Set Of Initial Parameters Research Articles

КОМПЛЕКСНИЙ АНАЛІЗ ВЛАСТИВОСТЕЙ КОДУ ХЕММІНГА В СУПЕРПОЗИЦІЇ ТЕОРІЙ КОДУВАННЯ, ЗАВАДОСТІЙКОСТІ ТА ІНФОРМАЦІЇ

In the modern era, the task of transferring and preserving information is extremely important. Errors can often occur when data is transmitted over channels with interference or when it is stored on an unreliable medium. Scientific studies of the properties of electrical signals, which are information carriers in electronic communication systems, traditionally have three directions: the theory of immunity, the theory of information, and the theory of probability. Each of these theories has its own historical lineage, outstanding achievements and untapped potential. But the peculiarity of these studies is that more often they are implemented independently of each other, using their own set of initial parameters, criteria and performance indicators, quantitative characteristics. The article examines the properties of Hamming codes, which have been actively used in practice for more than 70 years, in the context of determining their most effective area of use in various fields. The specific interest in Hamming codes is also caused by the fact that they have a unique property: they are - according to the classical definition - perfect codes. The difficulty lies in the fact that the concept of "the most effective area of use" in each of the mentioned theories is special, therefore, a comprehensive analysis of the properties of the Hamming code in the superposition of the theories of coding, immunity and information is a relevant and interesting research task. From the standpoint of coding theory, Hamming codes have the same correcting ability - they correct only one error in a block. After all, as the length of the block n increases, the redundancy decreases. At the same time, the degree of closeness of the Hamming codes to the Plotkin limit increases. Therefore, preference according to these criteria should be given to Hamming codes with the largest block length n. From the standpoint of the theory of immunity, the best is the code that provides the best correction indicators at the smallest value of the data transmission channel parameter h2 (signal/noise ratio) under the conditions of ensuring the required reliability. Compared to the case of no coding, the shortest code – the Hamming code (7,4) – gives the greatest gain. From the point of view of information theory, the transmission of signals using Hamming codes requires speed correction (acceleration) in the communication channel in order to prevent the loss of source symbols due to the redundancy of the correction code. At the same time, the advantage of the Hamming code (7,4) over other, longer codes remains. According to the integral indicator - the energy costs to achieve the required reliability without losing the speed of information transmission from the source - preference should be given to the Hamming code with parameters (7, 4) with a gain in the signal-to-noise ratio Δh2 = 2.72 dB compared to the case without coding.

Optimization Application of Deep Belief Network in Gear Fault Diagnosis

Deep belief networks (DBNs) in the process of the application of the gear vibration signal fault diagnosis, vector network depth, the number of neurons in hidden layer, set the initial parameters, such as there is a certain blindness, and for different data sets, the influence of initial parameters is different also, in order to get ideal fault recognition rate, the initial parameters have to be repeatedly debugging, This process is not only time-consuming and laborious, but also disadvantageous to its universal application in fault diagnosis. To solve this problem, this paper mainly optimizes the number of hidden layer neurons and learning rate of DBNs. The diagnosis example shows that after optimization, the initial parameter setting is more accurate and efficient, and the fault identification rate is significantly improved.

Uticaj parametara mreže i sagorevanja na CFD simulaciju motora sa varničkim paljenjem

In the current paper, a CFD simulation study on a pent-roof, spark ignition engine was performed. The scope was to analyze the basic parameters that influence the performances of the simulation: mesh configuration, mesh refinement, turbulence model, and flame wrinkling model parameters. It was used as the OpenFOAM solver for spark ignition engines. The solver was modified by adding the layer addition/removal mechanism for the mesh. Further, a Zeldovich extended NOx model was implemented. Four different mesh configurations were tested with different refinement levels. A set of different turbulence models and initial parameters and different flame wrinkling model parameters were tested. It was observed how all these parameters influence the rate of heat release, cylinder pressure, temperature, the level of turbulent kinetic energy, the flame wrinkling factor, and finally the resulting concentration of nitride monoxide.

