/44.14, and the lateral and axial resolutions also improved from 5.9/2.4 to 2.9/2.0, respectively. Compared with other post-processing filtering methods, the proposed CNN method provides better approximation to the original tissue echogenicity by completely removing speckle noises and improving the image resolution together with the capability for real-time implementation.
Multifrequency Vector Measurement System for Reliable Vehicle Magnetic Profile Assessment
Authors: Marszalek Zbigniew , Duda Krzysztof
Published: August 2020

This paper describes the design and the performance of simultaneous, multifrequency impedance measurement system for four inductive-loop (IL) sensors which have been developed for vehicle parameters measurement based on vehicle magnetic profile (VMP) analysis. Simultaneous impedance measurement on several excitation frequencies increases the VMP measurement reliability because typical electromagnetic interferences (EMI) are narrowband, and should not simultaneously affect, in the same way, all measurement bands that are spread in the frequency, i.e., it is expected that at least one measurement band is disturbance-free. The system consists of two standard and two slim IL sensors, specially designed and installed, the analogue front-end, and an industrial computer with digital-to-analogue and analogue-to-digital converters accessed via field-programmable gate array (FPGA). The impedance of the IL sensors is obtained by vector measurement of voltages from auto-balancing bridge (ABB) front-end. Complex voltages are demodulated from excitation frequencies with FIR filters designed with the flat-top windows. The system is capable of delivering VMPs in real-time mode, and also storing voltages for off-line postprocessing and analysis. Field distributions and sensitivities of slim and standard IL sensors are also discussed. Field test confirmed assumed increased reliability of VMP measurement for proposed simultaneous multifrequency operational mode.
Achieving Portfolio Diversification for Individuals with Low Financial Sustainability
Authors: Lee Yongjae , Kim Woo Chang , Kim Jang Ho
Published: August 2020

While many individuals make investments to gain financial stability, most individual investors hold under-diversified portfolios that consist of only a few financial assets. Lack of diversification is alarming especially for average individuals because it may result in massive drawdowns in their portfolio returns. In this study, we analyze if it is theoretically feasible to construct fully risk-diversified portfolios even for the small accounts of not-so-rich individuals. In this regard, we formulate an investment size constrained mean-variance portfolio selection problem and investigate the relationship between the investment amount and diversification effect.
Informal Seed Traders: The Backbone of Seed Business and African Smallholder Seed Supply
Authors: Sperling Louise , Gallagher Patrick , McGuire Shawn , March Julie , Templer Noel
Published: August 2020

To work well and be sustainable, seed systems have to offer a range of crops and varieties of good quality seed and these products have to reach farmers, no matter how remote or poor they may be. Formal seed sector interventions alone are not delivering the crop portfolio or achieving the social and geographic breadth needed, and the paper argues for focus on informal seed channels and particularly on traders who move ‘potential seed’ (informal or local seed) even to high stress areas. This paper provides the first in-depth analysis on potential seed trader types and actions, drawing on data collected on 287 traders working in 10 African countries. The research delves into four themes: the types and hierarchies of traders; the technical ways traders manage seed using 11 core practices; the price differential of +50% of potential (local) seed over grain, and the pivotal roles which traders play in remote and crisis contexts. Traders are the backbone of smallholder seed security and need to be engaged, not ignored, in development and relief efforts. An action framework for leveraging seed trader skills is presented, with the paper addressing possible legal and donor constraints for engaging such market actors more fully.