bluuebunny/crossref_metadata_2025 huggingface dataset:
0D Dynamic Modeling and Experimental Characterization of a Biomass Boiler with Mass and Energy Balance
Authors: Mameri Fateh , Delacourt Eric , Morin Céline , Schiffler Jesse
Published: January 2022

The paper presents an experimental study and a 0D dynamic modeling of a biomass boiler based on the Bond Graph formalism from mass and energy balance. The biomass boiler investigated in this study is an automatic pellet boiler with a nominal power of 30 kW with a fixed bed. The balances allow to model as time function the flue gas enthalpy flux variation and the thermal transfers between the flue gas and the walls of the boiler subsystems. The main objective is to build a model to represent the dynamic thermal behavior of the boiler. Indeed, small domestic boilers have discontinuous operating phases when the set temperature is reached. The global thermal transfer coefficients for the boiler subsystems are obtained according to an iterative calculation by inverse method. The boiler has an average efficiency of 67.5% under our operating conditions and the radiation is the dominant thermal transfer by reaching 97.6% of the total thermal transfers inside the combustion chamber. The understanding of the dynamic behavior of the boiler during the operating phases allows to evaluate its energy performances. The proposed model is both stimulated and validated using experimental results carried out on the boiler.
Thermodynamics Irreversibilities Analysis of Oxy-Fuel Diffusion Flames: The Effect of Oxygen Concentration
Authors: Yan Huibo , Tang Guangtong , Wang Chaoyang , Li Lujiang , Zhou Yuanke , Zhang Zhongnong , Lou Chun
Published: January 2022

In studies on the combustion process, thermodynamic analysis can be used to evaluate the irreversibility of the combustion process and improve energy utilization efficiency. In this paper, the combustion process of a laminar oxy-fuel diffusion flame was simulated, and the entropy generation due to the irreversibilities of the radiation process, the heat conduction and heat convection process, the mass diffusion process, and the chemical reaction process was calculated. The effect of the oxygen concentration in the oxidizer on the entropy generation was analyzed. The results indicated that, as the oxygen concentration in the oxidizer increases, the radiative entropy generation first increases and then decreases, and the convective and conductive entropy generation, the mass diffusion entropy generation, the chemical entropy generation, and the total entropy generation gradually increase.
Ensemble Approach for Detection of Depression Using EEG Features
Authors: Avots Egils , Jermakovs Klāvs , Bachmann Maie , Päeske Laura , Ozcinar Cagri , Anbarjafari Gholamreza
Published: January 2022

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.
Relevant Information for the Accountability of Private Institutions of Social Solidarity: Results from Fieldwork
Authors: Inácio Helena , Costa Alberto J. , Bandeira Ana Maria , Ferreira Augusta , Tomé Brízida , Joaquim Carla , Santos Carlos , Góis Cristina , Curi Denise , Meira Deolinda , Azevedo Graça , Jesus Mafalda , Teixeira Maria Goreti , Monteiro Patrícia , Duarte Rúben , Marques Rui Pedro
Published: January 2022

The social economy (SE) has emerged as an interesting alternative for dealing with social problems. However, there are some concerns related to the abilities of these institutions regarding accountability. Thus, the present work aimed to determine if private social solidarity institutions (IPSS) are prepared to meet management requirements by increasing their accountability. In Portugal, IPSS are social economy organisations. Using an exploratory focus, we conducted qualitative research on 31 Portuguese IPSS. Interviews with those responsible for these entities took place between June and July 2019. The interviews were guided based on a semistructured script that was created based on a literature review. After content analysis, it was found that, in most of the institutions interviewed, the board does not use management tools, such as performance analysis, social impact assessments, strategic planning, and quality management systems, even though they recognise the importance of using them. This is due to the lack of access or knowledge about their use. In addition, the majority of the IPSS interviewed showed concern about the transparency and ethics of managers. Current strategic management practices are remarkably targeted at companies in the for-profit sector and can compromise the principles of investment in human and social issues.
Self-Concept and Feeling of Belonging as a Predictor Variable of the Attitude towards the Study from the PISA 2018 Report
Authors: Gutiérrez Nieves , Mercader Isabel , Carrión José Juan , Trigueros Rubén
Published: January 2022

