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Multi-wall carbon nanotubes (MWCNTs) characterize innovative nanoparticles that progress the thermal characteristics of base fluids, compelling them appropriate for utilizing in renewable energy, heat exchanger, and automotive engineering. In this analysis, the buoyancy-driven flow in a superposed spherical enclosure packed with amalgamated porous (Fe 3 O 4 -MWCNTs/H 2 O) hybrid nanofluid layers was explored by employing the procedure of Levenberg–Marquardt with backpropagated artificial neural networks (LMB-ANN) for two temperature models. The exterior wall of enclosure was kept at a constant frigid condition, while the inner surface received partial heating to create a heat flux. The flow situation within the porous cavity was modeled using the Darcy–Boussinesq model. To evaluate the model equations, the control volume-based finite element method (CVFEM) was adopted. The results obtained from numerical method explain the reference data of LMB-ANN for several situations of porous cavity by modifying model variables. By varying the model parameters within the scope of the present numerical approach, a set of proposed data LMB-ANN is generated for cases. The proposed model has equaled for perfection after the numerical findings of various instances have been evaluated using the LMB-ANN train, test, and validating strategy. Several error graphs and statistical visualizations focused on mean square errors, error histogram, and regression assessment are designed to support the proposed methodology (LMB-NN). The proposed approach (LMB-ANN) has been verified based on the correlation of the suggested and benchmark (numerical) outputs, with a validity level ranging from 10 –02 to 10 –09 . Also, the principal findings revealed that elevating the Rayleigh and Darcy numbers improves energy transmission inside the enclosure.

The access of new energy power source makes the traditional transmission line structure become complex, and due to the influence of power electronic device control strategy, the fault characteristics have been fundamentally changed, resulting in the traditional transmission line fault localization method can not be applied to the new energy sending line. To address the above problems, this paper analyzes the transient current characteristics of different power supply faults, and learns that there are obvious differences in the transient currents on both sides of the fault point inside and outside the transmission line area, and then proposes a new energy transmission line fault localization method based on the Pearson correlation coefficient, which determines the fault location by calculating the correlation coefficients of the fault waveforms of the neighboring monitoring points. Finally, comparative experiments are carried out under different fault types and fault locations to further verify the applicability of the proposed method.

This project intends to use a combination of genetic algorithm and particle swarm optimization (GAPSO) to reasonably allocate energy between nodes in the wireless energy transmission system. First, considering the influence of channel attenuation and transmission distance on energy distribution, a mathematical model of the energy transmission system is established. Secondly, the genetic algorithm is used to optimize the system globally, PSO is used to speed up the local optimization speed, and finally, the optimal power allocation is achieved. Simulation experiments show that compared with the traditional single optimization method, the GA-PSO method has obvious advantages in energy transmission efficiency, node energy consumption and stability performance. The algorithm can effectively improve the system's transmission performance and reduce the system's energy consumption under different network topologies and channel conditions. At the same time, the GA-PSO algorithm has good convergence and computational complexity.

Integrated Energy Transmission Systems (IETSs) are essential to bridge the geographical gap between where energy is produced and where it is needed, transporting power from resource-rich regions to distant load centers. The fundamental challenge is to resolve the inherent asymmetry between an intermittent power supply and distant load demand. Conventional approaches, focusing only on capacity, fail to address this issue while achieving an effective economic and reliable balance. To address the concerns above, a bilevel optimization framework is proposed to optimize the capacity configuration of IETSs, including wind power, photovoltaic (PV), thermal power, and pumped storage. The optimal capacity of wind and PV is determined by the upper-level model to minimize electricity price, whereas the lower-level model optimizes the system’s operational dispatch for given configuration to minimize operational expenses. A detailed IETS model is also developed to accurately capture the operational characteristics of diverse power sources. Furthermore, the proposed model integrates carbon emission costs and High-Voltage Direct Current (HVDC) utilization constraints, thereby allowing for a comprehensive assessment of their economic efficiency and reliability for capacity configuration. Case studies are conducted to verify the proposed method. The results show that the capacities of wind and PV are optimized, and the electricity costs of IETSs are minimized while satisfying reliability constraints.

This paper proposes a machine learning-based modeling and multi-objective optimization method for transcutaneous energy transfer coils to address the problem that current transcutaneous energy transfer coils with single-objective optimization design methods have difficulty achieving optimal solutions. From modeling to multi-objective optimization design, the whole transcutaneous energy transfer coil process is covered by this approach. This approach models transcutaneous energy transfer coils using the Extreme Learning Machine, and the Gray Wolf Optimization algorithm is used to tune the Extreme Learning Machine’s parameters in order to increase modeling accuracy. The Non-Dominated Sorting Whale Optimization algorithm is utilized for multi-objective optimization of the transcutaneous energy transfer coils, which is based on the established model. Using the optimization of planar helical coils applied in artificial detrusors as an example, a verification analysis was conducted, and the final optimization analysis results were demonstrated. The results indicate that the Gray Wolf Optimization algorithm significantly outperforms the comparison algorithms in tuning the parameters of the Extreme Learning Machine model, and it exhibits good convergence ability and stability. The established transcutaneous energy transfer coil prediction model outperforms the comparative prediction model in terms of evaluation metrics for predicting the three outputs (transmission efficiency, coupling coefficient, and secondary coil diameter), demonstrating excellent prediction performance. The Non-Dominated Sorting Whale Optimization algorithm performs well in the multi-objective optimization process of transcutaneous energy transfer coils, showing excellent results. The Pareto optimal solutions obtained using this algorithm have errors of 3.03%, 0.1%, and 1.7% for transmission efficiency, coupling coefficient, and secondary coil diameter, respectively, when compared to the simulation and experimental calculations. The small errors validate the correctness and effectiveness of the proposed method.

