bluuebunny/crossref_metadata_2025 huggingface dataset:
Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data Derived from sUAS Imagery
Authors: Lawrence Brett
Published: January 2022

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of Red-cockaded Woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.
Forest Fire Detection via Feature Entropy Guided Neural Network
Authors: Guan Zhenwei , Min Feng , He Wei , Fang Wenhua , Lu Tao
Published: January 2022

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.
Multifractal Company Market: An Application to the Stock Market Indices
Authors: Chorowski Michał , Kutner Ryszard
Published: January 2022

Using the multiscale normalized partition function, we exploit the multifractal analysis based on directly measurable shares of companies in the market. We present evidence that markets of competing firms are multifractal/multiscale. We verified this by (i) using our model that described the critical properties of the company market and (ii) analyzing a real company market defined by the S&P500 index. As the valuable reference case, we considered a four-group market model that skillfully reconstructs this index’s empirical data. We point out that a four-group company market organization is universal because it can perfectly describe the essential features of the spectrum of dimensions, regardless of the analyzed series of shares. The apparent differences from the empirical data appear only at the level of subtle effects.
Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network’s Learnt Parameters
Authors: Alsaghir Eyad , Shi Xiyu , De Silva Varuna , Kondoz Ahmet
Published: January 2022

Deep learning, in general, was built on input data transformation and presentation, model training with parameter tuning, and recognition of new observations using the trained model. However, this came with a high computation cost due to the extensive input database and the length of time required in training. Despite the model learning its parameters from the transformed input data, no direct research has been conducted to investigate the mathematical relationship between the transformed information (i.e., features, excitation) and the model’s learnt parameters (i.e., weights). This research aims to explore a mathematical relationship between the input excitations and the weights of a trained convolutional neural network. The objective is to investigate three aspects of this assumed feature-weight relationship: (1) the mathematical relationship between the training input images’ features and the model’s learnt parameters, (2) the mathematical relationship between the images’ features of a separate test dataset and a trained model’s learnt parameters, and (3) the mathematical relationship between the difference of training and testing images’ features and the model’s learnt parameters with a separate test dataset. The paper empirically demonstrated the existence of this mathematical relationship between the test image features and the model’s learnt weights by the ANOVA analysis.
Learn Quasi-Stationary Distributions of Finite State Markov Chain
Authors: Cai Zhiqiang , Lin Ling , Zhou Xiang
Published: January 2022

We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution. Based on the fixed-point formulation of quasi-stationary distribution, we minimize the KL-divergence of two Markovian path distributions induced by candidate distribution and true target distribution. To solve this challenging minimization problem by gradient descent, we apply a reinforcement learning technique by introducing the reward and value functions. We derive the corresponding policy gradient theorem and design an actor-critic algorithm to learn the optimal solution and the value function. The numerical examples of finite state Markov chain are tested to demonstrate the new method.
Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN
Authors: Paing May Phu , Sento Adna , Bui Toan Huy , Pintavirooj Chuchart
Published: January 2022

Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.
Incorporating Climate Uncertainty into Conservation Planning for Wildlife Managers
Authors: Wilkening Jennifer L. , Magness Dawn Robin , Harrington Anita , Johnson Kurt , Covington Scott , Hoffman Jennie Ruth
Published: January 2022

The U.S. Fish and Wildlife Service (USFWS) is one of the oldest conservation organizations in the United States and is the only federal agency solely charged with conserving fish, wildlife, plants and their habitats. The agency leads numerous conservation initiatives, such as protecting and recovering endangered species, managing almost 600 wildlife refuges throughout all states and territories, enforcing federal wildlife laws, and regulating international wildlife trade. In the past, these activities have not accounted for climate change. The accelerating biodiversity crisis, in combination with climate uncertainty, adds to the existing complexity associated with responding to multiple anthropogenic stressors. Here we describe current practice and thinking related to climate uncertainty and management of USFWS resources. We focus on three agency domains which represent various conservation planning responsibilities: evaluating species to be listed as threatened or endangered, Habitat Conservation Plans for listed species, and land management techniques on wildlife refuges. Integrating climate considerations into agency planning documents is complex and we highlight effective current applications and suggest future improvements. Additionally, we identify outstanding research needs or management applications, and updates to existing policy that will aid in developing improved conservation strategies. Our synthesis contributes to ongoing efforts to incorporate climate uncertainty into conservation planning, natural resource management, and related policy revisions.
‘The Game of the Sea’: An Interdisciplinary Educational Board Game on the Marine Environment and Ocean Awareness for Primary and Secondary Students
Authors: Arboleya-García Elena , Miralles Laura
Published: January 2022

