The application of carbon-rich char-based admixtures, including biochar and plastic char, in construction products has received substantial attention from global industries due to their potential to “lock in” carbon for the long term, thus mitigating the climatic impacts of future constructions. Furthermore, a sharp rise in plastic waste generation and uncontrolled landfilling threatens natural ecosystems. Depending on type, plastic waste can be used as fuel, and the generated char (solid residue) can be reintegrated into the construction value chain by utilizing it as a carbon-sequestering admixture in construction materials. This article discusses critical factors, including the synthesis temperature, heating rate, and different activation pathways, for tuning plastic char’s porosity and surface properties, contributing to enhanced carbon fixation and CO2 uptake. Chemical pyrolysis using alkaline agents produces microporous structure (< 2 nm) with high surface areas (> 1000 m2g−1) and CO2 uptake, ranging up to 4.6 mmolg−1 while acidic agents produce a higher fraction of mesopores (> 2 nm) with lower surface areas < 1500 m2g−1 and CO2 uptake capacities (up to 1.8 mmolg−1). The review finds that surface functionalization of plastic char and altering its physicochemical properties improve the engineering properties of construction binders. The locked carbon in the char, complemented by additional CO2 uptake in the engineered pore and surface sites, can be instrumental in mitigating the embodied carbon of construction products. However, future investigations should study the microstructural interactions of engineered char within construction binders and conduct a holistic life-cycle assessment to fully realize the benefits of using engineered plastic char as a supplementary additive.
Materials of engineering and construction. Mechanics of materials
Inertia is a critical factor in maintaining the frequency stability of power systems. However, the growing integration of power electronics-based renewable energy sources (RESs) has significantly reduced system inertia. AC and dc microgrids have emerged as key solutions for integrating RESs. Unlike traditional synchronous generators, power electronic converters interfacing RESs lack inherent inertia and damping, posing challenges to the control and stability of these microgrids. To address these challenges, virtual inertia control strategies, which emulate the behavior of synchronous generators, have been widely adopted to enhance the stability of ac microgrids. Drawing on the analogies between ac and dc systems, similar virtual inertia concepts have been extended to dc microgrids, demonstrating their potential to improve system stability. This article provides a comprehensive review of inertia enhancement strategies for dc microgrids, examining their key features, benefits, and limitations. The analogy between synchronous generators/dc machines and energy storage systems is explored, with a particular focus on the implementation of virtual inertia and damping control in energy storage converters as a promising solution to mitigate power fluctuations. In addition, this article investigates the grid-forming and grid-following converter analogies in ac and dc microgrids. Various stability analysis methods applied to inertia enhancement strategies are also reviewed, offering readers a comprehensive understanding of the current state of research. By addressing the conceptual and technical analogies between ac and dc systems, this review aims to provide valuable insights for developing advanced control strategies for next-generation microgrids.
In recent years generative artificial intelligence has been used to create data to support scientific analysis. For example, generative adversarial networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the advantage that a GAN creates data in a significantly reduced computing time. $N$ training events for a GAN can result in $NG$ generated events with the gain factor $G$ being greater than one. This appears to violate the principle that one cannot get information for free. This is not the only way to amplify data so this process will be referred to as data amplification which is studied using information theoretic concepts. It is shown that a gain greater than one is possible whilst keeping the information content of the data unchanged. This leads to a mathematical bound, $2\log (\text{Generated}\ \text{Events}) \unicode{x2A7E} {\text{3log(Training Events)}}$ , which only depends on the number of generated and training events. This study determined the conditions for both the underlying and reconstructed probability distributions to ensure this bound. In particular, the resolution of variables in amplified data is not improved by the process but the increase in sample size can still improve statistical significance. The bound was confirmed using computer simulation and analysis of GAN generated data from the literature.
Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder, economic, and emotional behaviour. In the stock market, incessant unsettlement is the main reason why financiers give away at the wrong time and frequently fail to get a profit. While financing in the stock market, the stakeholders should not disremember the gamble of payment rule and reveal their assets to greater dangers. Discovering economic time series data and exhibiting the relationship between the stock trend and past data is the main method to resolve the issue. Machine learning (ML), a conventional technique, has also been considered for its ability to predict financial markets. This manuscript proposes a new Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization (PSPMCDL-TTMO) method. The PSPMCDL-TTMO methodology employs an optimal deep learning model to forecast stock price movements, determining whether prices will rise or fall. At the primary stage, the PSPMCDL-TTMO model utilizes data pre-processing using Z-score normalization to ensure that the input features are standardized for consistent performance. For feature selection (FS), the dingo optimizer algorithm (DOA) is employed to optimize the most relevant and impactful features from historical stock data. In addition, the multi-head attention bi-directional gated recurrent unit (MHA-BiGRU) model is used for stock price movement prediction. Finally, the hyperparameter range of the MHA-BiGRU model is implemented by the design of the equilibrium optimizer (EO) model. The experimentation outcome analysis of the PSPMCDL-TTMO approach takes place, and the results are inspected using various features. The investigational validation of the PSPMCDL-TTMO technique attained a superior CORR value of 0.9999 over existing models.
