bluuebunny/crossref_metadata_2025 huggingface dataset:
A Negative Index Nonagonal CSRR Metamaterial-Based Compact Flexible Planar Monopole Antenna for Ultrawideband Applications Using Viscose-Wool Felt
Authors: Hossain Kabir , Sabapathy Thennarasan , Jusoh Muzammil , Abdelghany Mahmoud A. , Soh Ping Jack , Osman Mohamed Nasrun , Yasin Mohd Najib Mohd , Rahim Hasliza A. , Al-Bawri Samir Salem
Published: August 2021

In this paper, a compact textile ultrawideband (UWB) planar monopole antenna loaded with a metamaterial unit cell array (MTMUCA) structure with epsilon-negative (ENG) and near-zero refractive index (NZRI) properties is proposed. The proposed MTMUCA was constructed based on a combination of a rectangular- and a nonagonal-shaped unit cell. The size of the antenna was 0.825 λ0 × 0.75 λ0 × 0.075 λ0, whereas each MTMUCA was sized at 0.312 λ0 × 0.312 λ0, with respect to a free space wavelength of 7.5 GHz. The antenna was fabricated using viscose-wool felt due to its strong metal–polymer adhesion. A naturally available polymer, wool, and a human-made polymer, viscose, that was derived from regenerated cellulose fiber were used in the manufacturing of the adopted viscose-wool felt. The MTMUCA exhibits the characteristics of ENG, with a bandwidth (BW) of 11.68 GHz and an NZRI BW of 8.5 GHz. The MTMUCA was incorporated on the planar monopole to behave as a shunt LC resonator, and its working principles were described using an equivalent circuit. The results indicate a 10 dB impedance fractional bandwidth of 142% (from 2.55 to 15 GHz) in simulations, and 138.84% (from 2.63 to 14.57 GHz) in measurements obtained by the textile UWB antenna. A peak realized gain of 4.84 dBi and 4.4 dBi was achieved in simulations and measurements, respectively. A satisfactory agreement between simulations and experiments was achieved, indicating the potential of the proposed negative index metamaterial-based antenna for microwave applications.
Phosphorous-Nitrogen Modification of Epoxy Grafted Poly-Acrylic Resin: Synergistic Flame Retardment Effect
Authors: Liu Chao , Qiao Hui , Xu Guilong , Liang Yun , Yang Jin , Hu Jian
Published: August 2021

A novel high-efficient flame retardant epoxy grafted poly-acrylic resin modified by phosphorus and nitrogen was successfully synthesized by radical grafting polymerization and solution polymerization simultaneously. The flame retardancy of copolymer resin was investigated using thermogravimetric analysis (TGA), cone calorimetric test (CONE), limiting oxygen index (LOI) and so on. The micro-morphology and chemical composition of char formed after a CONE calorimetric test was analyzed using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS), respectively. The Kissinger method was used to evaluate the kinetics of thermal decomposition on copolymer resin. The results showed that the flame retardant property of copolymer resin increased with the increase in phosphorus content. With the increase in nitrogen content, however, the flame retardant property first increased and then decreased. The flame retardant property of the resin was the best and the limiting oxygen index could reach 34.3% when the phosphorus content and nitrogen content of the copolymer resin were 6.45 wt% and 2.33 wt%, respectively. Meanwhile, nitrogen-containing compounds will interact with phosphorus-containing compounds to form P-N intermediates during combustion, which have stronger dehydration and carbonization and could further enhance the flame retardant performance of the resin and generate phosphorus-nitrogen synergistic interactions.
Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production
Authors: Na’amnh Saeed , Salim Muath Bani , Husti István , Daróczi Miklós
Published: August 2021

Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making.
Anti-Smog Building and Civil Engineering Structures
Authors: Stanaszek-Tomal Elżbieta
Published: August 2021

