bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthetic Polymers for Organ 3D Printing
Authors: Liu Fan , Wang Xiaohong
Published: August 2020

Three-dimensional (3D) printing, known as the most promising approach for bioartificial organ manufacturing, has provided unprecedented versatility in delivering multi-functional cells along with other biomaterials with precise control of their locations in space. The constantly emerging 3D printing technologies are the integration results of biomaterials with other related techniques in biology, chemistry, physics, mechanics and medicine. Synthetic polymers have played a key role in supporting cellular and biomolecular (or bioactive agent) activities before, during and after the 3D printing processes. In particular, biodegradable synthetic polymers are preferable candidates for bioartificial organ manufacturing with excellent mechanical properties, tunable chemical structures, non-toxic degradation products and controllable degradation rates. In this review, we aim to cover the recent progress of synthetic polymers in organ 3D printing fields. It is structured as introducing the main approaches of 3D printing technologies, the important properties of 3D printable synthetic polymers, the successful models of bioartificial organ printing and the perspectives of synthetic polymers in vascularized and innervated organ 3D printing areas.
Preparation, Thermal Analysis, and Mechanical Properties of Basalt Fiber/Epoxy Composites
Authors: Karvanis Konstantinos , Rusnáková Soňa , Krejčí Ondřej , Žaludek Milan
Published: August 2020

In this study, basalt fiber-reinforced polymer (BFRP) composites with epoxy matrix, 20 layers, and volume fraction of fibers Vf = 53.66%, were prepared by a hand lay-up compression molding combined method. The fabric of the basalt fibers is in twill 2/2 weave. Through dynamic mechanical analysis (DMA), their viscoelastic behavior at elevated temperatures and in various frequencies was explored, whereas thermomechanical analysis (TMA) took part in terms of creep recovery and stress-relaxation tests. Moreover, the glass transition temperature (Tg) of the BFRP composites was determined through the peak of the tanδ curves while the decomposition of the BFRP composites and basalt fibers, in air or nitrogen atmosphere, was explored through thermogravimetric analysis (TGA). The mechanical behavior of the BFRP composites was investigated by tensile and three-point bending experiments. The results showed that as the frequency is raised, the BFRP composites can achieve slightly higher Tg while, under the same circumstances, the storage modulus curve obtains a less steep decrease in the middle transition region. Moreover, the hand lay-up compression molding hybrid technique can be characterized as efficient for the preparation of polymer matrix composites with a relatively high Vf of over 50%. Remarkably, through the TGA experiments, the excellent thermal resistance of the basalt fibers, in the temperature range 30–900 °C, was revealed.
Plant Extracts as Green Corrosion Inhibitors for Different Metal Surfaces and Corrosive Media: A Review
Authors: Miralrio Alan , Espinoza Vázquez Araceli
Published: August 2020

Natural extracts have been widely used to protect metal materials from corrosion. The efficiency of these extracts as corrosion inhibitors is commonly evaluated through electrochemical tests, which include techniques such as potentiodynamic polarization, electrochemical impedance spectroscopy, and weight loss measurement. The inhibition efficiency of different extract concentrations is a valuable indicator to obtain a clear outlook to choose an extract for a particular purpose. A complementary vision of the effectiveness of green extracts to inhibit the corrosion of metals is obtained by means of surface characterizations; atomic force microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy analysis are experimental techniques widely used for this purpose. Moreover, theoretical studies are usually addressed to elucidate the nature of the corrosion inhibitor—metal surface interactions. In addition, calculations have been employed to predict how other organic substances behave on metal surfaces and to provide experimental work with fresh proposals. This work reports a broad overview of the current state of the art research on the study of new extracts as corrosion inhibitors on metal surfaces in corrosive media. Most constituents obtained from plant extracts are adsorbed on the metal, following the Langmuir adsorption model. Electron-rich regions and heteroatoms have been found to be responsible for chemisorption on the metal surface, whereas physisorption is due to the polar regions of the inhibitor molecules. The plant extracts compiled in this work obtained corrosion inhibition efficiencies above 60%, most of them around 80–90%. The effect of concentration, extraction solvent, temperature, and immersion time were studied as well. Additional studies regarding plant extracts as corrosion inhibitors on metals are needed to produce solutions for industrial purposes.
The Changes of Flavonoids in Honey during Storage
Authors: Šarić Goran , Vahčić Nada , Bursać Kovačević Danijela , Putnik Predrag
Published: August 2020

