bluuebunny/crossref_metadata_2025 huggingface dataset:
Calculating the Inrush Current of Superconducting Transformers
Authors: Komarzyniec Grzegorz
Published: October 2021

Under certain circumstances, after connecting a superconducting transformer to the power network, a high value current may flow through its windings. This current can exceed the critical value of the superconductor many times and cause the windings to lose their superconductive state. Loss of superconductive state of the windings may result in thermal interruption of their continuity as a result of conduction of a current of very high density. The mathematical relationships used to calculate the inrush current of conventional transformers do not work well for the calculation of superconducting transformers. This is due to the properties of superconducting materials used in the windings, first of all to the stepwise changes of the windings’ resistance when exiting the superconducting state and when returning to this state. The article presents the mathematical dependencies allowing to calculate the pulse waveforms of the inrush current of these transformers are derived. Basic electrical circuit sizes are used in the calculations, making the calculations quick and easy. Using the formulas, calculations of the inrush current of 8.5 kVA and 13.5 kVA superconducting transformers. The results were verified with the results of the inrush current measurements, achieving good compliance.
Differences in the Domestic Energy Consumption in Hungary: Trends between 2006–2017
Authors: Győri Ágnes , Huszár Ákos , Balogh Karolina
Published: October 2021

Goal 7 of the UN’s Sustainable Development Goals, adopted in 2015, sets out universal access to affordable, reliable and sustainable energy, but even in developed countries, this is still difficult to achieve. European comparative studies show that in Mediterranean and Central European countries, including Hungary, access to adequate energy remains a serious problem for certain social groups. The aim of the study is to examine the inequalities in access to and consumption of energy in Hungary. We pay special attention to presenting changes over time and examine what changes have taken place in household energy consumption since the years before the global economic crisis. We also explore the major socio-economic and building-related factors that increase the risk of possible energy vulnerability. For our analysis, we draw on data from a large sample survey conducted in 2007, 2013 and 2018 on a representative probability sample. Our results showed on the one hand the fundamental role of different combinations of energy sources used by households in the intensity of energy consumption, and on the other hand that besides the characteristics of the property concerned, the energy use and behaviour patterns of households are determined by the socio-demographic characteristics of the household as well.
Green Growth in the OECD Countries: A Multivariate Analytical Approach
Authors: Gavurova Beata , Megyesiova Silvia , Hudak Matej
Published: October 2021

Green growth is about merging environmental and social protection with economic growth. The OECD countries follow the progress toward greening though a set of indicators. The aim of the study is to analyze the condition and development of the OECD countries using a set of green growth indicators. The univariate and multivariate statistical approach was used to identify the main features of green growth development in two time spans. The achieved success of the OECD countries toward the green growth was measured from period 1 (years 2000–2009) to period 2 (years 2010–2019). For stimulant indicators, an increase was achieved, while for the destimulant variables, a decrease was reached between the analyzed periods. CO2 productivity increased by more than 31%, material productivity by 25%, and the energy productivity by nearly 21%. From the ecological point of view, a positive sign was achieved by an intensive increase of the percentage of municipal waste treatment by recycling or composting. The real GDP increased between periods in each of the OECD countries, except in Greece. The destimulant indicators decreased over time. The mortality declined by about 20% from exposure to ambient PM2.5 and thus the welfare costs of premature death from exposure to PM2.5 also declined. The decline of the mean population exposure to PM2.5 by 12.5% on average for the OECD countries is a positive signal for environmental protection and public health of the OECD population. Some uncertainty exists as the municipal waste generated per capita decreased only slightly by 2%.
High-Frequency Non-Invasive Magnetic Field-Based Condition Monitoring of SiC Power MOSFET Modules
Authors: Naghibi Javad , Mehran Kamyar , Foster Martin P.
Published: October 2021

Current distribution anomaly can be used to indicate the onset of package-related failures modes in Silicon Carbide power MOSFET modules. In this paper, we propose to obtain the wire bond’s magnetic field profile using an array of Tunnel Magneto-Resistance (TMR) sensors, and characterise the small changes in the current density distribution to find the onset of the wire bond degradation processes, including wire bond lift-off, wire bond cracking, and wire bond fracture. We propose a novel condition monitoring technique where a non-galvanic high-bandwidth sensing and a reliability model monitor the health of the power switches. We designed a dedicated calibration set-up to examine the sensor array and calibrated to demonstrate the adequate sensitivity to a minimum 5% current anomaly detection in a single wire bond of the switching devices operating with 50 kHz switching frequency. We use a hardware-in-the-loop (HIL) experimental set-up to replicate wire bond-related failures in a 1200 V/55 A SiC MOSFET power module of a DC/DC Boost converter. Signal conditioning circuits are further designed to amplify and buffer the sensor readings. Experimental results showed the proposed technique is able to detect a wide range of package-related failures.
Comparison of Energy Prediction Algorithms for Differential and Skid-Steer Drive Mobile Robots on Different Ground Surfaces
Authors: Góra Krystian , Kujawinski Mateusz , Wroński Damian , Granosik Grzegorz
Published: October 2021

