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Context: The study delves into the adsorption process of acrolein (AC) onto both an untainted and a titanium-doped aluminum nitride nanotube (AlNNT) using computations based on density functional theory. As AC approaches the pure AlNNT, it exhibits a calculated adsorption energy (Ead) of -5.3 kcal/mol, underscoring the feeble nature of the adsorption. Furthermore, there has been very little change to the AlNNT's natural electrical characteristics. On the contrary, the introduction of titanium (Ti) enhances the performance of AlNNT, rendering it more susceptible and reactive to AC signals. Analyzing the conventional Gibbs free energy of formation computationally, we ascertain that replacing a nitrogen (N) atom with a titanium (Ti) atom within the aluminum nitride nanotube (AlNNT) structure presents a more advantageous prospect. Notably, there is a substantial alteration in the energy of adsorption (Ead) for AC as a Ti atom is incorporated onto the AlNNT surface, resulting in a shift from -5.3 to -24.6 kcal/mol. Methods: Energy calculations and geometric optimizations were conducted utilizing the dispersion-augmented B3LYP method, known as B3LYP-D. In this approach, Grimme's dispersion term, referred to as the "D" term, was employed to account for dispersion forces. The basis set adopted was 6–31 + + G** (d), and all computational procedures were executed using the GAMESS software program. Following the incorporation of titanium (Ti), this adjustment leads to a substantial enhancement in sensing capability, reaching a value of 93.7. This indicates an improved electrical conductivity of the aluminum nitride nanotube (AlNNT). Remarkably, the Ti-doped AlNNT demonstrates the ability to detect AC distinctly, even in the presence of HCN, formaldehyde, ethanol, toluene, and acetone. The swift recovery process becomes evident as AC desorbs from the surface of Ti-doped AlNNT, with a calculated recovery time of 14.0 s. [ABSTRACT FROM AUTHOR]

During peak demand hours, hydroelectric energy is one of the most significant sources of energy. Power sector restructuring has increased competition among the country's electricity providers. Estimating the future price of energy is critical for producers in order to enhance investment profit and make better use of resources. One of the most significant technologies of artificial intelligence, Artificial Neural Networks (ANN), has various applications in estimating and forecasting phenomena. Combining artificial intelligence models with optimization models (e.g. Artificial Bee Colonoy [ABC]) has recently become quite popular for improving the performance of artificial intelligence models. The goal of this study is to look at the effectiveness of ANN and ABC-ANN models in forecasting the dispersed and sinusoidal data of Angola's daily peak power price. The findings reveal that in this case study, the employment of the ABC-ANN model is not superior to the ANN model and has not resulted in enhanced performance and forecasting of power market data. As a result, the R2 of the ANN and ABC-ANN models is 0.88 and 0.85, respectively. [ABSTRACT FROM AUTHOR]

At present, the use of smart systems to reduce energy consumption has been developing more and more, and the need to use these systems plays a significant role in energy systems. Smart systems can play a significant role when there are many energy production sources and there are various loads in the system. In this research, the smart system of the sports stadium is evaluated considering the renewable energy resources, and the electric vehicles are also one of the demanded loads. The studied system uses a solar cell to supply the electric load, which is used to model the equipment using GAMS software. Different scenarios have been evaluated considering the uncertainty in solar radiation intensity. Furthermore, the conditional value at risk measure is considered to manage and reduce the risk of smart sports stadium energy supply cost. Finally, the effects of the battery capacity of electric vehicles on the installed solar cell power and the cost of the produced energy have been evaluated. The results indicate that in a scenario based on a system consisting of an electric vehicle under a photovoltaic cell and a battery, the energy cost will be equal to around 111.2 cents, which represents a 34% reduction compared to a different scenario. This can be an important achievement in the design of sports stadiums under smart energy systems. Furthermore, the developed strategy in this article can manage the load shifting and the operation of the electric vehicle and the storage system during the charging and discharging periods. [ABSTRACT FROM AUTHOR]

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system. [ABSTRACT FROM AUTHOR]

