Design of a two-dimensional self-similar acoustic metamaterial with ultra-wide band gaps via genetic algorithm optimization

Quality enhancement and insertion loss reduction of a rectangular resonator by employing an ultra-wide band gap diamond phononic crystal

Low-frequency vibration and noise control present enduring engineering challenges that garner extensive research attention. Despite numerous active and passive control solutions, achieving multiple ultra-wide attenuation regions remains elusive. Addressing vibration and noise control across a multidirectional broad low-frequency spectrum, three-dimensional metastructures have emerged as innovative solutions. This study introduces a novel three-dimensional composite metastructure featuring multiple ultra-wide three-dimensional complete band gaps. The research emphasizes the design strategy of elastic ligaments to achieve multiple ultra-wide attenuation regions spanning from 0.7 to 40 kHz. The band structures are elucidated through modal analysis and further substantiated by an analytical model based on a spring-mass chain with an additional resonator. The underlying physical mechanism for the formation of multiple ultra-wide band gaps is revealed through novel vibration modes from finite element analyses. Furthermore, we demonstrate that the distribution and the relative width of the ultra-wide band gaps can be tuned by modifying the geometric parameters of the metastructure. Utilizing additive manufacturing, prototypes are fabricated, and low-amplitude vibration tests are conducted to evaluate real-time vibration attenuation properties. Consistency is observed among theoretical, numerical, and experimental results. The proposed structure shows significant potential for high-performance meta-devices aimed at controlling noise and vibration across an extremely wide low-frequency spectrum.

Ultrawide band gap materials have numerous potential applications in deep ultraviolet optoelectronics, as well as next-generation high-power and radio frequency electronics. Through the first-principles calculations based on density functional theory calculations, we demonstrate that the As2O3 bulk and monolayer structures have excellent energetic, mechanical, and thermal stabilities. The bulk and monolayer of As2O3 come in two distinct structures, namely st1-As2O3, and st2-As2O3. We show that the st1-As2O3 and st2-As2O3 monolayer and bilayer could be mechanically exfoliated from their bulk material and found that the cleavage energy values are significantly lower than those reported for similarly layered materials. By performing Perdew–Burke–Ernzerhof (PBE) and Heyd–Scuseria–Ernzerhof (HSE06) band structure calculations, we found that the bulk and monolayers of As2O3 structures exhibit wide (PBE) and ultra-wide (HSE06) indirect band gaps. We further evaluate the As2O3 layered thickness-dependent band gaps and found that band gap decreases uniformly as the number of st1-As2O3 and st2-As2O3 layers increases. Our findings demonstrate the potential of the As2O3 structures for the future design of ultra-wide band gap semiconductor electronic devices.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

An original approach for designing a one dimensional phononic crystal strip with an ultra-wide band gap is presented. The strip consists of periodic pillars erected on a tailored beam, enabling the generation of a band gap that is due to both Bragg scattering and local resonances. The optimized combination of both effects results in the lowering and the widening of the main band gap, ultimately leading to a gap-to-midgap ratio of 138%. The design method used to improve the band gap width is based on the flattening of phononic bands and relies on the study of the modal energy distribution within the unit cell. The computed transmission through a finite number of periods corroborates the dispersion diagram. The strong attenuation, in excess of 150 dB for only five periods, highlights the interest of such ultra-wide band gap phononic crystal strips.

Measuring the maximum operating temperature within the channel of ultrawide band-gap transistors is critically important since the temperature dependence of the device reliability sets operational limits such as maximum operational power. Thermoreflectance imaging (TTI) is an optimal choice to measure the junction temperature due to its submicrometer spatial resolution and submicrosecond temporal resolution. Since TTI is an imaging technique, data acquisition is orders of magnitude faster than point measurement techniques such as Raman thermometry. Unfortunately, commercially available LED light sources used in thermoreflectance systems are limited to energies less than ∼3.9 eV, which is below the band gap of many ultrawide band-gap semiconductors (>4.0 eV). Therefore, the semiconductors are transparent to the probing light sources, prohibiting the application of TTI. To address this thermal imaging challenge, we utilize an MoS2 coating as a thermoreflectance enhancement coating that allows for the measurement of the surface temperature of (ultra)wide band-gap materials. The coating consists of a network of MoS2 nanoflakes with the c axis aligned normal to the surface and is easily removable via sonication. The method is validated using electrical and thermal characterization of GaN and AlGaN devices. We demonstrate that this coating does not measurably influence the electrical performance or the measured operating temperature. A maximum temperature rise of 49 K at 0.59 W was measured within the channel of the AlGaN device, which is over double the maximum temperature rise obtained by measuring the thermoreflectance of the gate metal. The importance of accurately measuring the peak operational temperature is discussed in the context of accelerated stress testing.

Under the background of the rapid development of computer network technology, the algorithm used in engineering is also making continuous progress. The multi-objective integrated management of engineering projects must adapt to the pace of development of The Times, and constantly improve the optimization algorithm, so that the algorithm can serve the field of engineering projects. The optimization and improvement of algorithm are of great significance to engineering project management, especially multi-objective integrated management of engineering project. It is necessary to strengthen the research of genetic hybrid optimization algorithm for engineering project development. This article studied the genetic hybrid optimization algorithm based on the study of the multi-objective integrated project management, is introduced to define genetic hybrid optimization algorithm, principle of multi-objective project related theory and knowledge, etc., by using functional analysis method based on genetic hybrid optimization algorithm of the multi-objective integrated project management effect comparison, Compare the advantages of genetic hybrid optimization algorithm. This paper uses data to analyze the effect of engineering projects, which has significant advantages in many aspects of multi-objective integrated management of engineering projects. Through data testing, the comprehensive evaluation rate of engineering projects reaches 89.12%, 90.23%, 95.32%, 98.10% respectively.