Currently, people worldwide, in the period from September to April, observe with their own eyes and feel the pollution of the air, called smog, in their own breath. The biggest cause of smog and the source of air pollution is burning rubbish in stoves. Other causes include exhaust fumes from large factories, burning coal in furnaces, and car exhaust fumes. Smog is an unnatural phenomenon, directly related to human activity. The weather is becoming worse. On no-wind, foggy days, the smog phenomenon is the most troublesome for city dwellers. Smog persists in European countries from November to April, during the heating season. The harmful effect of smog affects almost the entire human body. Every year, air pollution causes the death of approximately 26,000–48,000 people. At the same time, poor air quality reduces life expectancy by up to a year. The purpose of this article is to present buildings and finishing elements that can help in the fight against air pollution.
Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System
Authors: Kim Seul-Gi , Park Donghyun , Jung Jae-Yoon
Published: August 2021

Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.
Defect Detection on a Wind Turbine Blade Based on Digital Image Processing
Authors: Deng Liwei , Guo Yangang , Chai Borong
Published: August 2021

Wind power generation is a widely used power generation technology. Among these, the wind turbine blade is an important part of a wind turbine. If the wind turbine blade is damaged, it will cause serious consequences. The traditional methods of defect detection for wind turbine blades are mainly manual detection and acoustic nondestructive detection, which are unsafe and time-consuming, and have low accuracy. In order to detect the defects on wind turbine blades more safely, conveniently, and accurately, this paper studied a defect detection method for wind turbine blades based on digital image processing. Because the log-Gabor filter used needed to extract features through multiple filter templates, the number of output images was large. Firstly, this paper used the Lévy flight strategy to improve the PSO algorithm to create the LPSO algorithm. The improved LPSO algorithm could successfully solve the PSO algorithm’s problem of falling into the local optimal solution. Then, the LPSO algorithm and log-Gabor filter were used to generate an adaptive filter, which could directly output the optimal results in multiple feature extraction images. Finally, a classifier based on HOG + SVM was used to identify and classify the defect types. The method extracted and identified the scratch-type, crack-type, sand-hole-type, and spot-type defects, and the recognition rate was more than 92%.
Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers
Authors: Trinh Cindy , Meimaroglou Dimitrios , Hoppe Sandrine
Published: August 2021

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.
Estimating the Remaining Useful Life of Proton Exchange Membrane Fuel Cells under Variable Loading Conditions Online
Authors: Wang Penghao , Liu Hao , Hou Ming , Zheng Limin , Yang Yue , Geng Jiangtao , Song Wei , Shao Zhigang
Published: August 2021

The major challenges for the commercialization of proton exchange membrane fuel cells (PEMFCs) are durability and cost. Prognostics and health management technology enable appropriate decisions and maintenance measures by estimating the current state of health and predicting the degradation trend, which can help extend the life and reduce the maintenance costs of PEMFCs. This paper proposes an online model-based prognostics method to estimate the degradation trend and the remaining useful life of PEMFCs. A non-linear empirical degradation model is proposed based on an aging test, then three degradation state variables, including degradation degree, degradation speed and degradation acceleration, can be estimated online by the particle filter algorithm to predict the degradation trend and remaining useful life. Moreover, a new health indicator is proposed to replace the actual variable loading conditions with the simulated constant loading conditions. Test results using actual aging data show that the proposed method is suitable for online remaining useful life estimation under variable loading conditions. In addition, the proposed prognostics method, which considers the activation loss and the ohmic loss to be the main factors leading to the voltage degradation of PEMFCs, can predict the degradation trend and remaining useful life at variable degradation accelerations.
Experimental Study on Axial Temperature Profile of Jet Fire of Oil-Filled Equipment in Substation
Authors: Sun Ruibang , Yang Xing , Wang Juncai , Chen Peng , Wu Liusuo
Published: August 2021

