bluuebunny/crossref_metadata_2025 huggingface dataset:
Imidazole and Imidazolium Antibacterial Drugs Derived from Amino Acids
Authors: Valls Adriana , Andreu Jose J. , Falomir Eva , Luis Santiago V. , Atrián-Blasco Elena , Mitchell Scott G. , Altava Belén
Published: December 2020

The antibacterial activity of imidazole and imidazolium salts is highly dependent upon their lipophilicity, which can be tuned through the introduction of different hydrophobic substituents on the nitrogen atoms of the imidazole or imidazolium ring of the molecule. Taking this into consideration, we have synthesized and characterized a series of imidazole and imidazolium salts derived from L-valine and L-phenylalanine containing different hydrophobic groups and tested their antibacterial activity against two model bacterial strains, Gram-negative E. coli and Gram-positive B. subtilis. Importantly, the results demonstrate that the minimum bactericidal concentration (MBC) of these derivatives can be tuned to fall close to the cytotoxicity values in eukaryotic cell lines. The MBC value of one of these compounds toward B. subtilis was found to be lower than the IC50 cytotoxicity value for the control cell line, HEK-293. Furthermore, the aggregation behavior of these compounds has been studied in pure water, in cell culture media, and in mixtures thereof, in order to determine if the compounds formed self-assembled aggregates at their bioactive concentrations with the aim of determining whether the monomeric species were in fact responsible for the observed antibacterial activity. Overall, these results indicate that imidazole and imidazolium compounds derived from L-valine and L-phenylalanine—with different alkyl lengths in the amide substitution—can serve as potent antibacterial agents with low cytotoxicity to human cell lines.
Pharmacokinetic Characterization and External Evaluation of a Quantitative Framework of Sublingual Buprenorphine in Patients with an Opioid Disorder in Puerto Rico
Authors: Santiago Darlene , Mangas-Sanjuan Victor , Melin Kyle , Duconge Jorge , Zhao Wenchen , Venkataramanan Raman
Published: December 2020

Background: The aim of this analysis was to characterize the pharmacokinetics (PK) of sublingual buprenorphine (BUP) and its metabolites (buprenorphine glucuronide; BUP-g, norbuprenorphine; Nor-BUP, and norbuprenorphine glucuronide; Nor-BUP-g) in opioid use disorder (OUD) patients in Puerto Rico (PR) as a first step of evidence-based BUP dosing strategies in this population. Methods: BUP and metabolites concentrations were measured from 0 to 8 h after the administration of sublingual buprenorphine/naloxone films in 12 stable OUD subjects. Results: PK non-compartmental characteristics showed considerable variability in parameters between the subjects over the 8-h sampling time (tmax = 1.5 ± 0.7 h, Co = 1.6 ± 1.4 ng/mL, Cmax= 7.1 ± 6 ng/mL, and AUC0–8h = 26.8 ± 17.8 h·ng/mL). Subjects had a significantly higher tendency towards CYP-mediated N-demethylation, with the AUC0–8h ratios of the molar concentrations of [Nor-BUP + Nor-BUP-g] to BUP being (3.4 ± 1.9) significantly higher compared with BUP-g to BUP (0.19 ± 0.2). A two-compartment population-PK model with linear absorption (ka = 2.54 h−1), distribution (k12= 2.34 h−1, k14 = 1.29 h−1), metabolism (k24 = 1.28 × 10−1 h−1, k23 = 6.43 × 10−2 h−1, k35 = 1.23 × 10−1 h−1, k45 = 8.73 × 10−1 h−1), and elimination (k30 = 3.81 × 10−3 h−1, k50 = 1.27 × 10−1 h−1) adequately described the time-course of BUP and its metabolites, which has been externally validated using published data. Conclusions: Although limited in sampling time and number of recruited subjects, this study presents specific BUP PK characteristics that evidenced the need for additional PK studies and subsequent modeling of the data for the development of evidence-based dosing approaches in Puerto Rico.
Intranasal Nanoemulsions for Direct Nose-to-Brain Delivery of Actives for CNS Disorders
Authors: Bahadur Shiv , Pardhi Dinesh M. , Rautio Jarkko , Rosenholm Jessica M. , Pathak Kamla
Published: December 2020