Medical physics. Medical radiology. Nuclear medicine, Nuclear engineering. Atomic power
With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Motion intent recognition research is one of the key challenges in achieving human-robot collaboration in rehabilitation robots. In the traditional method, intention recognition is performed based on the complete sEMG, however, due to the muscle atrophy of stroke patients, the complete sequences are not captured at the early stage of rehabilitation, so in this paper, three sEMG segments of 1/2, 1/4, and 1/8 of three selected activity of daily living (ADL) movements of the upper limb are investigated, respectively, and comparing with the complete sEMG sequences, a novel method of motor intention prediction is proposed. In order to achieve optimal recognition accuracy and speed, the Kernel Extreme Learning Machine (KELM) algorithm optimized by the Sparrow Search Algorithm (SSA) algorithm was used for prediction. It was found that the SSA-KELM algorithm based on segmented sEMG has better recognition accuracy and recognition speed in each segment compared to other algorithms. The recognition accuracy in 1/8 sEMG segments is 98.4%, and the recognition time is 0.0102s, which shows how well the method works and what it means for rehabilitation robots working together with people.
Electric vehicles (EVs) are a promising solution to reduce carbon dioxide (CO<sub>2</sub>) emissions, but this reduction depends on the fraction of renewable sources used to generate electricity. Wind energy is thus a vital candidate and has experienced a remarkable surge recently, establishing itself as a leading renewable power source worldwide. The research on Direct-Driven Permanent Magnet Synchronous Generator (PMSG)-based type 4 wind farms has indicated that the Phase-locked Loop (PLL) bandwidth significantly impacts Sub-Synchronous Resonance (SSR). However, the influence of PLL architecture on SSR remains unexplored and warrants investigation. Therefore, this paper investigates PLL architectural variations in PLL Loop Filter (LF) to understand their impact on SSR in type 4 wind farms. Specifically, an in-depth analysis of the Notch Filter (NF)-based enhanced PLL is conducted using eigenvalue analysis of the admittance model of a PMSG-based type 4 wind farm. The findings demonstrate that the NF-based enhanced PLL exhibits superior performance and improved passivity in the sub-synchronous frequency range, limiting the risk of SSR below 20 Hz. Additionally, Nyquist plots are employed to assess the impact on system stability resulting in increased stability margins. In the future, it is recommended to further investigate and optimize the PLL to mitigate SSR in wind farms.
The pH effect on the surface and interfacial films on η-phase (MgZn2) in aqueous solutions under acidic, neutral, and alkaline conditions has been evaluated using time of flight-secondary ion spectroscopy (TOF-SIMS), Atomic force microscopy (AFM) and scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX). TOF-SIMS depth profile plots reveal that under an acidic environment (pH2) deep corrosion penetration occurs with a dispersion of corrosion products which claims a considerable depth matrix cross-section. Under near neutral environments (pH 6), the corrosion film is seen to be stratified into two layers of different compositions, while in a slightly alkaline environment (pH 10) the film appears not to be distinctly differentiated, whereas in a very alkaline environment (pH 13) a compact film rich in hydroxides develops. TOF-SIMs surface and depth profile maps were consistent with the depth profile plots. SEM and AFM images reveal that the surface roughness increased in with a decrease in pH value from the acidic to the alkaline environments. EDX elemental composition analysis also indicated a severe drop in the zinc content of the film in the alkaline environment. Largely, metallic zinc enrichment occurs following the initial magnesium dissolution whose stability is greatly affected by the near-surface pH of the bulk solution, thus, giving rise to different film structures.
Materials of engineering and construction. Mechanics of materials, Industrial electrochemistry
Indresh Yadav, Sulabh Sachan, Sanjay Kumar Maurya
et al.
