bluuebunny/crossref_metadata_2025 huggingface dataset:
ZnO Particles Modified MgAl Coatings with Improved Photocatalytic Activity Formed by Plasma Electrolytic Oxidation of AZ31 Magnesium Alloy in Aluminate Electrolyte
Authors: Stojadinović Stevan , Radić Nenad , Vasilić Rastko
Published: November 2022

MgAl and MgAl/ZnO coatings were prepared by plasma electrolytic oxidation (PEO) of AZ31 magnesium alloy in aluminate electrolyte (5 g/L NaAlO2) without and with addition of ZnO particles in various concentrations. The MgAl coating was partially crystallized and contained MgO and MgAl2O4 phases. The addition of ZnO particles to aluminate electrolyte had no significant effect on the surface morphology of formed coatings, while the Zn content increased with ZnO particle concentrations. X-ray diffraction confirmed the incorporation of ZnO particles in the coatings. The photodegradation of methyl orange (10 cm3 of 8 mg/L) was used to measure the photocatalytic activity (PA) of MgAl and MgAl/ZnO coatings. The PA of MgAl coating after 8 h of irradiation was around 58%, while the PA of MgAl/ZnO coatings formed in aluminate electrolyte with the addition of ZnO particles in concentrations of 4 g/L, 8 g/L, and 12 g/L were around 69%, 86%, and 97%, respectively.
Analysis of the Catalytic Effects Induced by Alkali and Alkaline Earth Metals (AAEMs) on the Pyrolysis of Beech Wood and Corncob
Authors: Wang Wei , Lemaire Romain , Bensakhria Ammar , Luart Denis
Published: November 2022

The catalytic pyrolysis of beech wood and corncob was experimentally investigated considering six additives containing alkali and alkaline earth metals (Na2CO3, NaOH, NaCl, KCl, CaCl2 and MgCl2). Thermogravimetric analyses (TGA) were carried out with raw feedstocks and samples impregnated with different concentrations of catalysts. In a bid to better interpret observed trends, measured data were analyzed using an integral kinetic modeling approach considering 14 different reaction models. As highlights, this work showed that cations (Na+, K+, Ca2+, and Mg2+) as well as anions (i.e., CO32−, OH−, and Cl−) influence pyrolysis in selective ways. Alkaline earth metals were proven to be more effective than alkali metals in fostering biomass decomposition, as evidenced by decreases in the characteristic pyrolysis temperatures and activation energies. Furthermore, the results obtained showed that the higher the basicity of the catalyst, the higher its efficiency as well. Increasing the quantities of calcium- and magnesium-based additives finally led to an enhancement of the decomposition process at low temperatures, although a saturation phenomenon was seen for high catalyst concentrations.
Effect of Cr on a Ni-Catalyst Supported on Sibunite in Bicyclohexyl Dehydrogenation in Hydrogen Storage Application
Authors: Kustov Leonid M. , Kalenchuk Alexander N.
Published: November 2022

A comparison of the activity of mono- and bimetallic Ni-Cr/C catalysts deposited on a carbon carrier (sibunite) in the bicyclohexyl dehydrogenation reaction as a stage of hydrogen evolution in hydrogen storage systems is carried out. The interaction of Ni and Cr supported onto the carbon carrier—sibunite in bimetallic NiCr systems affects the change in the parameters of the crystal lattice of nickel, compared with the FCC lattice of Ni, as shown by the methods of XPS, TPR, XRD, high-resolution TEM and electron diffraction.
Enhanced Sensing Performance of Electrospun Tin Dioxide Nanofibers Decorated with Cerium Dioxide Nanoparticles for the Detection of Liquefied Petroleum Gas
Authors: Liu Xichen , Zhang Jianhua , Zhang Hao , Chen Can , Zhang Dongzhi
Published: November 2022

Tin dioxide (SnO2) nanofibers and cerium dioxide (CeO2) nanoparticles were prepared by electrospinning and hydrothermal methods, respectively. The morphology and structure of the synthesized SnO2/CeO2 samples were characterized by a variety of methods. The gas-sensing properties of the SnO2/CeO2 sensor were investigated for liquefied petroleum gas (LPG) detection at room temperature. Compared with pure SnO2 nanofibers, the SnO2/CeO2 composite sensor showed a much higher response and shorter response time for LPG sensing after doping with CeO2 nanoparticles. Furthermore, the SnO2/CeO2 composite sensor had better resistance to interference from humidity than the pure SnO2 sensor. The significantly enhanced sensing performance of the SnO2/CeO2 composite sensor for LPG can be attributed to the modification with CeO2 to increase oxygen vacancies and form a heterostructure with SnO2 nanofibers. Meanwhile, the LPG detection circuit was built to realize real-time concentration display and alarm for practical applications.
Numerical Simulation of Phase Transitions in Porous Media with Three-Phase Flows Considering Steam Injection into the Oil Reservoir
Authors: Bublik Sergey , Semin Mikhail
Published: November 2022