A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system.
Social Enterprise in Small Hydropower (SHP) Owned by a Limited Liability Partnership (LLP) between a Food Cooperative and a Social Venture Company; a Case Study of the 20 kW Shiraito (Step3) SHP in Itoshima City, Fukuoka (Japan)
Authors: Alam Zafar , Watanabe Yoshinobu , Hanif Shazia , Sato Tatsuro , Fujimoto Tokihiko
Published: October 2021

There was an unprecedented growth of SHP (small hydropower) in Japan during recent years because the government has provided a high FIT (USD 0.32/kWh) for SHP development projects of less than 200 kW. The public and private sectors are committed to harnessing this natural energy to achieve the renewable energy goal as well as to improve local communities social and economic conditions. This paper will discuss about renewable energy initiatives by a food corporation enterprise (FCOOP) based in Fukuoka prefecture of Japan to reduce their net carbon emissions. A detailed approach for social enterprises-based SHP development has been discussed which will be a role model concept for other social enterprises in Japan and in the world that are willing to reduce CO2 emissions. Also, this kind of project connects urban stake holders with local community where resources are available and it leads to development of the local community living standards. As a demonstrated example, a case study of a 20 kW SHP installed in Japan by a FCOOP has been discussed.
Contaminant Source Identification from Finite Sensor Data: Perron–Frobenius Operator and Bayesian Inference
Authors: Sharma Himanshu , Vaidya Umesh , Ganapathysubramanian Baskar
Published: October 2021

Sensors in the built environment ensure safety and comfort by tracking contaminants in the occupied space. In the event of contaminant release, it is important to use the limited sensor data to rapidly and accurately identify the release location of the contaminant. Identification of the release location will enable subsequent remediation as well as evacuation decision-making. In previous work, we used an operator theoretic approach—based on the Perron–Frobenius (PF) operator—to estimate the contaminant concentration distribution in the domain given a finite amount of streaming sensor data. In the current work, the approach is extended to identify the most probable contaminant release location. The release location identification is framed as a Bayesian inference problem. The Bayesian inference approach requires considering multiple release location scenarios, which is done efficiently using the discrete PF operator. The discrete PF operator provides a fast, effective and accurate model for contaminant transport modeling. The utility of our PF-based Bayesian inference methodology is illustrated using single-point release scenarios in both two and three-dimensional cases. The method provides a fast, accurate, and efficient framework for real-time identification of contaminant source location.
Effect of Light Irradiation on the Diffusion Rate of the Charge Carrier Hopping Mechanism in P3HT–ZnO Nanoparticles Studied by μ+SR
Authors: Pratikna Eka , Safriani Lusi , Riveli Nowo , Adiperdana Budi , Winarsih Suci , Aprilia Annisa , Sari Dita Puspita , Watanabe Isao , Risdiana Risdiana
Published: October 2021

Blended regio-regular P3HT–ZnO nanoparticles are a hybrid material developed as an active layer for hybrid solar cells. The study of the hopping mechanisms and diffusion rates of regio-regular P3HT–ZnO nanoparticles is significant for obtaining intrinsic charge transport properties that provide helpful information for preparing high-performance solar cells. The temperature dependences of the parallel and perpendicular diffusion rates in regio-regular P3HT–ZnO nanoparticles determined from muon spin relaxation measurements were investigated by applying various longitudinal fields. We investigated the effect of light irradiation on the diffusion rates in regio-regular P3HT–ZnO nanoparticles. We found that with increasing temperature, the parallel diffusion rate decreased, while the perpendicular diffusion rate increased. The ratio of the parallel to perpendicular diffusion rate (D‖/D⊥) can be used to indicate the dominant charge carrier hopping mechanism. Without light irradiation, perpendicular diffusion dominates the charge carrier hopping, starting at 25 K, with a ratio of 1.70×104, whereas with light irradiation, the perpendicular diffusion of the charge carrier starts to dominate at the temperature of 10 K, with a ratio of 2.40×104. It is indicated that the additional energy from light irradiation affects the diffusion, especially the charge diffusion in the perpendicular direction.
Role of Architectural Design in Creating Circadian-Effective Interior Settings
Authors: Alkhatatbeh Baraa J. , Asadi Somayeh
Published: October 2021

