Simple Implementation Research Articles

A Hermite-type collocation mesh-free approach for simulating incompressible viscous fluid flows

In this work, a novel Hermite-type mesh-free model for incompressible viscous fluid flows is proposed, as a way, to overcome the numerical difficulties experienced by solving under weak or strong formulation the pure stream-function Navier–Stokes equation. In this model a dimensionless virtual domain is utilized, in order to make an accurate dimensionless shape-functions of the recently developed Hermite-type collocation Weighted Least Square approximation, by using dimensionless scale parameters. Then, a Jacobian transformation matrix is built to express the shape-functions spatial derivatives from the virtual to the considered domain. The resolution of the obtained nonlinear algebraic system is performed by the High Order Continuation Method. Numerical tests were investigated on incompressible viscous isothermal fluid flows in lid-driven cavity, backward-facing step, sudden expansions and around various obstacle shapes. They show the advantages of the proposed model compared to the reference Hermite-type collocation models used for pure stream-function equation and the weak Galerkin procedures as Finite Element Method and Element Free Galerkin method, in term of computation accuracy, number of degree of freedom, implementation simplicity and computation time. The numerical and experimental results of literature confirm the validity and capability of the proposed dimensionless model to simulate fluid flows in a expended-lengths inflow–outflow geometry and around obstacles of circular, inclined elliptical and Joukowski airfoil shapes.

CRISPR-powered quantitative keyword search engine in DNA data storage

Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

A direct transcription method for solving distributed-order fractional optimal control problems

This paper considers the numerical solution of distributed-order Fractional Optimal Control Problems (FOCPs). The common approaches for solving these problems involve approximating the problem with a multi-term FOCP and subsequently discretizing the resulting multi-term problem using either a direct or indirect method. However, this paper bypasses the need for approximation with a multi-term FOCP by introducing innovative approximation formulas for distributed-order fractional derivatives. These approximation formulas are directly derived from the conventional Grünwald–Letnikov, L1, and trapezoidal formulas. Building upon these formulas, we develop the relevant fractional-order distributed-order operational matrices. Utilizing the operational matrix, we easily discretize the distributed-order FOCP to a Non-Linear Programming (NLP), which can be solved by a suitable NLP-solver. Additionally, to enhance the efficiency of solving the resulting NLP, we determine the closed-forms of both the Jacobian of constraints and the gradient of the objective function. The presented method is characterized by its speed, simplicity, and ease of implementation. Moreover, it can be used to solve a wide range of distributed-order FOCPs, such as those involving nonlinear dynamics, free final times, free terminal conditions, and path constraints. By means of ample numerical tests, the accuracy and efficiency of the proposed method are assessed.

An adaptative differential evolution with enhanced diversity and restart mechanism

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the Adaptative Differential Evolution with Diversity Maintenance and Restart Mechanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants.

Progressive Multiple Alignment of Graphs

The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more precisely, a multipartite matching between the vertex sets. There, the goal is to match vertices that are similar in terms of labels and local neighborhoods. Alignments of sequences and ordered forests, however, have a second aspect that does not seem to be considered for graph comparison, namely the idea that an alignment is a superobject from which the constituent input objects can be recovered faithfully as well-defined projections. Progressive alignment algorithms are based on the idea of computing multiple alignments as a pairwise alignment of the alignments of two disjoint subsets of the input objects. Our formal framework guarantees that alignments have compositional properties that make alignments of alignments well-defined. The various similarity-based graph matching constructions do not share this property and solve substantially different optimization problems. We demonstrate that optimal multiple graph alignments can be approximated well by means of progressive alignment schemes. The solution of the pairwise alignment problem is reduced formally to computing maximal common induced subgraphs. Similar to the ambiguities arising from consecutive indels, pairwise alignments of graph alignments require the consideration of ambiguous edges that may appear between alignment columns with complementary gap patterns. We report a simple reference implementation in Python/NetworkX intended to serve as starting point for further developments. The computational feasibility of our approach is demonstrated on test sets of small graphs that mimimc in particular applications to molecular graphs.

TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks

In recent years, two of the largest open-source ground station (GS) networks capable of enabling Earth–satellite communication have emerged: TinyGS and SatNOGS. These open-source projects enable anyone to build their own GS inexpensively and easily, integrate into a GS network, and receive data from satellites listed in the database. Additionally, it enables satellite developers to add satellites to the databases of these projects and take advantage of this GS network to receive data from the satellites. This article introduces the TinyGS and SatNOGS projects and conducts a comparative analysis between them. Generally, the TinyGS project seems to have simpler implementation as well as lower associated costs. In a deeper analysis, it was observed that on the 29 July 2023, the TinyGS project had a higher number of online GSs and a more favorable geographic distribution. On the other hand, the SatNOGS project managed to communicate and decode a larger number of satellites up to 29 July 2023. Additionally, in both projects, it was noted that frequencies between 436 and 437 had the highest number of satellites with decoded data. Ultimately, the choice between these projects depends on critical parameters defined by the reader.

Accelerating Analytic-Continuation GW Calculations with a Laplace Transform and Natural Auxiliary Functions.

We present a simple and accurate GW implementation based on a combination of a Laplace transform (LT) and other acceleration techniques used in post-self-consistent field quantum chemistry, namely, natural auxiliary functions and the frozen-core approximation. The LT-GW approach combines three major benefits: (a) a small prefactor for computational scaling, (b) easy integration into existing molecular GW implementations, and (c) significant performance improvements for a wide range of possible applications. Illustrating these advantages for systems consisting of up to 352 atoms and 7412 basis functions, we further demonstrate the benefits of this approach combined with an efficient implementation of the Bethe-Salpeter equation.

Experimental demonstration of a photonic reservoir computing system based on Fabry Perot laser for multiple tasks processing

Abstract Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a simple hardware implementation method of RC, has attracted widespread attention. In this paper, we present and experimentally demonstrate a time delay RC system based on a Fabry Perot (FP) laser for multiple tasks processing. Here, the various tasks are attempted to perform in parallel in the multiple longitudinal modes of the FP laser. It is found that the time delay RC system based on the FP laser can successfully handle different tasks across multiple longitudinal modes simultaneously. The experimental results demonstrate the potential of the time delay RC system based on the FP laser to achieve multiple tasks processing, providing various possibilities for improving the information processing ability of neural morphology RC systems, and promoting the development of RC systems.

Fast Power Coefficient vs. Tip–Speed Ratio Curves for Small Wind Turbines with Single-Variable Measurements following a Single Test Run

Small wind turbines (SWTs) face tremendous challenges in being developed into a more reliable and widespread energy solution, with a number of efficiency, reliability, and cost issues that are yet to be resolved. As part of the development stages of an SWT, testing the resulting efficiency and determining appropriate working ranges are of high importance. In this paper, a methodology is presented for testing SWTs to obtain characteristic performance curves such as Cp (power coefficient) vs. TSR (tip–speed ratio), and torque vs. ω, in a simpler and faster yet accurate manner as an alternative energy solution when a wind tunnel is not available. The performance curves are obtained with the SWT mounted on a platform moving along a runway, requiring only a few minutes of data acquisition. Furthermore, it is only required to measure a single variable, i.e., the generator output voltage. A suitable physics-based mathematical model for the system allows for deriving the desired performance curves from this set of minimal data. The methodology was demonstrated by testing a prototype SWT developed by the authors. The tested prototype had a permanent magnet synchronous generator, but the methodology can be applied to any type of generator with a suitable mathematical model. Given its level of simplicity, accuracy, low cost, and ease of implementation, the proposed testing method has advantages that are helpful in the development process of SWTs, especially if access to a proper wind tunnel is prevented for any reason. To validate the methodology, Cp vs. TSR curves were obtained for an SWT prototype tested under different test conditions, arriving always at the same curve as would be expected. In this case, the test prototype reached a maximum power coefficient (Cp) of 0.35 for wind velocities from 20 to 50 km/h for a TSR of 5.5.