A radial flux permanent magnet generator can be effectively used in generating wind power at low speeds. This generator uses a combination of permanent magnets and a stator to produce electricity from the wind's kinetic energy. The permanent magnets are arranged in a radial pattern, which reduces the amount of heat lost and therefore increases the generator's efficiency. This design allows for a low rotational speed, which makes it suitable for applications such as wind turbines. The air gap between the rotor and the stator affects the output voltage and power due to the decrease in magnetic flux. This study aims to increase the generator's output voltage and output power by fitting stator teeth widths to the stator teeth. The stator teeth width fitting design method adjusts the air gap between the stator and the rotor to a uniform size. This helps to reduce the pulsating torque and ensure that the output voltage and power are maximized. The three variables are the stator slot width, the rotor slot width, and the air gap width. By adjusting these variables, the stator teeth width fitting design method can adjust the air gap between the stator and rotor. [ABSTRACT FROM AUTHOR]

In this paper, the nonparaxial solitons in a dimensionless coupled nonlinear Schrödinger system with cross-phase modulation, which enables the propagation of ultra-broad nonparaxial pulses in a birefringent optical waveguide are studied. By noticing that the system is a non-integrable one, and also diverse forms of solitary wave solutions by using the Hirota's bilinear scheme are reached. The binary bell polynomials and bilinear transformation and also the related theorem for getting to the bilinear form of nonlinear system are noticed. In particular, five forms of function solution including soliton, bright soliton, singular soliton, periodic wave and singular form of solutions are investigated. To achieve this, an illustrative example of the coupled nonlinear Helmholtz systems is provided to demonstrate the feasibility and reliability of the procedure is used in this study. The effect of the free parameters on the behavior of acquired figures of a few obtained solutions for two nonlinear rational exact cases was also discussed. We believe that our results would pave a way for future research generating optical memories based on the nonparaxial solitons. [ABSTRACT FROM AUTHOR]

It has been confirmed that structures with micro dimensions display size-dependent thermomechanical behaviors. Moreover, according to the findings of empirical and theoretical researches, thermoelastic damping (TED) has been recognized as one of inescapable causes of energy dissipation in microstructures. The current article is an effort to provide a novel size-dependent framework for approximating the amount of TED in microring resonators with rectangular cross section. To include size effect into structural and thermal constitutive relations, the modified couple stress theory (MCST) and the Moore–Gibson–Thompson (MGT) heat equation are utilized, respectively. By solving the coupled heat equation in the purview of MGT model, the fluctuation temperature throughout the ring is determined. By employing the obtained temperature distribution and constitutive relations of MCST, the peak values of strain and wasted thermal energies during one cycle of vibration are computed. Based on the description of TED in the energy dissipation (ED) method, a mathematical expression containing the scale parameters of MCST and MGT model is derived for estimating TED value. To ensure the correctness and veracity of the established solution, a comparative study is carried out on the basis of the data released by other researchers for more plain models. A section is also designated for an all-out study to ascertain the association between TED spectrum and some influential factors like scale parameters of MCST and MGT model, vibration mode number, one-dimensional (1D) and two-dimensional (2D) heat conduction, geometry and material. The extracted data enlighten that the impact of applying MCST and MGT model on TED has a close relationship with the vibration mode number of the ring. [ABSTRACT FROM AUTHOR]

The natural frequency of a clamped–clamped functionally graded porous (FGP) nanobeam is predicted in this study. Material distribution is considered based on monotonous, symmetric, and non-symmetric patterns in the thickness direction. This paper deals with governing equations of nanobeams based on third-order shear deformation beam theory in conjunction with nonlocal strain gradient theory (NSGT) and surface effects. Artificial neural network (ANN) is utilized to predict the effect of eight parameters including temperature gradient, residual surface stress, porosity distribution pattern, porosity parameter, nonlocal and material length scale parameters, and elastic and shear coefficients of Pasternak foundation on the fundamental frequency of FGP nanobeam. Different training methods are selected to simulate input and output dependency. Results show that the dependency of the natural frequency is inverse to the temperature gradient and nonlocal parameter in the sense that increasing these factors will decrease the natural frequency. Also, increasing the material length scale parameter grows the effect of the nonlocal parameter. Residual surface stress, material length scale, and Pasternak foundation parameters have a direct effect on the output and among them; the material length scale parameter has a more noticeable effect. Finally, it was found that by increasing the porosity parameter value, the diversity of natural frequency levels up drastically [ABSTRACT FROM AUTHOR]