The purpose of this study was to determine the changes in the contents of flavonoids that were the most prevalent in acacia and multifloral honey during one year of storage. Samples were stored in transparent glass containers, at room temperature, on open shelves exposed to light during daytime. Eight individual flavonoids identified and quantified using HPLC-Diode Array Detector (DAD) belongs to three subgroups: flavonols (quercetin, luteolin, kaempferol and galangin), total flavanons (hesperetin and pinocembrin) and total flavones (apigenin and chrysin). Obtained results revealed that multifloral honey had more total flavonoids than acacia samples did. On average from all of the samples, multifloral honey had more of quercetin, hesperetin, luteolin, kaempferol and apigenin than acacia honey did. Content of flavonoids increased in samples between the 1st and 6th month of storage and then started to decrease until the 9th month, when they remained relatively constant all the way until the 12th month of storage. In conclusion, acacia and multifloral honey after one-year of storage still can be a valuable source of flavonoids.
The Evolution of Biodiesel Policies in China over the Period 2000–2019
Authors: Chung Chao-chen , Zhang Yapeng , Liu Lina , Wang Yixuan , Wei Ziji
Published: August 2020

The Chinese government launched policies supporting biodiesel production and innovation to meet sustainable energy demands under rapid economic and social development. This study systematically investigates the dynamic transitions of biodiesel policies in China regarding policy contents and policy networks. We performed a content analysis in this study and used NVivo12 software to support the analysis process. The results highlighted some issues. First, the Chinese government supported the development of biodiesel from multiple policy objectives and adopted various policy instruments, eventually transforming biodiesel in China from the first- to second-generation. Second, the State Council was the main institution to promote biodiesel policies, and the National Development and Reform Commission under the State Council burdened most of the responsibilities of policy implementation. Most of the policies were issued at a lower level of administration rather than a higher level of decision-making. Biodiesel policies in China were gradually detailed, and they constantly established a system of technology and product innovation.
Analysis of Chemical and Biochemical Parameters of Petrol-Contaminated Soil after Biostimulation with an Enzyme Reagent
Authors: Curyło Kornel , Telesiński Arkadiusz , Jarnuszewski Grzegorz , Krzyśko-Łupicka Teresa , Cybulska Krystyna
Published: August 2020

This study aimed to assess the effect of petrol and the Fyre Zyme reagent on selected chemical and biochemical properties of loamy sand. The experiment was conducted under laboratory conditions. First, petrol was introduced into the soil at doses of 0 and 50 g k−1dry matter (DM). Next, 6% Fyre-Zyme enzyme reagent solution was added to the samples contaminated and uncontaminated with petrol, in the following combinations: 0 (control), once at 40 cm3 kg−1 DM, twice at 20 cm3 kg−1 DM at two-week intervals, and four times at 10 cm3 kg−1 DM at weekly intervals. Contamination of loamy sand with petrol caused slight changes in the determined chemical parameters and stimulated dehydrogenase activities, but inhibited the activity of phosphatases. The introduction of the enzyme reagent into the soil increased the Corg and Ntot content. The greatest changes were observed in the activity of phosphatases. The receiver operating characteristic (ROC) curves revealed that the application of the enzyme reagent at the application of 4 × 10 cm3 kg−1 DM was the most beneficial. However, the results of the η2 analysis indicate that the greatest influence on the determined experimental parameters was found in the soil contaminated with petrol.
Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review
Authors: He Q. Peter , Wang Jin
Published: August 2020

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.
Application of Theoretical and Experimental Findings for Optimization of Mixing Processes and Equipment
Authors: Jirout Tomáš , Jiroutová Dita
Published: August 2020