Games are a proven tool for learning at all ages and in many contexts. They increase the attractiveness of learning processes through arousing interest and enhancing motivation, and aid with the development of social skills. Educational games provide teachers with different approaches to teaching. ‘The Game of the Sea’ is an interdisciplinary board game, specifically designed to teach its players about the marine environment, regardless of their age. Through its 68 sections, coloured according to particular topics and organised as a fish shape, players encounter a wide range of questions and activities. Through playing this game, players acquire a broad knowledge of science, the marine environment and its importance, and literature. The game uses an interdisciplinary approach with question cards on a variety of topics (including maths, physics, biology, chemistry, art, etc.). A total of 222 players (111 children, aged 11–15, and 111 adults, aged 18–72) tested the game. These players were enrolled in different formal and non-formal educational contexts and had different educational backgrounds. For a qualitative analysis of game sessions (participant observation), researchers acted as game moderators and, while doing so, made subtle observations of players playing the game. On top of this, the value of the game, as a didactic tool, was evaluated with a test that players took before and after the game. After playing the game, knowledge of the marine environment, increased in both children and adults, with a slightly higher increase in children. Therefore, ‘The Game of the Sea’ is suitable for teaching all ages about the marine environment. Further, this game can impart to its players the importance of the marine environment and the importance of protecting this environment.
Modification of Cu(111) Surface with Alkylphosphonic Acids in Aqueous and Ethanol Solution—An Experimental and Theoretical Study
Authors: Mehmeti Valbonë , Podvorica Fetah
Published: January 2022

Alkylphosphonic acids are well known for their ability to form self-assembled monolayers on hydroxide surfaces. A crucial step to understanding fundamentally how these surfaces are created is the elucidation of the interaction process that leads to such interface creation. In this study, we employed electrochemical impedance spectroscopy (EIS), Monte Carlo and molecular dynamics to understand this process. The interaction with the Cu(111) surface of three different alkylphosphonic acids (hexyl-, octyl- and decylphosphonic acids) is evaluated in an aqueous acidic and in an ethanol solution by Monte Carlo and molecular dynamics simulations, while EIS measurements are used to put in evidence the impact of the layer made in ethanol on copper protection. Nyquist diagrams of copper samples modified with an alkylphosphonic monolayer showed a higher polarization resistance that mitigates the copper corrosion in an aqueous acid medium. The phase–frequency Bode plots had higher and broader phase maxima for a modified copper surface with phosphonic moieties, which confirmed the ability of this organic layer to prevent copper corrosion.
Strengthening Quality of Chaotic Bit Sequences
Authors: Melosik Michal , Marszalek Wieslaw
Published: January 2022

We discuss chaos and its quality as measured through the 0-1 test for chaos. When the 0-1 test indicates deteriorating quality of chaos, because of the finite precision representations of real numbers in digital implementations, then the process may eventually lead to a periodic sequence. A simple method for improving the quality of a chaotic signal is to mix the signal with another signal by using the XOR operation. In this paper, such mixing of weak chaotic signals is considered, yielding new signals with improved quality (with K values from the 0-1 test close to 1). In some sense, such a mixing of signals could be considered as a two-layer prevention strategy to maintain chaos. That fact may be important in those applications when the hardware resources are limited. The 0-1 test is used to show the improved chaotic behavior in the case when a continuous signal (for example, from the Chua, Rössler or Lorenz system) intermingles with a discrete signal (for example, from the logistic, Tinkerbell or Henon map). The analysis is presented for chaotic bit sequences. Our approach can further lead to hardware applications, and possibly, to improvements in the design of chaotic bit generators. Several illustrative examples are included.
Impact of Molecular Electrostatics on Field-Coupled Nanocomputing and Quantum-Dot Cellular Automata Circuits
Authors: Ardesi Yuri , Beretta Giuliana , Vacca Marco , Piccinini Gianluca , Graziano Mariagrazia
Published: January 2022