Daylight variability throughout the day makes it an ideal light source for the stimulation of humans’ circadian systems. However, the key criteria, including proper quantity, quality, and hours of access to daylight, are not always present inside the built environment. Therefore, artificial light is necessary to complement the human’s visual and non-visual needs for light. Architectural design parameters, such as window area, orientation, glazing material, and surface reflectance alter the characteristics of both daylight and artificial light inside buildings. These parameters and their impact on lighting design should be considered from the early design stages to attain a circadian-effective design. In response to this need, a design approach called Human-Centric Lighting (HCL) was introduced. HCL places humans, and their visual and non-visual needs, in the center of the design process. It manipulates the light-related factors, such as spectrum and intensity, within the built environment for circadian benefits. The effect of HCL on lighting energy efficiency is still not clear. This paper reviews essential architectural design parameters and their impacts on circadian lighting design, considers the HCL design process and explores the most widely used circadian lighting metrics and standards.
Design and Optimization of a High-Speed Switched Reluctance Motor
Authors: Kocan Stefan , Rafajdus Pavol , Bastovansky Ronald , Lenhard Richard , Stano Michal
Published: October 2021

Currently, one of the most used motor types for high-speed applications is the permanent-magnet synchronous motor. However, this type of machine has high costs and rare earth elements are needed for its production. For these reasons, permanent-magnet-free alternatives are being sought. An overview of high-speed electrical machines has shown that the switched reluctance motor is a possible alternative. This paper deals with design and optimization of this motor, which should achieve the same output power as the existing high-speed permanent-magnet synchronous motor while maintaining the same motor volume. The paper presents the initial design of the motor and the procedure for analyses performed using analytical and finite element methods. During the electromagnetic analysis, the influence of motor geometric parameters on parameters such as: maximum current, average torque, torque ripple, output power, and losses was analyzed. The analysis of windage losses was performed by analytical calculation. Based on the results, it was necessary to create a cylindrical rotor shape. The rotor modification method was chosen based on mechanical analysis. Using thermal analysis, the design was modified to meet thermal limits. The result of the work was a design that met all requirements and limits.
Analysis of the Energy Efficiency Improvement in a Load-Sensing Hydraulic System Built on the ISO Plate
Authors: Lisowski Edward , Filo Grzegorz , Rajda Janusz
Published: October 2021

The article presents a proposal to reduce energy consumption in a hydraulic system with a single pump and multiple receivers. The proposed Load-Sensing Basic (LSB) solution consists of expanding a typical hydraulic system by using additional logic valves and a dedicated differential valve. The modification is aimed at decrease in operating pressure and, thus, reduction in energy consumption. The LSB system is compact as all components are built on a single ISO plate. A detailed mathematical model of the system was formulated, then a simulation model was built and numerical tests were carried out in the Matlab/Simulink environment. The obtained results indicate that the use of the proposed LSB system for the implementation of typical working cycles with three actuators may reduce energy consumption by 4–30%, and under certain conditions even up to 70%.
Experimental Analysis on the Influence of Operating Profiles on High Temperature Polymer Electrolyte Membrane Fuel Cells
Authors: Chinese Tancredi , Ustolin Federico , Marmiroli Benedetta , Amenitsch Heinz , Taccani Rodolfo
Published: October 2021

The Energy System lab at the University of Trieste has carried out a study to investigate the reduction in performance of high temperature polymer electrolyte membrane (HTPEM) fuel cell membrane electrode assemblies (MEAs) when subjected to different ageing tests. In this study, start and stop cycles, load cycles, open circuit voltage (OCV) permanence and constant load profile were considered. Polarization curves (PC) together with electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) data were recorded during the ageing tests to assess the fuel cell performance. In this paper, experimental data are presented to confirm the test methodology previously proposed by the authors and to quantitatively correlate the performance degradation to the operational profiles. It was demonstrated that OCV condition, start and stop and load cycling increase degradation of the MEAs with respect to constant load operation. As expected, the OCV is the operational condition that influences performance degradation the most. Finally, the MEAs were analyzed with synchrotron small angle X-ray scattering (SAXS) technique at the Austrian SAXS beamline at Elettra-Sincrotrone Trieste to analyze the nano-morphological catalyst evolution. As for the catalyst morphology evolution, the ex situ SAXS methodology proposed by the authors is confirmed in its ability to assess the catalyst nanoparticles aggregation.
The Optimal Pumping Power under Different Ice Slurry Concentrations Using Evolutionary Strategy Algorithms
Authors: Hao Shuai , Zhou Wenjie , Lu Junliang , Wang Jiajun
Published: October 2021