The importance of self-concept and the feeling of belonging to a school have been highlighted in numerous studies as predictors of school success. However, there are hardly any studies that link the relationship between self-concept and the feeling of belonging to the school. Thus, the aim of this study is to analyze the influence of personal and educational self-concept on the feeling of belonging to school, based on the data provided by the PISA 2018 report. To achieve this objective, a correlational methodology based on structural equation modelling has been used. The sample of participants is composed of 35,943 students of Compulsory Secondary Education from different Spanish high schools that participated in the PISA 2018 Report. The results obtained have shown that personal and educational self-concept correlate positively. In addition, both factors were positively related to the feeling of belonging to the educational centre. This study allows us to infer the idea of the existence of a direct and positive correlation between school and personal self-concept and the feeling of belonging, which would allow schools to develop educational intervention programmes with the aim of promoting the student’s self-concept in order for them to feel better integrated into the school.
Problem Solving and Digital Transformation: Acquiring Skills through Pretend Play in Kindergarten
Authors: Hollenstein Lena , Thurnheer Stefanie , Vogt Franziska
Published: January 2022

One of the crucial 21st-century digital skills, in the context of digital transformation, is problem solving—equally so in the fields of science, technology, engineering, and mathematics (STEM). In the context of kindergarten, learning through play is central; therefore, pretend play, and particularly guided pretend play, is suggested as an innovative way to foster skills for digital problem solving. As yet, the potential of pretend play for children’s learning about digital transformation and digital problem-solving processes has hardly been researched. The paper examines how children solve digital problems in guided pretend play. In an explorative intervention study “We play the future”, an information technology center (IT center) is introduced as one of the play corners for pretend play in kindergartens, together with other inputs such as a smart home corner (Internet of Things) or autonomous vehicles. Children’s play was video recorded. From the 15 participating kindergartens, 13 h of sequences involving the IT center were analyzed using content analysis. The findings indicate that children identify problems in a play situation and solve them using problem-solving strategies, such as devising new applications and installing software. Furthermore, the findings show that the kindergarten teacher’s participation in the pretend play is important for enabling longer and more complex problem-solving processes. Consequences for further teacher training to foster problem-solving skills during guided pretend play are discussed.
Improved Human Activity Recognition Using Majority Combining of Reduced-Complexity Sensor Branch Classifiers
Authors: Webber Julian , Mehbodniya Abolfazl , Arafa Ahmed , Alwakeel Ahmed
Published: January 2022

Human activity recognition (HAR) employs machine learning for the automated recognition of motion and has widespread applications across healthcare, daily-life and security spaces. High performances have especially been demonstrated using video cameras and intensive signal processing such as the convolutional neural network (CNN). However, lower complexity algorithms operating on low-rate inertial data is a promising approach for portable use-cases such as pairing with smart wearables. This work considers the performance benefits from combining HAR classification estimates from multiple sensors each with lower-complexity processing compared with a higher-complexity single-sensor classifier. We show that while the highest single-sensor classification accuracy of 91% can be achieved for seven activities with optimized number of hidden units and sample rate, the classification accuracy is reduced to 56% with a reduced-complexity 50-neuron classifier. However, by majority combining the predictions of three and four low-complexity classifiers, the average classification accuracy increased to 82.5% and 94.4%, respectively, demonstrating the efficacy of this approach.
TheraProx: Capacitive Proximity Sensing
Authors: Čoko Duje , Stančić Ivo , Dujić Rodić Lea Dujić , Čošić Dora
Published: January 2022

This paper presents the development of a novel contactless omnidirectional capacitive proximity sensor. The presented device has been designed to be energy-efficient (≈5 mW power consumption) by means of duty-cycling the power supply. A comprehensive methodological experiment has been carried out to extensively evaluate the performance within the sensing range (5–10 cm). A simple boot-up self-adjustment mechanism has been implemented using a digital potentiometer. This feature allows for an effortless utilization of the proposed device in a wide variety of potential applications, including mobile robotics and human–machine interaction.
Uncertainty-Based Rejection in Machine Learning: Implications for Model Development and Interpretability
Authors: Barandas Marília , Folgado Duarte , Santos Ricardo , Simão Raquel , Gamboa Hugo
Published: January 2022

Uncertainty is present in every single prediction of Machine Learning (ML) models. Uncertainty Quantification (UQ) is arguably relevant, in particular for safety-critical applications. Prior research focused on the development of methods to quantify uncertainty; however, less attention has been given to how to leverage the knowledge of uncertainty in the process of model development. This work focused on applying UQ into practice, closing the gap of its utility in the ML pipeline and giving insights into how UQ is used to improve model development and its interpretability. We identified three main research questions: (1) How can UQ contribute to choosing the most suitable model for a given classification task? (2) Can UQ be used to combine different models in a principled manner? (3) Can visualization techniques improve UQ’s interpretability? These questions are answered by applying several methods to quantify uncertainty in both a simulated dataset and a real-world dataset of Human Activity Recognition (HAR). Our results showed that uncertainty quantification can increase model robustness and interpretability.
Flux Intensifying Feature of Permanent Magnet Assisted Synchronous Reluctance Motor with High Torque Density
Authors: Hsieh Min-Fu , Ngo Duc-Kien , Thao Nguyen Gia Minh
Published: January 2022