The treatment of various central nervous system (CNS) diseases has been challenging, despite the rapid development of several novel treatment approaches. The blood–brain barrier (BBB) is one of the major issues in the treatment of CNS diseases, having major role in the protection of the brain but simultaneously constituting the main limiting hurdle for drugs targeting the brain. Nasal drug delivery has gained significant interest for brain targeting over the past decades, wherein the drug is directly delivered to the brain by the trigeminal and olfactory pathway. Various novel and promising formulation approaches have been explored for drug targeting to the brain by nasal administration. Nanoemulsions have the potential to avoid problems, including low solubility, poor bioavailability, slow onset of action, and enzymatic degradation. The present review highlights research scenarios of nanoemulsions for nose-to-brain delivery for the management of CNS ailments classified on the basis of brain disorders and further identifies the areas that remain unexplored. The significance of the total dose delivered to the target region, biodistribution studies, and long-term toxicity studies have been identified as the key areas of future research.
3D-Printed Veterinary Dosage Forms—A Comparative Study of Three Semi-Solid Extrusion 3D Printers
Authors: Sjöholm Erica , Mathiyalagan Rathna , Rajan Prakash Dhayakumar , Lindfors Lisa , Wang Qingbo , Wang Xiaoju , Ojala Samuli , Sandler Niklas
Published: December 2020

Currently, the number of approved veterinary medicines are limited, and human medications are used off-label. These approved human medications are of too high potencies for a cat or a small dog breed. Therefore, there is a dire demand for smaller doses of veterinary medicines. This study aims to investigate the use of three semi-solid extrusion 3D printers in a pharmacy or animal clinic setting for the extemporaneous manufacturing of prednisolone containing orodispersible films for veterinary use. Orodispersible films with adequate content uniformity and acceptance values as defined by the European Pharmacopoeia were produced with one of the studied printers, namely the Allevi 2 bioprinter. Smooth and flexible films with high mechanical strength, neutral pH, and low moisture content were produced with a high correlation between the prepared design and the obtained drug amount, indicating that the Allevi 2 printer could successfully be used to extemporaneously manufacture personalized doses for animals at the point-of-care.
THz Spectroscopy as a Versatile Tool for Filler Distribution Diagnostics in Polymer Nanocomposites
Authors: Gorokhov Gleb , Bychanok Dzmitry , Gayduchenko Igor , Rogov Yuriy , Zhukova Elena , Zhukov Sergei , Kadyrov Lenar , Fedorov Georgy , Ivanov Evgeni , Kotsilkova Rumiana , Macutkevic Jan , Kuzhir Polina
Published: December 2020

Polymer composites containing nanocarbon fillers are under intensive investigation worldwide due to their remarkable electromagnetic properties distinguished not only by components as such, but the distribution and interaction of the fillers inside the polymer matrix. The theory herein reveals that a particular effect connected with the homogeneity of a composite manifests itself in the terahertz range. Transmission time-domain terahertz spectroscopy was applied to the investigation of nanocomposites obtained by co-extrusion of PLA polymer with additions of graphene nanoplatelets and multi-walled carbon nanotubes. The THz peak of permittivity’s imaginary part predicted by the applied model was experimentally shown for GNP-containing composites both below and above the percolation threshold. The physical nature of the peak was explained by the impact on filler particles excluded from the percolation network due to the peculiarities of filler distribution. Terahertz spectroscopy as a versatile instrument of filler distribution diagnostics is discussed.
Dope-Dyeing of Polyvinyl Alcohol (PVA) Nanofibres with Remazol Yellow FG
Authors: Fadil Fatirah , Adli Farah Atiqah , Affandi Nor Dalila Nor , Harun Ahmad Mukifza , Alam Mohammad Khursheed
Published: December 2020