This study focuses on the analysis of an approach to the simulation of the phase transition in porous media when hot steam is injected into the oil reservoir. The reservoir is assumed to consist of a porous medium with homogeneous thermal properties. Its porous space is filled with a three-phase mixture of steam, water, and oil. The problem is considered in a non-stationary and non-isothermal formulation. Each phase is considered to be incompressible, with constant thermal properties, except for the dynamic viscosity of oil, which depends on the temperature. The 1D mathematical model of filtration, taking into account the phase transition, consists of continuity, Darcy, and energy equations. Steam injection and oil production in the model are conducted via vertical or horizontal wells. In the case of horizontal wells, the influence of gravity is also taken into account. The Lee model is used to simulate the phase transition between steam and water. The convective terms in the balance equations are calculated without accounting for artificial diffusion. Spatial discretization of the 1D domain is carried out using the finite volume method, and time discretization is implemented using the inverse (implicit) Euler scheme. The proposed model is analyzed in terms of the accuracy of the phase transition simulation for various sets of independent phases and combinations of continuity equations. In addition, we study the sensitivity of the model to the selected independent phases, to the time step and spatial mesh parameters, and to the intensity of the phase transition. The obtained results allow us to formulate recommendations for simulations of the phase transition using the Lee model.
Antibodies to Commonly Circulating Viral Pathogens Modulate Serological Response to Severe Acute Respiratory Syndrome Coronavirus 2 Infection
Authors: Sarker Protim , Akhtar Evana , Akter Sharmin , Rajia Sultana , Kuddusi Rakib Ullah , Ahmed Razu , Jakarea Md. , Islam Mohammad Zahirul , Hoque Dewan Md Emdadul , Ahmed Shehlina , Raqib Rubhana
Published: November 2022

The purpose of this study was to determine the seropositivity of circulating viral pathogens and their association with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity. In a cross-sectional design, inhabitants (aged 10–60 years) of the slum and surrounding non-slum areas of Dhaka and Chattogram Metropolitan cities in Bangladesh were enrolled from October 2020 to February 2021. Antibodies to SARS-CoV-2, influenza B, parainfluenza, respiratory syncytial virus (RSV), human coronavirus HKU1 (HCoV-HKU1), dengue and chikungunya viruses were determined in plasma. The association of SARS-CoV-2 seropositivity with seropositivity to other viruses was assessed using the multi-variate logistic regression model. Seroprevalence of SARS-CoV-2, influenza B, RSV, dengue, chikungunya, HCoV-HKU1 and the parainfluenza virus were 68.3%, 98%, 50.0%, 16.5%, 15.5%, 3.36% and 0.0%, respectively. Individuals seropositive for RSV had lower odds (OR = 0.60; 95% CI= 0.49, 0.73) of SARS-CoV-2 seropositivity compared to RSV-seronegative individuals. Conversely, higher odds of SARS-CoV-2 seropositivity were observed in participants seropositive for dengue (OR= 1.73; 95% CI = 1.14, 2.66, only in slum) or chikungunya (OR = 1.48; 95% CI = 1.11, 1.95) compared to their seronegative counterparts. The study findings indicated that exposure to vector-borne virus dengue or chikungunya enhance, while antibodies to respiratory virus RSV decrease, the serological response to SARS-CoV-2.
A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis
Authors: Li Haiyang , Wang Fei , Huang Weini
Published: November 2022

Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is essential for the treatment of renal cancer and the long-term survival of patients. In this study, blood samples of patients were collected and a pre-defined set of blood indicators were measured. A random forest (RF) model was established to predict based on each indicator in the blood, and was trained with all relevant indicators for comprehensive predictions. In our study, we found that there was a high statistical significance (p < 0.001) for all indicators of healthy individuals and early cancer patients, except for uric acid (UA). At the same time, ccRCC also presented great differences in most blood indicators between males and females. In addition, patients with ccRCC had a higher probability of developing a low ratio of albumin (ALB) to globulin (GLB) (AGR < 1.2). Eight key indicators were used to classify and predict renal cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) of the eight-indicator model was as high as 0.932, the sensitivity was 88.2%, and the specificity was 86.3%, which are acceptable in many applications, thus realising early screening for renal cancer by blood indicators in a simple blood-draw physical examination. Furthermore, the composite indicator prediction method described in our study can be applied to other clinical conditions or diseases, where multiple blood indicators may be key to enhancing the diagnostic potential of screening strategies.
Quality of End-of-Life Care in Gastrointestinal Cancers: A 13-Year Population-Based Retrospective Analysis in Ontario, Canada
Authors: Lees Caitlin SR , Seow Hsien , Chan Kelvin KW , Gayowsky Anastasia , Sinnarajah Aynharan
Published: November 2022

