bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Characterization of Electrospun Fiber-Based Poly(ethylene-co-vinyl Alcohol) Films of Application Interest as High-Gas-Barrier Interlayers in Food Packaging
Authors: Melendez-Rodriguez Beatriz , Torres-Giner Sergio , Zavagna Lorenzo , Sammon Chris , Cabedo Luis , Prieto Cristina , Lagaron Jose M.
Published: June 2021

In the present study, poly(ethylene-co-vinyl alcohol) with 44 mol % ethylene content (EVOH44) was managed to be processed, for the first time, by electrospinning assisted by the coaxial technology of solvent jacket. In addition to this, different suspensions of cellulose nanocrystals (CNCs), with contents ranging from 0.1 to 1.0 wt %, were also electrospun to obtain hybrid bio-/non-bio nanocomposites. The resultant fiber mats were thereafter optimally annealed to promote interfiber coalescence at 145 °C, below the EVOH44 melting point, leading to continuous transparent fiber-based films. The morphological analysis revealed the successful distribution of CNCs into EVOH44 up to contents of 0.5 wt %. The incorporation of CNCs into the ethylene-vinyl alcohol copolymer caused a decrease in the crystallization and melting temperatures (TC and Tm) of about 12 and 7 °C, respectively, and also crystallinity. However, the incorporation of CNCs led to enhanced thermal stability of the copolymer matrix for a nanofiller content of 1.0 wt %. Furthermore, the incorporation of 0.1 and 0.5 wt % CNCs produced increases in the tensile modulus (E) of ca. 38% and 28%, respectively, but also yielded a reduction in the elongation at break and toughness. The oxygen barrier of the hybrid nanocomposite fiber-based films decreased with increasing the CNCs content, but they were seen to remain high barrier, especially in the low relative humidity (RH) regime, i.e., at 20% RH, showing permeability values lower than 0.6 × 10−20 m3·m·m−2·Pa−1·s−1. In general terms, an optimal balance in physical properties was found for the hybrid copolymer composite with a CNC loading of 0.1 wt %. On the overall, the present study demonstrates the potential of annealed electrospun fiber-based high-barrier polymers, with or without CNCs, to develop novel barrier interlayers to be used as food packaging constituents.
Gold-Nanoparticle-Deposited TiO2 Nanorod/Poly(Vinylidene Fluoride) Composites with Enhanced Dielectric Performance
Authors: Kum-onsa Pornsawan , Chanlek Narong , Manyam Jedsada , Thongbai Prasit , Harnchana Viyada , Phromviyo Nutthakritta , Chindaprasirt Prinya
Published: June 2021

Flexible dielectric polymer composites have been of great interest as embedded capacitor materials in the electronic industry. However, a polymer composite has a low relative dielectric permittivity (ε′ < 100), while its dielectric loss tangent is generally large (tanδ > 0.1). In this study, we fabricate a novel, high-permittivity polymer nanocomposite system with a low tanδ. The nanocomposite system comprises poly(vinylidene fluoride) (PVDF) co-filled with Au nanoparticles and semiconducting TiO2 nanorods (TNRs) that contain Ti3+ ions. To homogeneously disperse the conductive Au phase, the TNR surface was decorated with Au-NPs ~10–20 nm in size (Au-TNRs) using a modified Turkevich method. The polar β-PVDF phase was enhanced by the incorporation of the Au nanoparticles, partially contributing to the enhanced ε′ value. The introduction of the Au-TNRs in the PVDF matrix provided three-phase Au-TNR/PVDF nanocomposites with excellent dielectric properties (i.e., high ε′ ≈ 157 and low tanδ ≈ 0.05 at 1.8 vol% of Au and 47.4 vol% of TNRs). The ε′ of the three-phase Au-TNR/PVDF composite is ~2.4-times higher than that of the two-phase TNR/PVDF composite, clearly highlighting the primary contribution of the Au nanoparticles at similar filler loadings. The volume fraction dependence of ε′ is in close agreement with the effective medium percolation theory model. The significant enhancement in ε′ was primarily caused by interfacial polarization at the PVDF–conducting Au nanoparticle and PVDF–semiconducting TNR interfaces, as well as by the induced β-PVDF phase. A low tanδ was achieved due to the inhibited conducting pathway formed by direct Au nanoparticle contact.
Shrinkage Stress and Temperature Variation in Resin Composites Cured via Different Photoactivation Methods: Insights for Standardisation of the Photopolymerisation
Authors: dos Santos Sousa Guilherme , Guimarães Gabriel Felipe , Marcelino Edilmar , Rodokas José Eduardo Petit , de Oliveira Júnior Arilson José , Cesarino Ivana , Leão Alcides Lopes , dos Santos Riccardi Carla , Arjmand Mohammad , Simões Rafael Plana
Published: June 2021