The molecular Field-Coupled Nanocomputing (FCN) is a promising implementation of the Quantum-dot Cellular Automata (QCA) paradigm for future low-power digital electronics. However, most of the literature assumes all the QCA devices as possible molecular FCN devices, ignoring the molecular physics. Indeed, the electrostatic molecular characteristics play a relevant role in the interaction and consequently influence the functioning of the circuits. In this work, by considering three reference molecular species, namely neutral, oxidized, and zwitterionic, we analyze the fundamental devices, aiming to clarify how molecule physics impacts architectural behavior. We thus examine through energy analysis the fundamental cell-to-cell interactions involved in the layouts. Additionally, we simulate a set of circuits using two available simulators: SCERPA and QCADesigner. In fact, ignoring the molecular characteristics and assuming the molecules copying the QCA behavior lead to controversial molecular circuit proposals. This work demonstrates the importance of considering the molecular type during the design process, thus declaring the simulators working scope and facilitating the assessment of molecular FCN as a possible candidate for future digital electronics.
Enhancement of the Evaporation and Condensation Processes of a Solar Still with an Ultrasound Cotton Tent and a Thermoelectric Cooling Chamber
Authors: Alwan Naseer T. , Ahmed Ayad S. , Majeed Milia H. , Shcheklein Sergey E. , Yaqoob Salam J. , Nayyar Anand , Nam Yunyoung , Abouhawwash Mohamed
Published: January 2022

In this paper, an experimental investigation study was conducted to show the effect of enhancing the evaporation and condensation processes inside a modified solar still by placing ultrasonic humidifiers inside a cotton mesh tent in the basin water and by installing a cooling chamber with thermoelectric elements on top of the solar still. Various parameters were recorded every hour, such as temperatures at different points within the solar still, the weather conditions (e.g., solar irradiance intensity, ambient air temperature, and wind speed), the yield of distilled water, and thermal efficiency on 29 July 2021 at the Ural Federal University (Russia). The production cost of distilled water from modified and traditional solar stills was also estimated. The experimental results showed that the productivity of the modified solar still increased by 124% compared with the traditional solar still, and the highest thermal efficiency was recorded at 2:00 p.m. (approximately 95.8% and 35.6% for modified and traditional solar stills, respectively). Finally, the productivity cost of distillate water (1 L) was approximately 0.040 and 0.042 $/L for the modified and traditional solar stills, respectively. The current work has contributed to increasing solar still productivity by applying simple and new technologies with the lowest possible capital and operational costs.
A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments
Authors: Du Jinze , Yuan Chao , Yue Min , Ma Tao
Published: January 2022

Indoor localization algorithms based on the received signal strength indicator (RSSI) in wireless sensor networks (WSNs) have higher localization accuracy than other range-free methods. This paper considers indoor localization based on multilateration and averaged received signal strength indicator (RSSI). We propose an approach called weighted three minimum distances method (WTM) to deal with the poor accuracy of distances deduced from RSSI. Using a practical localization system, an experimental channel model is deduced to assess the performance of the proposed localization algorithm in realistic conditions. Both simulated data and measured data are used to verify the proposed method. Compared with nonlinear least squares (NLS), Levenberg–Marquardt algorithm (LM) and semidefinite programming method (SDP), simulations show that the proposed method exhibits better localization accuracy but consumes more calculation time.
Development and Implementation of the Smart Village Concept as a Challenge for the Modern Power Industry on the Example of Poland
Authors: Budziewicz-Guźlecka Agnieszka , Drożdż Wojciech
Published: January 2022