The lack of aesthetic properties of electrospun nanofibres in terms of colour appearance is the drive in this preliminary study. This research is conducted to study the dyeing behaviour and colorimetric properties of electrospun nanofibres blended with Remazol Yellow FG reactive dye using dope-dyeing method via electrospinning process. This paper reports the colorimetric properties of dyed poly vinyl alcohol (PVA) nanofibres within the range of 2.5 wt.% to 12.5 wt.% dye content. The electrospinning parameters were fixed at the electrospinning distance of 10 cm, constant feed rate of 0.5 mL/h and applied voltage of 15 kV. The resulting impregnated dye of 10 wt.% exhibits acceptable colour difference of dyed PVA nanofibres, with a mean fibre diameter of 177.1 ± 11.5 nm. The SEM micrographs show the effect of dye content on morphology and fibre diameter upon the increment of dye used. Further increase of dye content adversely affects the jet stability during the electrospinning, resulting in macroscopic dropping phenomenon. The presence of all prominent peaks of Remazol dye in the PVA nanofibers was supported with FTIR analysis. The addition of dye into the nanofibres has resulted in the enhancement of thermal stability of the PVA as demonstrated by TGA analysis.
Modified Fish Gelatin as an Alternative to Mammalian Gelatin in Modern Food Technologies
Authors: Derkach Svetlana R. , Voron’ko Nikolay G. , Kuchina Yuliya A. , Kolotova Daria S.
Published: December 2020

This review considers the main properties of fish gelatin that determine its use in food technologies. A comparative analysis of the amino acid composition of gelatin from cold-water and warm-water fish species, in comparison with gelatin from mammals, which is traditionally used in the food industry, is presented. Fish gelatin is characterized by a reduced content of proline and hydroxyproline which are responsible for the formation of collagen-like triple helices. For this reason, fish gelatin gels are less durable and have lower gelation and melting temperatures than mammalian gelatin. These properties impose significant restrictions on the use of fish gelatin in the technology of gelled food as an alternative to porcine and bovine gelatin. This problem can be solved by modifying the functional characteristics of fish gelatin by adding natural ionic polysaccharides, which, under certain conditions, are capable of forming polyelectrolyte complexes with gelatin, creating additional nodes in the spatial network of the gel.
The Bonding Mechanism of the Micro-Interface of Polymer Coated Steel
Authors: Liu Jiyang , Zhang Qingdong , Zhang Boyang , Yu Mingyang
Published: December 2020

As food and beverages require more and more green and safe packaging products, the emergence of polymer coated steel (PCS) has been promoted. PCS is a layered composite strip made of metal and polymer. To probe the bonding mechanism of PCS micro-interface, the substrate tin-free steel (TFS) was physically characterized by SEM and XPS, and cladding polyethylene terephthalate (PET) was simulated by first-principles methods of quantum mechanics (QM). We used COMPASS force field for molecular dynamics (MD) simulation. XPS pointed out that the element composition of TFS surface coating is Cr(OH)3, Cr2O3 and CrO3. The calculation results of MD and QM indicate that the chromium oxide and PET molecules compound in the form of acid-base interaction. The binding energies of Cr2O3 (110), (200), and (211) with PET molecules are −13.07 eV, −2.74 eV, and −2.37 eV, respectively. We established a Cr2O3 (200) model with different hydroxyl concentrations. It is proposed that the oxygen atom in C=O in the PET molecule combines with –OH on the surface of TFS to form a hydrogen bond. The binding energy of the PCS interface increases with the increase of the surface hydroxyl concentration of the TFS. It provides theoretical guidance and reference significance for the research on the bonding mechanism of PCS.
Adenine as Epoxy Resin Hardener for Sustainable Composites Production with Recycled Carbon Fibers and Cellulosic Fibers
Authors: Merighi Stefano , Mazzocchetti Laura , Benelli Tiziana , Giorgini Loris
Published: December 2020