The literature has shown that there is no consensus regarding the best resin composite photoactivation protocol. This study evaluated the efficiency of the conventional, soft-start, pulse-delay and exponential protocols for photoactivation of resin composites in reducing the shrinkage stress and temperature variation during the photopolymerisation. The photoactivation processes were performed using a photocuring unit and a smartphone app developed to control the irradiance according each photoactivation protocol. These photoactivation methods were evaluated applying photoactivation energies recommended by the resins manufactures. Three brands of resin composites were analysed: Z-250, Charisma and Ultrafill. The cure effectiveness was evaluated through depth of cure experiments. All results were statistically evaluated using one-way and multi-factor analysis of variance (ANOVA). The use of exponential and pulse-delay methods resulted in a significant reduction of the shrinkage stress for all evaluated resins; however, the pulse-delay method required too long a photoactivation time. The increases on the temperature were lower when the exponential photoactivation was applied; however, the temperature variation for all photoactivation protocols was not enough to cause damage in the restoration area. The evaluation of the depth of cure showed that all photoactivation protocols resulted in cured resins with equivalent hardness, indicating that the choice of an alternative photoactivation protocol did not harm the polymerisation. In this way, the results showed the exponential protocol as the best photoactivation technique for practical applications.
The Development and Characterization of a Cotton–Chitosan Composite for Lead Removal from Water
Authors: Alonso-Segura Diana , Hernández-García Luis , Menchaca-Arredondo Jorge , Sánchez Mario , Chamorro-Garza Belén , Garza-Hernández Raquel
Published: June 2021

Heavy metals in water are a serious environmental problem due to their accumulation and toxicity; there are several processes we can use to address this issue, but adsorption is the most popular due to its simplicity and efficiency. Polysaccharides such as cellulose have received attention as adsorbents for heavy metals, and cotton–chitosan composites (CCs) were developed here with nontoxic reagents such as carboxylic acids as crosslinkers and NaH2PO4 as a catalyst to achieve chitosan covalent crosslinkage into oxidized cotton textiles with H2O2. The composites were characterized by fourier-transform infrared spectroscopy (FTIR), elemental analysis (EA), X-ray photoelectron spectroscopy (XPS), atomic-force and scanning electron microscopy (AFM and SEM), and tensile strength; the adsorption of lead ions (Pb) was evaluated with cotton–chitosan composites and quantified by microwave plasma atomic emission spectroscopy (MP-AES). The composites showed a maximum incorporation of chitosan of 27.62 mg per gram of cotton textile. A tensile strength analysis of the composite showed a Young’s modulus approximately 1 MPa higher than that of cotton textile. The adsorption of lead ions with composites in an aqueous solution at pH 5 and 25 °C was circa 74% after 6 h of contact, as determined by MP-AES. This work is an approach to demonstrate the potential of these polysaccharides, modified by “green” procedures to remove pollutants from water.
Empirical Study of Foundry Efficiency Improvement Based on Data-Driven Techniques
Authors: Chen Kuentai , Wang Chien-Chih , Kuo Chi-Hung
Published: June 2021