Nowadays, while cities are often subject to research in terms of their development, especially smart development, studies on rural areas are rare. However, the development of the latter is very important. It is important that rural areas develop economically and socially. Smart villages are a challenge for the modern energy sector. The authors of the article try to answer the question: What are the challenges for the modern energy sector in the context of rural development? The aim of this article is to identify challenges for the modern power industry in the concept of smart countryside development. The article begins with the presentation of the essence of smart villages and the essence of energy policy. The research facilitated the identification of basic challenges that prevent or slow down the development of the smart villages in terms of modern energy solutions, as perceived by experts and residents, and farmers and entrepreneurs operating in rural areas. The article identifies a number of energy challenges in the context of a smart village. They include, among others, a lack of awareness regarding the impact of energy on the environment, a low level of public knowledge about new energy solutions, and a lack of social trust in modern energy solutions in rural areas. The research was conducted in rural areas in the north-western part of Poland. At the end, the article presents a model of rural development in the context of the modern energy sector. The research also allowed the creation of a smart village development model that focuses on smart economy, intelligent environment, intelligent people, and intelligent governance. Since these are universal solutions, they can be used as a proposition for other countries.
Synchronization of Electrical Drives via EtherCAT Fieldbus Communication Modules
Authors: Paprocki Marcin , Erwiński Krystian
Published: January 2022

Synchronization between devices (in particular drive systems) is paramount for multi-axis motion control systems used in Computerized Numerical Control (CNC) machines, robots, and specialized technology machines used in many areas of the manufacturing industry. EtherCAT is an Ethernet-based network that is one of the most popular industrial networks for multi-axis motion control systems. EtherCAT is standardized in the IEC 61158 and IEC 61784 standards. In the article, an EtherCAT communication network for electrical drives is presented. The article focuses on the synchronization in the EtherCAT network consisting of one master device and slave servo drive devices. Special attention is given to synchronization mechanisms in EtherCAT, such as distributed clocks in slave servo drives devices. For this purpose, a laboratory stand was built consisting of two prototype servo drive devices with BLDC motors equipped with EtherCAT communication modules. A description of the working developed EtherCAT communication modules is given. Authors in communication modules ware used an EtherCAT Slave Controller (ESC) chip (AX58100) to implement lower EtherCAT layers. EtherCAT application layer was implemented in software form on a 32-bit microcontroller, based on CANopen over EtherCAT (CoE) CAN in Automation 402 (CiA402) profile. This research’s main contribution was to show the time dependencies regarding synchronization in terms of data flow in the EtherCAT communication stack in slave servo drive devices. The research results showed that the synchronous operation of drives is mainly influenced not by the mechanism of distributed clocks that ensures synchronization in the EtherCAT network but the implementation of the highest layer of the communication stack in slave servo drive devices. Experimental results are presented that prove the modules’ adequacy for use in high-performance motion control systems.
Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model
Authors: Chen Peng , Deng Yumin , Zhang Xuegui , Ma Li , Yan Yaoliang , Wu Yifan , Li Chaoshun
Published: January 2022

The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.
Coupling a Chlor-Alkali Membrane Electrolyzer Cell to a Wind Energy Source: Dynamic Modeling and Simulations
Authors: Thummar Krunalkumar , Abang Roger , Menzel Katharina , de Groot Matheus Theodorus
Published: January 2022

Renewable energy sources are becoming a greater component of the electrical mix, while being significantly more volatile than conventional energy sources. As a result, net stability and availability pose significant challenges. Energy-intensive processes, such as chlor-alkali electrolysis, can potentially adjust their consumption to the available power, which is known as demand side management or demand response. In this study, a dynamic model of a chlor-alkali membrane cell is developed to assess the flexible potential of the membrane cell. Several improvements to previously published models were made, making the model more representative of state-of-the-art CA plants. By coupling the model with a wind power profile, the current and potential level over the course of a day was simulated. The simulation results show that the required ramp rates are within the regular operating possibilities of the plant for most of the time and that the electrolyte concentrations in the cell can be kept at the right level by varying inlet flows and concentrations. This means that a CA plant can indeed be flexibly operated in the future energy system.
Enhancement of Sensorless Control for Non-Sinusoidal Multiphase Drives-Part I: Operation in Medium and High-Speed Range
Authors: Mini Youssouf , Nguyen Ngac Ky , Semail Eric , Vu Duc Tan
Published: January 2022