This paper investigates the flux intensifying (FI) feature of permanent magnet assisted synchronous reluctance motor (PMa-SynRM) in order to achieve high torque density with a small amount of permanent magnet (PM). This motor is thus denoted “FI-PMa-SynRM”. The performance of the developed FI-PMa-SynRM is compared with that of two other counterparts, i.e., an inset surface permanent magnet synchronous motor (SPMSM) and a synchronous reluctance motor (SynRM) to highlight the strengths of the FI-PMa-SynRM. In addition, an analysis on partial demagnetization and torque density of the developed FI-PMa-SynRM is conducted to demonstrate its advantages. The finite element method (FEM) is employed for the analysis in terms of flux linkage and inductances so that the effectiveness of the FI characteristics is validated. The analysis shows that a high torque density (24.79 Nm/L or even greater) can be achieved with a small amount of PM (0.72% motor volume). The ability of anti-demagnetization is also fulfilled. Furthermore, experimental results are provided to validate the analysis findings.
A Vacuum Transistor Based on Field-Assisted Thermionic Emission from a Multiwalled Carbon Nanotube
Authors: He Yidan , Li Zhiwei , Mao Shuyu , Zhan Fangyuan , Wei Xianlong
Published: January 2022

Vacuum triodes have been scaled down to the microscale on a chip by microfabrication technologies to be vacuum transistors. Most of the reported devices are based on field electron emission, which suffer from the problems of unstable electron emission, poor uniformity, and high requirement for operating vacuum. Here, to overcome these problems, a vacuum transistor based on Field-Assisted thermionic emission from individual carbon nanotubes is proposed and fabricated using microfabrication technologies. The carbon nanotube vacuum transistor exhibits an ON/OFF current ratio as high as 104 and a subthreshold slope of ~4 V·dec−1. The gate controllability is found to be strongly dependent on the distance between the collector electrodes and electron emitter, and a device with the distance of 1.5 μm shows a better gate controllability than that with the distance of 0.5 μm. Benefiting from Field-Assisted thermionic emission mechanism, electric field required in our devices is about one order of magnitude smaller than that in the devices based on field electron emission, and the surface of the emitters shows much less gas molecule absorption than cold field emitters. These are expected to be helpful for improving the stability and uniformity of the devices.
Automatic RTL Generation Tool of FPGAs for DNNs
Authors: Jang Seojin , Liu Wei , Park Sangun , Cho Yongbeom
Published: January 2022

With the increasing use of multi-purpose artificial intelligence of things (AIOT) devices, embedded field-programmable gate arrays (FPGA) represent excellent platforms for deep neural network (DNN) acceleration on edge devices. FPGAs possess the advantages of low latency and high energy efficiency, but the scarcity of FPGA development resources challenges the deployment of DNN-based edge devices. Register-transfer level programming, hardware verification, and precise resource allocation are needed to build a high-performance FPGA accelerator for DNNs. These tasks present a challenge and are time consuming for even experienced hardware developers. Therefore, we propose an automated, collaborative design process employing an automatic design space exploration tool; an automatic DNN engine enables the tool to reshape and parse a DNN model from software to hardware. We also introduce a long short-term memory (LSTM)-based model to predict performance and generate a DNN model that suits the developer requirements automatically. We demonstrate our design scheme with three FPGAs: a zcu104, a zcu102, and a Cyclone V SoC (system on chip). The results show that our hardware-based edge accelerator exhibits superior throughput compared with the most advanced edge graphics processing unit.
Image Forgery Detection Using Deep Learning by Recompressing Images
Authors: Ali Syed Sadaf , Ganapathi Iyyakutti Iyappan , Vu Ngoc-Son , Ali Syed Danish , Saxena Neetesh , Werghi Naoufel
Published: January 2022

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.
High-Voltage Drivers Based on Forming Lines with Extended Quasi-Rectangular Pulses for High-Power Microwave Oscillators
Authors: Rostov Vladislav V. , Stepchenko Alexei S. , Vykhodtsev Pavel V. , Tsygankov Ruslan V.
Published: January 2022

The paper considers such modifications of an ordinary pulse-forming line (PFL) as double-width and triple-width forming lines (DWFL, TWFL) built around the PFL by nesting one and two additional uncharged lines, respectively, into its free volume inside the inner conductor of the PFL. The theoretical analysis is supported by simulation and experimental data, showing that the TWFL provides a 3-fold increase in the voltage pulse width and that it can be further increased by an arbitrary integer factor k. The results of the numerical simulations also show the electric field behavior and other features, including the edge effect in the TWFL. The proposed method opens up new opportunities for designing compact high-power microwace (HPM) sources.
Acknowledgment to Reviewers of Electronics in 2021
Authors:   Electronics Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition
Authors: Nam Jae-Won , Cho Young-Kyun , Lee Youn Kyu
Published: January 2022