With the widespread use of substations around the world, oil jet fire accidents from transformer oil-filled equipment in substations caused by faults have occurred from time to time. In this paper, a series of transformer oil jet fire experiments are carried out by changing the external heat source (30 cm and 40 cm) and the inner diameter of the container (5 cm, 8 cm and 10 cm) to study the axial centerline temperature distribution of the transformer oil jet fire plume of the transformer oil-filled equipment in the substation. The experiment uses K-type thermocouple, electronic balance and CCD to measure and assess the temperature distribution of the axial centerline of the fire plume of the transformer oil jet. The result demonstrates that the axial centerline temperature of the fire plume increases with the external heat release rate and the inner diameter of the container. In addition, a novel axial temperature distribution prediction model of the transformer oil jet fire plume is established. This model can effectively predict the oil jet fire plume temperature of transformer oil- filling equipment in substations, and provide help for substation fire control.
Risk Assessment and Material Suitability Evaluation on Static Equipment of Hydrofluoric Acid Alkylation Unit
Authors: Wang Weihua , Lu Duhui , Li Xiang , Liang Lin
Published: August 2021

In 2005, a 60 kt/a alkylation (ALK) unit began to resume production in the Second Oil Refinery Plant of Beijing Yanshan Petrochemical Company. There have been many leak cases from pipeline welds, valve body, flange, etc. After a half-year period of operation, production process is stable. However, the operation of the hydrofluoric (HF) acid ALK unit has been suffered from corrosion problems. There are no clear answers and references to the following problems. What types of corrosion are currently in the main equipment for HF acid ALK unit? What does cause equipment corrosion? What are the main influencing factors for corrosion? What measures can be taken to reduce the corrosion of HF acid? In this paper, considering the acid-related conditions of the ALK unit, the damage mechanism and damage rate analysis were carried out to calculate the safety risk of the static equipment of the ALK unit. Based on the damage mechanism and failure history, the material suitability of the ALK unit was investigated. The anti-corrosion measures and recommended materials for important corrosion parts of the ALK unit were proposed. It is meaningful for reducing the number of shutdowns of ALK units and maintaining safe and stable operation of the unit.
Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems
Authors: Rezaei Mostafa , Dampage Udaya , Das Barun K. , Nasif Omaima , Borowski Piotr F. , Mohamed Mohamed A.
Published: August 2021

One of the many barriers to decarbonization and decentralization of the energy sector in developing countries is the economic uncertainty. As such, this study scrutinizes economics of three grid-independent hybrid renewable-based systems proposed to co-generate electricity and heat for a small-scale load. Accordingly, the under-study systems are simulated and optimized with the aid of HOMER Pro software. Here, a 20-year average value of discount and inflation rates is deemed a benchmark case. The techno-economic-environmental and reliability results suggest a standalone solar/wind/electrolyzer/hydrogen-based fuel cell integrated with a hydrogen-based boiler system is the best alternative. Moreover, to ascertain the impact of economic uncertainty on optimal unit sizing of the nominated model, the fluctuations of the nominal discount rate and inflation, respectively, constitute within the range of 15–20% and 10–26%. The findings of economic uncertainty analysis imply that total net present cost (TNPC) fluctuates around the benchmark value symmetrically between $478,704 and $814,905. Levelized energy cost varies from an amount 69% less than the benchmark value up to two-fold of that. Furthermore, photovoltaic (PV) optimal size starts from a value 23% less than the benchmark case and rises up to 55% more. The corresponding figures for wind turbine (WT) are, respectively, 21% and 29%. Eventually, several practical policies are introduced to cope with economic uncertainty.
Beauveria bassiana Ribotoxin (BbRib) Induces Silkworm Cell Apoptosis via Activating Ros Stress Response
Authors: Ma Xiaoke , Ge Qi , Taha Rehab , Chen Keping , Yuan Yi
Published: August 2021