In this paper, a data-driven approach was applied to improve a furnace zone of a foundry in Taiwan. Improvements are based on the historical production records, order-scheduling, and labor-scheduling data. To resolve the bottleneck provided by the company, historical data were analyzed, and the existence of large variance in the process was found. Statistical analysis was performed to identify the primal factors causing the variance, and suggestions were made and implemented to the production line. As a result, daily production increased steadily to more than 30 pots of molten metal, while the original production was 20–30 pots of molten metal and are not controllable. Such significant improvement was mainly made by standardizing the input and reducing the variance of processes. The average cycle time of each pot of molten metal was reduced from 219 min to 135 min. Our suggested improvements also reduced the foundry’s electricity consumption cost by almost $240,000NT per month. In summary, data analysis can help traditional industries in identifying the main factors causing the bottleneck.
Successful Pass Schedule Design in Open-Die Forging Using Double Deep Q-Learning
Authors: Reinisch Niklas , Rudolph Fridtjof , Günther Stefan , Bailly David , Hirt Gerhard
Published: June 2021

In order to not only produce an open-die forged part with the desired final geometry but to also maintain economic production, precise process planning is necessary. However, due to the incremental forming of the billet, often with several hundred strokes, the process design is arbitrarily complicated and, even today, often only based on experience or simple mathematical models describing the geometry development. Hence, in this paper, fast process models were merged with a double deep Q-learning algorithm to enable a pass schedule design including multi-objective optimization. The presented implementation of a double deep Q-learning algorithm was successfully trained on an industrial-scale forging process and converged stably against high reward values. The generated pass schedules reliably produced the desired final ingot geometry, utilized the available press force well without exceeding plant limits, and, at the same time, minimized the number of passes. Finally, a forging experiment was performed at the institute of metal forming to validate the generated results. Overall, a proof of concept for the pass schedule design in open-die forging via double deep Q-learning was achieved which opens various starting points for future work.
A QM/MM Evaluation of the Missing Step in the Reduction Mechanism of HMG-CoA by Human HMG-CoA Reductase
Authors: Mihaljević-Jurič Paula , Sousa Sérgio F.
Published: June 2021

Statins are important drugs in the regulation of cholesterol levels in the human body that have as a primary target the enzyme β-hydroxy-β-methylglutaryl-CoA reductase (HMGR). This enzyme plays a crucial role in the mevalonate pathway, catalyzing the four-electron reduction of HMG-CoA to mevalonate. A second reduction step of this reaction mechanism has been the subject of much speculation in the literature, with different conflicting theories persisting to the present day. In this study, the different mechanistic hypotheses were evaluated with atomic-level detail through a combination of molecular dynamics simulations (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. The obtained Gibbs free activation and Gibbs free reaction energy (15 kcal mol−1 and −40 kcal mol−1) show that this hydride step takes place with the involvement of a cationic His405 and Lys639, and a neutral Glu98, while Asp715 remains in an anionic state. The results provide an atomic-level portrait of this step, clearly demonstrating the nature and protonation state of the amino acid residues involved, the energetics associated, and the structure and charge of the key participating atoms in the several intermediate states, finally elucidating this missing step.
Docetaxel for Breast Cancer Treatment-Side Effects on Ocular Surface, a Systematic Review
Authors: Stoicescu Elena Andreea , Burcea Marian , Iancu Raluca Claudia , Zivari Mirela , Popa Cherecheanu Alina , Bujor Inna Adriana , Rastoaca Cristina , Iancu George
Published: June 2021