In this work, Adenine is proposed, for the first time, as a cross-linker for epoxy resins. Adenine is an amino-substituted purine with heterocyclic aromatic structure showing both proton donors, and hydrogen bonding ability. DSC studies show that adenine is able to positively cross-link a biobased DGEBA-like commercial epoxy precursor with good thermal performance and a reaction mechanism based on a 1H NMR investigation has been proposed. The use of such a formulation to produce composite with recycled short carbon fibers (and virgin ones for the sake of comparison), as well as jute and linen natural fibers as sustainable reinforcements, leads to materials with high compaction and fiber content. The curing cycle was optimized for both carbon fiber and natural fiber reinforced materials, with the aim to achieve the better final properties. All composites produced display good thermal and mechanical properties with glass transition in the range of HT resins (Tg > 150 °C, E’ =26 GPa) for the carbon fiber-based composites. The natural fiber-based composites display slightly lower performance that is nonetheless good compared with standard composite performance (Tg about 115–120 °C, E’ = 7–9 GPa). The present results thus pave the way to the application of adenine as hardener system for composites production.
Personalised 3D Printed Fast-Dissolving Tablets for Managing Hypertensive Crisis: In-Vitro/In-Vivo Studies
Authors: Hussain Amjad , Mahmood Faisal , Arshad Muhammad Sohail , Abbas Nasir , Qamar Nadia , Mudassir Jahanzeb , Farhaj Samia , Nirwan Jorabar Singh , Ghori Muhammad Usman
Published: December 2020

Hypertensive crisis (HC) is an emergency health condition which requires an effective management strategy. Over the years, various researchers have developed captopril based fast-dissolving formulations to manage HC; however, primarily, the question of personalisation remains unaddressed. Moreover, commercially these formulations are available as in fixed-dose combinations or strengths, so the titration of dose according to patient’s prerequisite is challenging to achieve. The recent emergence of 3D printing technologies has given pharmaceutical scientists a way forward to develop personalised medicines keeping in view patients individual needs. The current project, therefore, is aimed at addressing the limitations as mentioned above by developing fast-dissolving captopril tablets using 3D printing approach. Captopril unloaded (F1) and loaded (F2-F4) filaments were successfully produced with an acceptable drug loading and mechanical properties. Various captopril formulations (F2–F4) were successfully printed using fused deposition modelling technique. The results revealed that the formulations (F2 and F3) containing superdisintegrant had a faster extent of dissolution and in-vivo findings were endorsing these results. The present study has successfully exhibited the utilisation of additive manufacturing approach to mend the gap of personalisation and manufacturing fast-dissolving captopril 3D printed tablets. The procedure adopted in the present study may be used for the development of fused deposition modelling (FDM) based fast-dissolving 3D printed tablets.
Biopolymer Coatings for Biomedical Applications
Authors: Nathanael A. Joseph , Oh Tae Hwan
Published: December 2020

Biopolymer coatings exhibit outstanding potential in various biomedical applications, due to their flexible functionalization. In this review, we have discussed the latest developments in biopolymer coatings on various substrates and nanoparticles for improved tissue engineering and drug delivery applications, and summarized the latest research advancements. Polymer coatings are used to modify surface properties to satisfy certain requirements or include additional functionalities for different biomedical applications. Additionally, polymer coatings with different inorganic ions may facilitate different functionalities, such as cell proliferation, tissue growth, repair, and delivery of biomolecules, such as growth factors, active molecules, antimicrobial agents, and drugs. This review primarily focuses on specific polymers for coating applications and different polymer coatings for increased functionalization. We aim to provide broad overview of latest developments in the various kind of biopolymer coatings for biomedical applications, in order to highlight the most important results in the literatures, and to offer a potential outline for impending progress and perspective. Some key polymer coatings were discussed in detail. Further, the use of polymer coatings on nanomaterials for biomedical applications has also been discussed, and the latest research results have been reported.
Insight into the Structure and Dynamics of Polymers by Neutron Scattering Combined with Atomistic Molecular Dynamics Simulations
Authors: Arbe Arantxa , Alvarez Fernando , Colmenero Juan
Published: December 2020

Combining neutron scattering and fully atomistic molecular dynamics simulations allows unraveling structural and dynamical features of polymer melts at different length scales, mainly in the intermolecular and monomeric range. Here we present the methodology developed by us and the results of its application during the last years in a variety of polymers. This methodology is based on two pillars: (i) both techniques cover approximately the same length and time scales and (ii) the classical van Hove formalism allows easily calculating the magnitudes measured by neutron scattering from the simulated atomic trajectories. By direct comparison with experimental results, the simulated cell is validated. Thereafter, the information of the simulations can be exploited, calculating magnitudes that are experimentally inaccessible or extending the parameters range beyond the experimental capabilities. We show how detailed microscopic insight on structural features and dynamical processes of various kinds has been gained in polymeric systems with different degrees of complexity, and how intriguing questions as the collective behavior at intermediate length scales have been faced.
Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps
Authors: Maxwell Aaron , Bester Michelle , Guillen Luis , Ramezan Christopher , Carpinello Dennis , Fan Yiting , Hartley Faith , Maynard Shannon , Pyron Jaimee
Published: December 2020

