Hasil untuk "Electricity and magnetism"

Y. Tokura, S. Seki

Wenqi Zhang, Qibin Wang, Guangyi Shen et al.

Abstract Aramid nanofiber (ANF)‐based composites have drawn tremendous interest in high‐voltage electrical systems due to their superior insulation strength, thermal stability, and mechanical endurance. However, the filler agglomeration and interface compatibility have retarded further improvement of the dielectric performance. Herein, the nano‐titanium dioxide (TiO2) particles treated by aminopropyl triethoxysilane (APTES) serve as the inorganic fillers, which are doped in the ANF to prepare the composite nano‐paper via the blade coating method. The electrostatic interaction between the ANF and fillers highly promotes their uniform distribution. Compared to the pure ANF paper, the composite paper has a denser structure with reduced pores and defects, which significantly improves its dielectric performance with inhibited partial discharge development. At a filler loading of 3 wt% (mass fraction), the breakdown strength is increased by 70.5% to a maximum value of 358.1 kV/mm, while the bulk conductivity is minimised to 5.2 × 10−17 S/m, representing an 88.1% decrease. By analysing the energy band structure of each component, energy barriers at the interface for electrons (1.48 eV) and holes (0.40 eV) are determined. These values indicate deepened trap energy levels, which greatly strengthen the carrier trapping effect for improved dielectric performance.

Ziwen Huang, Lufen Jia, Wenwen Gu et al.

Abstract This study proposes a novel transformer oil micro‐water detection method based on the ultrasonic pulse‐echo technique, optimised by a sparrow search algorithm (SSA) to enhance the prediction performance of a random forest (RF) model. Initially, finite element simulations were conducted to select optimal ultrasonic frequencies of 2 and 2.5 MHz. An accelerated thermal ageing experiment was performed using #25 Karamay oil samples, and ultrasonic pulse‐echo signals were collected via a custom‐built detection platform. Variational mode decomposition was employed to extract effective echoes from the raw pulse‐echo signals. Temporal and frequency domain analyses yielded 162 dimensional features, which were subsequently filtered to 88 key parameters using the maximum information coefficient method. A transformer oil micro‐water detection model was then developed by integrating the SSA with RF and trained using K‐fold cross‐validation. The model achieved an impressive average prediction accuracy of 97.34% over 10 cross‐validation runs. The testing set demonstrated a prediction accuracy of 96.40%, a remarkable improvement of 16.53% compared to the unoptimised RF model. The findings provide a solid foundation for the rapid detection of micro‐water content in transformer oil using the ultrasonic pulse‐echo method.

Haoqing LI, Dian YU, Changchun PAN et al.

Modern radar systems face increasingly complex challenges in tasks such as detection, tracking, and identification. The diversity of task types, limited data resources, and strict execution time requirements make radar task scheduling a strongly NP-hard problem. However, existing scheduling algorithms struggle to efficiently handle multiradar collaborative tasks involving complex logical constraints. Therefore, Artificial Intelligence (AI)-based scheduling algorithms have gained significant attention. However, their efficiency is heavily dependent on effectively extracting the key features of the problem. The ability to quickly and comprehensively extract common features of multiradar scheduling problems is essential for improving the efficiency of such AI scheduling algorithms. Therefore, this paper proposes a Model Knowledge Embedded Graph Neural Network (MKEGNN) scheduling algorithm. This method frames the radar task collaborative scheduling problem as a heterogeneous network graph, leveraging model knowledge to optimize the training process of the Graph Neural Network (GNN) algorithm. A key innovation of this algorithm is its capability to capture critical model knowledge using low-complexity calculations, which helps to further optimize the GNN model. During the feature extraction stage, the algorithm employs a random unitary matrix transformation. This approach utilizes the spectral features of the random Laplacian matrix from the task’s heterogeneous graph as global features, enhancing the GNN’s ability to extract shared problem features while downplaying individual characteristics. In the parameterized decision-making stage, the algorithm leverages the upper and lower bound knowledge derived from guiding and empirical solutions of the problem model. This strategy significantly reduces the decision space, enabling the network to optimize quickly and accelerating the learning process. Extensive simulation experiments confirm the effectiveness of the MKEGNN algorithm. Compared to existing approaches, it demonstrates improved stability and accuracy across all task sets, boosting the scheduling success rate by 3%～10% and the weighted success rate by 5%～15%. For particularly challenging task sets involving complex multiradar collaborations, the success rate improves by over 4%. The results highlight the algorithm’s stability and robustness.

WEN Yulin, LI Gaiying, WU Yupeng et al.

Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) measures metabolite apparent diffusion coefficients (ADC) to characterize cellular microstructures. However, physiological motion artifacts and low reproducibility limit its clinical application. This study investigates the effects of different gating methods and cycling modes on DW-MRS results. A total of 21 healthy subjects were included: 6 underwent DW-MRS scans under electrocardiogram (ECG) gating, respiratory gating, and no gating; the remaining 15 were scanned using internal and external b-value cycling to evaluate the impact of cycling modes on ADC reproducibility. Results showed that ECG gating reduced motion artifacts and mitigated ADC overestimation, while internal cycling improved reproducibility. The combination of ECG gating and internal cycling enhances ADC reliability, supporting the broader clinical application of DW-MRS.

Dx Dy Dx

We next consider the effect of magnetic fields on materials. We expect that the effect will be similar to that with dielectrics – the field will induce a magnetic dipole in the material. Anticipating this we define a magnetization M  as the magnetic dipole moment/volume. What will the effect of this be? Consider a tiny cube with faces parallel to the xy, yz, xz planes and one corner at (x,y,z). Consider first the plane at x + dx. Then there will be a dipole moment:   p M x,y,z dxdydz    in the cube. Hence   y y p M x,y,z dxdydz  This will mean a current in the –z direction of:   y z y p I M x ,y ,zd y dx dz   Now consider the next cube out in the x direction (corner at x + dx,y,z). It will produce a current at the same face given by:   z y I M x dx,y,z dy   Hence the y component of M  will produce a net current at the face in the z direction of:

Y. Tokunaga, N. Furukawa, H. Sakai et al.

Putri Aprilia, Mira Amelia, C. Panggabean

Various skills that must be mastered by students to be able to prepare themselves to match the human resources of developed countries are today's challenges. One of these skills is critical thinking. The application of the STEM approach is a new era to meet the learning objectives in the 21st century. And by applying the HOTS-based STEM approach will improve students' critical thinking skills. The research in this article uses the meta-analysis method by finding the effect size calculation value of each journal analyzed. Thus, it is found that the learning model that has a major effect on the application of the HOTS-based STEM approach is the experimental learning model, with the material of Electricity and Magnetism and with the help of simulation media at the XII grade high school level.Keywords: STEM Approach, HOTS, Critical Thinking

Daxing WANG, Yan Ning, Jingpei WANG et al.

The development of microgrid with high proportion of renewable energy is one of the important means to construct new modern power systems so as to achieve energy security and low carbon emissions. However, amid the analysis of the dynamic characteristics of microgrid-integrated power system, the current equivalent models appear to be not robust enough. Specifically, these models can well reproduce the behaviors of actual system under the faults in training set, they may not be able to reflect actual system responses under other unknown faults (non-training faults). In regard to this, k-means++ is introduced first to effectively distinguish the typical operation condition of microgrid such that the randomness and time-varying characteristics of the system can be represented. Next, key parameter selection-based parameter identification method is applied to avoid the issue of multiple solutions in parameter identification process. Then, the convolutional neural network is used to generalize the model parameters with respect to different typical system operation conditions. Additionally, online matching of equivalent model parameters is achieved by virtue of Fisher discriminant analysis. Finally, the effectiveness of the proposed method has been verified in a real microgrid system in China.

Pengfei FAN, Baoqin LI, Jiangwei HOU et al.

Based on the robust optimization method, considering the safe operation and economic objectives, the capacity of distributed power generation and energy storage in the distribution network is optimized. According to historical data and probability distributions of the uncertain wind, solar and load, multiple scenarios of these uncertainties are generated, and the uncertainty set describing these uncertainties is established using these operation scenarios. Then, based on the uncertainty set, a two-level robust optimization model is established. The outer-level model searches for the economically worst operation scenario in the uncertainty set. The inner-level model optimizes the capacity of wind, solar and energy storage in the worst scenario, taking into account the security constraints of distribution network. Compared with the capacity allocation results of the traditional method using typical operation scenarios, the capacities allocated in this paper are smaller, and can meet the condition of safe operation in the distribution network, with higher economic performance.

Jian ZHOU, Nan FENG, Yiping JI et al.

Facing the complex and changeable international situation and the increasing number of extreme events, it is of great significance to study the system recovery scheme under the high proportion of new energy access to improve the new power system security defense system. In this context, a decision-making optimization method for grid reconfiguration of power system with new energy is proposed. Firstly, the uncertainty of new energy output is analyzed and modeled based on the kernel density method. Secondly, considering the requirements of new energy grid connection and operation on the strength of the grid, the linearized model of multiple renewable energy stations short-circuit ratio constraint is realized. On this basis, a grid reconfiguration optimization model that can coordinate new energy, energy storage, conventional units and transmission network restoration is established and a bi-level optimization strategy is proposed to improve the efficiency of model solution. The example results based on the New England 10-machine 39-bus system verify the effectiveness of the proposed method.