The homogenization of the agitated batch and ensuring the suspension of particles are the most frequently encountered requirements in terms of mixing applications. These operations are affected by the flow of the agitated batch. The geometrical parameters of the mixing system, especially the shape of the agitator blade, affect flow and circulation in the agitated batch. The present work provides a general description of the most common processes in the agitated batch (blending and particle suspension), hydrodynamic parameters (flow in agitated batches, pumping and circulation capacity of impellers) and the geometrical configurations of the mixing equipment (shape of vessel, baffle and impeller, and their mutual arrangement) that influence the process. The dimensionless process characteristics of the agitator were derived by theoretical analysis. These characteristics were applied to evaluate an extensive set of experimental data with various geometric configurations of the mixing equipment. This study shows how the flow in the agitated batch, caused by the pumping and circulating effects of the agitators, affects the parameters and energy efficiency of these processes, depending on the geometric configuration of the mixing equipment. Moreover, the benefits of the hydrofoil impellers used for these mixing processes are presented.
An Overview of Natural Extracts with Antioxidant Activity for the Improvement of the Oxidative Stability and Shelf Life of Edible Oils
Authors: Blasi Francesca , Cossignani Lina
Published: August 2020

Many plant materials, such as fruits and vegetables as well as herbs and spices, represent valuable sources of antioxidants. In recent years, wastes from agriculture and food industrial processes have been shown to be interesting sources for bioactive compound recovery, strongly contributing to the circular economy. Nowadays, because of their possible adverse effects on human health, there is a tendency to replace synthetic antioxidants with natural compounds. This review attempts to critically summarize the current evidence on plant bioactives, extracted from food or waste, added to unsaturated vegetable oils, in order to obtain high added-value products and to ameliorate their oxidative stability and shelf life. The goal of this review is to demonstrate the current status of the research on edible oils added with natural plant bioactives, highlighting new approaches in the field of health-promoting foods.
Evaluation of Postharvest Processing of Hazelnut Kernel Oil Extraction Using Uniaxial Pressure and Organic Solvent
Authors: Gürdil Gürkan Alp Kağan , Kabutey Abraham , Selvi Kemal Çağatay , Mizera Čestmír , Herák David , Fraňková Adéla
Published: August 2020

Uniaxial loading and organic solvent are small-scale oil expression methods used to evaluate the mechanical behavior, oil content, and oil efficiency of oil-bearing materials aimed at designing a low-cost mechanical pressing system. Bulk kernels of pressing height 40 mm were heated from 40 to 60 °C and compressed at maximum force of 60 kN and speeds from 4 to 8 mm/min. Relaxation times between 3 and 12 min were applied to assess the kernel oil efficiency. The kernel oil point was identified at deformation levels between 15 and 25 mm at a speed of 4 mm/min using a litmus test. The kernel oil was analyzed for peroxide value and free fatty acid. Kernel oil content was determined by Soxhlet extraction. Increased speed caused a serration effect on the force–deformation curve leading to lower oil yield. Lower and upper oil point forces at 6.21 ± 0.58 and 10.61 ± 0.71 kN were observed to be useful for predicting the pressure for maximum output oil. The peroxide value and free fatty acid content of kernel oil decreased with increasing temperature, indicating its quality usage. The relaxation time of 12 min after compression increased kernel oil efficiency of 15.6%. In designing new presses, there is a need to consider compression and relaxation processes to reduce the residual kernel cake oil.
Investigation on Unsteady Flow Characteristics in an Axial-Flow Fan under Stall Conditions
Authors: Jiang Chenlong , Li Mengjiao , Li Enda , Zhu Xingye
Published: August 2020

Based on Shear Stress Transport (SST) turbulence model for unsteady simulation of an axial-flow fan, this paper studies the time-frequency information in the hump region, and investigates the disturbance information of spike and modal wave under different flow coefficients based on continuous wavelet transform (CWT). The results show that before the hump point, the low-frequency modal wave occupies the main disturbance form and circularly propagates at 1/10 of the rotor speed, and the axial-flow fan does not enter the stall stage; while after the flow coefficient reduces to the hump point, the spike wave with higher frequency replaces the modal wave as the main disturbance mode while the axial-flow fan enters the stall stage. Through in-depth investigation of unsteady flow characteristics under the hump point, it is found that after experiencing the emerging spike, with the sharp increase of incidence angle, some flow distortions appear on the intake surface, and further induce some flow paths to form stall vortices. When a path goes into stall stage, the airflow state is greatly affected, the inverse flow and air separation phenomenon in the rim region increase significantly, and the flow capacity decreases significantly, so the flow capacity in the hub region increases correspondingly. The flow path distortion of tip leakage flow (TLF) and leading edge (LE) spillage caused by the stall vortices are the main inducements of rotating stall.
Heat and Mass Transfer during Lignocellulosic Biomass Torrefaction: Contributions from the Major Components—Cellulose, Hemicellulose, and Lignin
Authors: Tanoue Ken-ichiro , Hikasa Kentaro , Hamaoka Yuuki , Yoshinaga Akihiro , Nishimura Tatsuo , Uemura Yoshimitsu , Hideno Akihiro
Published: August 2020