The BbRib gene participates in the infection process of Beauveria bassiana (B. bassiana). It also helps pathogenic fungi to escape and defeat the insect host immune defense system by regulating the innate immune response. However, model insects are rarely used to study the mechanism of fungal ribosomal toxin protein. In this study, BbRib protein was produced by prokaryotic expression and injected into silkworm (Bombyx mori) larvae. The physiological and biochemical indexes of silkworm were monitored, and the pathological effects of BbRib protein on immune tissues of silkworm were examined by Hematoxylin and Eosin (HE) staining. BbRib protein can significantly affect the growth and development of the silkworm, causing poisoning, destroying the midgut and fat body and producing physiological changes. The ROS stress response in the adipose tissue and cells of the silkworm was activated to induce apoptosis. These results indicated that the BbRib gene not only participates in the infection process of B. bassiana, it also helps the pathogenic fungi escape the immune system by regulating the innate immune system of the silkworm, allowing it to break through the silkworm’s immune defense. This study reveals the potential molecular mechanism of BbRib protein to insect toxicity, and provides a theoretical basis and material basis for the development and use of novel insecticidal toxins.
Effect of an Increased Particulate COD Load on the Aerobic Granular Sludge Process: A Full Scale Study
Authors: Toja Ortega Sara , Pronk Mario , de Kreuk Merle K.
Published: August 2021

High concentrations of particulate COD (pCOD) in the influent of aerobic granular sludge (AGS) systems are often associated to small granule diameter and a large fraction of flocculent sludge. At high particulate concentrations even granule stability and process performance might be compromised. However, pilot- or full-scale studies focusing on the effect of real wastewater particulates on AGS are scarce. This study describes a 3-month period of increased particulate loading at a municipal AGS wastewater treatment plant. The pCOD concentration of the influent increased from 0.5 g COD/L to 1.3 g COD/L, by adding an untreated slaughterhouse wastewater source to the influent. Sludge concentration, waste sludge production and COD and nutrient removal performance were monitored. Furthermore, to investigate how the sludge acclimatises to a higher influent particulate content, lipase and protease hydrolytic activities were studied, as well as the microbial community composition of the sludge. The composition of the granule bed and nutrient removal efficiency did not change considerably by the increased pCOD. Interestingly, the biomass-specific hydrolytic activities of the sludge did not increase during the test period either. However, already during normal operation the aerobic granules and flocs exhibited a hydrolytic potential that exceeded the influent concentrations of proteins and lipids. Microbial community analysis also revealed a high proportion of putative hydrolysing and fermenting organisms in the sludge, both during normal operation and during the test period. The results of this study highlight the robustness of the full-scale AGS process, which can bear a substantial increase in the influent pCOD concentration during an extended period.
Numerical Study on the Influence of Well Layout on Electricity Generation Performance of Enhanced Geothermal Systems
Authors: Zeng Yuchao , Sun Fangdi , Zhai Haizhen
Published: August 2021

The energy efficiency of the enhanced geothermal system (EGS) measures the economic value of the heat production and electricity generation, and it is a key indicator of system production performance. Presently there is no systematic study on the influence of well layout on the system energy efficiency. In this work we numerically analyzed the main factors affecting the energy efficiency of EGS using the TOUGH2-EOS1 codes at Gonghe Basin geothermal field, Qinghai province. The results show that for the reservoirs of the same size, the electric power of the three horizontal well system is higher than that of the five vertical well system, and the electric power of the five vertical well system is higher than that of the three vertical well system. The energy efficiency of the three horizontal well system is higher than that of the five vertical well system and the three vertical well system. The reservoir impedance of the three horizontal well system is lower than that of the three vertical well system, and the reservoir impedance of the three vertical well system is lower than that of the five vertical system. The sensitivity analysis shows that well spacing has an obvious impact on the electricity production performance; decreasing well spacing will reduce the electric power, reduce the energy efficiency and only have very slight influence on the reservoir impedance. Fracture spacing has an obvious impact on the electricity production performance; increasing fracture spacing will reduce the electric power and reduce the energy efficiency. Fracture permeability has an obvious impact on the electricity production performance; increasing fracture permeability will improve the energy efficiency and reduce the reservoir impedance.
Model Discrimination for Hydrogen Peroxide Consumption towards γ-Alumina in Homogeneous Liquid and Heterogeneous Liquid-Liquid Systems
Authors: Di Menno Di Bucchianico Daniele , Perez-Sena Wander Y. , Casson Moreno Valeria , Salmi Tapio , Leveneur Sébastien
Published: August 2021