Ozden Sengul

This action research study was conducted in a physics education class focusing on electricity and magnetism. The instructor aimed to integrate three-dimensional learning into curriculum, lesson planning, and instruction to understand successes and challenges of teaching through a new approach and students’ perceptions of their learning process. The data collection included instructor’s lesson planning, pre- and post-lesson reflections, student artifacts, and students’ reflections. The qualitative data were analyzed through constant comparative method to identify theory-driven and data-driven codes, determine their frequency to categorize and construct themes. The results were provided with three themes: (1) the instructor’s integration of three-dimensional learning, (2) the strengths and challenges of the implementation, and (3) students’ experiences. These findings suggested the need for focusing on developing teachers’ knowledge in different domains connected to each other such as scientific practices, crosscutting concepts, subject matter knowledge, and nature of science for student conceptions and instructional strategies.

Peng Zhao, Shijie Gu, Liutao Li et al.

As a new generation of microwave and millimeter-wave devices, HEMTs have already become core components of microwave mixers, oscillators, and other such equipment. However, as integration levels continue to rise and device sizes shrink, microelectronic devices are becoming increasingly sensitive to various types of electromagnetic interference and thermal effects, which can cause damage. Therefore, it is necessary to study the damage effects and mechanisms of HEMT devices under electromagnetic interference. This research is conducted to address the issue that the core devices of radio frequency power amplifier integrated circuits are prone to interference or damage from strong electromagnetic pulses. It clarifies the interference or damage process of the core devices under the influence of strong electromagnetic pulses, establishes a GaAs HEMT device model under the coupling conditions of electricity, heat and magnetism, and analyzes the triggering process and failure mechanism. It investigates the impact of different electromagnetic parameters on the failure process, obtains the sensitive locations and parameters and establishes an interference model that can reflect different electromagnetic parameters. This research work provides a theoretical foundation for establishing and simulating the electromagnetic interference damage model of HEMT devices, and also accelerates research in related fields.

Kexin Xi, Zishu Xiang

A particular class of simulated enzymes known as nanoenzymes performs biocatalytic activities in addition to having the distinctive characteristics of nanomaterials. Nanoenzymes offer distinct benefits over real enzymes and other artificial enzymes. On the one hand, nanoenzymes are endowed with several properties such as optical, electricity, and magnetism by the chemical makeup of nanomaterials. The building of precise biological analysis probes is made easier by the large specific surface area and rich surface chemical characteristics of nanomaterials, which enable nanoenzymes to chemically change and attach to biological recognition molecules. It's significant to note that the structure, content, morphology, surface modification layer, etc. of nanoenzymes are all directly connected to their catalytic activity. By regulating their activity, various aspects of the performance of nanoenzymes can be greatly improved, and it has become a research hotspot in multiple fields. Therefore, this article introduces the latest progress in the regulation of nanoenzyme activity from six aspects, with a focus on its applications in biomedical fields such as cancer treatment, antibacterial, and biosensing. In addition, this article also prospects the development prospects of nanoenzymes, aiming to provide more references for further research.

Weidong Xu, Jiong Wang, Wenyi Ye et al.

Nripesh Trivedi

Electricity is generated by magnet as described in [1]. It produces heat and light which are the properties of fire. Thus, electricity is fire. Since electricity is generated by magnet [1], electricity is magnetic. Since electricity is fire, thus fire is also magnetic. The energy of the mass is fire as described in [2].  Since fire is magnetic (as described above) and is the energy of the mass [2], the strength of the material or mass lies in magnetism. The strength of the material is magnetism (a short-range force as evident by the magnetic force between two magnets), thus strength of the material lies in more magnitude of material in less volume. This is because, the material is joined by a short-range force (magnetism) as described in above statement that energy of the mass is magnetism. From previous statement, it could be seen that strength of mass/material lies in density. It is density since higher density means higher magnetic force in the mass for joining mass together.