An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor (FinFET) and silicon-on-insulator (SOI), have been proposed to provide the higher computation performance required to meet various design specifications. As a result, the latest research on AMS design optimization has enabled enormous resource savings in AMS design procedures but remains limited with regard to reflecting the intended operating conditions in the design parameters. Hereby, we propose what is termed a supervised learning artificial neural network (ANN) as a means by which to define an AMS regression model. This approach allows for rapid searches of complex design dimensions, including variations in performance metrics caused by process–voltage–temperature (PVT) changes. The method also reduces the considerable computation expense compared to that of simulation-program-with-integrated-circuit-emphasis (SPICE) simulations. Hence, the proposed AMS circuit design flow generates highly promising output to meet target requirements while showing an excellent ability to match the design target performance. To verify the potential and promise of our design flow, a successive approximation register analog-to-digital converter (SAR ADC) is designed with a 14 nm process design kit. In order to show the maximum single ADC performance (6-bit∼8-bit resolution and few GS/s conversion speed), we have set three different ADC performance targets. Under all SS/TT/FF corners, ±6.25% supply voltage variation, and temperature variation from −40 ∘C to 80 ∘C, the designed SAR ADC using our proposed AMS circuit optimization flow yields remarkable figure-of-merit energy efficiency (approximately 15 fJ/conversion step).
Bidding Strategy for VPP and Economic Feasibility Study of the Optimal Sizing of Storage Systems to Face the Uncertainty of Solar Generation Modelled with IGDT
Authors: Maceas Henao Michelle , Espinosa Oviedo Jairo José
Published: January 2022

Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI).
Special Issue “Selected Papers from the 8th International OTEC Symposium”
Authors: Kim Albert S.
Published: January 2022

Ocean thermal energy conversion (OTEC) aims to use the temperature difference between surface and deep seawater to generate electricity and (possibly) freshwater [...]
Effect of MXene Loaded on g-C3N4 Photocatalyst for the Photocatalytic Degradation of Methylene Blue
Authors: Nasri Muhammad Syahmi Irfan , Samsudin Mohamad Fakhrul Ridhwan , Tahir Asif Ali , Sufian Suriati
Published: January 2022

Photocatalytic degradation is one of the environmentally friendly methods used in treating dye wastewater. In this study, a series of MXene/g-C3N4 heterostructure photocatalysts with different loading amounts of MXene (1, 4, 8, and 12 wt.%) were successfully synthesized via the wet impregnation method and their photocatalytic activity was evaluated via the degradation of methylene blue under visible-light irradiation. As such, the 1 wt.% MXene/g-C3N4 heterostructure photocatalyst achieved a high degradation of methylene blue compared to the pure g-C3N4 under visible-light illumination of 180 min. This significant improvement was attributed to the intimate interfacial contact, evidently from the FESEM analysis, which allows the smooth photocharge carriers to transport between g-C3N4 and MXene. Additionally, the larger BET surface area demonstrated by the 1 wt.% MXene/g-C3N4 heterostructure allowed this sample to have higher adsorption of dye molecules and provided a higher number of reactive sites, which was beneficial for the enhancement of the photocatalytic activity. Nevertheless, it was found that the excessive loading of MXene can substantially impede photocatalytic activity. This was attributed to the decrease in the active sites, as well as the weakened crystallinity of the MXene/g-C3N4 heterostructure photocatalyst, evident from the FTIR and XRD analysis. All in all, this study has shown the potential of the MXene/g-C3N4 photocatalyst as a promising photocatalyst for highly efficient wastewater treatment applications.
A Shallow Neural Network Approach for the Short-Term Forecast of Hourly Energy Consumption
Authors: Manno Andrea , Martelli Emanuele , Amaldi Edoardo
Published: January 2022

The forecasts of electricity and heating demands are key inputs for the efficient design and operation of energy systems serving urban districts, buildings, and households. Their accuracy may have a considerable effect on the selection of the optimization approach and on the solution quality. In this work, we describe a supervised learning approach based on shallow Artificial Neural Networks to develop an accurate model for predicting the daily hourly energy consumption of an energy district 24 h ahead. Predictive models are generated for each one of the two considered energy types, namely electricity and heating. Single-layer feedforward neural networks are trained with the efficient and robust decomposition algorithm DEC proposed by Grippo et al. on a data set of historical data, including, among others, carefully selected information related to the hourly energy consumption of the energy district and the hourly weather data of the region where the district is located. Three different case studies are analyzed: a medium-size hospital located in the Emilia-Romagna Italian region, the whole Politecnico di Milano University campus, and a single building of a department belonging to the latter. The computational results indicate that the proposed method with enriched data inputs compares favorably with the benchmark forecasting and Machine Learning techniques, namely, ARIMA, Support Vector Regression and long short-term memory networks.
Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller
Authors: Wu Weiqiang , Gong Guofang , Chen Yuxi , Zhou Xinghai
Published: January 2022

