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This paper aims to evaluate the effect of optimal use of rooftop photovoltaic (PV) systems on improving the loss of life (LOL) of distribution transformers, reducing power losses as well as the unbalance rate of the 69-bus distribution network.

The problem is studied in three scenarios, considering different objective functions as multi-objective optimization in balanced and unbalanced operations. Meta-heuristic golden ratio optimization method (GROM) is used to determine the optimal size of the rooftop PV in the network.

The simulation results show that in all scenarios, the GROM by optimally installing the rooftop PV is significantly capable to reduce the transformer distribution loss of loss, unbalance rate and power loss as well as reduce the temperature of the oil and transformer winding. Also, the lowest %LOL, power loss and unbalance rate occurred in the second scenario for the balanced network and first scenario, respectively. In addition, the results showed that the unbalance of the network results in increased power losses and LOL of the distribution transformer.

The better capability of GROM is proved compared with the grey wolf optimization algorithm with better objective function and by achieving better values of LOL, unbalance rate and power loss. The results also showed that the %LOL, unbalance and power losses are weakened compared to without considering the PV cost but the achieved results are realistic and cost-effective.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

The purpose of this paper is to present an optimization process for the design of a 2×2 patch antenna phased array with application for an UHF RFID system.

The optimization process is based on a method of moment (MoM)-solver, which was individually made to create such patch antenna phased arrays and simulate the radiated field pattern. In combination with this MoM-solver, a GUI, which gives the opportunity to change every physical antenna factor and create the antenna structure within a few minutes is presented. Furthermore the golden section search method is used to produce an even better solution in a more efficient way compared to the first attempt. After the simulation, different types of presentation of results can be chosen for a fast and easy optimization.

The design process is discussed while the authors try to optimize the distance between the elements and the difference of input phase for each patch element. The final goal is to create an antenna with maximum directivity and coverage of field pattern.

A physical implementation of an optimized patch antenna phased array and the results of measurement are presented in the end.

An optimization process for the design of a 2×2 patch antenna phased array with application for an UHF RFID system is presented. Furthermore the golden section search method is combined with the design process to increase the accuracy of the solution and decrease the time effort.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This paper aims to study the sensitivity enhancement effect of the gold nanorod on fiber surface plasmon resonance (SPR) sensor. It proposes modeling the sensing effects of fiber SPR sensor decorated with metal nanoparticles. By using simulation and experiment, the sensitivity enhancement effect of the gold nanorod was studied and demonstrated.

The paper opted for an exploratory study using simulation approach of finite-difference time-domain. Specifically, the effect of ratios and aspect ratios of gold nanorod on sensing performance are investigated theoretically. Based on the mathematical models, the validation experiments by using the gold nanorod with the aspect ratios of 5.1 were done to verify the sensitivity enhancement effect of the gold nanorod.

In conclusion, it is evident that with the increases of the aspect ratios, the sensing sensitivity of the refractive index increases first, then gradually stabilizes or decreases. After parameter optimization, the ratios and aspect ratios of gold nanorod are chosen to be 8 nm and 12.5, respectively, which makes the optimal refractive index sensitivity of 4465.53 nm/RIU be realized. In addition, the validation experiments by using the gold nanorod with the aspect ratios of 5.1 verify the sensitivity enhancement effect of the gold nanorods.

This paper proposes and demonstrates a new method for the sensitivity enhancement of fiber SPR sensor. After parameter optimization, the maximum sensitivity of 4465.53 nm/RIU was achieved by using 8 nm gold nanorods with the aspect ratios of 12.5. To verify the sensitivity enhancement of the gold nanorods, the authors also did the validation experiments. The testing results indicated that after the decoration of the gold nanorods, the sensitivity of the sensing probe increases from 2190.57 nm/RIU to 2693.24 nm/RIU, which demonstrates the sensitivity enhancement effect of the gold nanorods.

To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness.

The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.

Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity.

The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.

This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19.

This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures.

The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off.

To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

The interface between two conducting fluids in a magnetic fluid dynamics (MFD) problem was identified by means of external magnetic field measurements. Genetic algorithms (GA) were applied to solve the inverse problem.The principal component analysis (PCA) was used to speed up the process of interface reconstruction.

With respect to the experimental results we have designed a general technique for mode identification and/or interface reconstruction. Two main procedures are available to solve the inverse problem, the full interface reconstruction and the principle component analysis (PCA) mode. In the case of full reconstruction, it can be decided whether an algorithm for fast identification of the dominant modes applying a FFT module should be performed or not. The full interface reconstruction applies stochastic optimization methods ((GA) or evolution strategies (ES)) for the estimation of the interface shape characteristics. The main goal of the PCA mode is to find the dominant mode of the interface shape and its amplitude. The PCA mode is realized by means of stochastic optimization methods (GA, ES) and a simple direct searching (DS) using the golden section technique.

PCA with GA procedure enables the identification of the dominant mode of the interface shape between two conducting fluids with sufficient accuracy for simulated magnetic fields. Time of identification is strongly reduced due to a redefinition of the genotype representations in the PCA mode. Accuracy of reconstruction depends on the noise level, i.e. signal to noise ratio and a geometrical model used in the reconstruction phase. The correlation between the noise level and values of cost function for identified modes has been found if a proper geometry modelling is applied.

The paper describes a new, fast technique for solving an inverse field problem of a MFD problem where the interface between two conducting fluids has to be identified using a magnetic field tomography measuring system.