Chunxiang Mo, Rui Luo, Yuping Chen

The stimuli-responsiveness of injectable hydrogel has been drastically developed for the controlled release of drugs and achieved encouraging curative effects in a variety of diseases including wounds, cardiovascular diseases and tumors. The gelation, swelling and degradation of such hydrogels respond to endogenous biochemical factors (such as pH, reactive oxygen species, glutathione, enzymes, glucose) and/or to exogenous physical stimulations (like light, magnetism, electricity and ultrasound), thereby accurately releasing loaded drugs in response to specifically pathological status and as desired for treatment plan and thus improving therapeutic efficacy effectively. In this paper, we give a detailed introduction of recent progresses in responsive injectable hydrogels and focus on the design strategy of various stimuli-sensitivities and their resultant alteration of gel dissociation and drug liberation behaviour. Their application in disease treatment is also discussed. This article is protected by copyright. All rights reserved.

Endalamaw Dessie, Desta Gebeyehu, Fikadu Eshetu

This study investigates the impact of three different instructional models, direct instructional model (DIM), experiential learning model (ELM), and their combinations (DIM-ELM) on enhancing critical thinking, metacognition, and conceptual understanding in an introductory physics course. The study included 84 first-year pre-engineering students aged 18-24 years who were enrolled in the introductory physics course at two public science and technology universities in Ethiopia. A quasi-experimental design was used with three intact classes randomly assigned to one of three treatment groups: ELM, DIM, and DIM-ELM. The instruments used to measure the outcomes were the critical thinking test in electricity and magnetism, electricity and magnetism conceptual assessment, and metacognitive awareness and regulation scale in electricity and magnetism. The study used one-way analysis of covariance to examine the impact of instructional models on students’ conceptual understanding and critical thinking on the topic of electricity and magnetism, while a one-way analysis of variance was used to analyze the effects of instructional models on metacognition. Results showed that ELM was more effective than DIM and DIM-ELM in enhancing post-test conceptual understanding scores. ELM was also more effective than DIM-ELM method in improving post-test critical thinking scores, with the DIM-ELM showing better results than DIM. However, there were no significant differences in the effects of instructional approaches on metacognition. These findings suggest that ELM may be more effective than DIM and DIM-ELM in improving students’ conceptual understanding and critical thinking in physics.

N. Politakos

3D printing is a manufacturing technique in constant evolution. Day by day, new materials and methods are discovered, making 3D printing continually develop. 3D printers are also evolving, giving us objects with better resolution, faster, and in mass production. One of the areas in 3D printing that has excellent potential is 4D printing. It is a technique involving materials that can react to an environmental stimulus (pH, heat, magnetism, humidity, electricity, and light), causing an alteration in their physical or chemical state and performing another function. Lately, 3D/4D printing has been increasingly used for fabricating materials aiming at drug delivery, scaffolds, bioinks, tissue engineering (soft and hard), synthetic organs, and even printed cells. The majority of the materials used in 3D printing are polymeric. These materials can be of natural origin or synthetic ones of different architectures and combinations. The use of block copolymers can combine the exemplary properties of both blocks to have better mechanics, processability, biocompatibility, and possible stimulus behavior via tunable structures. This review has gathered fundamental aspects of 3D/4D printing for biomaterials, and it shows the advances and applications of block copolymers in the field of biomaterials over the last years.

Y. Essouda, H. T. Diep, M. Ellouze et al.

This work presents the remarkable experimental magnetocaloric properties of the perovskites Pr$_{0.9}$Sr$_{0.1}$Mn$_{0.9}^{3+}$Mn$_{0.1}^{4+}$O$_{3}$, including the magnetic entropy change $|ΔS_m|$ and the Relative Cooling Power (RCP). To understand these striking properties, we elaborate in this paper a model and use Monte Carlo (MC) simulations to study it for comparison. For the model, we take into account nearest-neighbor (NN) interactions between magnetic ions Mn$^{3+}$($S=2$) and Mn$^{4+}$($S=3/2$) and the interactions between these Mn ions with the magnetic Pr ions. The crystal is a body-centered tetragonal lattice where the corner sites are occupied by Mn ions and the center sites by Pr and Sr ions in their respective concentrations given in the compound formula. We use an Ising-like spin model. We show that pairwise interactions between ions cannot reproduce the large plateau of the magnetization experimentally observed below the phase-transition temperature. By introducing for the first time a many-spin interaction between Mn ions, we obtain an excellent agreement with experiments. Fitting the experimental Curie temperature $T_C$ with the MC transition temperature, we estimate the value of the effective exchange interaction in the system. From this value, we estimate various exchange interactions between ions: the dominant one is that between Mn$^{3+}$ and Mn$^{4+}$ which is at the origin of the ferromagnetic ordering below $T_C$. We also studied the applied-field effect on the magnetization in the region below and above $T_C$. The obtained MC results for $|ΔS_m|$ are in agreement with experiments performed for applied fields from 1 to 5 Tesla. MC results of RCP are also shown and compared to experimental ones.