A suitable ice slurry fluid with a suitable ice concentration ratio can save operational costs. The design of the optimal ice slurry concentration focuses on finding an evolution strategy, which can further minimize the power consumption of the pump. A theoretical model was established to simulate the effect of different ice concentrations and flow rates on the performance of the pump. The data obtained were fitted by curve-fitting function. The process was modeled in the MATLAB evolutionary strategy algorithm to obtain the configuration scheme of the ice concentration and flow under different refrigeration capacities. The simulation results showed that when the required cooling capacity was 13.889 kWh, ice concentration was set to 19.68%, and flow rate was set to 2.1075 × 10−4 m3/s, the power consumption could be reduced by 23%.
Pr- and Sm-Substituted Layered Perovskite Oxide Systems for IT-SOFC Cathodes
Authors: Woo Sung Hun , Song Kyeong Eun , Baek Seung-Wook , Kang Hyunil , Choi Wonseok , Shin Tae Ho , Park Jun-Young , Kim Jung Hyun
Published: October 2021

In this study, the phase synthesis and electrochemical properties of A/A//A///B2O5+d (A/: Lanthanide, A//: Ba, and A//: Sr) layered perovskites in which Pr and Sm were substituted at the A/-site were investigated for cathode materials of Intermediate Temperature-Operating Solid Oxide Fuel cells (IT-SOFC). In the PrxSm1−xBa0.5Sr0.5Co2O5+d (x = 0.1–0.9) systems, tetragonal (x < 0.4) and orthorhombic (x ≥ 0.5) crystalline structures were confirmed according to the substitution amount of Pr, which has a relatively large ionic radius, and Sm, which has a small ionic radius. All of the layered perovskite oxide systems utilized in this study presented typical metallic conductivity behavior, with decreasing electrical conductivity as temperature increased. In addition, Pr0.5Sm0.5Ba0.5Sr0.5Co2O5+d (PSBSCO55), showing a tetragonal crystalline structure, had the lowest conductivity values. However, the Area-Specific Resistance (ASR) of PSBSCO55 was found to be 0.10 Ω cm2 at 700 °C, which is lower than those of the other compositions.
Decentralized Phase Shedding with Low Power Mode for Multiphase Converter
Authors: Cousineau Marc , Monroy Martin , Lorenzi Pol William , Hureau Loic , Aulagnier Guillaume , Goyhenetche Philippe , Rolland Eric , Flumian Didier
Published: October 2021

With a multiphase converter, the phase-shedding function dedicated to maximizing the power efficiency, in a manner that is dependent on the load current, is always provided by a centralized controller that induces a Single Point of Failure (SPOF). The objective of this study is to obtain a decentralized control approach to implement this function by removing any SPOF. The method consists of using identical local controllers, each associated with a converter phase, that communicate with each other in a daisy-chain structure. Instead of measuring the global output current to determine the optimal number of active phases required, each local controller measures its own leg current and takes a local decision based on threshold crossing management and inter-controller communications. Functional simulations are carried out on a 5-leg 12 V/1.2 V 60 W multiphase converter supplying a modern microcontroller. They demonstrate that the number of active phases is well adjusted, in a dynamic manner, depending on the load current level. Specific events such as load current inrush or the start-up sequence are analyzed to guarantee optimal transient responses. A maximum power efficiency tracking ability is also demonstrated. Finally, it is shown that this control strategy allows phase shedding to be implemented using as many phases as desired, in a modular manner, thereby avoiding any centralized processing.
Sizing and Energy Management of Parking Lots of Electric Vehicles Based on Battery Storage with Wind Resources in Distribution Network
Authors: Shahrokhi Saman , El-Shahat Adel , Masoudinia Fatemeh , Gandoman Foad H. , Abdel Aleem Shady H. E.
Published: October 2021

In this paper, an optimal sizing and placement framework (OSPF) is performed for electric parking lots integrated with wind turbines in a 33-bus distribution network. The total objective function is defined as minimizing the total cost including the cost of grid power, cost of power losses, cost of charge and discharge of parking lots, cost of wind turbines as well as voltage deviations reduction. In the OSPF, optimization variables are selected as electric parking size and wind turbines, which have been determined optimally using an intelligent method named arithmetic optimization algorithm (AOA) inspired by arithmetic operators in mathematics. The load following strategy (LFS) is used for energy management in the OSPF. The OSPF is evaluated in three cases of the objective function such as minimizing the cost of power losses, minimizing the network voltage deviations, and minimizing the total objective function using the AOA. The capability of the AOA is compared with the well-known particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms for solving the OSPF in the last case. The findings show that the power loss, voltage deviations, and power purchased from the grid are reduced considerably based on the OSPF using the AOA. The results show the lowest total cost of energy and also minimum network voltage deviation (third case) by the AOA in comparison with the PSO and ABC with a higher convergence rate, which confirms the better capability of the proposed method. The results of the first and second cases show the high cost of power purchased from the main grid as well as the high total cost. Therefore, the comparison of different cases confirms that considering the cost index along with losses and voltage deviations causes a compromise between different objectives, and thus the cost of purchasing power from the main network is significantly reduced. Moreover, the voltage profile of the network improves, and also the minimum voltage of the network is also enhanced using the OSPF via the AOA.
A Data-Driven Multi-Regime Approach for Predicting Energy Consumption
Authors: Kahraman Abdulgani , Kantardzic Mehmed , Kahraman Muhammet Mustafa , Kotan Muhammed
Published: October 2021