Solar energy is an excellent source of renewable energy, despite its intermittent nature that can pose a challenge. To meet load demand, a converter is required to integrate the system. The converter acts based on control signals from the controller, which is trained according to the end demand and availability of Sun Irradiance. This paper utilizes the Incremental Conductance (IC) and Perturb and Observe (P&O) algorithms, which are widely accepted in the industry and easy to implement. This study aims to design and compare a Step Voltage (SV) controller and a Step-Duty (SD) Maximum Point Tracking (MPPT) IC controller-based DC to DC boost converter. The paper compares the performance of SV and SD controller-based DC to DC boost converters under different environmental conditions, evaluates the system’s effectiveness by comparing the oscillations in load power for both conditions and discusses the impact of battery charging on the Load. The system performance is tested using MATLAB Simulink/coding, considering the Indian solar radiation intensity (SRI) scenario and temperature variations. Overall, this study provides a comprehensive analysis of the performance of the proposed system, which can contribute to the development and optimization of solar energy systems in various applications. From the comparative analysis of IC SD, SV and P&O SD, SV it is observed the performance of IC SD is superior. The impact of battery charging using IC SD controller on the load and MPPT point is also discussed.
Unlike controllable switches, the on/off states of diodes are uncertain, which often results in various unknown operation modes for converters under different working conditions. Thus, the comprehensive operation analysis of converters is usually hard to achieve with the conventional manual analysis method. Aiming to overcome this shortcoming, a computer-aided automatic analysis method of dc–dc converters is proposed in this article. First, the converter is mathematically modeled on the basis of electrical network theory, so that its analysis can be further processed in the computer program. Then, all possible working states of diodes in different switching modes are automatically evaluated according to fundamental circuit law. Finally, by arbitrarily combining the feasible submodes, comprehensive operation modes of converters satisfying the power electronics principle are automatically derived. More than that, their corresponding steady-state performance analysis results including operation mode boundaries, voltage gains, and components' voltage/current would be obtained simultaneously. Therefore, the proposed computer-aided automatic analysis method is not only capable of conveniently obtaining a comprehensive understanding of different converters after considering uncertain states of diodes, but also is beneficial for the converter design in engineering applications.
Physics is also known as a science of observation and experiment. In this paper, the most representative experimental verification of general relativity, "Mercury perihelion precession", is used for mathematical derivation and analysis, and the serious errors in the experimental verification are expounded deeply. Furthermo2re, the Lorentz transformation, the core formula of special relativity, is mathematically deduced and analyzed, and the contradictions and errors existing in the Lorentz transformation are profoundly revealed. These contradictions and errors involve the deep relationship between physics and mathematics, and are also the core problems faced by theoretical physics. Minkowski's four-dimensional spacetime contradicts the "principle of constant speed of light", and there are serious errors in the "relativity of simultaneity". The so-called experimental verification of the theory of relativity is false, and there is a fundamental error in the theory of relativity, which will lead to the collapse of the whole theoretical building, including general relativity. The basic formula of quantum mechanics, "matter wave" wavelength λ=h/p , is also based on the wrong Einstein "massenergy equation". Therefore, the wave function of "matter wave" established on the wavelength λ=h/p of "de Broglie wave" and the Schrodinger equation are all pseudo-formulas without any physical significance. Schrodinger equation is the core formula of quantum mechanics. This is enough to cause the collapse of quantum mechanics.The mathematical derivation of the Compton effect was completely based on the pseudo-concepts of special relativity such as "mass-energy equation" and "moving mass", while Einstein's explanation of the photoelectric effect was limited by the scientific and technological level at that time. The Compton effect and the photoelectric effect, as the two most important experimental verification of the "waver-particle duality" of light, were not valid. "Wade-particle duality" is another misdirection of Einstein to human beings, which "inspired" de Broglie to put forward the famous "matter wave" hypothesis, and the "matter wave" concept is the root that led theoretical physics to fall into the abyss of quantum mechanics. In addition, there are serious errors in the basic concept of quantum mechanics. "Monochromatic radiant outdegree" is an incomplete definition of physics, which is also the root cause of the "ultraviolet disaster" problem. The defective physics concept of "monochromatic radiant outdegree" is also an important reason for theoretical physics to fall into the "quantization" quake-pit. The "ultraviolet catastrophe" problem of blackbody radiation is a physical accident caused by the artificially defined defect concept of "monochromatic radiance outdegree". Boer's hydrogen atom theory is the foundation theory of quantum mechanics. Like relativity theory, this theory also has the problem that the same mathematical symbol expresses two contradictory physical meanings at the same time. Quantum tunneling has the problem of different physical interpretation of mathematical formulas. No matter the premise or the core formula, there are serious contradictions and errors in quantum mechanics, and quantum mechanics is also a wrong theory. The