Historic topographic maps, which are georeferenced and made publicly available by the United States Geological Survey (USGS) and the National Map’s Historical Topographic Map Collection (HTMC), are a valuable source of historic land cover and land use (LCLU) information that could be used to expand the historic record when combined with data from moderate spatial resolution Earth observation missions. This is especially true for landscape disturbances that have a long and complex historic record, such as surface coal mining in the Appalachian region of the eastern United States. In this study, we investigate this specific mapping problem using modified UNet semantic segmentation deep learning (DL), which is based on convolutional neural networks (CNNs), and a large example dataset of historic surface mine disturbance extents from the USGS Geology, Geophysics, and Geochemistry Science Center (GGGSC). The primary objectives of this study are to (1) evaluate model generalization to new geographic extents and topographic maps and (2) to assess the impact of training sample size, or the number of manually interpreted topographic maps, on model performance. Using data from the state of Kentucky, our findings suggest that DL semantic segmentation can detect surface mine disturbance features from topographic maps with a high level of accuracy (Dice coefficient = 0.902) and relatively balanced omission and commission error rates (Precision = 0.891, Recall = 0.917). When the model is applied to new topographic maps in Ohio and Virginia to assess generalization, model performance decreases; however, performance is still strong (Ohio Dice coefficient = 0.837 and Virginia Dice coefficient = 0.763). Further, when reducing the number of topographic maps used to derive training image chips from 84 to 15, model performance was only slightly reduced, suggesting that models that generalize well to new data and geographic extents may not require a large training set. We suggest the incorporation of DL semantic segmentation methods into applied workflows to decrease manual digitizing labor requirements and call for additional research associated with applying semantic segmentation methods to alternative cartographic representations to supplement research focused on multispectral image analysis and classification.
Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network
Authors: Samarin Maxim , Zweifel Lauren , Roth Volker , Alewell Christine
Published: December 2020

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25–0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F1-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method suitable to efficiently and successfully capture the temporal trends and spatial heterogeneity of degradation in alpine grasslands. Additionally, U-Net is a powerful and robust tool to map erosion sites in a predictive manner utilising large amounts of new aerial imagery.
Estimation of Land Surface Incident and Net Shortwave Radiation from Visible Infrared Imaging Radiometer Suite (VIIRS) Using an Optimization Method
Authors: Zhang Yi , Liang Shunlin , He Tao , Wang Dongdong , Yu Yunyue
Published: December 2020

Incident surface shortwave radiation (ISR) is a key parameter in Earth’s surface radiation budget. Many reanalysis and satellite-based ISR products have been developed, but they often have insufficient accuracy and resolution for many applications. In this study, we extended our optimization method developed earlier for the MODIS data with several major improvements for estimating instantaneous and daily ISR and net shortwave radiation (NSR) from Visible Infrared Imaging Radiometer Suite observations (VIIRS), including (1) an integrated framework that combines look-up table and parameter optimization; (2) enabling the calculation of net shortwave radiation (NSR) as well as daily values; and (3) extensive global validation. We validated the estimated ISR values using measurements at seven Surface Radiation Budget Network (SURFRAD) sites and 33 Baseline Surface Radiation Network (BSRN) sites during 2013. The root mean square errors (RMSE) over SURFRAD sites for instantaneous ISR and NSR were 83.76 W/m2 and 66.80 W/m2, respectively. The corresponding daily RMSE values were 27.78 W/m2 and 23.51 W/m2. The RMSE at BSRN sites was 105.87 W/m2 for instantaneous ISR and 32.76 W/m2 for daily ISR. The accuracy is similar to the estimation from MODIS data at SURFRAD sites but the computational efficiency has improved by approximately 50%. We also produced global maps that demonstrate the potential of this algorithms to generate global ISR and NSR products from the VIIRS data.
Small Scale Rain Field Sensing and Tomographic Reconstruction with Passive Geostationary Satellite Receivers
Authors: Csurgai-Horváth László
Published: December 2020