This two-part study proposes a new sensorless control strategy for non-sinusoidal multiphase permanent magnet synchronous machines (PMSMs), especially integrated motor drives (IMDs). Based on the Sliding Mode Observer (SMO), the proposed sensorless control strategy uses the signals (currents and voltages) of all fictitious machines of the multiphase PMSMs. It can estimate the high-accuracy rotor positions that are required in vector control. This proposed strategy is compared to the conventional sensorless control strategy that applies only current and voltage signals of the main fictitious machine, including the fundamental component of back electromotive force (back EMF) of non-sinusoidal multiphase PMSMs. Therefore, in order to choose an appropriate sensorless control strategy for the non-sinusoidal multiphase PMSMs, these two sensorless control strategies will be highlighted in terms of precision with respect to rotor position and speed estimation. Simulations and the experimental results obtained with a non-sinusoidal seven-phase PMSM will be shown to verify and compare the two sensorless control strategies. In this part of the study (part I), only sensorless control in the medium and high-speed range is considered. Sensorless control at the zero and low-speed range will be treated in the second part of this study (part II).
Non-Isolated Interleaved Hybrid Boost Converter for Renewable Energy Applications
Authors: Ramanathan Girish Ganesan , Urasaki Naomitsu
Published: January 2022

DC-DC boost converters are necessary to extract power from solar panels. The output voltage from these panels is far lower than the utility voltage levels. One of the main functions of the boost converter is to provide a considerable step-up gain to interface the panel to the utility lines. There are several techniques used to boost the low panel voltage. Some of the issues faced by these topologies are a high duty ratio operation, complex design with multiple active switches and discontinuous input current that affects the power drawn from the panel. This paper presents a boost converter topology that combines the advantages of an interleaved structure, a voltage lift capacitor and a passive voltage multiplier network. A mathematical analysis of the proposed converter during its various modes of operation is presented. A 100 W prototype of the proposed converter is designed and tested. The prototype is controlled by a PIC16F18455 microcontroller. The converter is capable of achieving a gain of 10 without operating at extremely high duty ratios. The voltage stress of the switch is far lower than the maximum output voltage.
The Impact of Torrefaction Temperature on the Physical-Chemical Properties of Residual Exotic Fruit (Avocado, Mango, Lychee) Seeds
Authors: Dyjakon Arkadiusz , Sobol Łukasz , Noszczyk Tomasz , Mitręga Jakub
Published: January 2022

A large portion of food loss and waste (FSL) is comprised of seeds and stones. Exotic fruits such as mangoes, lychees and avocados, in which the seeds account for a significant part of the weight and volume of the entire product, are most affected by this problem. The seeds contain a large quantity of polyphenols and essential nutrients, which makes them a good material for extraction. However, conventional extraction techniques are considered time-consuming, and therefore significantly limit their use on an industrial scale. An alternative method of managing the seeds may be their energy utilization. In this study, torrefaction was proposed as a method for the valorization of exotic fruit seeds (mango, lychee, avocado). Thus, the influence of torrefaction temperature (200–300 °C) on the physical-chemical properties of substrates was investigated. The obtained results revealed that, in relation to the unprocessed raw materials, the torreficates are characterized by improved hydrophobic properties (all materials are classified as extremely hydrophobic), higher heating value (at 300 °C the values increased from 17,789 to 24,842 kJ∙kg−1 for mango, from 18,582 to 26,513 kJ∙kg−1 for avocado, and from 18,584 to 25,241 kJ∙kg−1 for lychee), higher fixed carbon content (which changed from 7.87–15.38% to 20.74–32.47%), and significant mass loss, by 50–60%. However, as a side effect of thermal treatment, an increase in ash content (approx. 2–3 times but still less than in coal) was observed. Therefore, the torreficates may be competitive with coal. The possibility of using residues from the food processing sector as a substrate for energy purposes is important from the point of view of environment protection and is a part of the functioning of the circular economy.
Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning
Authors: Ding Zhenhuan , Huang Xiaoge , Liu Zhao
Published: January 2022

Voltage regulation in distribution networks encounters a challenge of handling uncertainties caused by the high penetration of photovoltaics (PV). This research proposes an active exploration (AE) method based on reinforcement learning (RL) to respond to the uncertainties by regulating the voltage of a distribution network with battery energy storage systems (BESS). The proposed method integrates engineering knowledge to accelerate the training process of RL. The engineering knowledge is the chance-constrained optimization. We formulate the problem in a chance-constrained optimization with a linear load flow approximation. The optimization results are used to guide the action selection of the exploration for improving training efficiency and reducing the conserveness characteristic. The comparison of methods focuses on how BESSs are used, training efficiency, and robustness under varying uncertainties and BESS sizes. We implement the proposed algorithm, a chance-constrained optimization, and a traditional Q-learning in the IEEE 13 Node Test Feeder. Our evaluation shows that the proposed AE method has a better response to the training efficiency compared to traditional Q-learning. Meanwhile, the proposed method has advantages in BESS usage in conserveness compared to the chance-constrained optimization.
Advanced Polymer and Perovskite Solar Cells
Authors: Kong Jaemin
Published: January 2022

