bluuebunny/crossref_metadata_2025 huggingface dataset:
Enrichment of Refined Olive Oils with Phenolic Extracts of Olive Leaf and Exhausted Olive Pomace
Authors: Vidal Alfonso M. , Moya Manuel , Alcalá Sonia , Romero Inmaculada , Espínola Francisco
Published: January 2022

Refined olive oils (ROOs) are commonly enriched with synthetic antioxidants. Antioxidant extracts obtained from natural products can be used to improve the stability of these oils. In this study, ROOs were enriched through the addition of phenolic extracts from olive leaves (OLs) and exhausted olive pomace (EOP). In addition to replacing synthetic antioxidants with natural ones, this results in the valorization of these olive-derived biomasses. The most suitable method for mixing and enriching refined oils was probe-type ultrasonication using lecithin as the emulsifier. Thereafter, the change in the content of antioxidant compounds and the antioxidant capacity of the oils at 25, 35, and 45 °C were studied over 28 and 50 days of storage. The experimental results were fitted using a pseudo-first-order kinetic model. The oxidative stability index of the ROO enriched with a 2 g/L OL extract (70 h) was higher than that of a commercial ROO (46.8 h). Moreover, the oxidative stability index of the refined olive pomace oil (ROPO) enriched with a 2 g/L EOP extract (44.1 h) was higher than that of a commercial ROPO (38.9 h). In addition, the oxidative stabilities and antioxidant capacities of the oils were significantly correlated.
Combustion, Pyrolysis, and Gasification of Waste-Derived Fuel Slurries, Low-Grade Liquids, and High-Moisture Waste: Review
Authors: Vershinina Ksenia , Nyashina Galina , Strizhak Pavel
Published: January 2022

The article discusses the modern achievements in the field of thermal recovery of industrial and municipal waste. The average accumulation rate and calorific value of typical wastes were analyzed. The focus is on the opportunities to exploit the energy potential of high-moisture waste, low-grade liquid components, and fuel slurries. We consider the relevant results in the field of combustion, pyrolysis, and gasification of such fuels. The main attention is paid to synergistic effects, the influence of additives, and external conditions on the process performance. Vortex combustion chambers, boilers with burners, and nozzles for fuel injection, grate, and fluidized bed boilers can be used for the combustion of waste-derived liquid, high-moisture, and slurry fuels. The following difficulties are possible: long ignition delay, incomplete combustion, low combustion temperature and specific calorific value, high emissions (including particulate matter, polycyclic aromatic hydrocarbons), fast slagging, and difficult spraying. A successful solution to these problems is possible due to the use of auxiliary fuel; boiler modifications; oxy-fuel combustion; and the preparation of multi-component fuels, including the use of additives. An analysis of methods of waste recovery in the composition of slurries for fuel gas production showed that there are several main areas of research: pyrolysis and gasification of coal–water slurry with additives of oil waste; study of the influence of external conditions on the characteristics of final products; and the use of specialized additives and catalysts to improve the efficiency of the pyrolysis and gasification. The prospects for improving the characteristics of thermochemical conversion of such fuels are highlighted.
Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles
Authors: Razzaq Sheeba , Dar Amil Roohani , Shah Munam Ali , Khattak Hasan Ali , Ahmed Ejaz , El-Sherbeeny Ahmed M. , Lee Seongkwan Mark , Alkhaledi Khaled , Rauf Hafiz Tayyab
Published: January 2022

According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.
An Advanced Optimization Approach for Long-Short Pairs Trading Strategy Based on Correlation Coefficients and Bollinger Bands
Authors: Chen Chun-Hao , Lai Wei-Hsun , Hung Shih-Ting , Hong Tzung-Pei
Published: January 2022

In the financial market, commodity prices change over time, yielding profit opportunities. Various trading strategies have been proposed to yield good earnings. Pairs trading is one such critical, widely-used strategy with good effect. Given two highly correlated paired target stocks, the strategy suggests buying one when its price falls behind, selling it when its stock price converges, and operating the other stock inversely. In the existing approach, the genetic Bollinger Bands and correlation-coefficient-based pairs trading strategy (GBCPT) utilizes optimization technology to determine the parameters for correlation-based candidate pairs and discover Bollinger Bands-based trading signals. The correlation coefficients are used to calculate the relationship between two stocks through their historical stock prices, and the Bollinger Bands are indicators composed of the moving averages and standard deviations of the stocks. In this paper, to achieve more robust and reliable trading performance, AGBCPT, an advanced GBCPT algorithm, is proposed to take into account volatility and more critical parameters that influence profitability. It encodes six critical parameters into a chromosome. To evaluate the fitness of a chromosome, the encoded parameters are utilized to observe the trading pairs and their trading signals generated from Bollinger Bands. The fitness value is then calculated by the average return and volatility of the long and short trading pairs. The genetic process is repeated to find suitable parameters until the termination condition is met. Experiments on 44 stocks selected from the Taiwan 50 Index are conducted, showing the merits and effectiveness of the proposed approach.
A Study on the Calculation Method of Wave Crest Height and Panel Wave Force of a New Barrel-Pile Foundation Composite Structure Wharf
Authors: Ge Longzai , Chen Hanbao , Chen Songgui , Liu Haiyuan
Published: January 2022