Sensing of the rain rate and the movement of the rain field over a relatively small geographical area may often be necessary for agricultural purposes, water management objectives or transport management in airports or harbors. In this paper, an entirely passive method is suggested to sense rainfall rate and extension of a rain field, with reconstruction in two dimensions using a tomographic method. In order to achieve this goal, multiple colocated satellite receivers are proposed to measure the beacon signal levels of geostationary satellites received at different azimuth angles. The tomographic reconstruction of the rain field takes the advantage of the dis-placement of the rain field due to the movement of air masses, while the rain intensity along the receiving path is estimated by the attenuation of the radio signal between the satellite and the ground receiver. To realize the proposed system, a complete receiver setup and an existing, operational group of satellites are selected. A widely known rain field modeling method is applied to generate test data and evaluate the system. The feasibility of a technically realizable system is provided, and its capabilities are compared with a hypothetical, ideal system. The methods and algorithms are tested on an existing geographical location, simulating a real operating environment.
Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks
Authors: Hu Anna , Xie Zhong , Xu Yongyang , Xie Mingyu , Wu Liang , Qiu Qinjun
Published: December 2020

One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.
Development of CO2 Band-Based Cloud Emission and Scattering Indices and Their Applications to FY-3D Hyperspectral Infrared Atmospheric Sounder
Authors: Xia Xinlu , Zou Xiaolei
Published: December 2020

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Feng Yun-3D (FY-3D) satellite is the first Chinese hyperspectral infrared instrument. In this study, an improved cloud detection scheme using brightness temperature observations from paired HIRAS long-wave infrared (LWIR) and short-wave infrared (SWIR) channels at CO2 absorption bands (15-μm and 4.3-μm) is developed. The weighting function broadness and a set of height-dependent thresholds of cloud-sensitive-level differences are incorporated into pairing LWIR and SWIR channels. HIRAS brightness temperature observations made under clear-sky conditions during a training period are used to develop a set of linear regression equations between paired LWIR and SWIR channels. Moderate-resolution Imaging Spectroradiometer (MODIS) cloud mask data are used for selecting HIRAS clear-sky observations. Cloud Emission and Scattering Indices (CESIs) are defined as the differences in SWIR channels between HIRAS observations and regression simulations from LWIR observations. The cloud retrieval products of ice cloud optical depth and cloud-top pressure from the Atmospheric Infrared Sounder (AIRS) are used to illustrate the effectiveness of the proposed cloud detection scheme for FY-3D HIRAS observations. Results show that the distributions of modified CESIs at different altitudes can capture features in the distributions of AIRS-retrieved ice cloud optical depth and cloud-top pressure better than the CESIs obtained by the original method.
An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data
Authors: Xiang Kunlun , Yuan Wenping , Wang Liwen , Deng Yujiao
Published: December 2020

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.
Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure
Authors: Tonelli Daniel , Luchetta Michele , Rossi Francesco , Migliorino Placido , Zonta Daniele
Published: December 2020

The increasing number of bridges approaching their design life has prompted researchers and operators to develop innovative structural health monitoring (SHM) techniques. An acoustic emissions (AE) method is a passive SHM approach based on the detection of elastic waves in structural components generated by damages, such as the initiation and propagation of cracks in concrete and the failure of steel wires. In this paper, we discuss the effectiveness of AE techniques by analyzing records acquired during a load test on a full-size prestressed concrete bridge span. The bridge is a 1968 structure currently decommissioned but perfectly representative, by type, age, and deterioration state of similar bridges in operation on the Italian highway network. It underwent a sequence of loading and unloading cycles with a progressively increasing load up to failure. We analyzed the AE signals recorded during the load test and examined how far their features (number of hits, amplitude, signal strength, and peak frequency) allow us to detect, quantify, and classify damages. We conclude that AE can be successfully used in permanent monitoring to provide information on the cracking state and the maximum load withstood. They can also be used as a non-destructive technique to recognize whether a structural member is cracked. Finally, we noticed that AE allow classifying different types of damage, although further experiments are needed to establish and validate a robust classification procedure.
Integration of Industrially-Oriented Human-Robot Speech Communication and Vision-Based Object Recognition
Authors: Rogowski Adam , Bieliszczuk Krzysztof , Rapcewicz Jerzy
Published: December 2020

