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The number of electric vehicle charging stations (EVCSs) is increasing alongside electric vehicles (EVs). The unplanned rollout of EVCSs can lead to problems such as deterioration of the voltage profile, increased active power losses, and maximum load demand in distribution networks (DNs). Therefore, EVCSs should be placed in optimum locations on the DNs. The complexity of the problem requires efficiency and performance. In this paper, a novel modified Archimedes optimization algorithm (MAOA) is proposed for optimal placement of EVCSs in DNs. Five parameters of the AOA were modified using the Honey Badger Algorithm (HBA). This placement is based on a combination of indexes: power loss, voltage deviation, and voltage stability index (VSI). Next, Newton-Raphson based load flow analysis is used to compute the minimum cost. MAOA, AOA and eight other optimization algorithms are compared. The results provide the most appropriate buses in the DN. The efficiency of the proposed algorithm was tested on IEEE 33, 69, and 10-bus Hatay power network systems. In all test systems, the proposed MAOA provided the best results with consistently low standard deviation values. The results suggest that the proposed method is more effective in the integration of EVCSs into the DN. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Analyzing the landing dynamics response is essential for enhancing the success rate of surface explorations on celestial bodies like Mars, the moon, and asteroids. This paper develops a comprehensive dynamics model for a space lander equipped with four sets of buffer legs. Each set comprises a primary strut, two secondary struts, and a footpad. The compression force of the buffer struts is determined through interpolation of real experimental data, while the contact force between the footpad and the ground is calculated using Archimedes law for granular media. The Lagrange equation of the second kind is employed to construct the dynamical model for the lander with minimal degrees of freedom. The proposed dynamics model is validated through experiments involving touchdowns of a single legged lander and a four legged lander. Moreover, key physical quantities such as the buffer force, energy absorption and body acceleration are computed for both 1-2-1 and 2-2 landing configurations. The results indicate that the buffer strut is capable of absorbing approximately half of the impact energy, leading to a 50-fold reduction in the shock acceleration of the main body in both landing configurations. However, it is observed that the maximum compression of the buffer strut is greater in the 1-2-1 landing configuration than in the 2-2 landing configuration. Furthermore, these simulations can be completed within seconds, enabling extensive simulations for the design and optimization of soft landing processes. [ABSTRACT FROM AUTHOR]

Reducing the dependence on traditional energy sources and shifting towards the utilization of renewable energy sources (RES) of energy in the maritime sector is imperative for reducing greenhouse gas emissions. Inherently, RES sources like solar and wind are intermittent and variable, resulting in inconsistent power availability and hence leading to energy supply fluctuations and potential shortages. In this respect, an efficient control strategy to maintain system stability and address intermittency effectively is essential. This work considers a hybrid marine microgrid with various energy sources like photovoltaics (PV), wind energy conversion system (WECS), marine biodiesel generator, Archimedes wave power generation, solid oxide fuel cell, and batteries. A 2-degree of freedom (2DOF) structure is designed and implemented with the tilt-integral-derivative filter (TIDN) to address frequency variations. Furthermore, an Archimedes optimization algorithm (AOA) is used to optimize the 2DOF-TIDN controller. The stability of the proposed microgrid system is assessed under various combinations of RES availabilities, including real-time data from WECS and PV. The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. Simulation results obtained show that the AOA-based 2DOF-TIDN control strategy achieves shorter settling times in mitigating the changes of marine microgrid systems under different dynamic conditions as compared to the other algorithms. Finally, the controller being proposed in this paper was tested for robustness with parameter deviations of +25%, −20%, and − 40% from the nominal values, and proved to be the proposed 2DOF-TIDN controller parameters demonstrate significant robustness in effectively managing the uncertainties and parametric variations. • Maritime Microgrid system is designed for investigations with RE & storage units. • PV, Wind, MBG, AWPG, SOFC, and battery storage units are considered. • 2DOF-TIDN controller is developed to mitigate the changes in the system. • The AOA method is proposed for optimal gains of the 2DOF-TIDN under uncertainties. • Sensitivity analysis, Time delay analysis, and stability analysis are performed [ABSTRACT FROM AUTHOR]

• A novel MSSAO-ERNN technique is proposed to detect the PTSD symptom from speech. • The MSSAO-ERNN technique efficiently identifies PTSD symptoms from the dataset. • Three datasets namely NNE, FME hospital as well as TIMIT datasets are employed. • PTSD is mental illness that affects people when they experience traumatic events. • Performance is determined by computing various metrics for PTSD identification. Post-traumatic stress disorder (PTSD) is a stress-based disorder that occurs when a person is vulnerable to undesirable traumatic events like injury or harm. Numerous research works reported dissimilar complex issues for detecting PTSD severity in patients which makes the PTSD diagnosis process a complicated task. So, a novel Multi-Strategy Seeker Archimedes Optimization-based Elman Recurrent Neural Network (MSSAO-ERNN) technique to detect PTSD symptoms in earlier stages using speech samples. Our proposed MSSAO-ERNN technique efficiently identifies PTSD symptoms from the dataset and helps the physician to diagnose accurately. The proposed approach constitutes various significant phases that include the pre-processing phase, feature extraction phase, and classification phase to detect the presence of PTSD in the datasets. The performance of the proposed method is determined by computing various metrics for the accurate identification of PTSD. Finally, three different datasets namely the NNE dataset, the FME hospital dataset as well as TIMIT dataset are employed in this paper for PTSD diagnosis. The experimental results revealed that the proposed method attained a superior accuracy of 97% under different PTSD techniques. [ABSTRACT FROM AUTHOR]