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach.
Numerical Investigation of Microchannel Heat Sink with Trefoil Shape Ribs
Authors: Ali Sadiq , Ahmad Faraz , Akhtar Kareem , Habib Numan , Aamir Muhammad , Giasin Khaled , Vafadar Ana , Pimenov Danil Yurievich
Published: October 2021

The present study investigates the thermo-hydraulic characteristics of a microchannel sink with novel trefoil Shaped ribs. The motivation for this form of rib shape is taken from the design of lung alveoli that exchange oxygen and carbon dioxide. This study has been conducted numerically by using a code from the commercially available Fluent software. The trefoil shaped ribs were mounted on the centerline of different walls of the microchannel in three different configurations. These consisted of base wall trefoil ribs (MC-BWTR), sidewall trefoil ribs (MC-SWTR), all wall trefoil ribs (MC-AWTR) and smooth channel (MC-SC) having no ribs on its wall. The streamline distance between the ribs was kept constant at 0.4 mm, and the results were compared by using pressure drop (∆p), Nusselt number (Nu), thermal resistance (Rth) and thermal enhancement factor (η). The results indicated that the addition of trefoil ribs to any wall improved heat transfer characteristics at the expense of an increase in the friction factor. The trends of the pressure drop and heat transfer coefficient were the same, which indicated higher values for MC-AWTR followed by MC-SWTR and a lower value for MC-BWTR. In order to compare the thermal and hydraulic performance of all the configurations simultaneously, the overall performance was quantified in terms of the thermal enhancement factor, which was higher than one in each case, except for MC-AWTR, in 100 < Re < 200 regimes. The thermal enhancement factor in the ribbed channel was the highest for MC-SWTR followed by MC-BWTR, and it was the lowest for MC-AWTR. Moreover, the thermal enhancement factor increases with the Reynolds number (Re) for each case. This confirms that the increment in the Nusselt number with velocity is more significant than the pressure drop. The highest thermal enhancement factor of 1.6 was attained for MC-SWTR at Re = 1000, and the lowest value of 0.87 was achieved for MC-AWTR at Re = 100.
Mechanical Durability and Grindability of Pellets after Torrefaction Process
Authors: Dyjakon Arkadiusz , Noszczyk Tomasz , Mostek Agata
Published: October 2021

Renewable energy sources and their part in the global energy mix are beneficial to energy diversification and environment protection. However, raw biomass is characterized by low heating value, hydrophilic properties, various mechanical durability, and the logistic challenges related to transportation and storage. One frequently used process of combined biomass valorization is torrefaction and pelletization, which increase the heating value, homogeneity, and hydrophobicity of the fuel. However, industrial clients need fuel characterized by favorable grindability, whereas, the individual clients (householders) need fuel with high mechanical durability. Due to the different expectations of final customers regarding biomass fuel properties, it is necessary to investigate the influence of the torrefaction on the mechanical durability of the pellets. In this paper, five various types of pellets and their torreficates (obtained at a temperature of 200 and 300 °C) were examined. Then the mechanical durability index DU and the grindability of the untreated and torrefied pellets were determined. The results indicated that the mechanical durability of untorrefied pellets is significantly greater than torrefied pellets. Interestingly, no significant differences in mechanical durability between torrefied pellets at 200 and 300 °C were observed, For sunflower husk pellets, the DU index amounted to 95.28 ± 0.72 (untorrefied), 47.22% ± 0.28% (torrefied at 200 °C), and 46.34% ± 0.72% (torrefied at 300 °C). Considering the grindability, as the treatment temperature increased the energy demand for grindability decreased. For example, the grindability of pine tree pellets was 15.96 ± 3.07 Wh·kg−1 (untreated), 1.86 ± 0.31 Wh·kg−1 (torrefied at 200 °C), and 0.99 ± 0.17 Wh·kg−1 (torrefied at 300 °C). The highest difference between raw and torrefied pellets was determined for beetroot pomace pellet: 36.31 ± 2.06 Wh·kg−1 (untreated), 3.85 ± 0.47 Wh·kg−1 (torrefied at 200 °C), and 1.03 ± 0.12 Wh·kg−1 (torrefied at 300 °C).
A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters
Authors: Batsis Georgios , Partsinevelos Panagiotis , Stavrakakis Georgios
Published: October 2021