Population-based quality indicators of either aggressive or supportive care at end of life (EOL), especially when specific to a cancer type, help to inform quality improvement efforts. This is a population-based, retrospective cohort study of gastrointestinal (GI) cancer decedents in Ontario from 1 January 2006–31 December 2018, using administrative data. Quality indices included hospitalizations, emergency department (ED) use, intensive care unit admissions, receipt of chemotherapy, physician house call, and palliative home care in the last 14–30 days of life. Previously defined aggregate measures of both aggressive and supportive care at end of life were also used. In our population of 69,983 patients who died of a GI malignancy during the study period, the odds of experiencing aggressive care at EOL remained stable, while the odds of experiencing supportive care at EOL increased. Most of our population received palliative care in the last year of life (n = 65,076, 93.0%) and a palliative care home care service in the last 30 days of life (n = 45,327, 70.0%). A significant number of patients also experienced death in an acute care hospital bed (n = 28,721, 41.0%) or had a new hospitalisation in the last 30 days of life (n = 33,283, 51.4%). The majority of patients received palliative care in the last year of life, and a majority received a palliative care home service within the last 30 days of life. The odds of receiving supportive care at EOL have increased over time. Differences in care exist according to income, age, and rurality.
Freshwater Reservoir, Ecological Traps and Source-Sink Dynamics
Authors: Melendez Nina , Stevens Virginie M. , Baguette Michel
Published: November 2022

Odonata are biodiversity indicators that adequately represent many aquatic and semi-aquatic animal species. We recorded over two field seasons a rich lentic community of Odonata (18 species) in a large artificial freshwater reservoir (ca. 55 ha) built 40 years ago. The release of water from the dam in summer for crop irrigation leads to the desiccation of large parts of the reservoir, which prevents the reproduction of half of the species of this Odonata community. We identify two adaptations that allow eight species to cope with desiccation, i.e., a precocious breeding period allowing the emergence of adults before the retreat of water, or a delayed adult emergence due to egg diapause from oviposition to the end of winter. The reservoir acts thus as an ecological trap for individuals of 10 species that developed elsewhere and were attracted to the site without successfully breeding there. As consequence of the local population extinction at each generation, the presence of individuals of these 10 species at the reservoir depends on source-sink population dynamics in the landscape. In the context of global warming that encourages the creation of artificial freshwater reservoirs, the multiplication of such sinks could threaten the persistence in the landscape of species maladapted to desiccation.
New Strain of Cyphellophora olivacea Exhibits Striking Tolerance to Sodium Bicarbonate
Authors: Acs-Szabo Lajos , Papp Laszlo Attila , Csoma Hajnalka , Miklos Ida , Sipiczki Matthias
Published: November 2022

The cyanobacterium strain Synechococcus cedrorum SAG 88.79 stock culture has fungal contamination stated by the Sammlung von Algenkulturen der Universität Göttingen itself. In this recent work, this particular fungal strain was isolated, identified, and morphologically characterised. The fungal strain AGSC12 belongs to the species Cyphellophora olivacea, with respect to the sequence similarity, phylogeny, and morphology of the strain. Colony morphology and growth capability were examined on SMA, EMMA, PDA, MEA, YEA, and YPA plates. Growth of the colonies was the most successful on YPA plates, followed by PDA and MEA containing plates. Surprisingly, the AGSC12 strain showed extreme tolerance to NaHCO3, albeit it, is is considered a general fungistatic compound. Moreover, positive association between the AGSC12 and SAG 88.79 strains was revealed, as the SAG 88.79 strain always attained higher cell density in co-cultures with the fungus than in mono-cultures. Besides, a taxonomic note on the SAG 88.79 strain itself was also stated.
EUR/USD Exchange Rate Characterization: Study of Events
Authors: Carvalho Jorge , Couto Gualter , Pimentel Pedro
Published: November 2022

This study aims to evaluate the impact of major and minor changes in the Euro Zone and US interest rates on the EUR/USD exchange rate between 1 January 1999 and 31 December 2020. Therefore, twelve events are analyzed in this period, five related to changes in the US interest rate, six related to changes in the European interest rate, and finally, a single event in which both interest rates undergo an equal variation on the same date. The event study methodology was used, which, through the calculation of abnormal returns, makes it possible to evaluate whether there was a repercussion of the events on the value of the EUR/USD exchange rate. This methodology is used in several studies related to capital markets. The obtained results prove that there are abnormal returns with statistical significance on the event days, and, on the days that follow, changes in the interest rates have an impact on the EUR/USD exchange rate; however, there is no clear direction of the asset after the events occur.
Improvement in Error Recognition of Real-Time Football Images by an Object-Augmented AI Model for Similar Objects
Authors: Han Junsu , Kang Kiho , Kim Jongwon
Published: November 2022

In this paper, we analyze the recognition error of the general AI recognition model and propose the structure-modified and object-augmented AI recognition model. This model has object detection features that distinguish specific target objects in target areas where players with similar shapes and characteristics overlapped in real-time football images. We implemented the AI recognition model by reinforcing the training dataset and augmenting the object class. In addition, it is shown that the recognition rate increased by modifying the model structure based on the recognition errors analysis of general AI recognition models. Recognition errors were decreased by applying the modules of HSV processing and differentiated classes (overlapped player groups) learning to the general AI recognition model. We experimented in order to compare the recognition error reducing performance with the general AI model and the proposed AI model by the same real-time football images. Through this study, we have confirmed that the proposed model can reduce errors. It showed that the proposed AI model structure to recognize similar objects in real-time and in various environments could be used to analyze football games.
CNN-Based Surrogate Models of the Electrostatic Field for a MEMS Motor: A Bi-Objective Optimal Shape Design
Authors: Di Barba Paolo , Mognaschi Maria Evelina , Wiak Slawomir
Published: November 2022