The Generalized Long-Term Fault Memory Model and Applications to Paleoseismic Records

ABSTRACT Paleoseismic studies show large variability in earthquake interevent times along a fault, with short intervals often separated by long quiescent periods. Some paleoseismologists have interpreted this variability as a product of an earthquake’s partial strain release with the next earthquake occurring sooner than expected because of the remaining residual strain. However, commonly used probabilistic large earthquake recurrence models attribute this variability purely to chance, not the state of strain on the fault. Here, we present an alternative probabilistic model, built on the long-term fault memory model framework that better reflects the strain accumulation and release process. This generalized long-term fault memory model (GLTFM) predicts that this interevent time variability arises from both chance and the state of strain on the fault. Specifically, it estimates when residual strain is likely present and its impact on the timing of the next earthquake in the sequence. In addition, GLTFM assumes that additional accumulated strain always increases earthquake probability. In contrast, the commonly used lognormal and Brownian passage time models predict that the probability of a large earthquake stays constant or even decreases after it is “overdue” (past the observed average recurrence interval) so additional accumulated strain does not make an earthquake more likely. GLTFM’s simple implementation and versatility should make it a powerful tool in earthquake forecasting.

Single-Phase Charging of Six-Phase Integrated On-Board Battery Charger Using Predictive Current Control

Integrated On-Board Battery Chargers (IOBCs) have shown promise as an elegant charging solution for electric vehicles in recent literature. Although the three-phase charging technique of IOBCs has extensively been discussed in the literature, single-phase charging is still a challenging research topic. The Predictive Current Control (PCC) approach has shown many benefits, including a straightforward algorithm, simple implementation, comparatively quick response, and appropriate performance, when compared to conventional control techniques. This paper investigates the impact of single-phase charging of a six-phase-based IOBC system with different winding configurations using PCC, which, up to the best authors’ knowledge, has not been conceived thus far. Under single-phase charging, the zero-sequence current component is utilized to ensure zero torque production during charging mode. Since the impedance of the zero subspace is highly affected by the employed winding design, the performance of PCC with different winding layouts of either induction machine (IM) or permanent magnet synchronous machine (PMSM) is investigated and compared. The proposed method is experimentally validated using a 1.1kW six-phase IM and a 2 kW 12-slot/10-pole PMSM. Finite Element analysis is also carried out to investigate the effect of single-phase charging mode on the induced radial forces and vibration level when PM machine is employed.

Comprehensive Learning Particle Swarm Optimization Based on Optimal Particle Recombination

Particle swarm optimization (PSO) algorithm has been widely used in large-scale complex problems such as resource allocation in recent years because of its simple implementation and easy operation. However, the slow convergence speed and low solution accuracy of the algorithm also restrict its further application. To solve the above problems, this paper introduces the chromosome crossing characteristics of genetic algorithm (GA), and proposes a comprehensive learning particle swarm optimization based on optimal particle recombination. With the help of the ability of comprehensive learning strategy to efficiently explore the solution space, this method organically combines the excellent information explored by each particle through the optimal particle recombination, so as to obtain a better individual, speed up the convergence speed of the algorithm, and improve the solution accuracy of the problem. The experimental results of benchmark function show that the proposed algorithm has faster convergence speed and optimization accuracy than the original algorithm, and the results of Friedman test and Wilcoxon signed-rank test prove the feasibility of the optimal particle recombination operation in particle swarm optimization.