This paper presents a novel method for integration of industrially-oriented human-robot speech communication and vision-based object recognition. Such integration is necessary to provide context for task-oriented voice commands. Context-based speech communication is easier, the commands are shorter, hence their recognition rate is higher. In recent years, significant research was devoted to integration of speech and gesture recognition. However, little attention was paid to vision-based identification of objects in industrial environment (like workpieces or tools) represented by general terms used in voice commands. There are no reports on any methods facilitating the abovementioned integration. Image and speech recognition systems usually operate on different data structures, describing reality on different levels of abstraction, hence development of context-based voice control systems is a laborious and time-consuming task. The aim of our research was to solve this problem. The core of our method is extension of Voice Command Description (VCD) format describing syntax and semantics of task-oriented commands, as well as its integration with Flexible Editable Contour Templates (FECT) used for classification of contours derived from image recognition systems. To the best of our knowledge, it is the first solution that facilitates development of customized vision-based voice control applications for industrial robots.
Improved Self-Calibration of a Multilateration System Based on Absolute Distance Measurement
Authors: Nguyen Quoc Khanh , Kim Seungman , Han Seong-Heum , Ro Seung-Kook , Kim Seung-Woo , Kim Young-Jin , Kim Wooram , Oh Jeong Seok
Published: December 2020

Multilateration tracking systems (MLTSs) are used in industrial three-dimensional (3D) coordinate measuring applications. For high-precision measurement, system parameters must be calibrated properly in advance. For an MLTS using absolute distance measurement (ADM), the conventional self-calibration method significantly reduces estimation efficiency because all system parameters are estimated simultaneously using a complicated residual function. This paper presents a novel self-calibration method that optimizes ADM to reduce the number of system parameters via highly precise and separate estimations of dead paths. Therefore, the residual function to estimate the tracking station locations can be simplified. By applying a suitable mathematical procedure and solving the initial guess problem without the aid of an external device, estimation accuracy of the system parameters is significantly improved. In three self-calibration experiments, with ADM repeatability of approximately 3.4 µm, the maximum deviation of the system parameters estimated by the proposed self-calibration method was 68.6 µm, while the maximum deviation estimated by the conventional self-calibration method was 711.9 µm. Validation of 3D coordinate measurements in a 1000 mm × 1000 mm × 1000 mm volume showed good agreement between the proposed ADM-based MLTS and a commercial laser tracker, where the maximum difference based on the standard deviation was 17.7 µm. Conversely, the maximum difference was 98.8 µm using the conventional self-calibration method. These results confirmed the efficiency and feasibility of the proposed self-calibration method.
Simulation of Design Factors of a Clutch Pack for Power-Shift Transmission for an Agricultural Tractor
Authors: Siddique Md. Abu Ayub , Kim Wan-Soo , Kim Yeon-Soo , Baek Seung-Yun , Baek Seung-Min , Kim Yong-Joo , Park Seong-Un , Choi Chang-Hyun
Published: December 2020

The objective of this study is the simulation of the most affected design factors and variables of the clutch pack for the power-shift transmission (PST) of a tractor based measured data. The simulation model, the mathematical model of sliding velocity, a moment of inertia, and clutch engagement pressure of clutch pack were developed using the powertrain and configurations of the real PST tractor. In this study, the sensor fusion method was used to precisely measure the proportional valve pressure by test bench, which was applied to the simulation model. The clutch engagement times were found 1.20 s at all temperatures for determined factors. The engagement pressures have a significant difference at various temperatures (25 to 100 °C) of the hydraulic oils after the 1.20 s but the most affected factors were satisfied with the simulation conditions that ensure the clutch engagement on time. Finally, this sensor fusion method is believed to be helpful in realizing precision agriculture through minimization of power loss and maximum energy efficiency of tractors.
Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System
Authors: Song Shaoyu , Chen Hui , Sun Hongwei , Liu Meicen
Published: December 2020