theory of relativity and quantum mechanics is a lost on the journey of human truth exploration. The fundamental errors of relativity and quantum mechanics will lead to the comprehensive collapse of theoretical physics system including relativistic quantum mechanics, quantum field theory (quantum electrodynamics, quantum chromodynamics, unified theory of weak electricity, standard model of elementary particle physics), Yang Mills theory and so on. Fortunately, in the course of nearly a century of exploration, human beings have accumulated a large number of empirical results. Although the theory Journal of Electrical Electronics Engineering ISSN: 2834-4928 1 Department of Mathematical Science, College of Science, Tsinghua University, Beijing 100084, China 2 School of Physics, Peking University, Beijing 100091, China 3 School of Astronomy and Space Science, University of Science and Technology of China, Anhui 230026, China J Electrical Electron Eng, 2023 Volume 2 | Issue 4 | 381 of relativity is completely wrong, it does not affect the real existence of such an objective thing as the atomic bomb. Laser, transistor, tunnel microscope and so on have nothing to do with quantum mechanics, because quantum mechanics is also a fundamentally wrong theory, but this does not hinder the validity of the existing experimental results, the so-called experimental verification of relativity and quantum mechanics are forced to incorporate experimental results and physical phenomena into their own theoretical system. Special thanks to Academician Chen Jia-er for his theoretical guidance and help
A. I. P. G. A. Rinella, M. Agnello, B. Alessandro
et al.
A novel approach for designing the next generation of vertex detectors foresees to employ wafer-scale sensors that can be bent to truly cylindrical geometries after thinning them to thicknesses of 20-40$\mu$m. To solidify this concept, the feasibility of operating bent MAPS was demonstrated using 1.5$\times$3cm ALPIDE chips. Already with their thickness of 50$\mu$m, they can be successfully bent to radii of about 2cm without any signs of mechanical or electrical damage. During a subsequent characterisation using a 5.4GeV electron beam, it was further confirmed that they preserve their full electrical functionality as well as particle detection performance. In this article, the bending procedure and the setup used for characterisation are detailed. Furthermore, the analysis of the beam test, including the measurement of the detection efficiency as a function of beam position and local inclination angle, is discussed. The results show that the sensors maintain their excellent performance after bending to radii of 2cm, with detection efficiencies above 99.9% at typical operating conditions, paving the way towards a new class of detectors with unprecedented low material budget and ideal geometrical properties.
AdvancedCeramics are gaining a foothold in the lightweight aerospace, electronics, and structural engineering component markets. These ceramics could be extensively used in modern industry, such as ballistic body armour, ceramic carbon fibre composite automoti ve brakes, diesel particulate filters, prosthetic limbs, piezoelectric sensors, and computer memory products, due to their higher compressive strength, resistance to abrasion, lower thermal expansion coefficient, higher density, and chemical stability. Ceramics are notori ously difficult to handle due to the increased hardness and brittleness. Low electric-conductive ceramics, on the other hand, can be machined using the EDM technique, in which plasma energy is used to accurately remove the material by continuous sparking between the surface and the electrode submerged in dielectric. It is observed that EDM can be applied to the material having electrical resistivity below 100 ?.cm. Most recently it has been observed that EDM could be applied to insulating ceramics too. An attempt has been made in this paper to critically review the machining of ceramics by the EDM process.
M.S. Affia Thabassum, M. Thaha, N. Rajendran
et al.
This paper presents an experimental study conducted on employing Active Learning strategies towards enhancing the attainment of learning outcomes of a course "Linear Control Systems" in the undergraduate (UG) Electrical and Electronics Engineering (EEE) programme. The existing UG programme curriculum is retrofitted into outcome-based curriculum. The Programme Outcomes (POs) for this programme are derived from the Graduate Attributes of Washington Accord (WA). In order to meet the POs, Course Learning Outcomes (CLOs) are written predominantly in the Cognitive (Revised Bloom's taxonomy) and Psychomotor (Dave's taxonomy) domains for lecture-based courses and laboratory courses, respectively and very few are written in Affective (Bloom's taxonomy) domain. In this paper, four CLOs (i) Discuss the various classification of control systems (K2), (ii) Define the time and frequency response of the system (K2), (iii) Derive the overall transfer function of the given Block diagram of a system (K3) and (iv) Derive the overall transfer function of a Signal Flow Graph (SFG) of a system (K3) are considered to measure the effectiveness of active learning methods in attaining the outcome. Active learning methods such as Jigsaw, Think-pair-share and Peer instruction, and traditional lecture-based methods were employed. In addition, ClassComm software with student interactive devices was used to measure the students' learning in the classroom and subsequently the attainment of CLOs. This study compares the effectiveness of active learning methods with the traditional lecture-based instruction method in relation to the attainment of CLOs. The overall analysis results reveal that both instructors and students received the active learning methods very well, and they are very effective in enhancing the attainment of learning outcomes. However, instructors are of the view that the duration of the contact period should be at least two hours. Keywords— active learning, Bloom's taxonomy, concept map, control systems, learning outcomes
L. A. Sánchez-Gaspariano, Israel Vivaldo-de-la-Cruz, J. Muñoz-Pacheco
et al.