The use of hydrogen peroxide as an oxidizing agent becomes increasingly important in chemistry. The example of vegetable oil epoxidation is an excellent illustration of the potential of such an agent. This reaction is traditionally performed by Prileschajew oxidation, i.e., by the in situ production of percarboxylic acids. Drawbacks of this approach are side reactions of ring-opening and thermal runaway reactions due to percarboxylic acid instability. One way to overcome this issue is the direct epoxidation by hydrogen peroxide by using γ-alumina. However, the reaction mechanism is not elucidated: does hydrogen peroxide decompose with alumina or oxidize the hydroxyl groups at the surface? The kinetics of hydrogen peroxide consumption with alumina in homogeneous liquid and heterogeneous liquid-liquid systems was investigated to reply to this question. Bayesian inference was used to determine the most probable models. The results obtained led us to conclude that the oxidation mechanism is the most credible for the heterogeneous liquid-liquid system.
Exact Solutions for Time-Dependent Non-Hermitian Oscillators: Classical and Quantum Pictures
Authors: Zelaya Kevin , Rosas-Ortiz Oscar
Published: August 2021

We associate the stationary harmonic oscillator with time-dependent systems exhibiting non-Hermiticity by means of point transformations. The new systems are exactly solvable, with all-real spectra, and transit to the Hermitian configuration for the appropriate values of the involved parameters. We provide a concrete generalization of the Swanson oscillator that includes the Caldirola–Kanai model as a particular case. Explicit solutions are given in both the classical and quantum pictures.
Producing Solidarities: Theological Reflections on Humanity and Ecology in Animal’s People
Authors: Pruszinski Emily S.
Published: August 2021

This article examines Indra Sinha’s novel Animal’s People for insight into the intersection of theological anthropology and ecological theology. Set in the wake of a man-made ecological crisis, Sinha’s novel probes the definition of humanity, the interconnectedness between humans and the environment, and the toxic effects of the neoliberal order for humans and their environment. Drawing on Joerg Rieger and Kwok Pui-lan’s notion of an ecclesiology of solidarity, with insight from Rob Nixon’s work on ecological violence in the neoliberal order, this article considers the rich theological resources residing in Sinha’s work. Through a close reading of the text, the article highlights Sinha’s novel as a reflective resource both for eco-theology and for theological anthropology. It demonstrates that Sinha’s expansive vision of humanity effectively challenges the colonial hierarchy of humanity and the global system of borders which reinforces it. The witness of Animal’s People suggests that theological anthropology and eco-theology are inseparably interrelated and that responsible praxis in both spheres is necessary for developing global human and ecological solidarity.
Usefulness of Circulating CYFRA21-1 in Patients as a Biomarker in Patients Taking Sorafenib or Lenvatinib for Unresectable Hepatocellular Carcinoma
Authors: Takada Hitomi , Osawa Leona , Komiyama Yasuyuki , Kato Ryoh , Nakakuki Natsuko , Muraoka Masaru , Suzuki Yuichiro , Tatsumi Akihisa , Sato Mitsuaki , Takahashi Ei , Takano Shinichi , Fukasawa Mitsuharu , Yamaguchi Tatsuya , Inoue Taisuke , Maekawa Shinya , Enomoto Nobuyuki
Published: August 2021