The use of a convolutional neural network to develop a surrogate model of the electric field in MEMS devices is proposed. An electrostatic micromotor is considered as the case study. In particular, different CNNs are trained for the prediction of the torque profile and the maximum torque value at a no-load condition and the radial force which could arise in case of the radial displacement of the rotor during motion. The proposed deep learning approach is able to predict the abovementioned quantities with a low error and, in particular, it allows for a decrease in the computational cost, especially in case of optimization problems based on FE models.
Efficient PID Control Design for Frequency Regulation in an Independent Microgrid Based on the Hybrid PSO-GSA Algorithm
Authors: Zishan Farhad , Akbari Ehsan , Montoya Oscar Danilo , Giral-Ramírez Diego Armando , Molina-Cabrera Alexander
Published: November 2022

Microgrids are a part of the power system that consists of one or more units of distributed generation and are expected to remain in operation after being disconnected from the system. Since they rely on overlying networks, frequency control is very important for network-independent operation. Some of the most common problems in independently operating microgrids are frequency sustainability and its fluctuations. The main purpose of this study is to control the frequency of a microgrid in island mode in different scenarios. The objective function is defined based on time and changes in the system frequency. Thus, the variable parameters of the PID controller are transformed into an optimization problem and are solved through the hybrid PSO-GSA algorithm. The study considers four scenarios: (a) a microgrid dynamic model and optimal PID controller coefficients; (b) variable velocity disturbance applied to the studied system in order to observe power changes and the microgrid frequency; (c) stepped load changes applied to the studied system; and (d) the proposed methods on the standard test function. Simulations under different operating conditions are performed, indicating improvements in the stability of microgrid frequency fluctuations by means of the proposed control method.
Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis
Authors: Yehia Taha , Wahba Ali , Mostafa Sondos , Mahmoud Omar
Published: November 2022

Shale gas reservoirs have huge amounts of reserves. Economically evaluating these reserves is challenging due to complex driving mechanisms, complex drilling and completion configurations, and the complexity of controlling the producing conditions. Decline Curve Analysis (DCA) is historically considered the easiest method for production prediction of unconventional reservoirs as it only requires production history. Besides uncertainties in selecting a suitable DCA model to match the production behavior of the shale gas wells, the production data are usually noisy because of the changing choke size used to control the bottom hole flowing pressure and the multiple shut-ins to remove the associated water. Removing this noise from the data is important for effective DCA prediction. In this study, 12 machine learning outlier detection algorithms were investigated to determine the one most suitable for improving the quality of production data. Five of them were found not suitable, as they remove complete portions of the production data rather than scattered data points. The other seven algorithms were deeply investigated, assuming that 20% of the production data are outliers. During the work, eight DCA models were studied and applied. Different recommendations were stated regarding their sensitivity to noise. The results showed that the clustered based outlier factor, k-nearest neighbor, and the angular based outlier factor algorithms are the most effective algorithms for improving the data quality for DCA, while the stochastic outlier selection and subspace outlier detection algorithms were found to be the least effective. Additionally, DCA models, such as the Arps, Duong, and Wang models, were found to be less sensitive to removing noise, even with different algorithms. Meanwhile, power law exponential, logistic growth model, and stretched exponent production decline models showed more sensitivity to removing the noise, with varying performance under different outlier-removal algorithms. This work introduces the best combination of DCA models and outlier-detection algorithms, which could be used to reduce the uncertainties related to production forecasting and reserve estimation of shale gas reservoirs.
Robust Fractional MPPT-Based Moth-Flame Optimization Algorithm for Thermoelectric Generation Applications
Authors: Rezk Hegazy , Zaky Magdy M. , Alhaider Mohemmed , Tolba Mohamed A.
Published: November 2022

Depending on the temperature difference between the hot and cold sides of the thermoelectric generator (TEG), the output performance of the TEG can be produced. This means that it is necessary to force a TEG based on robust maximum power point tracking (MPPT) to operate close to its MPP at any given temperature or load. In this paper, an improved fractional MPPT (IFMPPT) is proposed in order to increase the amount of energy that can be harvested from TEGs. According to the suggested method, fractional order control is used. A moth-flame optimizer (MFO) was used to determine IFMPPT’s optimal parameters. A comparison of the results obtained by the MFO is made with those obtained by a particle swarm optimizer, genetic algorithm, gray wolf optimizer, seagull optimization algorithm, and tunicate swarm algorithm in order to demonstrate MFO’s superiority. IFMPPT’s primary objective is to enhance dynamic responses and exclude steady-state oscillations. Consequently, incremental resistance and perturb and observe are compared with the proposed strategy’s performance. It was revealed that IFMPPT provides superior tracking results both in dynamic and steady-state conditions when compared with traditional methods.
Solar Energy Implementation in Manufacturing Industry Using Multi-Criteria Decision-Making Fuzzy TOPSIS and S4 Framework
Authors: Ponce Pedro , Pérez Citlaly , Fayek Aminah Robinson , Molina Arturo
Published: November 2022