Reinforcement learning (RL) is a promising direction in automated parking systems (APSs), as integrating planning and tracking control using RL can potentially maximize the overall performance. However, commonly used model-free RL requires many interactions to achieve acceptable performance, and model-based RL in APS cannot continuously learn. In this paper, a data-efficient RL method is constructed to learn from data by use of a model-based method. The proposed method uses a truncated Monte Carlo tree search to evaluate parking states and select moves. Two artificial neural networks are trained to provide the search probability of each tree branch and the final reward for each state using self-trained data. The data efficiency is enhanced by weighting exploration with parking trajectory returns, an adaptive exploration scheme, and experience augmentation with imaginary rollouts. Without human demonstrations, a novel training pipeline is also used to train the initial action guidance network and the state value network. Compared with path planning and path-following methods, the proposed integrated method can flexibly co-ordinate the longitudinal and lateral motion to park a smaller parking space in one maneuver. Its adaptability to changes in the vehicle model is verified by joint Carsim and MATLAB simulation, demonstrating that the algorithm converges within a few iterations. Finally, experiments using a real vehicle platform are used to further verify the effectiveness of the proposed method. Compared with obtaining rewards using simulation, the proposed method achieves a better final parking attitude and success rate.
Platform for Analysis and Labeling of Medical Time Series
Authors: Fedjajevs Andrejs , Groenendaal Willemijn , Agell Carlos , Hermeling Evelien
Published: December 2020

Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.
Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed
Authors: Przybył Krzysztof , Wawrzyniak Jolanta , Koszela Krzysztof , Adamski Franciszek , Gawrysiak-Witulska Marzena
Published: December 2020

This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.
Video-Tachometer Methodology for Wind Turbine Rotor Speed Measurement
Authors: Natili Francesco , Castellani Francesco , Astolfi Davide , Becchetti Matteo
Published: December 2020

The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates.
Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau
Authors: Li Wenlong , Xue Pengfei , Liu Chenli , Yan Hepiao , Zhu Gaofeng , Cao Yapeng
Published: December 2020

As an important part of the wetland ecosystem, alpine wetland is not only one of the most important ecological water conservation areas in the Qinghai–Tibet Plateau region, but is also an effective regulator of the local climate. In this study, using three machine learning algorithms to extract wetland, we employ the landscape ecological index to quantitatively analyze the evolution of landscape patterns and grey correlation to analyze the driving factors of Zoige wetland landscape pattern change from 1995 to 2020. The following results were obtained. (1) The random forest algorithm (RF) performs best when dealing with high-dimensional data, and the accuracy of the decision tree algorithm (DT) is better. The performance of the RF and DT is better than that of the support vector machine algorithm. (2) The alpine wetland in the study area was degraded from 1995 to 2015, whereas wetland area began to increase after 2015. (3) The results of landscape analysis show the decrease in wetland area from 1995 to 2005 was mainly due to the fragmentation of larger patches into many small patches and loss of the original small patches, while the 2005 to 2015 decrease was caused by the loss of many middle patches and the decrease in large patches from the edge to the middle. The 2015 to 2020 increase is due to an increase in the number of smaller patches and recovery of original wetland area. (4) The grey correlation degree further shows that precipitation and evaporation are the main factors leading to the change in the landscape pattern of Zoige alpine wetland. The results are of great significance to the long-term monitoring of the Zoige wetland ecosystem.
Gamma-Ray Spectral Unfolding of CdZnTe-Based Detectors Using a Genetic Algorithm
Authors: Sarzi Amadè Nicola , Bettelli Manuele , Zambelli Nicola , Zanettini Silvia , Benassi Giacomo , Zappettini Andrea
Published: December 2020

The analysis of γ-ray spectra can be an arduous task, especially in the case of room temperature semiconductor detectors, where several distortions and instrumental artifacts conceal the true spectral shape. We developed a genetic algorithm to perform the unfolding of γ-spectra in order to restore the true energy distribution of the incoming radiation. We successfully validated our approach on experimental spectra of four radionuclides (241Am, 57Co, 137Cs and 133Ba) acquired with two CdZnTe-based detectors with different contact geometries (single pixel and drift strip). The unfolded spectra consist of δ-like peaks in correspondence with the radiation emissions of each radioisotope.