Optimization of single-beam multiple-intensity reconstruction technique: Select an appropriate diffraction distance

The multi-distance phase retrieval technique is widely used in phase imaging due to its high accuracy, simple implementation, and strong robustness. However, the artificial selection of the diffraction distance for capturing images has been a limitation. This limitation has sparked the desire to improve the accuracy of multi-distance phase retrieval by either capturing more images at different diffraction distances or iterating more times in the algorithm. Unfortunately, these approaches significantly reduce the efficiency of the technique. In this study, we implemented detailed simulations of the single-beam multiple-intensity reconstruction (SBMIR) technique to investigate the relationship between the diffraction distance and phase retrieval accuracy. We conducted experiments using an USAF-1951 sample generated by LCOS-SLM, a circular steps sample, and a biological sample. The results verified that SBMIR achieves higher phase retrieval accuracy and faster efficiency at an optimal diffraction distance. This research provides valuable support for selecting the appropriate diffraction distance in SBMIR, thereby enhancing its accuracy and efficiency.

Energy distribution of fragments from H2 and H2 + dissociation by electron impact for the use in numerical models applied to negative ion sources

In negative hydrogen ion sources, the kinetic energy of the atoms is directly related to the negative ion yield at the caesiated converter, with a larger contribution from hot atoms. The energy distribution of hydrogen atoms is related to the formation process: either the kinetic energy release, resulting from dissociation of the hydrogen molecules or molecular ions, or the proton neutralization either in the volume or during reflection at walls. The interpretation of recent experimental measurements related to the translational energy distribution of atoms or positive ions could profit from accurate inclusion of the initial energy distribution in numerical models. In this work, we focus on the calculation of the kinetic energy release for the various dissociation channels due to electron impact on H2 and H2 +, in the Franck-Condon and delta approximation. Since in negative ion sources non-equilibrium vibrational distributions of H2 are found, the energy distribution of fragments is calculated for all vibrational levels. The inverse cumulative distribution functions related to the main dissociation processes are given, as well as the cumulative distributions for all dissociation channels by electron impact, for simple implementation in Monte Carlo numerical simulations. Finally, the application of the method to few cases of interest for negative ion sources is discussed.

Modeling and simulation of a Renzoku puzzle pattern-based PV array configuration for a partially shaded PV system

The world depends heavily on electrical energy for accessing technologies. For the generation of electricity, technology can utilize renewable energy sources like solar energy and wind energy. Solar photovoltaic (PV) systems occupy space among consumers due to their feasibility, flexibility, cost, and simple implementation procedures. The solar PV system experiences many factors causing power loss like partial shading, hotspots, and diode failure. In this work, a new static PV array configuration, named Renzoku puzzle pattern-based array configuration, is proposed. This proposed configuration technique was designed to overcome the drawbacks of the previously proposed array configurations in terms of power generation, fewer mismatch losses, a high shade-dispersion rate, and consistent performance under any level of partial shading. The proposed array configuration has been validated using both simulation and hardware. The simulation is carried out in a 9 × 9 PV array in MATLAB/Simulink®. The performance analysis, results, and corresponding characteristic curves are presented in this manuscript.

Lithium-Induced Oxygen Vacancies in MnO2@MXene for High-Performance Zinc-Air Batteries.

The traditional methods for creating oxygen vacancies in materials present several challenges and limitations, such as high preparation temperatures, limited oxygen vacancy generation, and morphological destruction, which hinder the application of transition metal oxides in the field of zinc-air batteries (ZABs). In order to address these limitations, we have introduced a pioneering lithium reduction strategy for generating oxygen vacancies in δ-MnO2@MXene composite materials. This strategy stands out for its simplicity of implementation, applicability at room temperature, and preservation of the material's structural integrity. This research demonstrates that aqueous Ov-MnO2@MXene-5, with introduced oxygen vacancies, exhibits an outstanding oxygen reduction reaction (ORR) activity with an ORR half-wave potential reaching 0.787 V. DFT calculations have demonstrated that the enhanced activity could be attributed to adjustments in the electronic structure and alterations in adsorption bond lengths. These adjustments result from the introduction of oxygen vacancies, which in turn promote electron transport and catalytic activity. In the context of zinc-air batteries, cells with Ov-MnO2@MXene-5 as the air cathode exhibit outstanding performance, featuring a significantly improved maximum power density (198.3 mW cm-2) and long-term cycling stability. Through the innovative strategy of introducing oxygen vacancies, this study has successfully enhanced the electrochemical catalytic performance of MnO2, overcoming the limitations associated with traditional methods for creating oxygen vacancies. Consequently, this research opens up new avenues and directions for nonprecious metal catalyst application in ZABs.