The demand for electrical energy has increased since the population of and automation in factories have grown. The manufacturing industry has been growing dramatically due to the fast-changing market, so electrical energy for manufacturing processes has increased. As a result, solar energy has been installed to supply electrical energy. Thus, assessing a solar panel company could be a complex task for manufacturing companies that need to assess, install, and operate solar panels when several criteria with different hierarchies from decision-makers are involved. In addition, the stages of a solar panel system could be divided into analysis, installation, operation, and disposal, and all of them must be considered. Thus, the solar panel company must provide a holistic solution for each stage of the solar panel lifespan. This paper provides a fuzzy decision-making approach (Fuzzy TOPSIS) to deal with the assessment of solar companies using the S4 framework in which the sensing, smart, sustainable, and social features are labeled with linguistic values that allow the evaluation of companies using fuzzy values and linguistic labels, instead of using crisp values that are difficult to define when decision-makers are evaluating a solar company for installation of the solar panels. The S4 features are considered the benefits of the evaluation. In the case study presented, three solar panel companies with different alternatives are evaluated on the basis of three decision-makers from manufacturing companies using the S4 framework. This paper considers the benefits of solar companies in the context of decision-makers participating in a multi-decision selection of such a company to install solar panels, so that the selection process is more effective. Thus, the proposed Fuzzy TOPSIS method proved efficient when selecting a solar panel company from among many options that best meets the needs of manufacturing companies.
A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies
Authors: Vakili Ramin , Khorsand Mojdeh
Published: November 2022

Protective relays play a crucial role in defining the dynamic responses of power systems during and after faults. Therefore, modeling protective relays in stability studies is crucial for enhancing the accuracy of these studies. Modeling all the relays in a bulk power system is a challenging task due to the limitations of stability software and the difficulties of keeping track of the changes in the setting information of these relays. Distance relays are one of the most important protective relays that are not properly modeled in current practices of stability studies. Hence, using the Random Forest algorithm, a fast machine learning-based method is developed in this paper that identifies the distance relays required to be modeled in stability studies of a contingency, referred to as critical distance relays (CDRs). GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The method is tested using 2018 summer peak load data of Western Electricity Coordinating Council (WECC) for various system conditions. The results illustrate the great performance of the method in identifying the CDRs. They also show that to conduct accurate stability studies, only modeling the CDRs suffices, and there is no need for modeling all the distance relays.
Overview of Research Status of DC Bias and Its Suppression in Power Transformers
Authors: Sun Youliang , Zhang Li , Zhang Zhuangzhuang , Wang Dong
Published: November 2022

In this article, the sources of DC bias and its effects on power transformers are first summarized. Secondly, the article classifies and summarizes the current DC bias calculation problems of power transformers, and puts forward some interesting viewpoints on the research logic of related calculations. The current processing methods of DC bias effect are classified and discussed, their advantages and disadvantages are compared, and the logic flow of DC bias effect processing is proposed. Finally, the current research on DC bias voltage of power transformers is summarized, and the progress and deficiencies of current research are pointed out, which has certain reference value for future research on DC bias voltage and its suppression.
Simulation Study of the Swirl Spray Atomization of a Bipropellant Thruster under Low Temperature Conditions
Authors: Li Haifu , Feng Jihong , Cao Xinyue , Zhang Zhen , Liang Hongbo , Yu Yusong
Published: November 2022

The spray atomization of an injector significantly influences the performance and working life span of a bipropellant thruster of a spacecraft. Deep space exploration requires the thruster to be able to operate reliably at a low temperature range from −40 °C to 0 °C, so the effect of low temperature conditions on the atomization characteristics of injector spray is motivated to be comprehensively investigated. To study the swirl atomization characteristics of MMH (methylhydrazine), which is more difficult to atomize than NTO (nitrogen tetroxide), numerical simulations were conducted, employing the methods of VOF (volume of fluid) and LES (large eddy simulation) under low temperature conditions. The physical model with a nozzle size of 0.5 mm and boundary conditions with a velocity inlet of 3.89 m/s both follow the actual operation of thrusters. The development of spray atomization at low temperatures was observed through parametric comparisons, such as spray velocity, liquid total surface area, droplet particle size distribution, spray cone angle and breakup distance. When the temperature decreased from 20 °C to −40 °C at the same condition of flowrate inlet, those atomization characteristics of MMH propellant vary following these rules: the spray ejection velocity of MMH is significantly reduced by 7.7%, and gas-liquid disturbance sequentially decreases; the liquid film development is more stable, with a negative influence on atomization quality, causing difficulties for primary and secondary breakup, so the total surface area of droplets also decreases by 6.4%; the spatial distribution characteristics, spray cone angle and breakup distance vary less than 5%. Therefore, the low temperature condition can directly lower the combustion efficiency of thrusters with obvious performance degradation, but there are no significant changes in the propellant mixing and liquid film cooling. It is concluded that the bipropellant thruster can reliably work at low temperatures around −40 °C for deep space probe operation.
Analysis of the Energy and Material Use of Manure as a Fertilizer or Substrate for Biogas Production during the Energy Crisis
Authors: Mazurkiewicz Jakub
Published: November 2022