This special issue was designed to draw attention to photovoltaic technology, which harnesses sunlight—the most promising renewable energy source—for our sustainable future [...]
A Concept for Solving the Sustainability of Cities Worldwide
Authors: Margeta Karmen , Glasnovic Zvonimir , Zabukovec Logar Nataša , Tišma Sanja , Farkaš Anamarija
Published: January 2022

Considering that more than half of the world’s population today lives in cities and consumes about 80% of the world’s energy and that there is a problem with drinking water supply, this paper presents a way to solve the problem of the sustainability of cities by enabling their complete independence from external sources of energy and drinking water. The proposed solution entails the use of Seawater Steam Engine (SSE) technology to supply cities with electricity, thermal energy and drinking water. The system would involve the seasonal storage of electricity and thermal energy, supported by geothermal heat pumps. The strategy of the distribution network would be based on the original concept of the “loop”. In cities that do not have enough space, SSE collectors would be placed above the lower parts of the city like “canopies”. The city of Zagreb (Croatia) was selected as a case study due to its size, climate and vulnerability to natural disasters. The results show that Zagreb could become sustainable in 30 years with the allocation of less than 2% of GDP and could become a paradigm of sustainability for cities worldwide. This paper encourages the development of the “Philosophy of Sustainability” because the stated goals cannot be achieved without a change in consciousness.
Analysis of the Status of Research and Innovation Actions on Electrofuels under Horizon 2020
Authors: Marzi Emanuela , Morini Mirko , Gambarotta Agostino
Published: January 2022

Europe stated the ambitious target of becoming carbon neutral by 2050 to combat climate change and meet the requirements imposed by the Paris Agreement, and renewable energy has proved to be a promising solution for the decarbonization of many sectors. Nonetheless, their aleatory nature leads to grid unbalances due to the difference between supply and demand. Storage solutions are needed, and electrofuels become a key factor in this context: they are fuels produced from electricity, which leads to carbon-neutral fuels if it originates from renewable sources. These can constitute a key solution to store the surplus energy and to decarbonize the so-called hard-to-abate sectors. Electrofuel production technologies have not yet been fully developed, and, in this context, extensive study of the state-of-the-art of existing projects can be very useful for researchers and developers. This work researches the European projects funded by the Horizon 2020 Programme regarding electrofuel production. The projects were analyzed in-depth using specific features, and the results were presented.
Influence of Different Plates Arrangements on the Car Body
Authors: Kurec Krzysztof , Kamieniecki Konrad , Piechna Janusz
Published: January 2022

The purpose of this study was to investigate whether small plates covering the roof and the hood of the DrivAer estate vehicle can be used as airbrakes and increase its drag as well as the downforce. The presented results were obtained with the use of the commercial computational fluid dynamics software ANSYS® Fluent. The main findings of the article are that the aerodynamic devices such as flaps covering surfaces of the vehicle can have a significant impact on drag increase and can be used not only to make the design of the car more striking but also beneficial when utilized as a part of an active aerodynamic setup.
Effects of Increased Renewable Energy Consumption on Electricity Prices: Evidence for Six South American Countries
Authors: Correa-Quezada Ronny , Tituaña-Castillo María del Cisne , del Río-Rama María de la Cruz , Álvarez-García José
Published: January 2022

This research examines the relationship between renewable energy consumption and electricity prices in six South American countries (Argentina, Bolivia, Colombia, Chile, Ecuador and Peru). The methodology used is a panel econometric model with annual data for the period 1990–2015. The results show that the consumption of renewable energies influences the price of electricity paid by households, although its influence is very moderate. On the other hand, it was observed that the consumption of renewable energies has no relationship with the energy prices of the industrial sector and the commercial and services sector. In the countries analyzed, an increase in GDP causes an increase in the price of energy in the industrial sector. With more CO2 emissions and a rise in the international price of oil, the annual average price of the industrial sector decreases.
Comprehensive Analysis of the Pollutant Characteristics of Gasoline Vehicle Emissions under Different Engine, Fuel, and Test Cycles
Authors: Lv Zongyan , Yang Lei , Wu Lin , Peng Jianfei , Zhang Qijun , Sun Meng , Mao Hongjun , Min Jie
Published: January 2022