Due to the depletion of near-shore resources and the complex environment of offshore deep-water open port construction, a new barrel-pile foundation composite structure wharf is proposed. In order to provide technical support for the engineering design and application of the new structure, research is carried out on a physical model. Through setting up a series of group tests, on the basis of fully understanding the wave impact characteristics of the wharf panel of the new structure: (1) the relationship between the wave crest height, impact force, and various influence factors is established; (2) a calculation method and formula are put forward (compared with the physical model test data, the calculation formula results have good accuracy); (3) according to the formulas of wave crest height and panel impact force, a determination method for the wharf surface elevation of the new structure is given. In addition, due to the limitations of the test conditions, it is suggested that the physical model should be further verified for a complex transverse and longitudinal beam structure or extreme sea conditions, such as traveling wave breaking and wave inundation of the wharf.
Aerodynamic-Aeroacoustic Optimization of a Baseline Wing and Flap Configuration
Authors: Ju Shengjun , Sun Zhenxu , Guo Dilong , Yang Guowei , Wang Yeteng , Yan Chang
Published: January 2022

Optimization design was widely used in the high-lift device design process, and the aeroacoustic reduction characteristic is an important objective of the optimization. The aerodynamic and aeroacoustic study on the baseline wing and flap configuration was performed numerically. In the current study, the three-dimensional Large Eddy Simulation (LES) equations coupled with dynamic Smagorinsky subgrid model and Ffowcs–William and Hawkings (FW–H) equation are employed to simulate the flow fields and carry out acoustic analogy. The numerical results show reasonable agreement with the experimental data. Further, the particle swarm optimization algorithm coupled with the Kriging surrogate model was employed to determine optimum location of the flap deposition. The Latin hypercube method is used for the generation of initial samples for optimization. In addition, the relationship between the design variables and the objective functions are obtained using the optimization sample points. The optimized maximum overall sound pressure level (OASPL) of far-field noise decreases by 3.99 dB with a loss of lift-drag ratio (L/D) of less than 1%. Meanwhile, the optimized performances are in good and reasonable agreement with the numerical predictions. The findings provide suggestions for the low-noise and high-lift configuration design and application in high-lift devices.
Effect of Co-Encapsulated Natural Antioxidants with Modified Starch on the Oxidative Stability of β-Carotene Loaded within Nanoemulsions
Authors: Ali Ahmad , Rehman Abdur , Jafari Seid Mahdi , Ranjha Muhammad Modassar Ali Nawaz , Shehzad Qayyum , Shahbaz Hafiz Muhammad , Khan Sohail , Usman Muhammad , Kowalczewski Przemysław Łukasz , Jarzębski Maciej , Xia Wenshui
Published: January 2022

β-Carotene (vitamin A precursor) and α-tocopherol, the utmost energetic form of vitamin E (VE), are known to be fat-soluble vitamins (FSVs) and essential nutrients needed to enhance the growth and metabolic functions of the human body. Their deficiencies are linked to numerous chronic disorders. Loading of FSVs within nanoemulsions could increase their oxidative stability and solubility. In this research, VE and β-Carotene (BC) were successfully co-entrapped within oil-in-water nanoemulsions of carrier oils, including tuna fish oil (TFO) and medium-chain triglycerides (MCTs), stabilized by modified starch and Tween-80. These nanoemulsions and free carrier oils loaded with vitamins were stored for over one month to investigate the impact of storage circumstances on their physiochemical characteristics. Entrapped bioactive compounds inside the nanoemulsions and bare oil systems showed a diverse behavior in terms of oxidation. A more deficiency of FSVs was found at higher temperatures that were more noticeable in the case of BC. VE behaved like an antioxidant to protect BC in MCT-based nanoemulsions, whereas it could not protect BC perfectly inside the TFO-loaded nanoemulsions. However, cinnamaldehyde (CIN) loading significantly enhanced the oxidative stability and FSVs retention in each nanoemulsion. Purity gum ultra (PGU)-based nanoemulsions comprising FSVs and CIN presented a greater BC retention (42.3%) and VE retention (90.1%) over one-month storage at 40 °C than Twee 80. The superior stability of PGU is accredited to the OSA-MS capabilities to produce denser interfacial coatings that can protect the entrapped compounds from the aqueous phase. This study delivers valuable evidence about the simultaneous loading of lipophilic bioactive compounds to enrich functional foods.
A Comparative Review of the Current Methods of Analysis and Design of Stiffened and Unstiffened Steel Liners
Authors: Martin-Candilejo Araceli , Valdeolivas José L. G. , Granados Alfredo , Mosquera Feijoo Juan C.
Published: January 2022