Background: This study investigated the impact of serum cytokeratin 19 fragment (CYFRA21-1) level on the clinical outcomes of patients with unresectable hepatocellular carcinoma (HCC) treated with sorafenib (SOR) or lenvatinib (LEN). Methods: A total of 71 cases with unresectable HCC taking SOR or LEN were included. Univariate and multivariate analyses were performed to identify the prognostic factors in patients taking SOR or LEN. Results: Among the 71 patients taking SOR or LEN, the frequency of cases showing high CYFRA21-1 levels after administration increased compared to before the administration. There was no association between the CYFRA21-1 level and the result of treatment response using modified Response Evaluation Criteria in Solid Tumors (mRECIST) 12 weeks after the administration. Univariate analysis identified a maximum intrahepatic tumor diameter of 70 mm or more, extrahepatic metastasis, baseline alpha-fetoprotein (AFP) ≥ 2000 ng/mL, baseline AFP-L3 index ≥ 15%, baseline des-gamma-carboxy prothrombin (DCP) ≥ 1000 mAU/mL, baseline CYFRA21-1 > 3.5 ng/mL, 12-week mRECIST progressive disease (PD), 12-week DCP ratio ≥ 4, 12-week CYFRA21-1 ratio ≥ 2, administration period less than 12 weeks, ALBI grade 3 at PD, and no additional treatment after discontinuation of SOR/LEN as prognostic factors. Multivariate analysis revealed that AFP-L3 index ≥ 15%, 12-week mRECIST PD, 12-week DCP ratio ≥ 4, 12-week CYFRA21-1 ratio ≥ 2, administration period less than 12 weeks, and no additional treatment after discontinuation of SOR/LEN were independent factors. Conclusions: Patients with a high CYFRA21-1 level at baseline tend to have poor prognosis, and patients with a high CYFRA21-1 ratio 12 weeks after administration have poor prognosis. Serum CYFRA21-1 measurement may have additional effects on prognostic prediction, and it may be necessary to pay close attention to the transition to the next HCC treatment in cases whose CYFRA21-1 level is high.
Micro and Macroelements in Honey and Atmospheric Pollution (NW and Central Poland)
Authors: Gałczyńska Małgorzata , Gamrat Renata , Bosiacki Mateusz , Sotek Zofia , Stasińska Małgorzata , Ochmian Ireneusz
Published: August 2021

Urban vegetation is generally exposed to high levels of air pollution in airborne particles, with the greatest exposure in the EU being seen in Poland. With the continuing growth of urban populations, there is a need to confirm whether honey produced from urban areas is of similar high quality to that from rural areas. A total of 27 honey samples were collected from urban and rural apiaries and tested for the concentrations of 19 elements by ICP-OES. The results were compared with data on honey produced in old and new EU countries (metadata). Our evaluation used a novel approach to determine threshold values in the identification of the bioproduct contamination index. The analysed urban honey samples demonstrated higher concentrations of K, Sr, Ba, Ni, and Co, and lower levels of Mn and B than rural honey samples. Contamination by PM10 particles and the toxic elements contained in them proved to be a poor predictor of the content of these elements in honey, in contrast to the effect of atmospheric pollution measured during firework shows, which demonstrated higher concentrations of Ba, Pb, Ca, Cu, and Mg. The non-carcinogenic risk assessment indicated that the analysed honey samples are of good quality and are comparable or of even better quality than honey products from other EU countries.
A Particle Swarm Optimization Based Approach to Pre-tune Programmable Hyperspectral Sensors
Authors: Banerjee Bikram Pratap , Raval Simit
Published: August 2021

Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling and its realtime analysis for the identification of optimal spectral wavelengths. The band selection optimization workflow involves particle swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging, in a given environment. The criterion function, MEAC, greatly simplifies the in-field spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with diversity in vegetation species and communities. The optimal set of bands were found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This will additionally reduce the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient, and requires less data storage and computational resources for post-processing the data.
First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution
Authors: Sánchez de Miguel Alejandro , Bennie Jonathan , Rosenfeld Emma , Dzurjak Simon , Gaston Kevin J.
Published: August 2021

The global spread of artificial light is eroding the natural night-time environment. The estimation of the pattern and rate of growth of light pollution on multi-decadal scales has nonetheless proven challenging. Here we show that the power of global satellite observable light emissions increased from 1992 to 2017 by at least 49%. We estimate the hidden impact of the transition to solid-state light-emitting diode (LED) technology, which increases emissions at visible wavelengths undetectable to existing satellite sensors, suggesting that the true increase in radiance in the visible spectrum may be as high as globally 270% and 400% on specific regions. These dynamics vary by region, but there is limited evidence that advances in lighting technology have led to decreased emissions.
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks
Authors: Zhao Yujin , Zhao Liaoying , Zhang Huaguo , Fu Bin
Published: August 2021

Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value.
SAR Imaging Distortions Induced by Topography: A Compact Analytical Formulation for Radiometric Calibration
Authors: Imperatore Pasquale
Published: August 2021

Modeling of synthetic aperture radar (SAR) imaging distortions induced by topography is addressed and a novel radiometric calibration method is proposed in this paper. An analytical formulation of the problem is primarily provided in purely geometrical terms, by adopting the theoretical notions of the differential geometry of surfaces. The novel and conceptually simple formulation relies on a cylindrical coordinate system, whose longitudinal axis corresponds to the sensor flight direction. A 3D representation of the terrain shape is then incorporated into the SAR imaging model by resorting to a suitable parametrization of the observed ground surface. Within this analytical framework, the area-stretching function quantitatively expresses in geometrical terms the inherent local radiometric distortions. This paper establishes its analytical expression in terms of the magnitude of the gradient of the look-angle function uniquely defined in the image domain, thus resulting in being mathematically concise and amenable to a straightforward implementation. The practical relevance of the formulation is also illustrated from a computational perspective, by elucidating its effective discrete implementation. In particular, an inverse cylindrical mapping approach is adopted, thus avoiding the drawback of pixel area fragmentation and integration required in forward-mapping-based approaches. The effectiveness of the proposed SAR radiometric calibration method is experimentally demonstrated by using COSMO-SkyMed SAR data acquired over a mountainous area in Italy.
Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network
Authors: Ma Nan , Sun Lin , Zhou Chenghu , He Yawen
Published: August 2021

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.
Field Observations of Breaking of Dominant Surface Waves
Authors: Pivaev Pavel , Kudryavtsev Vladimir , Korinenko Aleksandr , Malinovsky Vladimir
Published: August 2021

The results of field observations of breaking of surface spectral peak waves, taken from an oceanographic research platform, are presented. Whitecaps generated by breaking surface waves were detected using video recordings of the sea surface, accompanied by co-located measurements of waves and wind velocity. Whitecaps were separated according to the speed of their movement, c, and then described in terms of spectral distributions of their areas and lengths over c. The contribution of dominant waves to the whitecap coverage varies with the wave age and attains more than 50% when seas are young. As found, the whitecap coverage and the total length of whitecaps generated by dominant waves exhibit strong dependence on the dominant wave steepness, ϵp, the former being proportional to ϵp6. This result supports a parameterization of the dissipation term, used in the WAM model. A semi-empirical model of the whitecap coverage, where contributions of breaking of dominant and equilibrium range waves are separated, is suggested.
Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning
Authors: Li Dan , Miao Yuxin , Gupta Sanjay K. , Rosen Carl J. , Yuan Fei , Wang Chongyang , Wang Li , Huang Yanbo
Published: August 2021

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.
Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images
Authors: Li Xuanye , Li Hongguang , Jiang Yalong , Wang Meng
Published: August 2021

Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets.
Relationship between Biological, Training, and Physical Fitness Variables in the Expression of Performance in Non-Professional Runners
Authors: Thuany Mabliny , Gomes Thayse Natacha , Almeida Marcos B.
Published: August 2021