The torrefaction of three representative types of biomass—bamboo, and Douglas fir and its bark—was carried out in a cylindrical-shaped packed bed reactor under nitrogen flow at 573 K of the reactor wall temperature. As the thermal energy for the torrefaction was supplied from the top and the side of the bed, the propagation of the temperature profile of the bed is a crucial factor for discussing and improving the torrefaction reactor performance. Therefore, the temperature and gas flow rate (vector) profiles throughout the bed were calculated by model simulation so as to scrutinize this point. The measured temperature at a certain representative location (z = 30 mm and r = 38 mm) of the bed was well reproduced by the simulation. The volume faction of the bed at temperatures higher than 500 K at 75 min was 0.89, 0.85, and 0.99 for bamboo, and Douglas fir and its bark, respectively. It was found that the effective thermal conductivity is the determining factor for this difference. The heat of the reactions was found to be insignificant.
Stretchable and Flexible Thin Films Based on Expanded Graphite Particles
Authors: Nauman Malik Muhammad , Mehdi Murtuza , Husain Dawood , Haji Zaini Juliana , Abu Bakar Muhammad Saifullah , Askari Hasan , Baig Babar Ali , Rehman Ahmed Ur , Abbas Hassan , Hussain Zahid , Zaki Danial
Published: August 2020

Stretchable and flexible graphite films can be effectively applied as functional layers in the progressively increasing field of stretchable and flexible electronics. In this paper, we focus on the feasibility of making stretchable and flexible films based on expanded graphite particles on a polymeric substrate material, polydimethylsiloxane (PDMS). The expanded graphite particles used in this work are prepared by utilizing bath sonication processes at the ultrasonic frequency of either the commercially available graphite flakes or graphite particles obtained through electrolysis under the interstitial substitution of silver and sulfate, respectively. The X-ray diffraction (XRD) patterns confirm that, due to the action of the bath sonication intercalation of graphite taking place, the resistances of the as-fabricated thin films is ultimately lowered. Mechanical characterizations, such as stretchability, flexibility and reliability tests were performed using home-made tools. The films were found to remain stretchable up to 40% tensile strain and 20% bending strain. These films were also found to remain functional when repeatedly flexed up to 1000 times.
Rapid TLC with Densitometry for Evaluation of Naproxen Stability
Authors: Parys Wioletta , Dołowy Małgorzata , Pyka-Pająk Alina
Published: August 2020

The purpose of the work was to develop such chromatographic conditions that allowed to separate as many naproxen degradation products as possible. In order to follow this process, thin-layer chromatography (TLC) coupled with densitometry and spectrodensitometry was used. A forced degradation study was performed using an ethanolic solution of naproxen spotted on silica gel plates, existing in the form of an aqueous solution at various pH values, and as solution prepared in saline and in hydrogen peroxide. Degradative effect of UV light on naproxen was watched in the context of naproxen spotted on plates precoated with silica gel and exposed to UV light, and also for its solution treated with UV light. However, the solution of naproxen prepared in water at pH ≈ 2.60 undergoes the largest changes as the results of its exposure to UV light during 10 h. Stressed samples of naproxen were analyzed by using a new and well validated TLC procedure including toluene (TOL)—acetone (ACE)—chloroform (CHL) (2:5:12, v/v/v) as mobile phase A and glacial acetic acid (AcOH)—n-hexane (Hex)—acetone (ACE)-(0.10:10:10, v/v/v) as mobile phase B. As the newly developed TLC-densitometric method can effectively separate the substances about pharmaceutical significance from products of its degradation, which are formed as a result of stress studies, is considered to be a good alternative and important tool in routine quality control and stability testing of naproxen in pharmaceutical formulations. These results indicate that proposed TLC-densitometric method is cost-effective, rapid, specific, accurate, and precise. This TLC procedure is comparable to HPLC and UPLC method in terms of detection the number of degradation products of naproxen. In addition, it realizes the criterion of linearity. A major advantage and novelty of proposed method is its low cost and ability to analyze examined drug and all degradation products simultaneously, including those which can be observed under intensive UV radiation exposure of naproxen solution which are not described by previous HPTLC studies available in the literature.
An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli
Authors: Kunna Mohammed Adam , Kadir Tuty Asmawaty Abdul , Remli Muhammad Akmal , Ali Noorlin Mohd , Moorthy Kohbalan , Muhammad Noryanti
Published: August 2020