Docetaxel is a very effective chemotherapeutic agent for the treatment of metastatic or locally advanced breast cancer. Epiphora (hyperlacrimation) has been shown to be the most common eye condition in patients receiving docetaxel-based chemotherapy. This symptom does not decrease visual acuity, but decreases the quality of life. Daily activities (reading, working on the computer, watching TV, and so on) are affected, with patients complaining about an alteration of daily life with the appearance of this symptom. The mechanism by which epiphora occurs is considered to be the canalicular stenosis, but the trials on the subject failed to reach statistical significance. The objective of this scoping review is to determine whether there is a treatment regimen-dependent relationship between docetaxel administration and the presence of epiphora in women with breast cancer. The inclusion criteria were met by 10 trials, from which one was excluded owing to data selection biases. Accordingly, nine studies were evaluated quantitatively and qualitatively in the present review. We included subjects with docetaxel as single treatment or docetaxel in combination with other chemotherapy compounds. The occurrence of epiphora among subjects treated with docetaxel, regardless of the therapeutic regimen used, was statistically significant (p = 0.005). The proportion of patients with epiphora after weekly administration of docetaxel (54 out of 131 subjects, 41.22%) was different compared with that of those who received docetaxel at three week intervals (112 out of 325 subjects, 34.15%), but the difference between the two was not statistically significant (p = 0.732). The present study demonstrates that epiphora occurs more frequently in patients receiving weekly docetaxel-based chemotherapy than those taking the three-weekly regimen, but the difference is not statistically significant. Ophthalmologic assessment of all patients starting this treatment is recommended. The causal relationship between canalicular stenosis and epiphora is not fully elucidated as long as this ocular symptom occurs in women who do not have stenosis of the lacrimal system. Further well-designed trials are required to bring new insights into the mechanisms of epiphora pathogenesis in subjects treated with docetaxel.
The Zinc-Air Battery Performance with Ni-Doped MnO2 Electrodes
Authors: Chowdhury Anuradha , Lee Kuan-Ching , Wei Lim Mitchell Shyan , Pan Kuan-Lun , Chen Jyy Ning , Chong Siewhui , Huang Chao-Ming , Pan Guan-Ting , Yang Thomas Chung-Kuang
Published: June 2021

A rechargeable zinc-air battery shows great promise because of its high energy density, low cost, greater safety, and its environment-friendly properties. However, rechargeable zinc-air battery development has been hindered by the lack of a satisfactory bi-functional electrode. In this research, we report on a solution which uses electro-deposition to dope nickel into manganese on the stainless-steel mesh. The result shows the hydroxyl group on the prepared samples improving its oxygen reduction reaction and oxygen evolution reaction performance, as well as boosting the ion diffusion rate and stabilizing the zinc-air battery charge-discharge performance (overall potential gap dropped from 0.84 V to 0.82 V after 1000 cycles). This study contributes to our understanding of a new method for the improvement of bi-functional electrodes.
The Role of Heterotrophic Microalgae in Waste Conversion to Biofuels and Bioproducts
Authors: Silva Teresa Lopes da , Moniz Patrícia , Silva Carla , Reis Alberto
Published: June 2021

In the last few decades, microalgae have attracted attention from the scientific community worldwide, being considered a promising feedstock for renewable energy production, as well as for a wide range of high value-added products such as pigments and poly-unsaturated fatty acids for pharmaceutical, nutraceutical, food, and cosmetic markets. Despite the investments in microalgae biotechnology to date, the major obstacle to its wide commercialization is the high cost of microalgal biomass production and expensive product extraction steps. One way to reduce the microalgae production costs is the use of low-cost feedstock for microalgae production. Some wastes contain organic and inorganic components that may serve as nutrients for algal growth, decreasing the culture media cost and, thus, the overall process costs. Most of the research studies on microalgae waste treatment use autotrophic and mixotrophic microalgae growth. Research on heterotrophic microalgae to treat wastes is still scarce, although this cultivation mode shows several benefits over the others, such as higher organic carbon load tolerance, intracellular products production, and stability in production all year round, regardless of the location and climate. In this review article, the use of heterotrophic microalgae to simultaneously treat wastes and produce high value-added bioproducts and biofuels will be discussed, critically analyzing the most recent research done in this area so far and envisioning the use of this approach to a commercial scale in the near future.
Activated Carbon for Pharmaceutical Removal at Point-of-Entry
Authors: Finn Michelle , Giampietro Gabrielle , Mazyck David , Rodriguez Regina
Published: June 2021