Vehicle exhaust emissions have seriously affected air quality and human health, and understanding the emission characteristics of vehicle pollutants can promote emission reductions. In this study, a chassis dynamometer was used to study the emission characteristics of the pollutants of two gasoline vehicles (Euro 5 and Euro 6) when using six kinds of fuels. The results show that the two tested vehicles had different engine performance under the same test conditions, which led to a significant difference in their emission characteristics. The fuel consumption and pollutant emission factors of the WLTC cycle were higher than those of the NEDC. The research octane number (RON) and ethanol content of fuels have significant effects on pollutant emissions. For the Euro 5 vehicle, CO and particle number (PN) emissions decreased under the WLTC cycle, and NOx emissions decreased with increasing RONs. For the Euro 6 vehicle, CO and NOx emissions decreased and PN emissions increased with increasing RONs. Compared with traditional gasoline, ethanol gasoline (E10) led to decreases in NOx and PN emissions, and increased CO emissions for the Euro 5 vehicle, while it led to higher PN and NOx emissions and lower CO emissions for the Euro 6 vehicle. In addition, the particulate matter emitted was mainly nucleation-mode particulate matter, accounting for more than 70%. There were two peaks in the particle size distribution, which were about 18 nm and 40 nm, respectively. Finally, compared with ethanol–gasoline, gasoline vehicles with high emission standards (Euro 6) are more suitable for the use of traditional gasoline with a high RON.
Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison
Authors: Surya Sumukh , Samanta Akash , Marcis Vinicius , Williamson Sheldon
Published: January 2022

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.
Combustion Characteristics of Premixed Hydrogen/Air in an Undulate Microchannel
Authors: Resende Pedro R. , Morais Leandro C. , Pinho Carlos , Afonso Alexandre M.
Published: January 2022

This work reports a numerical investigation of microcombustion in an undulate microchannel, using premixed hydrogen and air to understand the effect of the burner design on the flame in order to obtain stability of the flame. The simulations were performed for a fixed equivalence ratio and a hyperbolic temperature profile imposed at the microchannel walls in order to mimic the heat external losses occurred in experimental setups. Due to the complexity of the flow dynamics combined with the combustion behavior, the present study focuses on understanding the effect of the fuel inlet rate on the flame characteristics, keeping other parameters constant. The results presented stable flame structure regardless of the inlet velocity for this type of design, meaning that a significant reduction in the heat flux losses through the walls occurred, allowing the design of new simpler systems. The increase in inlet velocity increased the flame extension, with the flame being stretched along the microchannel. For higher velocities, flame separation was observed, with two detected different combustion zones, and the temperature profiles along the burner centerline presented a non-monotonic decrease due to the dynamics of the vortices observed in the convex regions of the undulated geometry walls. The geometry effects on the flame structure, flow field, thermal evolution and species distribution for different inlet velocities are reported and discussed.
Advanced Biofuel Value Chains through System Dynamics Modelling and Competitive Priorities
Authors: Christensen Thomas , Panoutsou Calliope
Published: January 2022

The greatest challenge in accelerating the realisation of a sustainable and competitive bioeconomy is to demonstrate that enshrining sustainability principles at the very heart of a production line can generate value and improve its overall system. Strategies for reducing emissions, pollutants, indirect land use change or soil depreciation are all perceived as costs or necessary inconveniences to comply with stringent, climate change-focused policy frameworks. System dynamics modelling and competitive priorities are tools that can accurately and intelligently expand on the cross-value chain approach, which integrates both technical and environmental performances, to address the issue of harmonising sustainability and technical operations as one overall dimension of performance. A stock-and-flow model is developed to map a full biofuel value chain and quantitatively and coherently integrate factors of emissions, carbon, land, production, and technology. As such, environmental and operational impacts of innovative practices are measured, and subsequently linked to a qualitative framework of competitive priorities, as defined by transparency, quality, innovation and flexibility. Sustainability and productivity functions are found to reinforce each other when all competitive priorities are optimised. Equally, the framework provides a clear understanding of trade-offs engendered by value chain interventions. Advantages and limitations in the accessibility, scope and transferability of the multi-pronged analytical approach are discussed.
Application of Ionic Liquids for the Recycling and Recovery of Technologically Critical and Valuable Metals
Authors: Inman Grace , Nlebedim Ikenna C. , Prodius Denis
Published: January 2022