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.
New Insights into Inflammatory Bowel Diseases from Proteomic and Lipidomic Studies
Authors: Longo Serena , Chieppa Marcello , Cossa Luca G. , Spinelli Chiara C. , Greco Marco , Maffia Michele , Giudetti Anna M.
Published: August 2020

Ulcerative colitis (UC) and Crohn’s disease (CD) represent the two main forms of chronic inflammatory bowel diseases (IBD). The exact IBD etiology is not yet revealed but CD and UC are likely induced by an excessive immune response against normal constituents of the intestinal microbial flora. IBD diagnosis is based on clinical symptoms often combined with invasive and costly procedures. Thus, the need for more non-invasive markers is urgent. Several routine laboratory investigations have been explored as indicators of intestinal inflammation in IBD, including blood testing for C-reactive protein, erythrocyte sedimentation rate, and specific antibodies, in addition to stool testing for calprotectin and lactoferrin. However, none has been universally adopted, some have been well-characterized, and others hold great promise. In recent years, the technological developments within the field of mass spectrometry (MS) and bioinformatics have greatly enhanced the ability to retrieve, characterize, and analyze large amounts of data. High-throughput research allowed enhancing the understanding of the biology of IBD permitting a more accurate biomarker discovery than ever before. In this review, we summarize currently used IBD serological and stool biomarkers and how proteomics and lipidomics are contributing to the identification of IBD biomarkers.
Exploratory Analysis of the Relationship between Happiness and Religious Participation within China
Authors: Liu Tianyuan , Wu Lin , Yang Yang , Jia Yu
Published: August 2020

Although the positive relationship between religion and happiness has aroused heated debate, empirical studies on this are limited in the Chinese cultural context. Furthermore, there is a lack of heterogeneity analysis concerning this influence. This paper aims to address this gap in the existing literature. Using the Chinese General Social Survey data from 2015 for empirical analysis, the results show that people with religious beliefs have an increased probability of feeling very happy. This positive association does not exist in urban and eastern groups, but it still holds up in other remaining sub-samples (i.e., rural group). This study further finds that the effect of religiosity on happiness varies by different religious identification. Muslims are more likely to feel very happy compared with non-Muslims, but people of the Christian faith do not rate themselves higher on the happiness scale than non-Christians. Moreover, the results also reveal that religious involvement is significantly and positively related to happiness. Specifically, vulnerable groups are more likely to perceive themselves to be happier from continuous religious participation, whereas advantaged groups do not. This is because vulnerable groups generally have a lack of social security, and religious practices provide them with social support among their members. They therefore can enjoy larger and denser social networks. These are vital mechanisms for them to cope with stress and risk.
Improved Faster RCNN Based on Feature Amplification and Oversampling Data Augmentation for Oriented Vehicle Detection in Aerial Images
Authors: Mo Nan , Yan Li
Published: August 2020