Pharmaceuticals are an increasing problem in waterways due to improper disposal and lack of removal at wastewater treatment plants. Long-term exposure impacts to humans are unknown but have been observed in model organisms (i.e., fish), impacting reproduction, changing temperament, and causing organ damage. The application of activated carbon (AC) for organic contaminant removal is widespread and applied successfully for water treatment. The objective of this study is to rapidly adsorb ibuprofen using AC to determine the feasibility as a point-of-entry treatment option for removal of pharmaceuticals in the toilet. AC factors analyzed include type of AC raw material, adsorbent particle size, contact time, and competitive adsorption of ibuprofen and common toilet bowl cleaner components such as chlorine and methylene blue dye. A coconut-based AC with a high surface area adsorbed the highest quantity of ibuprofen. There was no significant impact to ibuprofen adsorption upon the introduction of other compounds to the solution, thus demonstrating rapid adsorption and the potential for application at the point-of-entry.
The Influence of Greek Spirituality on Russian Culture
Authors: Zozuľak Ján
Published: June 2021

In this article, we will analyze the influence of Greek spirituality on Russian culture in the second half of the 18th century, when Enlightenment ideas infused Russian society. Russian intellectual circles and the upper social class were inspired by Western categories of thought. The absence of a living theology that would give man the true meaning of life has caused tension and a great spiritual crisis in Russian society. One possible solution was to start a fight against the Enlightenment and reject any Western ideas. The second solution was to pay attention to the forgotten tradition and look for inspiration in it for the renewal of spiritual life. The spiritual renewal, known as the philokalic movement, leaned towards the second solution, building upon the Byzantine hesychastic tradition of the 14th century. This paved the way for a new era of Orthodox spirituality, which significantly influenced thinking and spiritual life in Russia. The movement of spiritual renewal is associated with the translation and publication of manuscripts written by Byzantine niptic authors, which were published in the book Dobrotolublye (gr. Philokalia). This significantly contributed to the spread of the hesychastic tradition in Russia and became an impetus for a return to Byzantine spiritual values. This article examines the spiritual, literary, and cultural activities of the most important centers of Russian Hesychasm, such as Sarov, Valaam, and Optina, and their influence on Russian society, which has not yet been recognized sufficiently.
The Assessment of the Usefulness of Miscanthus x giganteus to Water and Soil Protection against Erosive Degradation
Authors: Mazur Andrzej , Kowalczyk-Juśko Alina
Published: June 2021

Water erosion is one of the major factors of soil degradation in the world. Various methods have been developed to prevent soil erosion. One of them is the use of anti-erosion belts on slopes, but it has both positive and negative effects. In order to minimize the negative effects, this study proposes the use of perennial grass in place of the most commonly used trees and shrubs. The paper presents studies on the erosion control effectiveness of a strip planted with Miscanthus x giganteus, established on a loess slope. Surface runoff of water and its constituents and erosion damage was studied on the experimental plot with a separate anti-erosion belt and the control plot. Obtained results indicate the anti-erosion efficiency of the established strip in the context of soil protection from water erosion and surface water protection from pollution, although, in the first years of vegetation, miscanthus has not yet reached the stage of full development. The average surface water runoff relating to precipitation causing the erosive event was 17.1% higher in the control plot than in the experimental plot. The volume of erosion damage in the form of rill erosion was 89.3% higher in the control plot. On the other hand, the volume of erosion damages in surface erosion and patches of deposited silts was lower by 14.7% and 21.6%, respectively. Soil losses from the control plot were 29% higher than those from the experimental plot. Dissolved plant nutrient runoff was also higher from the control plot by: 33.4% N-Ntot, 31.3% N-NH4, 42.7% N-NO3, 21.6% N-NO2, 22.9% P-Ptot, 24.1% K.
A Nowcast/Forecast System for Japan’s Coasts Using Daily Assimilation of Remote Sensing and In Situ Data
Authors: Miyazawa Yasumasa , Varlamov Sergey M. , Miyama Toru , Kurihara Yukio , Murakami Hiroshi , Kachi Misako
Published: June 2021