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.
Switching Arc Energy Limitation Approach for LV Circuit Breakers
Authors: Smugala Dariusz , Bonk Michal
Published: October 2021

This paper presents an experimentally verified approach to deriving switching arc energy limitations for low-voltage (LV) circuit breakers (CBs). Air-insulated contactors equipped with additional vacuum-insulated (VI) arcing contacts were tested for AC and DC current interruption efficiency, respectively. In the study, the contact arrangements of reed relay VI contact switches of low current breaking capacity combined with air-insulated contactors were examined. Tests were performed on selected LV CBs inductively loaded for LV power network rated voltages. A comparative analysis of the arc energy resulting from various arc time durations recorded during the switching-off operation was performed. Using a variety of either basic CB air-insulated contact systems or combined contact systems, a practical assessment of the proposed idea for enhancing the arc quenching efficiency was undertaken. As a result of the implementation of the proposed idea, the arc burning duration time was indicated as being hundreds of times shorter. In most cases, a complete arc reduction was achieved. Moreover, the resulting arc energy dissipation during the breaking operation was substantially minimized. Consequently, a significant increase in the total current breaking capacity of the tested CBs was achieved.
Energy Efficiency in the Industry 4.0 Era: Attributes of Teal Organisations
Authors: Miśkiewicz Radosław , Rzepka Agnieszka , Borowiecki Ryszard , Olesińki Zbigniew
Published: October 2021

The rapid development of innovations in the industry 4.0 era led to new or evolved companies. At the same time, the accepted concept of carbon-free development requires building a new philosophy for the company’s management. The paper aims to analyse the key attributes of teal organisations (as a new type of a company) from the energy sector (as a core sector for carbon-free transformation). The paper summarises the core features of teal organisations and their attributes. In the paper, three hypotheses are tested: innovations and technologies are the most used attributes among teal organisations from the energy sector; organisational and corporate culture are the least used attributes among teal organisations from the energy sector; in the energy sector, the companies that have the attributes of teal organisations primarily work in countries with a high level of innovation and information technologies (as a core indicator of Industry 4.0) and economic development. For testing the hypotheses, the following methods are applied: a Friedman test, a paired-samples t-test, the principal components analysis, a correlation analysis, an ANOVA test (analysis of variance), and a regression analysis. The online survey generates the data for analysis. The object of the research is the workers from the energy sector companies from five countries (Poland, Ukraine, Georgia, Slovakia, and Romania). The findings of the statistical analysis confirm the first and second hypotheses. Companies in the energy sector mostly use innovations and technologies as the attributes of teal organisations. The regression analysis results show that an increase of 1% of patent applications leads to an increased energy efficiency of 1.29%. Additionally, the implemented features of teal organisations in the energy sector allow for improving the country’s energy efficiency, which, as a consequence, then boosts carbon-free development.
The Effect of Sucrose Supplementation on the Micropropagation of Salix viminalis L. Shoots in Semisolid Medium and Temporary Immersion Bioreactors
Authors: Gago Diego , Vilavert Saladina , Bernal María Ángeles , Sánchez Conchi , Aldrey Anxela , Vidal Nieves
Published: October 2021

The effect of sucrose concentration on the micropropagation of axillary shoots of willow was investigated. The following factors were examined: the culture system (semisolid medium in glass jars versus liquid medium in temporary immersion bioreactors), the type of explant (apical and basal sections), the frequency of immersion, and CO2 enrichment. Shoots and leaf growth were significantly higher in RITA® bioreactors than in the jars for all the sucrose treatments. Apical or basal sections of willow cultured in bioreactors under high light intensity (150 µmol m−2 s−1) and ventilated six times a day with CO2-enriched air were successfully proliferated without sucrose, whereas shoots cultured in jars did not proliferate well if sucrose concentration was 0.5% or lower. More roots were formed when sucrose was added to the medium. Shoots cultured in bioreactors were successfully acclimatized irrespective of the sucrose treatment and the root biomass when transferred to ex vitro conditions. This is the first report of photoautotrophic willow micropropagation, our results confirm the importance of proper gaseous exchange to attain autotrophy during in vitro propagation.
Differential Responses of Soil Bacterial and Fungal Community to Short-Term Crop Tree Management in a Larix gmelinii Plantation
Authors: Ji Li , Yu Jiangbo , Zhang Xingzhe , Liu Yue , Yang Lixue
Published: October 2021