Vehicles in aerial images are generally with small sizes and unbalanced number of samples, which leads to the poor performances of the existing vehicle detection algorithms. Therefore, an oriented vehicle detection framework based on improved Faster RCNN is proposed for aerial images. First of all, we propose an oversampling and stitching data augmentation method to decrease the negative effect of category imbalance in the training dataset and construct a new dataset with balanced number of samples. Then considering that the pooling operation may loss the discriminative ability of features for small objects, we propose to amplify the feature map so that detailed information hidden in the last feature map can be enriched. Finally, we design a joint training loss function including center loss for both horizontal and oriented bounding boxes, and reduce the impact of small inter-class diversity on vehicle detection. The proposed framework is evaluated on the VEDAI dataset that consists of 9 vehicle categories. The experimental results show that the proposed framework outperforms previous approaches with a mean average precision of 60.4% and 60.1% in detecting horizontal and oriented bounding boxes respectively, which is about 8% better than Faster RCNN.
Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map
Authors: Zhang Yuzhen , Liang Shunlin
Published: August 2020

Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.
Convective Initiation Proxies for Nowcasting Precipitation Severity Using the MSG-SEVIRI Rapid Scan
Authors: Gallucci Donatello , De Natale Maria Pia , Cimini Domenico , Di Paola Francesco , Gentile Sabrina , Geraldi Edoardo , Larosa Salvatore , Nilo Saverio Teodosio , Ricciardelli Elisabetta , Viggiano Mariassunta , Romano Filomena
Published: August 2020

In this study, we investigate the ability of several convective initiation predictors based on satellite infrared observations to distinguish convective weak precipitation events from those leading to intense rainfall. The two types of precipitation are identified according to hourly rainfall, respectively less than 10 mm and greater than 30 mm. The analysis is conducted on a representative dataset containing 92 severe and weak precipitation events collected over the Italian peninsula in the period 2016–2019 over June-September. The events are selected to be short-lived (i.e., less than 12 h) and localized (i.e., less than 50×50km2). Italian National Radar Network products, namely the Vertical Maximum Intensity (VMI) and the Surface Rain Total (SRT) variables (from Dewetra Platform by CIMA, Italian Civil Protection Department), are used as indicators of convection (i.e., VMI greater than 35 dBZ echo intensity) and cumulated rainfall, respectively. The considered predictors are linear combinations of spectral infrared channels measured with the Rapid Scan Service (RSS) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) geostationary satellites. We select a 5×5 SEVIRI pixel-box centered on the storm core and perform a statistical analysis of the predictors up to 2.5 h around the event occurrence. We demonstrate that some of the proxies—describing growth and glaciation storm properties—show few degrees contrast between severe and nonsevere precipitation cases, hence carrying significant information to help discriminate the two types. We design a threshold scheme based on the three most informative predictors to distinguish weak and strong precipitation events. This analysis yields accuracy higher than 0.6 and the probability of false detection lower than 0.26; in terms of reducing false alarms, this method shows slight better performances compared to related works, at the expense of a lower probability of detection. The overall results, however, show limited capability for these infrared proxies as stand-alone predictors to distinguish severe from nonsevere precipitation events. Nonetheless, these may serve as additional tools to reduce the false alarm ratio in nowcasting algorithms for convective orographic storms. This study also provides further insight into the correlation between early infrared fields signatures prior to convection and subsequent evolution of the storms, extending previous works in this field.
Increasing Summer Rainfall and Asymmetrical Diurnal and Seasonal Warming Enhanced Vegetation Greenness in Temperate Deciduous Forests and Grasslands of Northern China
Authors: Yu Mei , Gao Qiong
Published: August 2020

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10−3 and 4.8 ± 0.116 × 10−3 yr−1, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.
A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
Authors: Mandel Nicolas , Milford Michael , Gonzalez Felipe
Published: August 2020

The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.
Analytical Study of Front-End Circuits Coupled to Silicon Photomultipliers for Timing Performance Estimation under the Influence of Parasitic Components
Authors: Calò Pietro Antonio Paolo , Petrignani Savino , Di Gioia Michele , Marzocca Cristoforo
Published: August 2020