The tunnel boring machine (TBM) is widely used in tunnel construction projects. The thrust system plays a crucial role to drive the machine ahead and support gripper shoes stably while tunneling. More and more attention has been paid to the pressure and velocity regulation efficiency as the TBM advances in complex rock conditions to ensure the stabilization of the tunneling process. A thrust hydraulic control system, assembled with a proportional pressure reducing valve, is established with system operating parameters. The mathematical model of the thrust electro-hydraulic system is revealed. To improve the control characteristics of the thrust system, a self-tuning fuzzy PID controller is introduced in the pressure and velocity regulation procedures. After that, tests on a Φ2.5 m scaled TBM test rig are carried out. The test results show that the thrust system adopting the fuzzy PID controller results in less oscillation and a smoother regulation process. It takes less time to reach the target goal of pressure regulation with less vibration during the pressure regenerating periods, and both systems of conventional PID controller and fuzzy PID controller are qualified in velocity regulation movements. The proposed control methods show better benefits in reduction of vibrations and shorter time of regulation to stable conditions, which extends the machine’s life and affects the acceleration of the tunneling process.
Novel Nonylphenol Polyethoxylated Based Surfactants for Enhanced Oil Recovery for High-Mineralization Carbonate Reservoir
Authors: Saifullin Emil R. , Putintseva Polina O. , Sagirov Rustam N. , Varfolomeev Mikhail A. , Yuan Chengdong , Pavelyev Roman S. , Nazarychev Sergey A. , Malahov Aleksei O. , Akimushkina Lucia , Zharkov Dmitrii A. , Bykov Alexey O. , Mirzakimov Ulukbek
Published: January 2022

Surfactant flooding can mobilize trapped oil and change the wettability of the rock to be more hydrophilic, which increases the oil recovery factor. However, the selection of surfactants is difficult in the case of high salinity conditions. In this work, we synthesized three novel anionic-nonionic surfactants based on widely used nonionic surfactant nonylphenol polyethoxylated (NPEO) and evaluated their efficiencies for enhanced oil recovery (EOR) in high salinity water (20% NaCl). The modified surfactants showed a decrease in interfacial tension (IFT) up to 10 times compared with the nonionic precursor. All surfactants had changed the wettability of rock to be more hydrophilic according to contact angle measurements. The effectiveness of surfactants was proved by spontaneous imbibition experiments, in which the synthesized surfactants showed a better displacement efficiency and increased oil production by 1.5–2 times. Filtration experiments showed an increase in oil recovery factor by 2–2.5 times in comparison with the nonionic NPEO. These promising results prove that the synthesis of new surfactants by modifying NPEO is successful and indicate that these novel surfactants have a great potential for EOR in high salinity reservoirs.
Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model
Authors: Jiang Yinfeng , Song Wenxiang , Zhu Hao , Zhu Yun , Du Yongzhi , Yin Huichun
Published: January 2022

The state of charge (SOC) of a lithium battery system is critical since it indicates the remaining operating hours, full charge time, and peak power of the battery. This paper recommends an extended Rauch–Tung–Striebel smoother (ERTSS) for estimating SOC. It is implemented based on an improved equivalent circuit model with hysteresis voltage. The smoothing step of ERTSS will reduce the estimation error further. Additionally, the genetic algorithm (GA) is employed for searching the optimal ERTSS’s smoothing time interval. Various dynamic cell tests are conducted to verify the model’s accuracy and error estimation deviation. The test results demonstrate that ERTSS’s SOC estimation error is limited to 4% with an initial error between −25 ∘C and 45 ∘C and that the root mean square error (RMSE) of ERTSS’s SOC estimation is approximately 5% lower than that of extended Kalman filter (EKF). The ERTSS improves the SOC estimation accuracy at all operating temperatures of batteries.
Indirect Current Control Method Based on Reference Current Compensation of an LCL-Type Grid-Connected Inverter
Authors: Li Zheng , Yang Linhang , Yang Daliang , Peng Zuwen , Shao Dashuai , Liu Jinbao
Published: January 2022