There are significant levels of concern about both the safety assessment and financial evaluation of the whole hydropower system, especially at early project stages. In addition, there is a variety of reliable and accurate methods for analysis, design, and optimization of steel pressure liners in hydropower plants. Several countries have developed specific regulations and codes for the design, installation, and safety evaluation of under-pressure piping, as well as estimates of the potential risks associated with failure. This paper reviews the current methodologies and codes available for design and safety assessment of either unstiffened or stiffened pressure steel liners in hydropower plants. After examining the main guidelines and practical conclusions drawn from recent research in the field of liners subjected to either internal or external pressure, this article then discusses the relevant regulations and codes. The scope of this work is to summarize the advantages, disadvantages, and main characteristics of the existing design criteria, analysis methods, and other issues related to steel liners. Afterwards, this paper compares the main reliable formulations and modelling techniques, even the most recent or sophisticated ones. Lastly, we draw some conclusions regarding their accuracy and scope of applicability.
MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation
Authors: Ardiansyah Ardiansyah , Ferdiana Ridi , Permanasari Adhistya Erna
Published: January 2022

Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Particle swarm optimization still suffers from two primary shortcomings, i.e., premature convergence and easy trapping in local optima. Therefore, this study proposes modified chaotic particle swarm optimization with uniform particle initialization to enhance the comprehensive performance of standard particle swarm optimization by introducing three additional schemes. Firstly, the initialized swarm is generated through a uniform approach. Secondly, replacing the linear inertia weight by introducing the nonlinear chaotic inertia weight map. Thirdly, by applying a personal learning strategy to enhance the global and local search to avoid trap in local optima. The proposed algorithm is examined and compared with standard particle swarm optimization, two recent particle swarm optimization variants, and a nature-inspired algorithm using three software effort estimation methods as benchmark functions: Use case points, COCOMO, and Agile. Detailed investigations prove that the proposed schemes work well to develop the proposed algorithm in an exploitative manner, which is created by a uniform particle initialization and avoids being trapped on the local optimum solution in an explorative manner and is generated by a personal learning strategy and chaotic-based inertia weight.
Virtual Reality and Digital Human Modeling for Ergonomic Assessment in Industrial Product Development: A Patent and Literature Review
Authors: da Silva Adailton Gonçalves , Mendes Gomes Marcus Vinicius , Winkler Ingrid
Published: January 2022

The late detection of ergonomic component assembly issues during manufacturing processes has an influence on operator well-being and productivity, as well as having a high cost of correction. Although virtual reality may enhance digital human modeling, there is a knowledge gap on the combination of these technologies to assess ergonomics. This study aims to analyze the application of virtual reality and digital human modeling for physical ergonomics assessment during product development in the industry, through a review of patents and the literature. We searched the Derwent Innovation Index, Scopus, and Web of Science databases and found 250 patents and 18 articles. We observed an exponential increase in patents, concentrated among major technological players, and a wide range of technologies being invented. A significant number of studies focuses on the automotive and aviation industries. Despite a relative consensus in the literature on the benefits of integrating virtual reality and digital human modeling to assess physical ergonomics in the early stages of product development, the technologies are seldom combined in the same analysis; moreover, most cases continue to focus on analyzing pre-designed production processes, when resources are completely deployed. These outcomes may provide a reference for practitioners and researchers to develop novel solutions for the early detection of physical ergonomics issues in the industry.
Production and Characterization of Yogurt-Like Fermented Beverage Based on Camelina (Camelina sativa L.) Seed Press Cake
Authors: Łopusiewicz Łukasz , Kwiatkowski Paweł , Drozłowska Emilia
Published: January 2022