With a growing interest on inorganic ceramics based solid-state electrolytes for all-solid-state batteries, there is a need to maximise their density to optimise electrochemical performance and fuel impermeability. In this paper, we demonstrate the sensitivity of terahertz time-domain spectroscopy (THz-TDS) combined with effective medium theory to quantify the porosity or density of sodium superionic conductor (NaSICON)-based solid-state electrolyte (SSEs) pellets prepared at densities in the range of 2.2–2.9 g cm−3, corresponding to 50–90 % relative densities sintered at 900–1150 °C. The results of which, have been validated against complementary Archimedes analysis and mercury porosimetry highlighting the potential of THz-TDS for rapid, contactless, non-destructive electrolyte characterisation. • Demonstrated sensitivity of terahertz radiation to NaSICON-based electrolyte. • NaSICON densities quantified using effective medium theory. • Estimated densities correlates to Archimedes analysis. • Estimated densities agrees with to mercury porosimetry data. [ABSTRACT FROM AUTHOR]

Optimization problem, as a hot research field, is applied to many industries in the real world. Due to the complexity of different search spaces, metaheuristic optimization algorithms are proposed to solve this problem. As a recently introduced optimization method inspired by physics, Archimedes Optimization Algorithm (AOA) is an efficient metaheuristic algorithm based on Archimedes' law. It has the advantages of fast convergence speed and balance between local and global search ability when solving continuous problems. However, discrete problems exist more in practical applications. AOA needs to be further improved in dealing with such problems. On this basis, to make Archimedes Optimization Algorithm better applied to solve discrete problems, a Binary Archimedes Optimization Algorithm (BAOA) is proposed in this paper, which incorporates a novel V-shaped transfer function. The proposed method applies the BAOA to COVID-19 classification of medical data, segmentation of real brain lesion, and the knapsack problem. The experimental results show that the proposed BAOA can solve the discrete problem well. [ABSTRACT FROM AUTHOR]

Traditional power system stabilizer (PSS) is an effective controller to damp out low frequency oscillation (LFO) through the excitation system. Adding proportional integral derivative (PID) controller improves the performance of PSS controller. This paper propose Archimedes optimization algorithm (AOA) to improve the performance of series PSS-PID controller through adjusting the tuning parameters to effectively damp out LFO and improve power system transient stability in western system coordinate council (WSCC) power system. AOA is used to optimize the system based on minimizing the integral time absolute error (ITAE) of rotor angular speed. A nonlinear model of WSCC system and PSS-PID controller are simulated under three phase short circuit fault applied at terminals of machines. The system has been tested without PSS-PID controller. Then, PSS-PID controller is optimized using AOA algorithm and whale optimization algorithm (WOA) to compare between their performances. The system under study is simulated in time-domain using MATLAB software. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bi-DCB specimens were designed and manufactured by the vacuum bag method, and bistable deformation processes of Bi-DCB specimens were achieved. • Based on the Tsai-Hill and maximum stress criteria, two nonlinear explicit finite element models were established for predicting the folding stable state of the Bi-DCB. • Numerical results of two finite element models were compared with experiments, and the three were in good agreement. The bistable deployable composite boom (Bi-DCB) can realize the bistable function by storing and releasing strain energy, which has a good application prospect in the aerospace field. In this paper, the folding stable state of the Bi-DCB was investigated using experimental and numerical approaches. Using the vacuum bag method, six Bi-DCB specimens were prepared. Bistable experiments of Bi-DCB specimens were conducted and linear fitting with Archimedes' helix was performed to determine the folding stable configuration. In addition, two Finite Element Models (FEMs) were established for predicting the folding stable state of the Bi-DCB. Two classical failure criteria were utilized to analyze the stress level of the folding stable state of the Bi-DCB. Numerical results of two FEMs agreed well with experimental results, including the bistable deformation process and the folding stable state. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we model the dependence structure between credit default swap (CDS) and jump risk using Archimedean copulas. The paper models and estimates the different relationships that can exist in different ranges of behaviour. It studies the bivariate distributions of CDS index spreads and the kurtosis of equity return distribution. To take into account nonlinear relationships and different structures of dependency, we employ three Archimedean copula functions: Gumbel, Clayton, and Frank. We adopt nonparametric estimation of copula parameters and we find an extreme co-movement of CDS and stock market conditions. In addition, tail dependence indicates the extreme co-movements and the potential for a simultaneous large loss in stock markets and a significant default risk. Ignoring the tail dependence would lead to underestimation of the default risk premium. [ABSTRACT FROM AUTHOR]

In convex analysis when studying function spaces of continuous affine functions, notions of a geometrical character like faces, split and parallel faces, exposed or Archimedean faces were investigated in detail by many authors. In this paper we transfer these notions to a more general setting of Choquet theory of abstract function spaces. We prefer a direct functional analytic approach to the treatment of problems instead of using a transfer of a function space to its state space. Methods invoked are based mainly on a measure theory and basic tools of functional analysis and are different from ones using a geometric visualization. [Copyright &y& Elsevier]