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons.

Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities. In this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter. Oxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i)a typical homogeneous reconstruction and (ii)a theoretical layered reconstruction. Results suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude). We have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.

The Potential of Diffusion-Based Near-Infrared Image Colorization.

Camera traps, an invaluable tool for biodiversity monitoring, capture wildlife activities day and night. In low-light conditions, near-infrared (NIR) imaging is commonly employed to capture images without disturbing animals. However, the reflection properties of NIR light differ from those of visible light in terms of chrominance and luminance, creating a notable gap in human perception. Thus, the objective is to enrich near-infrared images with colors, thereby bridging this domain gap. Conventional colorization techniques are ineffective due to the difference between NIR and visible light. Moreover, regular supervised learning methods cannot be applied because paired training data are rare. Solutions to such unpaired image-to-image translation problems currently commonly involve generative adversarial networks (GANs), but recently, diffusion models gained attention for their superior performance in various tasks. In response to this, we present a novel framework utilizing diffusion models for the colorization of NIR images. This framework allows efficient implementation of various methods for colorizing NIR images. We show NIR colorization is primarily controlled by the translation of the near-infrared intensities to those of visible light. The experimental evaluation of three implementations with increasing complexity shows that even a simple implementation inspired by visible-near-infrared (VIS-NIR) fusion rivals GANs. Moreover, we show that the third implementation is capable of outperforming GANs. With our study, we introduce an intersection field joining the research areas of diffusion models, NIR colorization, and VIS-NIR fusion.

Implementing perceptrons by means of water-based computing

Water-based computing emerged as a branch of membrane computing in which water tanks act as permeable membranes connected via pipes. Valves residing at the pipes control the flow of water in terms of processing rules. Resulting water tank systems provide a promising platform for exploration and for case studies of information processing by flow of liquid media like water. We first discuss the possibility of realizing a single layer neural network using tanks and pipes systems. Moreover, we discuss the possibility to create a multi-layer neural network, which could be used to solve more complex problems. Two different implementations are considered: in a first solution, the weight values of the connections between the network nodes are represented by tanks. This means that the network diagram includes multiplication structures between the weight tanks and the input tanks. The second solution aims at simplifying the network proposed in the previous implementation, by considering the possibility to modify the weight values associated to neuron by varying the diameter of the connecting pipes between the tanks. The multiplication structures are replaced with a timer that regulates the opening of the outlet valves of all the tanks. These two implementations can be compared to evaluate their efficiency, and considerations will be made regarding the simplicity of implementation.

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

AbstractA type‐2 fuzzy logic‐based adaptive backstepping control (T2FABSC) approach is designed for an H‐bridge inverter. This inverter has an LC filter to decrease the level of total harmonic distortion (THD) that can affect the efficiency of the system. Reduction of THD and stability insurance of the filter are challenging trade‐offs, tackled here by a controller design. The performance, however, is not suitable for actual applications under a wider range of disturbances, and the parameters need to be adjusted once more for more dependable operations. Backstepping control is given a Lyapunov definition‐based adaptive mechanism that can improve this scheme's stability and robustness in the face of numerous disturbances. Additionally, the system is viewed as a “Black box” without the need for a precise mathematical model, which might lead to a lighter computing burden and simpler implementation. Moreover, a fuzzy type‐2 structure is adopted that can optimize the gains of the adaptive Backstepping controller (ABSC) in challenging conditions. The antlion optimization algorithm is employed to optimize the parameters of the membership functions in the fuzzy system, hence improving the performance of the controller. In addition, compared with different optimization algorithms, it can more quickly and accurately locate the best solutions. Finally, the findings of both the simulations and the experimental outputs are examined, proving the T2FABSC's significant robustness and faster dynamics in a variety of challenging circumstances.