Plant-based fermented beverages are growing in popularity due to the rise in vegetarianism, health trends and ethical concerns. In this study, camelina (Camelina sativa L.) seed press cake (CPC, 15% and 20% w/w) was fermented using yogurt starter culture. The physicochemical properties of the samples, including pH, total acidity, color, viscosity, texture and rheological properties were investigated. Moreover, the lactic acid bacteria (LAB) viability, bioactive compounds and antioxidant activity were determined. During fermentation and 28-day refrigerated storage, the samples achieved a mean viable bacterial count of at least 1010 CFU/g, which is higher than the recommended bacteria level for traditional dairy yogurt (106 CFU/g). A significant acidification, consumption of reducing sugars, increase in free amino acids and polyphenolics was observed. In addition, CPC-based fermented samples showed good antioxidant potential. Textural and rheological characteristics were similar to dairy yogurt. Moreover, fermentation improved the sensory attributes of CPC, meeting consumers’ acceptance criteria. Thus, the study indicated that fermentation had a marked effect on the physicochemical, microbiological and functional properties of CPC. Therefore, the fermented CPC-based beverage has the potential to be a valid, value-added and novel alternative to dairy-based yogurt.
Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
Authors: Lucas Evan , Wang Zhaohui
Published: January 2022

Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one.
In Vitro Analysis of Organic Ester Functional Groups in Carious Dentine
Authors: Alturki Mohammed , Almhöjd Ulrica , Koller Garrit , Warburton Fiona , Banerjee Avijit
Published: January 2022

Background: With the implementation of minimally invasive selective caries removal protocols to treat cavitated, deep carious dentine lesions, there is a need to investigate specific biochemical moiety distributions to help characterise and distinguish between infected (contaminated) and affected (demineralised) zones within the dentine lesion. The present in vitro investigation aimed to compare the distribution of ester functional groups (1740 cm−1) within carious dentine tissue (infected and affected dentine). The null hypothesis stipulated that there are no differences in ester function intensity/distribution within carious dentine lesions. Materials and Methods: From a total of five extracted human molar teeth with carious dentine lesions, 246 points from 10 sections of carious dentine were examined using high-resolution Raman spectroscopy and characterised into infected, affected and sound dentine. The peak intensity of the characteristic vibration mode of the ester function was calculated from sample scans. Results: Analyses indicated a statistically significant difference in the spectroscopic vibration bands of esters between the infected and affected dentine zones. Conclusion: The ester functional group is higher in intensity in the caries-infected dentine zone compared to the affected tissue. This finding could be used to develop an objective indicator for the selective operative management of carious dentine.
Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
Authors: Hoffer Johannes G. , Geiger Bernhard C. , Kern Roman
Published: January 2022

The avoidance of scrap and the adherence to tolerances is an important goal in manufacturing. This requires a good engineering understanding of the underlying process. To achieve this, real physical experiments can be conducted. However, they are expensive in time and resources, and can slow down production. A promising way to overcome these drawbacks is process exploration through simulation, where the finite element method (FEM) is a well-established and robust simulation method. While FEM simulation can provide high-resolution results, it requires extensive computing resources to do so. In addition, the simulation design often depends on unknown process properties. To circumvent these drawbacks, we present a Gaussian Process surrogate model approach that accounts for real physical manufacturing process uncertainties and acts as a substitute for expensive FEM simulation, resulting in a fast and robust method that adequately depicts reality. We demonstrate that active learning can be easily applied with our surrogate model to improve computational resources. On top of that, we present a novel optimization method that treats aleatoric and epistemic uncertainties separately, allowing for greater flexibility in solving inverse problems. We evaluate our model using a typical manufacturing use case, the preforming of an Inconel 625 superalloy billet on a forging press.
The Effect of the Use of Essential Oils in the Feed of Bee Families on Honey Chemical Composition and Antimicrobial Activity
Authors: Lazăr Roxana Nicoleta , Alexa Ersilia , Obiștioiu Diana , Cocan Ileana , Pătruică Silvia
Published: January 2022