Fuzzy optimization of route networks urban passenger transport

The work aims to study methods for optimizing the route networks of urban passenger transport with fuzzy initial information since the process of passenger transportation in cities has many factors of uncertainty and stochasticity and is quite complex, including a whole range of initial data. Heuristic fuzzy optimization methods are proposed. The study's main result is the improvement of methods for optimizing the route networks of urban passenger transport. These methods today make it possible to take into account the opposing interests of the participants in the process of transporting passengers in the city, as well as to set a large set of initial parameters and restrictions for the mathematical model of optimizing the urban route network. A fuzzy transport problem has been solved, a computational experiment has been carried out, and results have been obtained to illustrate the proposed method. This method is easy to understand and apply to find the optimal solution in fuzzy transport models of real problems. An algorithm for finding L alternate routes and determining the ideal route for vehicle traffic control based on a graph database allows vehicle traffic control.

Two new mathematical models for two level electricity network design with distributed generation

In the new millennium, traditional electrical power systems have undergone a significant change driven by a set of requirements arising from evolving and changing technology. Thus, fundamental changes have occurred in the way electrical energy is produced, transmitted, and distributed. This situation has revealed the need to expand existing networks or to establish new networks. The available literature revealed that particular attention to the latter one is still limited due to the complexity of the power system. The purpose of this study is to contribute to the body of literature that tries to address the gap at overall design of a power distribution network. Moreover, distributed generation integration is also considered simultaneously with network design. In this paper, a two-level electricity distribution system design with distributed generation (TLEDS_DG) for green-field planning is considered. The TLEDS_DG is defined to find the locations of distribution transformers, to decide the number of distributed generators, and to design underlying two-level network in such a way that demand and capacity constraints are satisfied, and the overall design cost is minimized. Two mathematical models, a node-based and flow-based, are proposed and compared in terms of solution quality and CPU times. Within the flow-based formulation which is a new technique, called node cloning, is used to transform a two-level network into a single-level one. To validate the effectiveness and efficiency of the proposed approach (flow-based formulation with node cloning), we conduct numerical studies based on randomly generated instances up to 300 customers. Additionally, sensitivity analysis is also applied to demonstrate the impacts of initial parameter settings. Computational results on a large suite of test problems show that the proposed approach (flow-based formulation with node cloning) is efficient and highly effective for the generated test problems.

A Novel Functional Electrical Stimulation-Induced Cycling Controller Using Reinforcement Learning to Optimize Online Muscle Activation Pattern

This study introduces a novel controller based on a Reinforcement Learning (RL) algorithm for real-time adaptation of the stimulation pattern during FES-cycling. Core to our approach is the introduction of an RL agent that interacts with the cycling environment and learns through trial and error how to modulate the electrical charge applied to the stimulated muscle groups according to a predefined policy and while tracking a reference cadence. Instead of a static stimulation pattern to be modified by a control law, we hypothesized that a non-stationary baseline set of parameters would better adjust the amount of injected electrical charge to the time-varying characteristics of the musculature. Overground FES-assisted cycling sessions were performed by a subject with spinal cord injury (SCI AIS-A, T8). For tracking a predefined pedaling cadence, two closed-loop control laws were simultaneously used to modulate the pulse intensity of the stimulation channels responsible for evoking the muscle contractions. First, a Proportional-Integral (PI) controller was used to control the current amplitude of the stimulation channels over an initial parameter setting with predefined pulse amplitude, width and fixed frequency parameters. In parallel, an RL algorithm with a decayed-epsilon-greedy strategy was implemented to randomly explore nine different variations of pulse amplitude and width parameters over the same stimulation setting, aiming to adjust the injected electrical charge according to a predefined policy. The performance of this global control strategy was evaluated in two different RL settings and explored in two different cycling scenarios. The participant was able to pedal overground for distances over 3.5 km, and the results evidenced the RL agent learned to modify the stimulation pattern according to the predefined policy and was simultaneously able to track a predefined pedaling cadence. Despite the simplicity of our approach and the existence of more sophisticated RL algorithms, our method can be used to reduce the time needed to define stimulation patterns. Our results suggest interesting research possibilities to be explored in the future to improve cycling performance since more efficient stimulation cost dynamics can be explored and implemented for the agent to learn.