The main goal of the publication was to show the differences in profit when using manure directly as fertilizer (after the storage period) or as a substrate for biogas plants with a cogeneration unit, and then using the digestate for fertilization purposes. The comparison covers the streams of costs, revenues and profits over the year between 14 October 2021 and 14 October 2022. This period was chosen due to the energy and fertilization crisis caused by the war in Ukraine. Profitability forecasts for biogas investments (including the payback period) are presented, with the reduction of greenhouse gas emissions, i.e., methane and nitrous oxide, taken into account. The performed economic, energy and ecological calculations of manure management can be used as guidelines when considering investing in biogas plants, as well as what is recently becoming a new trend: the carbon footprint of dairy production. Input substrate parameters, gaseous emissions and biogas yields were obtained from own research (manure samples were collected) and from literature data, including guidelines for international and national IPCC protocols.
Sensitivity Analysis of Transcritical CO2 Cycle Performance Regarding Isentropic Efficiencies of Turbomachinery for Low Temperature Heat Sources
Authors: Lu Kun-Hsien , Chiang Hsiao-Wei D. , Wang Pei-Jen
Published: November 2022

The transcritical CO2 (T-CO2) power cycle using low temperature waste heat is a promising technique for energy saving and environmental protection. However, according to the literature, there is no commercialized unit in service yet. This study provides developers a reference to shorten the design phase of the T-CO2 cycle commercialization process. A sensitivity analysis of the system performance, i.e., thermal efficiency and net power output, regarding the isentropic efficiencies of pump (ηp) and expander (ηe) and the heat source temperature (Th,in) has been carried out using MATLAB and NIST REFPROP database. Simple and recuperative configurations are investigated based on their own optimal working pressures. The results show that the enhancement of ηe has a greater influence on improving the system performance, but the improvement will diminish as ηp, ηe, and Th,in increase. Although better system performance can be achieved with higher ηp, ηe, and Th,in, the cost of the system equipment will also increase due to the higher optimal working pressure. In addition, increasing ηp and ηe will negatively affect the effectiveness of the recuperator. Therefore, the turbomachinery efficiencies and the heat source temperature should be considered simultaneously for the most cost-effective system design.
E-Government Development in European Countries: Socio-Economic and Environmental Aspects
Authors: Zioło Magdalena , Niedzielski Piotr , Kuzionko-Ochrymiuk Ewa , Marcinkiewicz Jacek , Łobacz Katarzyna , Dyl Krzysztof , Szanter Renata
Published: November 2022

The purpose of the paper is to analyse the relationship between digitalisation of public services (e-government) and ESG factors (environmental, social and economic factors). As public administration is a major stakeholder influencing sustainable development and state governments are aiming to digitise their services, there is a need to better understand the effect of the digital revolution on ESG. This article aims to study the relationship between the E-Government Development Index (EGDI) and ESG factors in 26 European countries. The linear ordering method TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was used for the study, followed by the Perkal index and the method of optimal predictors’ selection—the Hellwig method. The study is based on the Eurostat data (in the period 2003–2020), and the analysis includes ten variables: Share of environmental taxes in GDP; Exposure to dust air pollution; Greenhouse gas emissions by source sector—carbon dioxide, methane, nitrous oxide, etc. (energy, industry, agriculture, forestry, waste management) (thousands of tonnes); Passenger transport volume in relation to GDP; At risk of poverty or social exclusion; Percentage of people satisfied with their financial situation among people aged 16+; Share of the ICT sector in GDP; Unemployment rate of people aged 20–64; Research and development expenditure in all sectors; GDP per capita. The in-depth analysis offers a matrix that represents the relationships between environmental taxes and the development of e-government. The main finding reveals statistically significant relationships between the EGDI and aggregate variables representing the environmental, social and economic spheres. It indicates a genuine, positive impact of digitalised administrative processes on sustainable development. It also seems to confirm that investments in digital infrastructure and government e-services bring multiple long-term benefits and contribute directly to all three domains relevant to the sustainability of modern development. The results of the analysis can be found useful by governments and governmental institutions as informing digitalisation strategies aimed at balancing the development of e-services and their support infrastructure. Nowadays, when planning strategic actions, one should take into account the social, economic and environmental impact of the digitalisation processes.
The Biogas Potential of Oxytree Leaves
Authors: Mazurkiewicz Jakub
Published: November 2022