Honey is a natural food with pharmacological properties. The present study was focused on the use of essential oils in the supplementary feeding of bee families for three weeks (spring). The purpose was to monitor the effect of essential oils (basil, thyme, juniper, cloves, mint, cinnamon, oregano, rosemary) on the chemical components of the resulting honey. The honey sampling period was carried out before the administration of essential oils in the supplementary feed of the bee families, after the administration of essential oils in the bee feed, respectively, after the first harvest (rapeseed). The honey samples were subjected to chemical analysis to determine humidity, impurities, ash, pH, acidity, total phenolic content (TPC) and flavonoid content (FC), reducing sugar content and antioxidant activity. In addition, the antimicrobial activity against nine strains was tested. We found out that all the essential oils used had a positive effect on the chemical composition of honey, especially the essential oil of oregano, mint, thyme, cinnamon. Experimental variants in which juniper and clove essential oil were introduced reduced the acidity of honey by 28.12% and 35.48%, respectively. Ash content varies between 0.23% and 0.46%, impurities content between 4.11% and 9.11%, while the values for pH were between 3.42 and 4.03. As for the TPC, they have increased considerably in all experimental variants to which essential oil has been added, compared to the batch fed only with sugar syrup, the highest value being recorded for the sample treated with cinnamon after the third harvest (163.94 mg/100 g). The FC values vary between 8.41–44.36 mg/100 g, depending of the treatment applied and the period of harvesting. Regarding the antimicrobial activity, the results highlighted that the essential oils present in the diet of bees produced honey with antimicrobial effect increased after two weeks after administration.
Modified Approach Using Mentha arvensis in the Synthesis of ZnO Nanoparticles—Textural, Structural, and Photocatalytic Properties
Authors: Stoyanova Daniela , Stambolova Irina , Blaskov Vladimir , Georgieva Petya , Shipochka Maria , Zaharieva Katerina , Dimitrov Ognian , Markov Pavel , Dyakova Vanya , Kostova Yoanna , Mladenova Ralitsa , Tzvetkov George , Boshkova Nelly , Boshkov Nikolai
Published: January 2022

Zinc oxide arouses considerable interest since it has many applications—in microelectronics, environmental decontaminations, biomedicine, photocatalysis, corrosion, etc. The present investigation describes the green synthesis of nanosized ZnO particles using a low-cost, ecologically friendly approach compared to the classical methods, which are aimed at limiting their harmful effects on the environment. In this study, ZnO nanoparticles were prepared using an extract of Mentha arvensis (MA) leaves as a stabilizing/reducing agent, followed by hydrothermal treatment at 180 °C. The resulting powder samples were characterized by X-ray diffraction (XRD) phase analysis, infrared spectroscopy (IRS), scanning electron microscopy (SEM), and electron paramagnetic resonance (EPR). The specific surface area and pore size distribution were measured by the Brunauer–Emmett–Taylor (BET) method. Electronic paramagnetic resonance spectra were recorded at room temperature and at 123 K by a JEOL JES-FA 100 EPR spectrometer. The intensity of the bands within the range of 400–1700 cm−1 for biosynthesized ZnO (BS-Zn) powders decreased with the increase in the Mentha arvensis extract concentration. Upon increasing the plant extract concentration, the relative proportion of mesopores in the BS-Zn samples also increased. It was established that the photocatalytic performance of the biosynthesized powders was dependent on the MA concentration in the precursor solution. According to EPR and PL analyses, it was proved that there was a presence of singly ionized oxygen vacancies (V0+) and zinc interstitials (Zni). The use of the plant extract led to changes in the morphology, phase composition, and structure of the ZnO particles, which were responsible for the increased photocatalytic rate of discoloration of Malachite Green dye.
Fake News Classification Based on Content Level Features
Authors: Lai Chun-Ming , Chen Mei-Hua , Kristiani Endah , Verma Vinod Kumar , Yang Chao-Tung
Published: January 2022

Due to the openness and easy accessibility of online social media (OSM), anyone can easily contribute a simple paragraph of text to express their opinion on an article that they have seen. Without access control mechanisms, it has been reported that there are many suspicious messages and accounts spreading across multiple platforms. Accordingly, identifying and labeling fake news is a demanding problem due to the massive amount of heterogeneous content. In essence, the functions of machine learning (ML) and natural language processing (NLP) are to enhance, speed up, and automate the analytical process. Therefore, this unstructured text can be transformed into meaningful data and insights. In this paper, the combination of ML and NLP are implemented to classify fake news based on an open, large and labeled corpus on Twitter. In this case, we compare several state-of-the-art ML and neural network models based on content-only features. To enhance classification performance, before the training process, the term frequency-inverse document frequency (TF-IDF) features were applied in ML training, while word embedding was utilized in neural network training. By implementing ML and NLP methods, all the traditional models have greater than 85% accuracy. All the neural network models have greater than 90% accuracy. From the experiments, we found that the neural network models outperform the traditional ML models by, on average, approximately 6% precision, with all neural network models reaching up to 90% accuracy.
Seismic Response Analysis of High-Voltage Bushings and Down Lead Transmission Line Systems in Substations
Authors: Zhang Meng , Wei Ran , Du Yuanxiang , Zhao Guifeng
Published: January 2022