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively represent oceanic phenomena with a spatial scale smaller than 100 km, we adopted a data assimilation scheme that explicitly separates larger and smaller horizontal scales from satellite sea surface temperature data. Our model is updated daily through data assimilation using the latest available remote-sensing data. Here we validate the data assimilation products of JCOPE-T DA using various kinds of in situ observational data. This validation proves that the JCOPE-T DA model output outperforms those of a previous version of JCOPE-T, which is based on nudging the values of temperature and salinity toward those provided by a different coarse grid data-assimilated model JCOPE2M. Parameter sensitivity experiments show that the selection of horizontal scale separation parameters considerably affects the representation of sea surface temperature. Additional experiments demonstrate that the assimilation of daily-updated satellite sea surface temperature data actually improves the model’s efficiency in representing typhoon-induced disturbances of sea surface temperature on a time scale of a few days. Assimilation of additional in situ data, such as temperature/salinity/ocean current information, further improves the model’s ability to represent the ocean currents near the coast accurately.
Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland
Authors: Yang Shu , Peng Fengchao , von Löwis Sibylle , Petersen Guðrún Nína , Finger David Christian
Published: June 2021

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.
Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning
Authors: Evans Fiona H. , Shen Jianxiu
Published: June 2021

Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year (R2 = 0.67, MAE = 0.25 t ha−1, RMSE = 0.33 t ha−1, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (R2 = 0.68, MAE = 0.25 t ha−1, RMSE = 0.32 t ha−1, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning.
Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria
Authors: Calamita Federico , Imran Hafiz Ali , Vescovo Loris , Mekhalfi Mohamed Lamine , La Porta Nicola
Published: June 2021

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.
Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size
Authors: Xiao Yingxin , Dong Yingying , Huang Wenjiang , Liu Linyi , Ma Huiqin
Published: June 2021

By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution were combined to construct the model, and its overall accuracy, sensitivity, and generalization ability were evaluated. First, the sensitive spectral features and bands of the UAV-derived hyperspectral images were obtained, and then texture features were selected. Subsequently, spectral features and texture features extracted from windows of different sizes were input to classify the area of severe FHB. According to the model comparison, the optimal window size was obtained. With the collinearity between features eliminated, the best performance of the logistic model reached, with an accuracy, F1 score, and area under the receiver operating characteristic curve of 0.90, 0.79, and 0.79, respectively, when the window size of the GLCM was 5 × 5 pixels on May 3, and of 0.90, 0.83, and 0.82, respectively, when the size was 17 × 17 pixels on May 8. The results showed that the selection of an appropriate GLCM window size for texture feature extraction enabled more accurate disease detection.
Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks
Authors: Ni Hao , Gong Peng , Li Xuecao
Published: June 2021

With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance Tier 1 data on the Google Earth Engine (GEE) platform, facilitated by Global Urban Boundary (GUB), Essential Urban Land Use Categories (EULUC) and Global Artificial Impervious Area (GAIA) data. The LandTrendr change detection algorithm was applied to extract detailed information from 14 band/index trajectories. These features were then used as inputs to two methods of old town identification: statistical thresholding and random forest classification. We assessed these two methods in a rapidly developing large city, Hangzhou, and subsequently obtained overall accuracies of 81.33% and 90.67%, respectively. Red band, NIR band and related indices show higher importance in random forest classification, and the magnitude feature plays an outstanding role. The final map of Hangzhou during the 2000–2018 period shows that the old towns were concentrated in the downtown region near West Lake within the urban boundaries in 2000, and far fewer than the renewed areas. The results could serve as references in the provincial and national planning of future urban developments.
Quantitatively Estimating of InSAR Decorrelation Based on Landsat-Derived NDVI
Authors: Chen Yaogang , Sun Qian , Hu Jun
Published: June 2021