Crop tree management (CTM) is a widely applicable silviculture technology that is used to improve the performance of individual trees. However, only little information is available about the effects of the CTM regime on the soil microbial community structure. We conducted a study to explore the effects of short-term (five years) CTM on the soil bacterial and fungal diversity, community composition, and structure in the 0–10 cm soil layer in a Larix gmelinii (Rupr.) Kuzen. plantation. We set out to investigate the differential response of bacterial and fungal communities to variations in soil properties mediated by short-term CTM. Compared with the control plots, the soil microbial biomass carbon and microbial biomass nitrogen in CTM increased significantly by 64.2% and 32.3%, respectively. CTM significantly promoted the content of soil organic carbon, dissolved organic carbon, and nitrate nitrogen, and reduced the content of dissolved organic nitrogen. CTM changed the Shannon and Simpson indices of soil fungi to a remarkable extent but had little effect on the α diversity of bacterial communities. The bacterial β diversity was more sensitive to CTM than fungi. The relative abundance of Verrucomicrobiae (the dominant class of soil bacteria) in CTM was significantly increased by 78.2%, while the relative abundance of Agaricomycetes (dominant class for soil fungi) was reduced by 43.3%. We observed a significantly increased number of unique OTUs for soil fungi in the CTM plots. Redundancy analysis showed that dissolved organic carbon, soil moisture, and total phosphorus content significantly affected the composition of bacterial communities, while soil dissolved organic nitrogen, C/N, and total phosphorus drove the high variation in fungal community composition. Overall, our results emphasize the divergent response of soil bacterial and fungal communities in Larix gmelinii plantations to short-term CTM. We must pay more attention to the functional role of soil microbiota in future forest management.
Tree Species Mapping on Sentinel-2 Satellite Imagery with Weakly Supervised Classification and Object-Wise Sampling
Authors: Illarionova Svetlana , Trekin Alexey , Ignatiev Vladimir , Oseledets Ivan
Published: October 2021

Information on forest composition, specifically tree types and their distribution, aids in timber stock calculation and can help to better understand the biodiversity in a particular region. Automatic satellite imagery analysis can significantly accelerate the process of tree type classification, which is traditionally carried out by ground-based observation. Although computer vision methods have proven their efficiency in remote sensing tasks, specific challenges arise in forestry applications. The forest inventory data often contain the tree type composition but do not describe their spatial distribution within each individual stand. Therefore, some pixels can be assigned a wrong label in the semantic segmentation task if we consider each stand to be homogeneously populated by its dominant species. Another challenge is the spatial distribution of individual stands within the study area. Classes are usually imbalanced and distributed nonuniformly that makes sampling choice more critical. This study aims to enhance tree species classification based on a neural network approach providing automatic markup adjustment and improving sampling technique. For forest species markup adjustment, we propose using a weakly supervised learning approach based on the knowledge of dominant species content within each stand. We also propose substituting the commonly used CNN sampling approach with the object-wise one to reduce the effect of the spatial distribution of forest stands. We consider four species commonly found in Russian boreal forests: birch, aspen, pine, and spruce. We use imagery from the Sentinel-2 satellite, which has multiple bands (in the visible and infrared spectra) and a spatial resolution of up to 10 meters. A data set of images for Leningrad Oblast of Russia is used to assess the methods. We demonstrate how to modify the training strategy to outperform a basic CNN approach from F1-score 0.68 to 0.76. This approach is promising for future studies to obtain more specific information about stands composition even using incomplete data.
Pollen Competition and Paternal Contribution during Artificially Controlled Pollination of Black Locust (Robinia pseudoacacia L.) without Castration
Authors: Sun Yuhan , Hu Ruiyang , Dong Li , Li Xiuyu , Zhang Zijie , Guo Qi , Cao Sen , Li Jiankang , Han Peiyao , Han Chao , Uddin Saleem , Long Cui , Fan Yingming , Li Yun
Published: October 2021

(1) Background: Considering the serious damage caused by castration and the extremely high outcrossing rate in nature, we hypothesized that artificial controlled pollination of black locust without castration could be conducted for hybridization breeding. (2) Methods: The study conducted controlled pollination on 20 mating combinations of black locust without castration using a single or mixed male parent. Offspring of different developmental stages and the leaves of parents were collected to extract DNA and perform paternity analysis using SSR molecular markers. (3) Results: The contribution rate of each male parent differed according to developmental stage after pollination using different pollens mixed in equal proportions. There were significant correlations between the genetic similarity between each male parent and female parent and contribution rate of each male parent at three different developmental stages after pollination. (4) Conclusions: The composition of offspring pollen donors showed no bias toward selfing or outcrossing when artificially pollinated without castration. Hybrid breeding of black locust by artificially controlled pollination without castration may not be feasible, given that our manual method resulted in a large number of abortive and abnormal offspring. Introduction of honeybees in a limited space to conduct controlled pollination of black locust for hybrid breeding may be feasible.
Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)
Authors: Otón Gonzalo , Pereira José Miguel C. , Silva João M. N. , Chuvieco Emilio
Published: October 2021

