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The Central Nervous System (CNS) acts as the main element of the biological system, regulating and commanding numerous organs in the human body. Neurons play a crucial role in the central nervous system, and it is necessary to thoroughly examine, replicate, simulate, and integrate various aspects of the CNS to develop a comprehensive neuronal system that can mimic the actual nervous system. In this research, a neuron model called the Quartic Neuron Model is employed to imitate the fundamental nervous functions of the human brain. The proposed method, known as Digital-QNM (D-QNM) is accomplished by employing power-2 based approximation and linear approaches to modify the fourth-degree function. These power-2 based functions are digital-friendly terms (high-accurate, low-cost and leads to high-frequency implementation). By eliminating the high-cost function, the presented model offers advantages such as low error, high speed, and efficient resource utilization compared to the basic main state. In order to validate the final hardware design, a digital FPGA board (specifically, the Xilinx Virtex-5 FPGA board) is employed. The process of digitally synthesizing the hardware demonstrates that our proposed approach can replicate the QNM with improved frequency, performance, and reduced hardware costs. The implementation outcomes show a significant reduction of 98% in FPGA resources cost and a higher operating frequency of the suggested model, reaching 190 MHz. This frequency is considerably higher than the original model’s 105 MHz.

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In this article, we focus on the left translation actions on noncommutative compact connected Lie groups with topological dimension 3 or 4, consisting of SU(2), U(2), SO(3), SO(3) × S1and Spinℂ(3). We define the rotation vectors (numbers) of the left actions induced by the elements in the maximal tori of these groups, and utilize rotation vectors (numbers) to give the topologically conjugate classification of the left actions. Algebraic conjugacy and smooth conjugacy are also considered. As a by-product, we show that for any homeomorphism f: L(p, −1) × S1→ L(p, −1) × S1, the induced isomorphism (τ◦ f◦ i)* maps each element in the fundamental group of L(p, −1) to itself or its inverse, where i: L(p, −1) → L(p, −1) × S1is the natural inclusion and τ: L(p, −1) × S1→ L(p, −1) is the projection.

The current collector serves as a crucial element in supercapacitors, acting as a medium between the electrode material and the substrate. Due to its excellent conductivity, a metal collector is typically favored. Enhancing the binding strength between the collector and the substrate as well as between the collector and the electrode material has emerged as a critical factor for enhancing the capacitance performance. In this study, a Ag film with a grass root-like structure was initially grown on a PI substrate through the surface modification and ion exchange (SMIE) process. This Ag interlocking structure contributes to strong binding between the PI substrate and Ag without compromising the mechanical properties of the Ag film. To further enhance the electrochemical properties at low scan rates, electroless-plated Cu was subsequently deposited on the Ag film to form the Cu/Ag current collector. Moreover, the Cu within the Cu/Ag current collector served as a precursor for the growth of FeOOH–Cu(OH)2via a two-step in situ method. The resulting FeOOH–Cu(OH)2/Cu/Ag structure as a whole is binder-free. Supercapacitors employing symmetric FeOOH–Cu(OH)2/Cu/Ag structures were assembled, and their energy storage properties were investigated. The solution-based low-temperature process used in this study offers the potential for cost-effective and large-scale applications.

This study proposes a metasurface-designing framework using a pre-established database of primary elements and characteristic models, which avoids tedious optimization during metasurface design. We adopt the surface susceptibility model (SSM) and surface porosity model (SPM) as primary characteristic models, and patch-like and fishnet-aperture unit cells as primary elements. First, we connect the efficient characteristic model of metasurfaces, SSM for patch-like unit cells, and SPM for fishnet-aperture unit cells, to $LC$ circuit components. Then, we extend the scale of SSM/SPM of the patch/fishnet-like unit cells with a periodicity in the order of $0.1\lambda _{0}$ , to establish a primary database. Finally, we design metasurfaces of various functionality using the pre-established database of the primary elements and characteristic models with the aid of classical circuit and transmission line theories.

Programmable metasurfaces have recently been proposed to dynamically manipulate electromagnetic (EM) waves in both temporal and spatial dimensions. With active components integrated into unit cells of the metasurface, states of the unit cells can be adjusted by digital codes. The metasurface can then construct complex spatial and temporal electromagnetic beams. Given the main parameters of the beam, the optimal codes can be computed by nonlinear optimization algorithms, such as genetic algorithm, particle swarm optimization, etc. The high computational complexity of these algorithms makes it very challenging to compute the codes in real time. In this study, we applied deep learning techniques to compute the codes. A deep convolutional neural network is designed and trained to compute the required element codes in milliseconds, given the requirement of the waveform. The average accuracy of the prediction reaches more than 94 percent. This scheme is validated on a 1-bit programmable metasurface and both experimental and numerical results agree with each other well. This study shows that machines may “learn” the physics of modulating electromagnetic waves with the help of the good generalization ability in deep convolutional neural networks. The proposed scheme may provide us with a possible solution for real-time complex beamforming in antenna arrays, such as the programmable metasurface.

In this article, we give a sufficient and necessary condition to that a degree ±1self-map on a (2n-1)-dimensional lens space for n≥2is homotopic to a self-homeomorphism. In fact, we show how the endomorphism induced by a homeomorphism can act on the fundamental group of the lens space. Moreover, we provide a specific description of a class of lens spaces which admit self-homeomorphisms inducing nontrivial automorphisms on the fundamental groups. Furthermore, the topologically conjugate classification for a special class of periodic homeomorphisms on S2n-1is obtained.

In this paper, the new novel optimization algorithm on the energy consumption of the represented green building with novel material is represented. The introduced green building is 320 square meters and takes advantage of a novel lay-up of composite material with the common materials. For this purpose, the supporting vector machine is firstly proposed to classify the properties of the building based on the energy consumption with high accuracy. As a matter of fact, the hyperparameters are optimized using the Particle Swarm Optimization method (PSO). Energy consumption of the building with a common material is calculated based on a fixed price with 99.98% accuracy. In fact, the highest demand for consumption from the administrative and residential sectors is close to 40% will be optimized for the first time. Based on the represented novel model, the layers of the novel represented composite are optimized for decreasing electricity usage as well as biogas. The results show a significant reduction in the amount of green building energy consumption by 28% for electricity and 42.44% for biogas compared to the common building. Furthermore, the remarkable decreasing cost of the green building with composite material to a value of 50% with compare to the common building proves the need to invest obviously in the construction of green buildings and the use of renewable resources in the world.