Based on an actual ultra-high voltage (UHV) substation, a finite element (FE) model for a high-voltage (HV) bushing, arrester, and down lead transmission line (DLTL) system was built using ANSYS software. The dynamic responses of the system under different seismic intensities were analyzed and compared with those of the corresponding single bushing and arrester. On this basis, the coupling vibration influence of the upper DLTL on the responses of the HV bushing and arrester is discussed. The results indicate that the DLTL adversely affects the responses of the HV bushing and arrester under seismic loading. As the seismic intensity increases, the structural displacements at the top of the HV bushing and arrester increase, accompanied by a reduction in the geometric length redundancy of the DLTL, resulting in a mutual pulling effect between the HV bushing and the arrester and the quick amplification of their respective dynamic responses in a nonlinear form. Under the action of an earthquake with a peak ground acceleration (PGA) of 0.4 g, the maximum stresses at the roots of the HV bushing and the arrester in the system separately increase by approximately 13.02% and 7.80% compared to the corresponding single HV bushing and the arrester. Overall, a geometric length redundancy of at least 200 mm in the DLTL in engineering design is recommended.
A Simple Analysis Method of Specific Anammox Activity Using a Respirometer
Authors: Yu Jaecheul , Kim Yeonju , Kim Jeongmi , Jeong Soyeon , Park Seongjae , Lee Taeho
Published: January 2022

Anaerobic ammonium oxidation (anammox) is a biological nitrogen removal process with attractive prospects, such as no carbon addition, less aeration, lower greenhouse gas generation, and lower sludge production. However, it is difficult to maintain a stable anammox process since the anammox bacteria have a slow growth rate and high sensitivity to many factors. Therefore, it is very important to analyze and maintain the anammox activity as a process indicator for its successful operation. The conventional method for measuring the concentration of nitrogen compounds, such as ammonium, nitrite, or nitrogen gas is inconvenient during the reaction time for specific anammox activity (SAA) analysis, which can result in an inaccurately determined SAA due to the substrate loss and temperature change. In this study, a respirometer was utilized to analyze the SAA. The SAA values from a respirometer (rSAA) showed a similar pattern to the SAA values (mSAA) from the conventional method. All of the SAA analyses showed the highest value at 35 °C with a granule size of <1 mm. Statistical analysis showed no significant differences regardless of the analysis method, since the p-values for the t-test and Wilcoxon rank-sum test were >0.05. Therefore, the respirometer can be used as a simple and efficient tool for SAA analysis. Moreover, the operating maintenance and management of the anammox process can be improved due to the simple SAA analysis in the field.
Comment on Cabrera et al. A User-Friendly Tool to Characterize the Moisture Transfer in Porous Building Materials: FLoW1D. Appl. Sci. 2020, 10, 5090
Authors: Janssen Hans
Published: January 2022

In July 2020, this journal published “A User-Friendly Tool to Characterize the Moisture Transfer in Porous Building Materials: FLoW1D”. That article presents and applies a forward-Euler-based tool, implemented in Visual Basic for Applications in Excel, for simulating moisture transfer in building materials. In that tool, the building materials’ hygric properties are described via the unimodal van Genuchten and Mualem equations. All parameters needed therein are inversely identified from only a capillary absorption experiment. These are three feats that, if valid, would strongly progress the state-of-the-art on moisture transfer simulation, hygric property description, and hygric property characterisation. This critique voices severe doubts, however, about the applicability of FLoW1D, the adequacy of the van Genuchten-Mualem equations, and (most fundamentally) the uniqueness of the inverse characterisation.
Quantifying the Influence of a Burn Event on Ammonia Concentrations Using a Machine-Learning Technique
Authors: Hu Jiabao , Liao Tingting , Lü Yixuan , Wang Yanjun , He Yuexin , Shen Weishou , Yang Xianyu , Ji Dongsheng , Pan Yuepeng
Published: January 2022

Although combustion is considered a common source of ammonia (NH3) in the atmosphere, field measurements quantifying such emissions of NH3 are still lacking. In this study, online measurements of NH3 were performed by a cavity ring-down spectrometer, in the cold season at a rural site in Xianghe on the North China Plain. We found that the NH3 concentrations were mostly below 65 ppb during the study period. However, from 18 to 21 November 2017, a close burn event (~100 m) increased the NH3 concentrations to 145.6 ± 139.9 ppb. Using a machine-learning technique, we quantified that this burn event caused a significant increase in NH3 concentrations by 411%, compared with the scenario without the burn event. In addition, the ratio of ∆NH3/∆CO during the burn period was 0.016, which fell in the range of biomass burning. Future investigations are needed to evaluate the impacts of the NH3 combustion sources on air quality, ecosystems, and climate in the context of increasing burn events worldwide.
Properties of Magnetic Field Fluctuations in Long-Lasting Radial IMF Events from Wind Observation
Authors: Pi Gilbert , Pitňa Alexander , Zhao Guo-Qing , Němeček Zdeněk , Šafránková Jana , Tsai Tsung-Che
Published: January 2022