Gradient-based parameter optimization method to determine membrane ionic current composition in human induced pluripotent stem cell-derived cardiomyocytes

Premature cardiac myocytes derived from human induced pluripotent stem cells (hiPSC-CMs) show heterogeneous action potentials (APs), probably due to different expression patterns of membrane ionic currents. We developed a method for determining expression patterns of functional channels in terms of whole-cell ionic conductance (Gx) using individual spontaneous AP configurations. It has been suggested that apparently identical AP configurations can be obtained using different sets of ionic currents in mathematical models of cardiac membrane excitation. If so, the inverse problem of Gx estimation might not be solved. We computationally tested the feasibility of the gradient-based optimization method. For a realistic examination, conventional 'cell-specific models' were prepared by superimposing the model output of AP on each experimental AP recorded by conventional manual adjustment of Gxs of the baseline model. Gxs of 4–6 major ionic currents of the 'cell-specific models' were randomized within a range of ± 5–15% and used as an initial parameter set for the gradient-based automatic Gxs recovery by decreasing the mean square error (MSE) between the target and model output. Plotting all data points of the MSE–Gx relationship during optimization revealed progressive convergence of the randomized population of Gxs to the original value of the cell-specific model with decreasing MSE. The absence of any other local minimum in the global search space was confirmed by mapping the MSE by randomizing Gxs over a range of 0.1–10 times the control. No additional local minimum MSE was obvious in the whole parameter space, in addition to the global minimum of MSE at the default model parameter.

Taguchi Analysis of Natural Frequency for Simply Supported Composite Stiffened Hypars with Perforation

Taguchi based optimization of natural frequency of perforated stiffened hypars is performed to consider the role of fibre lamination, width/thickness ratio of shell and position of perforation centre along x- and y-direction. Natural frequency of stiffened shell is obtained for simply supported boundary condition using finite element procedure based on L27 orthogonal array (OA) considering three settings of each parameter. Main effect plot is analyzed to identify the significant parameters. Natural frequency becomes maximum for a combination of 450 fibre lamination, width/thickness value of 20 and perforation centre position (0.4, 0.4). Interaction graphs identify the interaction parameters. ANOVA study provides the significant contribution of the parameters considered here. Present analysis identifies width/thickness as the most significant factor and other parameters yield very little significance while no interaction is found to be significant. Width/thickness value of shell yields major (98.64%) contribution to natural frequency and other factors yield very little significance. Residual analysis for natural frequency and confirmatory test validate the present study. S/N ratio gets improved by 38.3% at optimal condition compared to the initial parameter setting.

DPCF: A framework for imputing missing values and clustering data in drug discovery process

The advent of modern Internet of Things (IoT) architectures has led to ease in data collection and availability. The data generated from such architectures are of large volume and dimensionality. As a result, data missingness and data labeling are the commonly occurring problems in the data collection process when data volume is too large. Data clustering is a commonly used unsupervised pattern classification technique that helps in identifying the hidden structure of the datasets and clusters or groups similar data items together. In context to chemometrics, clustering techniques play a significant role in identifying the structure-property relationships and structure-activity relationships among different compounds in drug discovery process. The quantitative structure-property relationships (QSPR) are based on the similar property principle which states that if the compounds are clustered together based on structural descriptors, the compounds within the same cluster will have similar properties. The quantitative structure-activity relationships (QSAR) help in determining the empirical relationships between chemical structure and biological activity of a set of identical compounds. Hence, a variety of compounds can be divided into homogeneous subsets using cluster analysis. The effectiveness of any clustering algorithm mostly depends on how efficiently the initial cluster centers are identified. Traditional techniques that mostly rely on the random selection of initial cluster centers and different parameter settings may result in distinct clusters for the same dataset. Moreover, if the initial cluster centers selected are outliers then the clusters formed are of poor quality. The quality of each cluster can be determined by using distinct cluster validation indices (CVIs). This paper aims to solve the problems of data missingness as well as data labeling for unsupervised datasets. In continuation to the previous study where the NMVI (Nullify the Missing Values before Imputation) technique efficiently imputes the missing values in different datasets, in this paper the proposed DPCF (Data Partitioning-based Clustering Framework) framework utilize the NMVI technique to impute the missing values and a novel Z-Clust clustering algorithm is proposed that efficiently clusters the unlabeled data samples. The integration of the NMVI technique and Z-Clust clustering technique in the proposed framework makes it well suited for the analysis of unsupervised datasets having missing values. The performance evaluation of the proposed Z-Clust clustering technique is done by using five standard CVIs and the results are compared with the existing clustering techniques. The experimental results depict that the proposed Z-Clust clustering technique shows better cluster formation as compared to the existing clustering techniques. Henceforth, the proposed DPCF framework is well suited for the analysis of datasets without labels and having missing values.