Population growth has led to an increased demand for raw minerals and energy resources; however, their supply cannot easily be provided in the same proportions. Modern technologies contain materials that are becoming more finely intermixed because of the broadening palette of elements used, and this outcome creates certain limitations for recycling. The recovery and separation of individual elements, critical materials and valuable metals from complex systems requires complex energy-consuming solutions with many hazardous chemicals used. Significant pressure is brought to bear on the improvement of separation and recycling approaches by the need to balance sustainability, efficiency, and environmental impacts. Due to the increase in environmental consciousness in chemical research and industry, the challenge for a sustainable environment calls for clean procedures that avoid the use of harmful organic solvents. Ionic liquids, also known as molten salts and future solvents, are endowed with unique features that have already had a promising impact on cutting-edge science and technologies. This review aims to address the current challenges associated with the energy-efficient design, recovery, recycling, and separation of valuable metals employing ionic liquids.
Comparative Performance Assessment of Different Energy Storage Devices in Combined LFC and AVR Analysis of Multi-Area Power System
Authors: Kalyan CH. Naga Sai , Goud B. Srikanth , Reddy Ch. Rami , Bajaj Mohit , Sharma Naveen Kumar , Alhelou Hassan Haes , Siano Pierluigi , Kamel Salah
Published: January 2022

This paper made an attempt to put forward the comparative performance analysis of different energy storage devices (ESDs), such as redox flow batteries (RFBs), superconducting magnetic energy storage (SMES) device and ultra-capacitors (UCs), in the combined frequency and voltage stabilization of a multi-area interconnected power system (MAIPS). The investigative power system model comprises two areas, and each area consists of the power-generating sources of thermal, hydro and gas units. The intelligent control mechanism of fuzzy PID was used as a secondary controller optimized with a hybridized approach of the artificial electric field algorithm (HAEFA) subjected to the minimization of integral time absolute error (ITAE) objective function. However, the superiority of fuzzy PID in dampening the deviations of combined load frequency control (LFC) and automatic voltage regulator (AVR) responses was revealed upon comparison with conventional PI and PID. Further, the LFC-AVR combined analysis was extended to incorporate different ESDs one after the other. The simulation results reveal the efficacy of incorporating ESDs with the LFC-AVR system and the supremacy of RFBs in damping out the fluctuations in frequency and voltage.
Validated Analytical Model of 8/6 and 10/8 Switched Reluctance Motors
Authors: Bieńkowski Krzysztof , Łapczyński Sebastian , Szulborski Michał , Kozarek Łukasz , Gołota Karol , Cichecki Hubert , Kolimas Łukasz , Żelaziński Tomasz , Smolarczyk Adam , Babiński Adam , Owsiński Maciej
Published: January 2022

The aim of this work was to develop a parameterized analytical and FEM (Finite Element Method) model of a switched reluctance motor. The developed analytical model was used to assess the performance of these types of motors, and is determined to be a tool for comparing and evaluating switched reluctance motors of various designs. The aim of the work was also the systematization of knowledge related to the operation, structure and methods of determining the static electromagnetic torque generated by switched reluctance motors. The FEM model (ANSYS) was, also, made in order to verify physical phenomena occurring during operation of these motors. Four laboratory tests were executed as part of the work prototypes of switched reluctance motors built at Warsaw University of Technology. A parameterized analytical model was developed and implemented in MATLAB. The model operation tests were conducted and as a result, the characteristics describing the dependence of the electromagnetic torque as a function of the current and the position of the rotor were obtained. The model was validated by confronting the analytical calculations supported by FEM simulation results with the measurement results. The usefulness of the results obtained with the use of the procured models was assessed.