Long-lasting radial interplanetary magnetic field (IMF) intervals in which IMF points along the solar wind velocity for several hours have many interesting properties. We investigate the average parameters and the behavior of magnetic field fluctuations within 419 such radial intervals. The power spectral density (PSD) calculated over 1-h intervals of a radial IMF is compared with PSDs in adjacent regions prior to and after the radial IMF. We concentrate on (1) the power of IMF fluctuations, (2) the median slopes of PSDs in both inertial and kinetic ranges, (3) the proton temperature and its anisotropy, and (4) the occurrence rate of wavy structures and their polarization. We have shown that the fluctuation amplitude is low in the radial IMF intervals in both magnetohydrodynamic (MHD) and kinetic ranges, and the spectral power increases with the cone angle in the MHD range. We discuss this effect in the light of present knowledge on plasma turbulence and peculiarities of observations of magnetic field variations under the radial background magnetic field. We found that in the radial IMF events, the proton temperature is more isotropic, the occurrence rate of waves is higher, and the waves have no preferred polarization in the frequency range from 0.1 to 1 Hz. It suggests that the radial IMF structure leads to a different development of turbulence than the typical Parker-spiral structure.
Generalized Vector-Valued Hardy Functions
Authors: Carmichael Richard D.
Published: January 2022

We consider analytic functions in tubes Rn+iB⊂Cn with values in Banach space or Hilbert space. The base of the tube B will be a proper open connected subset of Rn, an open connected cone in Rn, an open convex cone in Rn, and a regular cone in Rn, with this latter cone being an open convex cone which does not contain any entire straight lines. The analytic functions satisfy several different growth conditions in Lp norm, and all of the resulting spaces of analytic functions generalize the vector valued Hardy space Hp in Cn. The analytic functions are represented as the Fourier–Laplace transform of certain vector valued Lp functions which are characterized in the analysis. We give a characterization of the spaces of analytic functions in which the spaces are in fact subsets of the Hardy functions Hp. We obtain boundary value results on the distinguished boundary Rn+i{0¯} and on the topological boundary Rn+i∂B of the tube for the analytic functions in the Lp and vector valued tempered distribution topologies. Suggestions for associated future research are given.
Maximum Principle and Second-Order Optimality Conditions in Control Problems with Mixed Constraints
Authors: Arutyunov Aram , Karamzin Dmitry , Pereira Fernando
Published: January 2022

This article concerns the optimality conditions for a smooth optimal control problem with an endpoint and mixed constraints. Under the normality assumption, which corresponds to the full-rank condition of the associated controllability matrix, a simple proof of the second-order necessary optimality conditions based on the Robinson stability theorem is derived. The main novelty of this approach compared to the known results in this area is that only a local regularity with respect to the mixed constraints, that is, a regularity in an ε-tube about the minimizer, is required instead of the conventional stronger global regularity hypothesis. This affects the maximum condition. Therefore, the normal set of Lagrange multipliers in question satisfies the maximum principle, albeit along with the modified maximum condition, in which the maximum is taken over a reduced feasible set. In the second part of this work, we address the case of abnormal minimizers, that is, when the full rank of controllability matrix condition is not valid. The same type of reduced maximum condition is obtained.
Diversity and Evolution of DNA Transposons Targeting Multicopy Small RNA Genes from Actinopterygian Fish
Authors: Kojima Kenji K.
Published: January 2022

Dada is a unique superfamily of DNA transposons, inserted specifically in multicopy RNA genes. The zebrafish genome harbors five families of Dada transposons, whose targets are U6 and U1 snRNA genes, and tRNA-Ala and tRNA-Leu genes. Dada-U6, which is inserted specifically in U6 snRNA genes, is found in four animal phyla, but other target-specific lineages have been reported only from one or two species. Here, vertebrate genomes and transcriptomes were surveyed to characterize Dada families with new target specificities, and over 120 Dada families were characterized from the genomes of actinopterygian fish. They were classified into 12 groups with confirmed target specificities. Newly characterized Dada families target tRNA genes for Asp, Asn, Arg, Gly, Lys, Ser, Tyr, and Val, and 5S rRNA genes. Targeted positions inside of tRNA genes are concentrated in two regions: around the anticodon and the A box of RNA polymerase III promoter. Phylogenetic analysis revealed the relationships among actinopterygian Dada families, and one domestication event in the common ancestor of carps and minnows belonging to Cyprinoidei, Cypriniformes. Sequences targeted by phylogenetically related Dada families show sequence similarities, indicating that the target specificity of Dada is accomplished through the recognition of primary nucleotide sequences.
Retinoic Acid-Differentiated Neuroblastoma SH-SY5Y Is an Accessible In Vitro Model to Study Native Human Acid-Sensing Ion Channels 1a (ASIC1a)
Authors: Kalinovskii Aleksandr P. , Osmakov Dmitry I. , Koshelev Sergey G. , Lubova Kseniya I. , Korolkova Yuliya V. , Kozlov Sergey A. , Andreev Yaroslav A.
Published: January 2022