NPC (neutral point clamped converter) is widely used in medium- and high-voltage grid connection because of its small power devices, simple overall structure, and easy control. This paper presents an improved indirect current control strategy for an LCL-type NPC. Firstly, the reference current is compensated to suppress the resonant peak of the LCL filter and improve the tracking accuracy of the output current. Then, with the relationship between state variables, the control structure is optimized by introducing the non-PLL control method, so that the sensors are reduced to only two sets. Finally, the traditional differential operator is improved to compensate the calculation delay in digital control. The experimental results show that the amplitude of thee grid-connected current is consistent with the reference value, the phase deviation is reduced from 7° to about 1°, and the transient response time is shortened to 1/3 of that of the traditional method.
Nonlinear Hierarchical Easy-to-Implement Control for DC MicroGrids
Authors: Siad Sabah B. , Iovine Alessio , Damm Gilney , Galai-Dol Lilia , Netto Mariana
Published: January 2022

In this work is considered the connection of a photovoltaics (PV) solar plant to the main grid through a Direct Current (DC) MicroGrid and a hybrid storage system, composed of a battery and a supercapacitor, in order to satisfy constraints of grid connection (the so-called Grid-Codes). The objective, and main contribution of this paper, is to stabilize the DC MicroGrid voltage in spite of large variations in production and consumption, using a nonlinear hierarchical easy-to-implement control strategy. Here is presented the MicroGrid’s control design based on detailed models of the photovoltaic energy sources and the storage systems. Such DC grids may present an unstable behavior caused by the PV’s intermittent output power, by switching ripples from the power converters and their power electronics, and oscillatory currents produced by some types of loads. Therefore, the system is subject to both fast and slow variations, and its stabilization is based on different technologies of storage, such as battery and supercapacitor, and control algorithms designed thanks to the use of time-scale separation between different components of the storage systems. The obtained nonlinear results are stronger than current linear controllers, allowing to keep operating margins around the voltage reference. At the same time, in this work, insights from power systems practice have been used, aiming to obtain a very simple and easy-to-implement control scheme. Detailed simulation results are provided to illustrate the behavior and effectiveness of the proposed stabilization technique.
Optimization of a Membraneless Microfluidic Fuel Cell with a Double-Bridge Flow Channel
Authors: Oh Ji-Hyun , Vuong Tien-Dung , Kim Kwang-Yong
Published: January 2022

In this work, a design optimization study was conducted to improve the performance of a membraneless microfluidic fuel cell with a double-bridge cross-section of the flow channel. Governing equations including Navier–Stokes, mass-transport, and Butler–Volmer equations were solved numerically to analyze the electrochemical phenomena and evaluate the performance of the fuel cells. Optimization was performed to maximize the peak power density using a genetic algorithm combined with a surrogate model constructed by radial basis neural network. Two sub-channel widths of the flow channel were selected as design variables for the optimization. As a result, a large increase in the inner channel width and a small decrease in the outer channel width effectively increased the peak power density of the MMFC. The optimal design increased the peak power density by 57.6% compared to the reference design.
Management of Plastic Waste and a Circular Economy at the End of the Supply Chain: A Systematic Literature Review
Authors: da Silva Luciano Ferreira , Resnitzkyd Maria Helena Costa , Santibanez Gonzalez Ernesto Del Rosario , de Melo Conti Diego , da Costa Priscila Rezende
Published: January 2022

This article aims to describe the current state of research on plastic waste management as a circular economy practice at the end of the supply chain. The methodological strategy chosen was a systematic literature review. The articles selected from the Web of Science and Scopus databases were screened, and the research corpus consisted of 201 articles published in journals between 2014 and 2021. The results present 13 study categories, showing topics such as pyrolysis, business models, Industry 4.0, and energy generation. The insertion of Industry 4.0 technologies is still in its initial stages, comprising mainly the generation of inputs to reuse waste for 3D printers’ materials. Regarding energy generation, the insertion of processes such as pyrolysis for fuel generation stands out. The proposed discussion in this article suggests a circular ecosystem in which wastes follow a reuse flow according to their properties; incineration can be an option depending on the stage and benefits generated from the removal of plastic waste from the ecosystem. The highlighted issue is the scalability of the developed processes in the research, which is only possible if the state, universities, and civil society integrate efforts in the construction of a circular ecosystem infrastructure for waste management.
Study of Induction Motor Inter-Turn Fault Part II: Online Model-Based Fault Diagnosis Method
Authors: Im Seong-Hwan , Gu Bon-Gwan
Published: January 2022