We present an analysis of the spatio-temporal trends derived from long-term burned area (BA) data series. Two global BA products were included in our analysis, the FireCCI51 (2001–2019) and the FireCCILT11 (1982–2018) datasets. The former was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m reflectance data, guided by 1 km active fires. The FireCCILT11 dataset was generated from Land Long-Term Data Record data (0.05°), which provides a consistent time series for Advanced Very High Resolution Radiometer images, acquired from the NOAA satellite series. FireCCILT11 is the longest time series of a BA product currently available, making it possible to carry out temporal analysis of long-term trends. Both products were developed under the FireCCI project of the European Space Agency. The two datasets were pre-processed to correct for temporal autocorrelation. Unburnable areas were removed and the lack of the FireCCILT11 data in 1994 was examined to evaluate the impact of this gap on the BA trends. An analysis and comparison between the two BA products was performed using a contextual approach. Results of the contextual Mann-Kendall analysis identified significant trends in both datasets, with very different regional values. The long-term series presented larger clusters than the short-term ones. Africa displayed significant decreasing trends in the short-term, and increasing trends in the long-term data series, except in the east. In the long-term series, Eastern Africa, boreal regions, Central Asia and South Australia showed large BA decrease clusters, and Western and Central Africa, South America, USA and North Australia presented BA increase clusters.
A Deep Learning Based Object Identification System for Forest Fire Detection
Authors: Guede-Fernández Federico , Martins Leonardo , de Almeida Rui Valente , Gamboa Hugo , Vieira Pedro
Published: October 2021

Forest fires are still a large concern in several countries due to the social, environmental and economic damages caused. This paper aims to show the design and validation of a proposed system for the classification of smoke columns with object detection and a deep learning-based approach. This approach is able to detect smoke columns visible below or above the horizon. During the dataset labelling, the smoke object was divided into three different classes, depending on its distance to the horizon, a cloud object was also added, along with images without annotations. A comparison between the use of RetinaNet and Faster R-CNN was also performed. Using an independent test set, an F1-score around 80%, a G-mean around 80% and a detection rate around 90% were achieved by the two best models: both were trained with the dataset labelled with three different smoke classes and with augmentation; Faster R-CNNN was the model architecture, re-trained during the same iterations but following different learning rate schedules. Finally, these models were tested in 24 smoke sequences of the public HPWREN dataset, with 6.3 min as the average time elapsed from the start of the fire compared to the first detection of a smoke column.
Effects of Different Drying Methods on the Characterization, Dissolution Rate and Antioxidant Activity of Ursolic Acid-Loaded Chitosan Nanoparticles
Authors: Zhao Lujie , Duan Xu , Cao Weiwei , Ren Xing , Ren Guangyue , Liu Panpan , Chen Junliang
Published: October 2021

To improve the water solubility of ursolic acid (UA), UA-loaded chitosan nanoparticles were firstly prepared by the ionotropic gelation method and dried by freeze drying (FD), microwave freeze drying (MFD) and spray drying (SD). The characterization of UA-loaded chitosan nanoparticles was performed with particle size, drug loading (DL), scanning electron microscope (SEM), fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), dissolution studies and antioxidant activity. The results demonstrated that UA was successfully encapsulated into chitosan nanoparticles using sodium tripolyphosphate (TPP) as a cross-linker, with a 79% encapsulation efficiency. The spray-dried, UA-loaded chitosan nanoparticles had the lowest drug loading (11.8%) and the highest particle size (496.9 ± 11.20 nm). The particle size of UA-loaded chitosan nanoparticles dried by MFD and FD was lower, at 240.8 ± 12.10 nm and 184.4 ± 10.62 nm, respectively, and their antioxidant activity was higher than those nanoparticles dried by SD. Moreover, the drying time and energy consumption of UA-loaded chitosan nanoparticles dried by MFD and SD were lower than that of FD. The dissolution rates of UA-loaded chitosan nanoparticles prepared by FD and MFD were 60.6% and 57.1%, respectively, in a simulated gastric fluid, which was a greater value than SD (55.9%). Therefore, the UA-loaded chitosan nanoparticles encapsulation method, combined with MFD technology, showed a promising potential to improve the water solubility of UA.