Improvement of the Classification Method for the Fire Hazard Level in Forests Using Meteorological and Aerospace Data

The article proposes improvements to the method for determining the fire hazard level in forests based on predictive meteorological and aerospace data. A brief analysis of various methods for the fire hazard level determining in forests and the sets of initial parameters used in this case was carried out in order to determine the final method's optimal characteristics and properties for further application. The calculation of the fire hazard assessment is based on the flammability basic indicator according to weather conditions and correction factors based on the vegetation indices values. Vegetation indices division into quartiles is proposed for a vegetation relative assessment in the territory under consideration. Maps of the forest blocks distribution by fire hazard classes for the Volozhinsky forestry enterprise territory were obtained according to the proposed and other methods. A comparative analysis of the numerical and spatial characteristics of the obtained assessments of forests fire hazard using various methods was carried out. According to the improved methodology assessment results, its application results correlate with the assessment according to the standardized methodology, but adjusted according to the vegetation assessment results. It is advisable to verify the proposed methodology during a fire hazard period in real conditions, followed by an assessment of the results and refinement if necessary.

Simulation of Soil Cutting and Power Consumption Optimization of a Typical Rotary Tillage Soil Blade

The rotary tillage knife roller, as one of the typical soil-touching parts of the tillage equipment cutting process, is in direct contact with the soil. During the cutting process, there are problems related to structural bending, deformation, and high power consumption, caused by impact and load, and it is difficult to observe the micro-change law of the rotary tillage tool and soil. In view of the above problems, we took the soil of the cotton experimental field in Shihezi, Xinjiang, and the soil-contacting parts of the rotary tillage equipment, specifically the rotary tiller roller, as the research subject. Using the finite-element method (FEM) to simulate the structure of the rotary tiller with different bending angle parameters, we obtained its average stress and deformation position information, and obtained a range linear relationship between the bending angle and the structural performance of the rotary tiller tool. Using discrete element method (DEM)-based simulation to build the corresponding contact model, soil particle model, and soil–rotary tillage knife roll interaction model to simulate the dynamic process of a rotary tillage knife roll cutting soil, we obtained the change rules of the soil deformation area, cutting process energy, cutting resistance, and soil particle movement. By using the orthogonal simulation test and the response surface method, we optimized the kinematic parameters of the rotary tiller roller and the key design parameters of a single rotary tiller. Taking the reduction of cutting power consumption as the optimization goal and considering the influence of the bending angle on its structural performance, the optimal parameter combination was obtained as follows: the forward speed was 900 m/h, the rotation speed was 100 rad/min, the bending angle was 115°, and the minimum power consumption of the cutter roller was 0.181 kW. The corresponding average stress and deformation were 0.983 mm and 41.826 MPa, which were 15.8%, 13%, and 7.9% lower than the simulation results of power consumption, stress, and deformation under the initial parameter setting, respectively. Finally, the effectiveness of the simulation optimization model in reducing power consumption and the accuracy of the soil-cutting simulation were verified by a rotary tilling inter-field test, which provided theoretical reference and technical support for the design and optimization of other typical soil-touching parts of tillage and related equipment, such as disc harrow, ploughshare, and sub-soiling shovel.

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications. However, improving the training process using proper parameter hyperisations and techniques, such as regularisation and Adam-based optimisation, remains a challenge in the design of DLNs for processing time-series data. Moreover, the most appropriate parameter for the DLN model is to solve the wind-power forecasting problem by considering the excess training algorithms, such as the optimiser, activation function, batch size, and dropout. Reinforcement learning (RN) schemes constitute a smart approach to explore the proper initial parameters for the developed DLN model, considering a balance between exploration and exploitation processes. Therefore, the present study focuses on determining the proper hyperparameters for DLN models using a Q-learning scheme for four developed models. To verify the effectiveness of the developed temporal convolution network (TCN) models, experiments with five different sets of initial parameters for the TCN model were determined by the output results of Q-learning computation. The experimental results showed that the TCN accuracy for 168 h wind power prediction reached a mean absolute percentage error of 1.41%. In evaluating the effectiveness of selection of hyperparameters for the proposed model, the performance of four DLN-based prediction models for power forecasting—TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models—were compared. The overall detection accuracy of the TCN model exhibited higher prediction accuracy compared to canonical recurrent networks (i.e., the GRU, LSTM, and RNN models).

A review of artificial fish swarm algorithms: recent advances and applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models.