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage.
Study on Non-Bragg Microwave Backscattering from Sea Surface Covered with and without Oil Film at Moderate Incidence Angles
Authors: Zheng Honglei , Zhang Jie , Khenchaf Ali , Li Xiao-Ming
Published: June 2021

In the past decades, Bragg scattering has been considered to be an important scattering mechanism of microwave backscattering from sea surfaces. However, as reported in many recent literatures, non-Bragg scattering (which is often attributed to wave breaking) also makes a significant impact on radar scattering, especially for Horizontal–Horizontal (HH) polarized radar signals. To date, we know far less about non-Bragg scattering than Bragg scattering. Herein, this paper carries out an investigation on non-Bragg scattering and its effect on radar echoes at moderate incidence angles, both for oil-free and oil-covered sea surfaces. This paper firstly presents a systematic comparison of several sea spectra commonly used for the simulation of microwave scattering from sea surfaces. It is found that none of them perform well for the description of Bragg waves. Then, the “pure” Bragg wave spectra are inverted in the framework of the two-scale model (TSM) and geophysical model functions (GMFs). The normalized radar cross sections (NRCS) related to total scattering, non-Bragg scattering, and “pure” Bragg scattering in C, X, and Ku-bands are simulated under various conditions (i.e., incidence angles, wind speeds, and wind directions). Quantitative assessments of the relative contributions of non-Bragg scattering to total scattering are conducted. We also perform a survey on the non-Bragg scattering from the oil-covered sea surface. This article provides some new insights for a better understanding of the non-Bragg microwave scattering from rough sea surfaces at moderate incidence angles.
Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest
Authors: Martins-Neto Rorai Pereira , Tommaselli Antonio Maria Garcia , Imai Nilton Nobuhiro , David Hassan Camil , Miltiadou Milto , Honkavaara Eija
Published: June 2021

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.
Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities
Authors: Ghazal Rubina , Malik Ahmad , Raza Basit , Qadeer Nauman , Qamar Nafees , Bhatia Sajal
Published: June 2021

Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.
Dual-Motor Synchronization Control Design Based on Adaptive Neural Networks Considering Full-State Constraints and Partial Asymmetric Dead-Zone
Authors: Jin Chunhong , Cai Mingjie , Xu Zhihao
Published: June 2021

This paper proposes a command filtering backstepping (CFB) scheme with full-state constraints by leading into time-varying barrier Lyapunov functions (T-BLFs) for a dual-motor servo system with partial asymmetric dead-zone. Firstly, for the convenience of the controller design, the conventional partial asymmetric dead-zone model was replaced with a new smooth differentiable model owing to its non-smoothness. Secondly, neural networks (NNs) were utilized to approximate the nonlinearity that exists in the dead-zone model, improving the control performance. In addition, CFB was utilized to deal with the inherent computational explosion problem of the traditional backstepping method, and an error compensation mechanism was introduced to further reduce the filtering errors. Then, by applying the T-BLF to the CFB process, the states of the system never violated the prescribed constraints, and all signals in the dual-motor servo system were bounded. The tracking error and synchronization error could converge to a small desired neighborhood of the origin. In the end, the effectiveness of the proposed control scheme was verified through simulations.
Self-Erasing Network for Person Re-Identification
Authors: Fan Xinyue , Lin Yang , Zhang Chaoxi , Zhang Jia
Published: June 2021