Purpose The thermal management system of an aircraft’s environmental control system (ECS) plays a critical role in ensuring a comfortable and suitable environment for both the cabin and avionic. As the civil aviation industry shifts toward more electric aircraft (MEA), the ECS is undergoing a change in its power source, moving from engine bleed air to electric power. As the industry moves toward electric power, it is crucial to address the challenges associated with power optimization for electric-driven ECS (EECS). The purpose of this paper is to present a model-based design methodology for improving the coefficient of performance (COP) of an EECS through prediction and genetic algorithm (GA) optimization. The evaluation process includes a concurrent analysis of entropy generation in both the current and optimized systems to determine the irreversibility of individual components and entire system. Design/methodology/approach In this study, the endo-reversible and irreversible thermodynamic model (ETM)-based COP correlation was selected as the main objective function that incorporates the breakdown model algorithm and a prediction of GA with 8 degrees of freedom to optimize the thermal performance of a three-wheel air cycle refrigeration system (ACS) in a state-of-the-art civil aircraft EECS. The implementation of the GA optimization approach was carried out using Python. Findings The results of the study show that after implementing GA optimization, the COP improved by 50% with the optimal values of the ten variables, and the entropy generation number of the system decreased by 8%. Originality/value Aircraft manufacturers can use a GA optimization approach to evaluate system performance and convey specifications to their parts suppliers. For comparable applications, GA optimization estimation can minimize the required number of tests and experiments.

Genetic algorithm (GA) optimization is applied to the design of planar monopole antennas, which exhibit both ultrawideband (UWB) operation and a narrow-band frequency notch. Such an antenna is useful in applications involving wideband communications where it is desired to mitigate interference with other radio systems colocated with the operating band. It is demonstrated in this paper that traditional band-notched planar monopole antennas exhibit asymmetry in radiation patterns within the notch band such that the attenuation provided by the antenna varies as a function of azimuth angle, which lowers the effective bandwidth of the notch. A GA optimizer, which uses of a weighted sum cost function related to impedance matching and radiation patterns at frequencies within both the wide operating band and narrow notch band, is used to improve the performance of the band-notch planar monopole antenna. A two-dimensional (2-D) matrix chromosome is used in the GA to represent a wide-range on planar element shapes. It is shown that the GA generates antenna designs that exhibit equal wideband performance as traditional band-notched designs, but have improved azimuth plane radiation pattern symmetry, which widens the effective notch bandwidth. The GA-generated antenna design is measured and compared with simulation

It is of great significance to carry out cities' air quality forecasting work for the prevention of the air pollution in urban areas and to the improvement of the living environment of urban residents. The air quality index (AQI) is a dimensionless index that quantitatively describes the state of air quality. In this paper, the data of air quality in Lanzhou released by china air quality online monitoring and analysis platform is dealt with, and then AQI prediction model based on back propagation (BP) neural network, AQI prediction model based on genetic algorithm optimization and AQI prediction model of BP neural network based on genetic simulated annealing algorithm optimization are established. By comparing and analyzing the prediction results, it is found that BP neural network based on genetic simulated annealing algorithm has strong generalization ability and global search ability, and has higher accuracy rate.

This paper provides a tutorial overview of a new approach to optimization for aerospace electromagnetics known as the Genetic Algorithm. Genetic Algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and GA is discussed and the details of GA optimization implementation are explored. The tutorial overview is followed by a number of applications in which GA has proved useful. The applications discussed include the design of lightweight, broad-band microwave absorbers, the reduction of array sidelobes in thinned arrays, the design of shaped beam antenna arrays, and the extraction of natural resonance modes of radar targets from the backscattered response data. Genetic Algorithm Optimization is shown to be suitable for optimizing a broad class of problems of interest to aerospace antennas and related electromagnetics.

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.

Although lots of ways can be used to estimate geo-stress state, estimation of geo-stress state without knowing geomechanical parameters such as pore pressure, tensile strength and Poisson’s ratio, etc., still remains one of the most challenging tasks in geotechnical engineering. The main contribution of this paper is to present a back-propagation neural network (BPNN) with genetic algorithm (GA) optimization to predict the geo-stresses based on wellbore pressures of hydraulic fracturing tests during drilling. In the suggested hybrid model, the BPNN is used establish a mapping between the recording pressures and the geo-stress state. Also the GA is used to carry out the optimization of the weights and thresholds of BPNN model for improving accuracy of prediction. Finally, based on the record pressures in hydraulic fracturing (HF) tests, the BPNN model with genetic algorithm optimization successfully predicts the geo-stresses at the corresponding formation in the event that these parameters such as pore pressure, tensile strength and Poisson’s ratio are unavailable. In the meantime, the geo-stress state has been calculated using the theoretical formula by assuming pore pressure and tensile strength of rock mass are known. Then results from theoretical equation, BPNN and BPNN with GA optimization are compared, which shows that the degree accuracy of geo-stresses predicted by using GA-BPNN model is more obviously improvement than the predicted results by the basic BPNN model.

The amputation and survival of patients with diabetic foot based on establishment of prediction model

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model.