EI‐SCAM is a very useful tool capable of carrying out symbolic, semi‐symbolic, and numerical solutions for the electrical variables of a circuit under analysis. The goal of the research reported herein is to assess the effectiveness of the program as a teaching tool for circuit analysis of Automotive System Engineering students. To this aim, EI‐SCAM has been used for at least 5 years, from 2017 to 2021, in the undergraduate course Interfaces, Communications, and Signal Processing, intended to Automotive System Engineering majors at the Electronics Faculty of the Benemérita Universidad Autónoma de Puebla, México, and whose main topics are related to interface circuits for signal conditioning from sensors and towards actuators in the car. EI‐SCAM was used as a complement to theoretical classes and it has also been used alone as a self‐study tool. A student survey indicated that EI‐SCAM was an excellent complement to the teaching process, providing a useful and convenient tool for circuit analysis tasks even when the user is unfamiliar with both the use of MATLAB and circuit analysis. Additionally, a test applied before and after the introduction of EI‐SCAM in the course showed an increase beyond the 60% in the assertiveness of the students to respond properly to the questions.
1 Multi-Modality Medical Imaging (M3i) Group, Faculty of Science and Technology, Technical Medical Centre 2386, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands 2 Robotics and Mechatronics (RaM) Group, Faculty of Electrical Engineering Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, The Netherlands 3 Department of Nuclear Medicine, Ziekenhuis Groep Twente, Hengelo, The Netherlands 4 Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands 5 Biomedical Photonic Imaging Group, Faculty of Science and Technology, Technical Medical Centre, University of Twente, Enschede, The Netherlands The original article contained a mistake.
The induction motor with ring windings (IMRW) was developed by Electrical Engineering Department of Ural Federal University for the processing of nuclear waste. Due to the ceramic insulation of the stator windings, the motor can operate in conditions of increased radiation and temperature for 2-3 years. In hard conditions traditional three-phase induction motors are designed with organic insulation for stator windings, and they have a service life about 2-5 months only. After 2-5 months they must be replaced because of the destruction of the stator windings insulation. Extending the lifetime of a nuclear waste engine is an important goal as IMRW will save human resources involved in replacing machines in high radiation conditions.The first IMRW prototype’s power is P=2.2 kW, the number of poles is 2p = 6; it was designed on the basis of the general industrial induction motor with squirrel cage of the AO2 series. IMRW has the same dimensions as the motor AO2-32-6. However, previous studies have shown that IMRW design has a number of disadvantages and the engine has to be improved, in particular, it is necessary to take into account the leakage flux in the stator, because it affects the magnitudes of the starting and maximum torque.In IMRW the leakage flux can be reduced by changing the number of pole pairs of IMRW or increasing the height of the motor rotation axis. The first method leads to the need to use a frequency converter; the second method involves increasing the diameter of the engine and decreasing its active length.
—The development of information technology and computers has an impact on the development of e-learning. Learning Management System (LMS) as one of the software used to manage e-learning in the last decade has been widely used in schools. The implementation of LMS in vocational schools has consequences for vocational school teachers to be able to use LMS in the learning process. To use the LMS, the teacher must have adequate ICT competence so that the learning process can run efficiently and effectively. This study aims to determine the implementation of LMS in the self-development of productive vocational teachers. This research was conducted at the state vocational high schools located in the city of Bandung, Indonesia and has a program of expertise in electrical engineering and electronics engineering. We use a survey method with a qualitative approach and semi-structured interviews and documentation for collecting data. The results of the study show that the implementation of LMS greatly helps the self-development of productive vocational teachers to improve pedagogic and professional competencies by using ICT in the learning process, although in practice it still faces several problems according to the conditions and abilities of each school.