This article describes the characteristics of th Oxytree (Paulownia) plant, both in terms of its impact on GHG emissions and its potential use to produce biofuel, i.e., biogas. The described research involved the physico-chemical and elemental analysis of the Oxytree leaf composition and its biogas efficiency depending on the harvesting method. Three different scenarios were considered: the freshest possible leaves—processed immediately after stripping from the living tree; after the first day of collection from pruned or harvested wood; after the first week of collection from pruned or harvested wood. The best results were achieved for the harvest of the freshest leaves—on average 430 m3/Mg (biogas) and 223 m3/Mg (methane) per dry organic mass. The highest yield of biogas in terms of fresh mass (FM) was obtained for leaves fallen and collected after 1 day—123 m3/Mg FM, and 59 m3/Mg FM (methane). Processing Oxytree leaves through anaerobic digestion will contribute to reducing the carbon footprint of wood biomass production and is an additional source of renewable energy and fertilizer product.
Large Eddy Simulation of Film Cooling: A Review
Authors: Ahn Joon
Published: November 2022

Film cooling has dramatically contributed to the performance improvement of gas turbines, as it is a very effective cooling technique for gas turbines. Large eddy simulation (LES) began to be used in the study of film cooling 20 years ago, and meaningful results have been found, but it has not yet been intensively reviewed. In this review paper, we analyze and introduce about 70 papers published on LES of film cooling over the past 20 years. Numerical instability must be overcome, and realistic inflow must be generated to perform LES of film cooling. This review summarizes how the groups that performed LES of film cooling solved these problems. In film-cooling research, the main topics are improving the film-cooling performance by preventing the lift-off of the injectant and the effect of flow conditions on film cooling. In addition, LES has also been conducted extensively on the above two topics, and this review focuses on them. Finally, turbulence statistics of film-cooling flow obtained from LES are introduced, and future challenges of film-cooling LES are predicted.
Structure and Thermophysical Properties of Molten Calcium-Containing Multi-Component Chlorides by Using Specific BMH Potential Parameters
Authors: Wei Xiaolan , Chen Dandan , Liu Shule , Wang Weilong , Ding Jing , Lu Jianfeng
Published: November 2022

Chloride molten salts have become a potential heat storage material for the design of a new generation of concentrating solar power (CSP) (>700 °C) due to its abundant reserves and low cost. The difficulty of measuring the high-temperature thermal properties of chlorides can be effectively solved by using molecular dynamics simulation. However, it is challenging to get the thermophysical properties of multi-component molten salts containing CaCl2 due to the lack of Born–Mayer–Huggins (BMH) potential parameters of CaCl2. Through comparative analysis of the structure and thermal properties of CaCl2, including density and thermal conductivity, a set of Born–Mayer–Huggins (BMH) potential parameters of CaCl2 named SP2 is determined in this study. The density, specific heat capacity, and thermal conductivity of nine eutectic molten salts are simulated, including NaCl-CaCl2, KCl-CaCl2, NaCl-CaCl2-MgCl2, and NaCl-CaCl2-KCl, and the simulation results are found to be in good agreement with the experimental results. It is also found that the SP2 parameters are able to predict the thermal properties and structure of molten multicomponent chlorides including calcium.
An Investigation of Scale-Resolving Turbulence Models for Supersonic Retropropulsion Flows
Authors: Nastac Gabriel , Frendi Abdelkader
Published: November 2022

Characterization of unsteady loads is critical for the development of control systems for next-generation air vehicles. Both Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) methods are prohibitively expensive, and existing Reynolds-Averaged Navier-Stokes (RANS) approaches have been shown to be inadequate in predicting both mean and unsteady loads. In recent years, scale-resolving methods, such as Partially Averaged Navier-Stokes (PANS) and Detached Eddy Simulation (DES), have been gaining acceptance and filling the gap between RANS and LES. In this study, we focus on a new variant of the PANS method, namely blended PANS or BPANS, which was shown to perform well in the incompressible regime for both wall-bounded and free shear flows. In this paper, we extend BPANS to compressible supersonic flows by adding a compressibility correction, leading to a new model called BPANS CC. The new model is tested using a well-known supersonic mixing layer case, and the results show good agreement with experimental data. The model is then used on a complex supersonic retropropulsion case and the results are in good agreement with experimental data.
Physicochemical and Sensory Properties Colored Whey Protein-Cellulose Nanocrystal Edible Films after Freeze-Thaw Treatment
Authors: Sun Hongbo , Liu Xinnan , Huang Yue , Leng Xiaojing
Published: November 2022

Balancing physicochemical properties and sensory properties is one of the key points in expanding edible packaging applications. The work consisted of two parts, one was to investigate the effects of cellulose nanocrystals (CNC) on the packaging-related properties of whey protein isolate films with natural colorants (curcumin, phycocyanin, and lycopene) under freeze-thaw (FT) conditions; the other was to test oral tactility and visual sensory properties of the edible films and their overall acceptability in packed ice cream. FT treatment reduced the mechanical strength and moisture content and increased the water vapor permeability of the films, as water-phase transformation not only disrupted hydrogen bonds but also the film network structure through physical stress. The oral tactility produced by CNC and the visual effect produced by colorants could affect participants’ preference for edible films. This study provides a good reference for the consumer-driven product development of packaged low-temperature products.
Recovery and Utilization of Pea Albumins as Acidic Emulsion Stabilizer by Complexation with Dextran Sulfate
Authors: Li Xingfei , Zhang Xinyu , Long Jie , Zhang Caimeng , Hua Yufei
Published: November 2022