Sports performance is a multifactorial trait that can be associated with individual and environmental characteristics. In this study, the sample comprised 35 male runners, enrolled in the “InTrack” project. Information regarding variables related to runners’ training was obtained via an online questionnaire, while anthropometric and body composition variables, as well as physical fitness components (muscular power, isometric strength, local muscular endurance, agility, and aerobic capacity) were measured, and a global physical fitness score (based on physical fitness components measured) was computed. The Weltman test (3200 m) was used to estimate runners’ pace and their stride frequency. Linear regression was used, taking the running pace as dependent variable. The final model, comprising biological, physical fitness, spatiotemporal, and training variables, explained 86% of the running performance variance. Muscular power (β = −1.02; 95% CI = (−1.69)–(−0.35)), abdominal muscle endurance (β = −4.81; 95% CI = (−7.52)–(−2.10)), isometric strength (β = −422.95; 95% CI = (−689.65)–(−156.25)), global physical fitness (β = 27.14; 95% CI = 9.52–45.03), and stride frequency (β = −2.99; 95% CI = (−4.29)–(−1.69)) were significantly associated with performance, meaning that better results in tests and increasing the stride frequency leads to better performance. Individual characteristics and physical fitness components were demonstrated to be significant predictors for running performance.
Evaluation of Petroleum Resin in Rubberized Asphalt Binder
Authors: Kim Hyun Hwan , Mazumder Mithil , Lee Moon-Sup , Lee Soon-Jae
Published: August 2021

The crumb rubber modified (CRM) binder was evaluated considering the general operating temperatures of high, intermediate, and low temperatures. CRM binders were produced with four different contents (0, 5, 10, and 15%) using the base asphalt binder (PG64-22). Then, they were artificially aged by a rolling thin-film oven (RTFO) and pressure aging vessel (PAV). Superpave binder tests using a rotational viscometer (RV), dynamic shear rheometer (DSR), and bending beam rheometer (BBR) was applied to characterize the performance of the original and aged binders. Multiple stress creep recovery (MSCR) tests were also performed for deeper rutting characterization. The results of this study are as follows: (1) the presence of PR increases the binder viscosity, (2) the integration of CRM greatly improved the rutting resistance of the binder, and it was found that PR also improved the rutting characteristics, and (3) it is observed that PR is detrimental to the cracking properties of CRM binders.
Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Kraków
Authors: Jasińska Elżbieta , Preweda Edward
Published: August 2021

The analysis of a city’s spatial development, in terms of a location that meets the needs of its inhabitants, requires many approaches. The preliminary assessment of the collected material showed that there was real estate in the database whose price did not have market characteristics. For the correct formulation of the valuation model, it is necessary to detect and eliminate or reduce the impact of these properties on the valuation results. In this study, multivariate analysis was used and three methods of detecting outliers were verified. The database of 8812 residential premises traded on the primary market in Kraków was analyzed. In order to detect outliers, the following indices were determined: projection matrix, Mahalanobis distances, standardized chi test and Cook distances. Critical values were calculated based on the formulas proposed in the publication. The probability level was P = 0.95. The article shows that the selected methods of eliminating outliers—the methods of standardized residuals and the Cook’s distance method give similar regression models. Further analysis (with the use of classification tree methods) made it possible to distinguish zones that are homogeneous in terms of price dispersion. In these zones, a set of features influencing real estate prices were determined.
Utilization of Carrot Pomace to Grow Mealworm Larvae (Tenebrio molitor)
Authors: Rovai Dominic , Ortgies Maxwell , Amin Samir , Kuwahara Sara , Schwartz Gregory , Lesniauskas Ruta , Garza Jeff , Lammert Amy
Published: August 2021

Edible insects are a sustainable food source to help feed the growing population. Mealworms (Tenebrio molitor) can survive on a variety of food wastes and alter their composition based on the feed source. Commercial carrot production produces an abundance of carotenoid-rich carrot pomace, which may be beneficial for mealworm larvae growth. This study uses an I-optimal response surface design to assess the effect of dehydrated carrot pomace concentrations (made up with wheat bran as the control) in the substrate and wet carrot pomace as the moisture source (potato and carrot as control moisture sources) in a mealworm-larvae-growing system. Using this design, statistical models were fit to determine the relationship between the substrate and moisture and dependent variables, which include mealworm larvae mortality, days to maturity, weight, protein content, fat content, moisture content, ash content, and total carotenoid content. An optimum diet was proposed, in which the best diet for improving commercial mealworm growth was found to contain 36% dehydrated carrot pomace in the substrate, with wet carrot pomace as the moisture source. This research provides an application for a commercial waste stream and provides insight to help improve the growth of a sustainable protein source.