The transmission efficiency of underwater acoustic is doubly constrained by absorption attenuation and geometric spreading losses, with the relative interaction between these loss mechanisms exhibiting complex dynamic variations across the frequency spectrum. Achieving dynamic equilibrium between these frequency-dependent loss mechanisms is key to enhancing acoustic energy transmission performance. To address this, this paper proposes a multi-variable coupled acoustic energy transmission model that systematically integrates the cumulative effects of the propagation distance, the geometric configuration of acoustic source arrays, and the interactive influences of critical environmental factors such as the salinity, temperature, and depth to comprehensively analyze the synergistic mechanisms of absorption loss and geometric spreading loss in practical underwater environments. Based on dynamic response analysis in the frequency dimension, the model identifies and determines the optimal working frequency ranges (i.e., dynamic equilibrium points) for maximizing the efficiency of energy transmission under various propagation conditions and environmental configurations. Both theoretical derivations and numerical simulations consistently reveal a frequency band within the low-to-mid frequency range (approximately 20–100 kHz) which is associated with significantly enhanced transmission efficiency under specific parameter settings. These research findings provide a scientific basis and engineering guidance for frequency selection and the structural optimization of underwater acoustic energy systems, offering substantial theoretical value and application prospects that can strongly support the development of acoustic technologies in ocean engineering, resource exploration, and national defense security.

This article presents a coil couple-based transcutaneous energy transmission system (TETS) for wirelessly powering implanted artificial hearts. In the TETS, the performance of the system is commonly affected by the change in the position of the coupling coils, which are placed inside and outside the skin. However, to some extent, the influence of coupling efficiency caused by misalignment can be reduced by optimizing the coil. Thus, different types of coils are designed in this paper for comparison. It has been found that the curved coil better fits the surface of the skin and provides better performance for the TETS. Various types of curved coils have been designed in response to observed bending deformations, dislocations, and other coupling variations in the curved coil couple. The numerical model of the TETS is established to analyze the effects of the different types of coils. Subsequently, a series of experiments are designed to evaluate the resilience to misalignment and to verify the heating of the coil under conditions of severe coupling misalignment. The results indicated that, in the case of misalignment of the coils used in artificial hearts, the curved transmission coil demonstrated superior efficiency and lower temperature rise compared to the planar coil.

Energy security is a crucial issue for political, environmental, and economic reasons. This article presents a modern approach to securing critical infrastructure in energy transmission networks, which are managed by advanced IT systems. This paper focuses on the integration of cryptographic mechanisms with biometric data, providing an additional layer of protection against cyber threats. The discussed solutions enable the protection of management systems in energy transmission networks, enhancing their resilience to cyberattacks. The use of the command-line interface (CLI) in combination with biometrics allows for precise execution of security tasks such as network monitoring, firewall management, and automation of security tasks. This makes these systems more reliable and secure, which is essential for the stability of energy systems.

The topology and parameter characteristics of the wireless energy transmission system are the main factors affecting the system’s performance. A series–parallel–series–parallel (spsp) topology for magnetically coupled wireless energy transmission is proposed to address the problems of low efficiency and low output power when transmitting electrical energy in the conventional magnetically coupled topology. The spsp topology is compared with the conventional topology based on circuit theory, and the two structures are modeled, characterized, and verified in detail. Simulations and tests are performed for the transmission conditions, an improved Gray Wolf optimization algorithm is proposed, and a physical system is built. Experiments show that the spsp structure is superior near the designed circuit parameters when the network works in a resonant state. The improved Gray Wolf optimization algorithm is then used to find the optimal parameters, and the transmission efficiency reaches 90.53%, which effectively improves the transmission performance of the system. The established physical system utilizes the optimized parameters for coil structure and coil offset experiments, and the average transmission efficiency is 83.75%, with an error of 6.78% calculated by data measurement. The rationality of the proposed structure and the correctness of the simulation parameter design method are verified, and it is hoped that the proposed system and circuit structure in this paper will provide a reference for the design of a magnetically coupled wireless energy transmission system.

We established a new simple approach to study phosphorus (P) and nitrogen (N) reserves at subcellular level potentially applicable to various types of cells capable of accumulating P- and/or N-rich inclusions. Here, we report on using this approach for locating and assessing the abundance of the P and N reserves in microalgal and cyanobacterial cells. The approach includes separation of the signal from P- or N-rich structures from noise on the energy-filtered transmission electron microscopy (EFTEM) P- or N-maps. The separation includes (i) relative entropy estimation for each pixel of the map, (ii) binary thresholding of the map, and (iii) segmenting the image to assess the inclusion relative area and localization in the cell section. The separation is based on comparing the a posteriori probability that a pixel of the map contains information about the sample vs. Gaussian a priori probability that the pixel contains noise. The difference is expressed as relative entropy value for the pixel; positive values are characteristic of the pixels containing the payload information about the sample. This is the first known method for quantification and locating at a subcellular level P-rich and N-rich inclusions including tiny (< 180 nm) structures. We demonstrated the applicability of the proposed method both to the cells of eukaryotic green microalgae and cyanobacteria. Using the new method, we elucidated the heterogeneity of the studied cells in accumulation of P and N reserves across different species. The proposed approach will be handy for any cytological and microbiological study requiring a comparative assessment of subcellular distribution of cyanophycin, polyphosphates or other type of P- or N-rich inclusions. An added value is the potential of this approach for automation of the data processing and evaluation enabling an unprecedented increase of the EFTEM analysis throughput.