In this work, pea albumins (PAs) were efficiently recovered by complexation with dextran sulfate (DS), and the emulsifying ability and stability of PA/DS complexes were studied. The largest amounts of PAs (81.25%) were recovered at r = 5:1 and pHmax (pH 3.41) by forming insoluble complexes; and only soluble complexes were formed at r = 2:1 and over the whole pH range (2.0–7.0). The emulsions stabilized by PA/DS soluble complexes remained stable under acidic conditions due to the highly negatively charge (from −45.10 ± 0.40 to −57.23 ± 0.66 mV) and small particle size (0.168 ± 0.010–0.448 ± 0.004 μm), while emulsions stabilized by PAs alone generated a strong creaming and serum separation at pH 5 and 6. In terms of emulsifying stability, all PA emulsions and unheated PA/DS emulsions became unstable with different creaming index after 14 days storage. SDS-PAGE results showed that the interface adsorption proteins of unheated emulsions mainly consisted of PA1a, which was unfavorable to the stability of the interface. On the contrary, heat treatment (95 °C, 30 min) and complexation (PA/DS = 2:1) enhanced the adsorption of PA2 and lectin at the interface, inhibiting the aggregation of PA2 and lectin. This resulted in long-term stability of the PA/DS emulsions under acidic conditions.
Development and Dynamics of Sediment Waves in a Complex Morphological and Tidal Dominant System: Southern Irish Sea
Authors: Creane Shauna , Coughlan Mark , O’Shea Michael , Murphy Jimmy
Published: November 2022

With the recent push for a transition towards a climate-resilient economy, the demand on marine resources is accelerating. For many economic exploits, a comprehensive understanding of environmental parameters underpinning seabed morphodynamics in tidally-dominated shelf seas, and the relationship between local and regional scale sediment transport regimes as an entire system, is imperative. In this paper, high-resolution, time-lapse bathymetry datasets, hydrodynamic numerical modelling outputs and various theoretical parameters are used to describe the morphological characteristics of sediment waves and their spatio-temporal evolution in a hydrodynamically and morphodynamically complex region of the Irish Sea. Analysis reveals sediment waves in a range of sizes (height = 0.1 to 25.7 m, and wavelength = 17 to 983 m), occurring in water depths of 8.2 to 83 mLAT, and migrating at a rate of 1.1 to 79 m/yr. Combined with numerical modelling outputs, a strong divergence of sediment transport pathways from the previously understood predominantly southward flow in the south Irish Sea is revealed, both at offshore sand banks and independent sediment wave assemblages. This evidence supports the presence of a semi-closed circulatory hydrodynamic and sediment transport system at Arklow Bank (an open-shelf linear sand bank). Contrastingly, the Lucifer–Blackwater Bank complex and associated sediment waves are heavily influenced by the interaction between a dominant southward flow and a residual headland eddy, which also exerts a strong influence on the adjacent banner bank. Furthermore, a new sediment transfer system is defined for offshore independent sediment wave assemblages, whereby each sediment wave field is supported by circulatory residual current cells originating from offshore sand banks. These new data and results improve knowledge of seabed morphodynamics in tidally-dominated shelf seas, which has direct implications for offshore renewable developments and long-term marine spatial planning.
Improvement of GOCE-Based Global Geopotential Models for Gravimetric Geoid Modeling in Turkey
Authors: Isik Mustafa Serkan , Çevikalp Muhammed Raşit , Erol Bihter , Erol Serdar
Published: November 2022

This study investigates the contribution of global geopotential models which are calculated with GOCE satellite mission data to the improvement of gravimetric geoid models in Turkey. In this context, direct (DIR), time-wise (TIM), space-wise (SPW), and GOCO satellite-only model series were considered. The research was carried out in two parts. The first part includes the validation of models in each series at 100 homogeneously distributed GNSS/leveling stations over the country utilizing spectrally enhanced geoid heights to determine the best performing model and its optimal expansion degree. According to obtained statistics, the TIM-R6 model was selected as the best model with an optimal expansion degree of 204. In the second part, the TIM-R6 model up to 204 degree/order was linearly blended with EGM2008 to obtain an improved version up to 360 degree/order of expansion. To clarify the contribution of the linearly blended model to the improvement of the regional geoid model, the gravimetric geoid models were computed adopting TIM-R6 up to 204 degree/order and its improved version up to 360 degree/order as reference models. To further emphasize the contribution of the GOCE mission’s data, the gravimetric geoid computations were repeated relying on EGM2008 up to 204 and 360 degrees of expansions, since EGM2008 does not contain GOCE data. In addition, we computed gravimetric geoids based on another combined model that includes GOCE mission data, the EIGEN-6C4 model. The calculated regional geoids were compared to each other and validated using GNSS/leveling data set. The obtained results revealed a ∼23% improvement in regional geoid model accuracy when the blended GOCE-based geopotential model was used as a reference. In addition, the results of this study presented the significance of GOCE contribution to mapping the gravity field in Turkey. The best accuracy obtained from this study was 7.7 cm for the Turkey geoid.