Human neuroblastoma SH-SY5Y is a prominent neurobiological tool used for studying neuropathophysiological processes. We investigated acid-sensing (ASIC) and transient receptor potential vanilloid-1 (TRPV1) and ankyrin-1 (TRPA1) ion channels present in untreated and differentiated neuroblastoma SH-SY5Y to propose a new means for their study in neuronal-like cells. Using a quantitative real-time PCR and a whole-cell patch-clamp technique, ion channel expression profiles, functionality, and the pharmacological actions of their ligands were characterized. A low-level expression of ASIC1a and ASIC2 was detected in untreated cells. The treatment with 10 μM of retinoic acid (RA) for 6 days resulted in neuronal differentiation that was accompanied by a remarkable increase in ASIC1a expression, while ASIC2 expression remained almost unaltered. In response to acid stimuli, differentiated cells showed prominent ASIC-like currents. Detailed kinetic and pharmacological characterization suggests that homomeric ASIC1a is a dominant isoform among the present ASIC channels. RA-treatment also reduced the expression of TRPV1 and TRPA1, and minor electrophysiological responses to their agonists were found in untreated cells. Neuroblastoma SH-SY5Y treated with RA can serve as a model system to study the effects of different ligands on native human ASIC1a in neuronal-like cells. This approach can improve the characterization of modulators for the development of new neuroprotective and analgesic drugs.
Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling
Authors: Li Zhichao , Gurgel Helen , Xu Lei , Yang Linsheng , Dong Jinwei
Published: January 2022

Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.
Microbiological Characteristics of Some Stations of Moscow Subway
Authors: Pochtovyi Andrei A. , Vasina Daria V. , Verdiev Bakhtiyar I. , Shchetinin Alexey M. , Yuzhakov Anton G. , Ovchinnikov Roman S. , Tkachuk Artem P. , Gushchin Vladimir A. , Gintsburg Alexander L.
Published: January 2022

The subway is one of the most actively used means of transport in the traffic infrastructure of large metropolitan areas. More than seven million passengers use the Moscow subway every day, which promotes the exchange of microorganisms between people and the surrounding subway environment. In this research, a study of the bacterial communities of two Moscow subway stations was conducted and the common subway microbiome was determined. However, there were differences in microbiological and antibiotic-resistance profiles, depending on the station. The station’s operational period since opening correlated with the taxonomic diversity and resistance of the identified bacteria. Moreover, differences between aerosol and surface bacterial communities were found at the two subway stations, indicating the importance of diversified sampling during the microbiome profiling of public areas. In this study, we also compared our data with previously published results obtained for the Moscow subway. Despite sample collection at different stations and seasons, we showed the presence of 15 common genera forming the core microbiome of the Moscow subway, which represents human commensal species, as well as widespread microorganisms from the surrounding environment.
Exercise Effects on the Biomechanical Properties of the Achilles Tendon—A Narrative Review
Authors: Yu Changxiao , Deng Liqin , Li Li , Zhang Xini , Fu Weijie
Published: January 2022

The morphological and mechanical properties (e.g., stiffness, stress, and force) of the Achilles tendon (AT) are generally associated with its tendinosis and ruptures, particularly amongst runners. Interest in potential approaches to reduce or prevent the risk of AT injuries has grown exponentially as tendon mechanics have been efficiently improving. The following review aims to discuss the effect of different types of exercise on the AT properties. In this review article, we review literature showing the possibility to influence the mechanical properties of the AT from the perspective of acute exercise and long-term training interventions, and we discuss the reasons for inconsistent results. Finally, we review the role of the habitual state in the AT properties. The findings of the included studies suggest that physical exercise could efficiently improve the AT mechanical properties. In particular, relatively long-term and low-intensity eccentric training may be a useful adjunct to enhance the mechanical loading of the AT.