This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I. The relationships among fault parameters, negative sequence current, and fault copper loss are analyzed with the ITF model. The analyses show that the fault severity index, a function of fault parameters, is directly related to the negative sequence and the copper loss. Therefore, the proposed model-based fault diagnosis method estimates the fault severity index from the negative sequence current and recognizes the ITF. With the estimated fault severity index, the fault copper loss by the ITF, causing thermal degradation, can be calculated. Finally, experiments were performed in various fault conditions to verify the proposed fault diagnosis method.
Performance Investigation on Mono-Block-Layer Build Type Solid Oxide Fuel Cells with a Vertical Rib Design
Authors: Lu Siyu , Zhang Man , Wu Jie , Kong Wei
Published: January 2022

Compared with planar-type solid oxide fuel cells (SOFCs), mono-block-layer build (MOLB)-type SOFCs have additional three-phase boundaries per unit volume, and their performance is severely limited by their longer current path. To resolve this issue, a vertical rib design, which was evaluated using a numerical method, was proposed. Compared with the conventional design, the power density for the vertical rib design increased by 12.32%. This is because the vertical rib design provides another short path for current, which not only reduces the ohmic loss in the cathode, but also decreases the ohmic polarization caused by the contact resistance. However, the vertical rib design hinders the transport of oxygen in the cathode and increases the concentration loss. Therefore, the vertical rib size design is crucial. Based on the influence of the vertical rib width, the vertical rib widths on the cathode and anode sides of 0.7 and 1 mm are recommended for different contact resistances, respectively.
Influence of Polarity Reversal Period and Temperature Gradient on Space Charge Evolution and Electric Field Distribution in HVDC Extruded Cable
Authors: Zhou Yifan , Gao Bo , Wang Wei , Song Ke
Published: January 2022

During the operation of HVDC extruded cables, voltage polarity reversal (VPR) is considered one of the most severe conditions for cable insulation. In this paper, a bipolar charge transport model developed for cylindrical geometry is improved by introducing ionic carriers from impurity dissociation for the simulation of space charge and electric field in an HVDC extruded cable with thick polymeric insulation under VPR, and the construction of the geometric model is based on a practical 160 kV DC polymeric cable. The influence of polarity reversal period (PRP) and temperature gradient (TG), formed by the load current flowing through the conductor, on the space charge evolution and the electric field distribution are investigated. The mechanisms of the charge dynamics and the field distortion affected by the PRP and the TG are also discussed. The results show that under TG, the maximum transient field appears near the interface between the conductor shield and insulation in the early stage of complete VPR. In addition, the longer the PRP, the more serious the maximum transient field distortion. Moreover, an increase in TG intensifies the maximum field distortion under steady and transient states due to the enhancement of heterocharge accumulation.
Determination of Dielectric Losses in a Power Transformer
Authors: Nadolny Zbigniew
Published: January 2022

The article presents a method of determining dielectric losses that occur in insulating materials in a power transformer. These losses depend mainly on the electric field stress, pulsation, dielectric loss coefficient, and electrical permittivity of insulating materials. These losses were determined by integrating an expression describing unit losses. The determined dielectric losses were compared with the total losses of the transformer. It turned out that dielectric losses are a fraction of a percent of the total losses. The influence of the electrical permittivity of the insulating liquid and paper insulation on the value of dielectric losses was investigated. This influence was ambiguous, which is characteristic of stratified systems made of materials with different permittivity. An analysis of the influence of the dielectric loss coefficient tan(delta) on the value of dielectric losses in the transformer was carried out. The impact of this coefficient on the amount of dielectric losses turned out to be directly proportional.
A Computer Tool Using OpenModelica for Modelling CO2 Emissions in Driving Tests
Authors: Tucki Karol , Orynycz Olga , Wasiak Andrzej , Świć Antoni , Mieszkalski Leszek , Mruk Remigiusz , Gola Arkadiusz , Słoma Jacek , Botwińska Katarzyna , Gawron Jakub
Published: January 2022

The transport sector is one of the main barriers to achieving the European Union’s climate protection objectives. Therefore, more and more restrictive legal regulations are being introduced, setting out permissible limits for the emission of toxic substances emitted into the atmosphere, promoted biofuels and electromobility. The manuscript presents a computer tool to model the total energy consumption and carbon dioxide emissions of vehicles with an internal combustion engine of a 2018 Toyota Camry LE. The calculation tool is designed in the OpenModelica environment. Libraries were used for this purpose to build models of vehicles in motion: VehicleInterfaces, EMOTH (E-Mobility Library of OTH Regensburg). The tool developed on the basis of actual driving test data for the selected vehicle provides quantitative models for the instantaneous value of the fuel stream, the model of the instantaneous value of the carbon dioxide emission stream as a function of speed and the torque generated by the engine. In the manuscript, the tests were conducted for selected driving cycles tests: UDDS (EPA Urban Dynamometer Driving Schedule), HWFET (Highway Fuel Economy Driving Schedule), EPA US06 (Environmental Protection Agency; Supplemental Federal Test Procedure (SFTP)), LA-92 (Los Angeles 1992 driving schedule), NEDC (New European Driving Cycle), and WLTP (Worldwide Harmonized Light-Duty Vehicle Test Procedure). Using the developed computer tool, the impact on CO2 emissions was analyzed in the context of driving tests with four types of fuels: petrol 95, ethanol, methanol, DME (dimethyl ether), CNG (compressed natural gas), and LPG (liquefied petroleum gas).
Recent Advances in Biomass Pretreatment Technologies for Biohydrogen Production
Authors: Singh Harshita , Tomar Sakshi , Qureshi Kamal A. , Jaremko Mariusz , Rai Pankaj K.
Published: January 2022