Person re-identification (ReID) plays an important role in intelligent surveillance and receives widespread attention from academics and the industry. Due to extreme changes in viewing angles, some discriminative local regions are suppressed. In addition, the data with similar backgrounds collected by a fixed viewing angle camera will also affect the model’s ability to distinguish a person. Therefore, we need to discover more fine-grained information to form the overall characteristics of each identity. The proposed self-erasing network structure composed of three branches benefits the extraction of global information, the suppression of background noise and the mining of local information. The two self-erasing strategies that we proposed encourage the network to focus on foreground information and strengthen the model’s ability to encode weak features so as to form more effective and richer visual cues of a person. Extensive experiments show that the proposed method is competitive with the advanced methods and achieves state-of-the-art performance on DukeMTMC-ReID and CUHK-03(D) datasets. Furthermore, it can be seen from the activation map that the proposed method is beneficial to spread the attention to the whole body. Both metrics and the activation map validate the effectiveness of our proposed method.
Irradiance Sensing through PV Devices: A Sensitivity Analysis
Authors: Laudani Antonino , Lozito Gabriele Maria , Riganti Fulginei Francesco
Published: June 2021

In this work, a sensitivity analysis for the closed-form approach of irradiance sensing through photovoltaic devices is proposed. A lean expression to calculate irradiance on a photovoltaic device, given its operating point, temperature and equivalent circuit model, is proposed. On this expression, the sensitivity towards errors in the measurement of the photovoltaic device operating point and temperature is analyzed, determining optimal conditions to minimize sensitivity. The approach is studied for two scenarios, a stand-alone sensor and irradiance sensing on an operating power-producing photovoltaic device. A low-cost realization of a virtual sensor employing the closed form for monitoring performance of photovoltaic module is also presented, showing the advantage of this kind of simple solution. The proposed solution can be used to create a wireless sensor network for remote monitoring of a photovoltaic plant, assessing both electrical and environmental conditions of the devices in real time.
Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
Authors: Núñez-Carmona Estefanía , Abbatangelo Marco , Sberveglieri Veronica
Published: June 2021

Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of different types of tea to provide a competitive tool in terms of time and costs to recognize and enhance the quality of the product in the food chain. Analyzed samples are representative of the three major types of tea: black, green, and white. VOCs were studied in parallel with different technologies and methods: gas chromatography coupled with mass spectrometer and solid phase microextraction (SPME-GC-MS) and a device called small sensor system, (S3). S3 is made up of tailor-made metal oxide gas sensors, whose operating principle is based on the variation of sensor resistance based on volatiloma exposure. The data obtained were processed through multivariate statistics, showing the full file of the pre-established aim. From the results obtained, it is understood how supportive an innovative technology can be, remotely controllable supported by machine learning (IoF), aimed in the future at increasing food safety along the entire production chain, as an early warning system for possible microbiological or chemical contamination.
Tactile Interaction Sensor with Millimeter Sensing Acuity
Authors: Choi Eunsuk , Kim Sunjin , Gong Jinsil , Sun Hyeonjeong , Kwon Minjin , Seo Hojun , Sul Onejae , Lee Seung-Beck
Published: June 2021

In this article we report on a 3 × 3 mm tactile interaction sensor that is able to simultaneously detect pressure level, pressure distribution, and shear force direction. The sensor consists of multiple mechanical switches under a conducting diaphragm. An external stimulus is measured by the deflection of the diaphragm and the arrangement of mechanical switches, resulting in low noise, high reliability, and high uniformity. Our sensor is able to detect tactile forces as small as ~50 mgf along with the direction of the shear force. It also distinguishes whether there is a normal pressure during slip motion. We also succeed in detecting the contact shape and the contact motion, demonstrating potential applications in robotics and remote input interfaces. Since our sensor has a simple structure and its function depends only on sensor dimensions, not on an active sensing material, in comparison with previous tactile sensors, our sensor shows high uniformity and reliability for an array-type integration.