Features of solving the problem of condensation of vapor containing solid particles on the edge

THE PURPOSE. To evaluate the effect on the condensation process of contaminants contained in the steam and deposited on the surface of the cooled fins, for which to formulate a mathematical model of the process, to investigate its properties and to obtain quantitative estimates of the characteristics.METHODS. The equations of the mathematical model are based on the laws of conservation of energy and mass, their study and evaluation are carried out using analytical methods of the theory of differential equations, methods of similarity theory and dimensions, as well as numerical methods for solving boundary value problems.RESULTS. The formulation of the problem of condensation of vapor containing solid particles on the edge is formulated. The presence of a self-similar solution is revealed, which is represented as a dimensionless function of one variable, uniform for all sets of initial parameters. The relations describing the distributions of the thickness δ(x, τ) of the sediment and the temperature ϑ(x, τ) on a straight edge of unlimited height (l = ∞) with an initially clean surface (h0 = 0) are obtained. These relations also almost accurately describe the initial stage of such a process on a straight edge of a finite height l, until a sediment of noticeable thickness covers the entire surface of the rib, as well as the initial moments in the case of an edge on a round pipe, until the width of the sediment zone with a noticeable thickness is significantly less than the radius of the pipe.CONCLUSION. Using these distributions as the initial ones for edges of limited height instead of, for example, introducing a uniform initial layer h0, will allow achieving high accuracy of numerical calculations without excessive thickening of the grid in coordinate and time.

Economic Fundamentals for Development of International Road Freight Transportation Logistics in the Republic of Belarus under Coronavirus Pandemic

The paper considers an issue of international logistics development in Belarus under world coronavirus COVID-19 pandemic. As a result of analytical reviewing of the up-to-date scientific literature on the problem under study, the expediency is stated for highlighting the logistics of international road freight transportation as an independent area of theoretical research and practical activity. Further development of international road freight transportation logistics in the Republic of Belarus requires a complex economic analysis of the present state and dynamics of the international road freight transportation market, which is the main purpose of the present publication. The paper contains the results of the analysis of the market for international and transit freight road transportation. Separately the paper points out the dynamics of transit goods transportation segment in this market in response to the reorientation of international freight flows. Interconnection is shown between the volume indices of international road freight transit through the territory of Belarus and the dynamics of goods import to the Russian Federation from the EU countries. For the first time a proposal is given to use the methodological tools of systemmatrix diagnostic analysis for investigation of the macroeconomic development process of international road freight transit logistics. The author’s set of initial parameters for the analysis has been formed, and its results are presented on the basis of the data of the State statistical reporting form for 2019–2020. A suggestion is given for joint usage of system-matrix diagnostic tools in combination with author’s methodology for assessing the effectiveness of transit (international) freight transportation while evaluating the competitiveness of economic entities in the industry. The most typical trends of the market development in the short term are stated, taking into account the more complicated market conditions.

Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs.

The accurate prediction of OATP1B‐mediated drug–drug interactions (DDIs) is challenging for drug development. Here, we report a physiologically‐based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin, and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP‐I) by CysA. In vivo Ki of unbound CysA for OATP1B (Ki,OATP1B), and the overall intrinsic hepatic clearance per body weight of CP‐I (CLint,all,unit) were optimized to account for the CP‐I data (Ki,OATP1B, 0.536 ± 0.041 nM; CLint,all,unit, 41.9 ± 4.3 L/h/kg). DDI simulation using Ki,OATP1B reproduced the dose‐dependent effect of CysA (20 and 75 mg) and the dosing interval (1 and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro Ki,OATP1B failed. The Cluster Gauss–Newton method was used to conduct parameter optimization using 1000 initial parameter sets for the seven pharmacokinetic parameters of CP‐I (β, CLint, all, FaFg, Rdif, fbile, fsyn, and v syn), and Ki,OATP1B and Ki,MRP2 of CysA. Based on the accepted 546 parameter sets, the range of CLint, all and Ki,OATP1B was narrowed, with coefficients of variation of 12.4% and 11.5%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP‐I is a promising translational approach to predict OATP1B‐mediated DDIs in drug development.

Prediction of Air Leakage Rate of Sintering Furnace Based on BP Neural Network Optimized by PSO

Aiming at the difficulty of air leakage detection in the sintering process of the sintering furnace, especially the problems of high detection cost and poor timeliness of detection results when traditional methods are used for detection, we propose an air leakage rate prediction algorithm. Firstly, we use the particle swarm optimization algorithm to optimize the initial parameters of the neural network based on back propagation and get the best set of initial parameters through continuous search. Secondly, the optimized parameters are substituted into the neural network to train them with training data, and the trained parameters are obtained. Finally, the air leakage rate of the test set data is predicted by using the trained parameters. Compared with traditional calculation methods such as gas analysis and calorimetry, the proposed method can greatly simplify the detection process, shorten the detection time, and control the error within 5%, allowing the user to deal with the air leakage problem more timely and improve the overall sintering quality.