Hydrogen is an economical source of clean energy that has been utilized by industry for decades. In recent years, demand for hydrogen has risen significantly. Hydrogen sources include water electrolysis, hydrocarbon steam reforming, and fossil fuels, which emit hazardous greenhouse gases and therefore have a negative impact on global warming. The increasing worldwide population has created much pressure on natural fuels, with a growing gap between demand for renewable energy and its insufficient supply. As a result, the environment has suffered from alarming increases in pollution levels. Biohydrogen is a sustainable energy form and a preferable substitute for fossil fuel. Anaerobic fermentation, photo fermentation, microbial and enzymatic photolysis or combinations of such techniques are new approaches for producing biohydrogen. For cost-effective biohydrogen production, the substrate should be cheap and renewable. Substrates including algal biomass, agriculture residue, and wastewaters are readily available. Moreover, substrates rich in starch and cellulose such as plant stalks or agricultural waste, or food industry waste such as cheese whey are reported to support dark- and photo-fermentation. However, their direct utilization as a substrate is not recommended due to their complex nature. Therefore, they must be pretreated before use to release fermentable sugars. Various pretreatment technologies have been established and are still being developed. This article focuses on pretreatment techniques for biohydrogen production and discusses their efficiency and suitability, including hybrid-treatment technology.
Entropy
Authors: Tsallis Constantino
Published: January 2022

The concept of entropy constitutes, together with energy, a cornerstone of contemporary physics and related areas. It was originally introduced by Clausius in 1865 along abstract lines focusing on thermodynamical irreversibility of macroscopic physical processes. In the next decade, Boltzmann made the genius connection—further developed by Gibbs—of the entropy with the microscopic world, which led to the formulation of a new and impressively successful physical theory, thereafter named statistical mechanics. The extension to quantum mechanical systems was formalized by von Neumann in 1927, and the connections with the theory of communications and, more widely, with the theory of information were respectively introduced by Shannon in 1948 and Jaynes in 1957. Since then, over fifty new entropic functionals emerged in the scientific and technological literature. The most popular among them are the additive Renyi one introduced in 1961, and the nonadditive one introduced in 1988 as a basis for the generalization of the Boltzmann–Gibbs and related equilibrium and nonequilibrium theories, focusing on natural, artificial and social complex systems. Along such lines, theoretical, experimental, observational and computational efforts, and their connections to nonlinear dynamical systems and the theory of probabilities, are currently under progress. Illustrative applications, in physics and elsewhere, of these recent developments are briefly described in the present synopsis.
Painful Legs and Moving Toes
Authors: Ri Songjin
Published: January 2022

Painful Legs and Moving Toes syndrome (PLMT) is a rare neurological movement disorder characterized by involuntary and irregular movements of one or more toes, often together, with moderate to severe diffuse pain at the foot and leg. It can appear on one or both sides and in the upper limbs as well as lower limbs. The etiology of PLMT is still unclear; therefore, the main symptoms of PLMT, including pain in limbs as well as involuntary movements of toes, can be treated only symptomatically, and the outcomes are often unsatisfactory. Central or peripheral nerve injuries have been assumed to be the possible causes of irregular involuntary movements, often accompanied by chronic pain; however, its etiology is unknown in most cases. The management of PLMT is very difficult because of its rare prevalence and obscure pathogenesis, and there have been no clear guidelines for the treatment of PLMT. Oral medications (e.g., GABAergic agents, benzodiazepines, antiepileptics, antidepressants, atypical antipsychotics, beta-blockers, corticosteroids, cannabis, calcitonin, and analgesics) and several therapeutic interventions (e.g., local botulinum neurotoxin-A injection, spinal cord stimulation, anesthetic, and surgical interventions) have been recommended, based on individual experiences from several cases. Its etiology, pathogenesis, and effective treatments should be further discussed.
Acknowledgment to Reviewers of Eng in 2021
Authors:   Eng Editorial Office
Published: January 2022

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