Full exploitation of the intrinsic fast timing capabilities of analog silicon photomultipliers (SiPMs) requires suitable front-end electronics. Even a parasitic inductance of a few nH, associated to the interconnections between the SiPM and the preamplifier, can significantly degrade the steepness of the detector response, thus compromising the timing accuracy. In this work, we propose a simple analytic expression for the single-photon response of a SiPM coupled to the front-end electronics, as a function of the main parameters of the detector and the preamplifier, taking into account the parasitic inductance. The model is useful to evaluate the influence of each parameter of the system on the slope of its response and to guide the designer in the definition of the architecture and the specifications for the front-end electronics. The results provided by the model have been successfully compared with experimental measurements from a front-end circuit with variable configuration based on a bipolar junction transistor (BJT), coupled to a 3 × 3 mm2 SiPM stimulated by a fast-pulsed laser source.
Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support
Authors: Nikitin Yury , Božek Pavol , Peterka Jozef
Published: August 2020

The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.
Peptides, DNA and MIPs in Gas Sensing. From the Realization of the Sensors to Sample Analysis
Authors: Gaggiotti Sara , Della Pelle Flavio , Mascini Marcello , Cichelli Angelo , Compagnone Dario
Published: August 2020

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in ‘point of care’ diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases.
High-Accuracy Positioning Based on Pseudo-Ranges: Integrated Difference and Performance Analysis of the Loran System
Authors: Yan Baorong , Li Yun , Guo Wei , Hua Yu
Published: August 2020

The Long Range Navigation (Loran) system as a backup of the Global Navigation Satellite System (GNSS) is a good choice. The dominant deterioration factors of position accuracy are the pseudo-range measurement errors and the geometric dilution of precision (GDOP). This paper focuses on the algorithm integrated difference with pseudo-ranges to improve the position accuracy. Firstly, the theoretical prediction of propagation delay and raw measurement are compared. The results show that the measured pseudo-range consists of a constant term and a temporal term, which reflect the propagation situation along the true path. Secondly, a position solution algorithm based on a pseudo-range and difference is presented, exceeding the limit of a single chain. Finally, some simulation tests are implemented utilizing the new proposed position algorithm to verify the differential performance. This method can reduce the GDOP conveniently through increasing the number of transmitters. In view of the amplitude and characteristics of errors in measurement, systematic error and random noise are distinguished and discussed. The absolute accuracy responds to the pseudo-range bias that is different from geometric distance and repeatable accuracy is mainly influenced by random noise. The difference method can improve the absolute accuracy via the correction degree without changing the geometry of the transmitters.
Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods
Authors: Wang Qinghua , Yu Yuexiao , Ahmed Hosameldin O. A. , Darwish Mohamed , Nandi Asoke K.
Published: August 2020

In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges’ currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection. The two-terminal MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN, the SoftMax classifier performed better in detection and classification accuracy as well as testing speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based DNN and SoftMax classifier.
Synthetic Aperture Sonar (SAS) without Navigation: Scan Registration as Basis for Near Field Synthetic Imaging in 2D
Authors: Bülow Heiko , Birk Andreas
Published: August 2020

Sonars are essential for underwater sensing as they can operate over extended ranges and in poor visibility conditions. The use of a synthetic aperture is a popular approach to increase the resolution of sonars, i.e., the sonar with its N transducers is positioned at k places to generate a virtual sensor with kN transducers. The state of the art for synthetic aperture sonar (SAS) is strongly coupled to constraints, especially with respect to the trajectory of the placements and the need for good navigation data. In this article, we introduce an approach to SAS using registration of scans from single arrays, i.e., at individual poses of arbitrary trajectories, hence avoiding the need for navigation data of conventional SAS systems. The approach is introduced here for the near field using the coherent phase information of sonar scans. A Delay and Sum (D&S) beamformer (BF) is used, which directly operates on pixel/voxel form on a Cartesian grid supporting the registration. It is shown that this pixel/voxel-based registration and the coherent processing of several scans forming a synthetic aperture yields substantial improvements of the image resolution. The experimental evaluation is done with an advanced simulation tool generating realistic 2D sonar array data, i.e., with simulations of a linear 1D antenna reconstructing 2D images. For the image registration of the raw sonar scans, a robust implementation of a spectral method is presented. Furthermore, analyses with respect to the trajectories of the sensor locations are provided to remedy possible grating lobes due to the gaping positions of the transmitter devices.
Intelligent Controller Design by the Artificial Intelligence Methods
Authors: Nowaková Jana , Pokorný Miroslav
Published: August 2020

With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.