Protocol for Directing Nudged Elastic Band Calculations to the Minimum Energy Pathway: Nurturing Errant Calculations Back to Convergence.

The combination of density functional theory (DFT) and the nudged elastic band (NEB) method offers a practical tool for the discovery of underlying reaction mechanisms related to the synthesis of functional materials. However, in practice, the lack of a standardized protocol for minimum energy pathway determination too often leads to an inefficient and computationally intensive design process. To that end, we define a verifiable DFT+NEB protocol for efficiently locating and confirming the transition state of a reaction. To test this assertion, we curate 226 unique reactions within 14 classes of reactions and investigate their performance in terms of the number of NEB iterations they require to locate the transition state and an estimate of the associated mean absolute error. Leveraging this protocol, we demonstrate its application for an initial set of parameters: number of frames, Nframes = 11; maximum step size, Smax = 0.04 Å; optimizer = LBFGS; and spring constant, kspr = 0.1 eV/Å2. We report a convergence rate of 73% and find that a root-mean-square force (FRMS) of 0.01 eV/Å provides a "rule of thumb" below which NEB simulations are likely to converge. Venturing beyond this baseline enquiry, we delineate the effect on performance of altering the number of frames, maximum step size, choice of optimizer and spring constant. We find improvements in performance with increasing Nframes and Smax, ostensibly approaching some asymptotic limit. We also see substantial improvement in efficiency with the LBFGS optimizer and a clear minimum in performance for the spring constant value of 0.1 eV/Å2. Finally, we provide five case studies that demonstrate typical convergence issues for NEB simulations and suggest methods to overcome them. Our results provide specific and transferable recommendations, offering a transparent and practical tool for beginner and expert researchers alike toward a more rational NEB simulation design.

Effects of rescanning parameters on densification and microstructural refinement of 316L stainless steel fabricated by laser powder bed fusion

• The effect of laser power and scan speed on microstructure of 316L SS is explored. • Cell size of single scan can guide microstructural expectation of multilayer scan. • Rescanning with a 2nd parameter achieved 70.2 % cell size refinement. • Laser rescanning increased the relative density by 0.21−0.42%. • Spatially heterogeneous microstructure was achieved by applying rescanning. A challenge with microstructural control and refinement in laser powder bed fusion (LPBF) is maintaining high density when choosing parameters for desired microstructures. Rescanning during LPBF has been reported to improve densification and decrease surface roughness for many different alloys. However, little has been reported regarding the effects of locally rescanning with varying processing parameters on sub-grain cell size refinement for 316L stainless steel (SS). This study presents a novel solution to enable high densification with microstructural control in 316L SS by using a set of initial scanning parameters to achieve densification and a different set of rescanning parameters to refine the microstructure. Results showed that rescanning resulted in heterogeneous microstructure with coarse cell size of 0.84 μm and locally refined cell size of 0.35 μm, while maintaining a high level of densification (99.96 %), therefore enabling potential variations in component strength and hardness. The spatial distribution of local microstructure refinement was dictated by the melt pool dimensions of initial scanning and rescanning relative to the powder layer thickness. To better understand the link between LPBF process parameters and microstructure, the Wilson-Rosenthal equation was used to predict cooling rate ( G × R ) and correlate with sub-grain cell size. Such variation in properties may be useful for applications requiring parts with hardened surfaces, or localized strengthening at stress concentrations and sites of expected failure.

Chaotic attractors that exist only in fractional-order case

IntroductionStudying chaotic dynamics in fractional- and integer-order dynamical systems has let researchers understand and predict the mechanisms of related non-linear phenomena. ObjectivesPhase transitions between the fractional- and integer-order cases is one of the main problems that have been extensively examined by scientists, economists, and engineers. This paper reports the existence of chaotic attractors that exist only in the fractional-order case when using the specific selection of parameter values in a new hyperchaotic (Matouk’s) system. MethodsThis paper discusses stability analysis of the steady-state solutions, existence of hidden chaotic attractors and self-excited chaotic attractors. The results are supported by computing basin sets of attractions, bifurcation diagrams and the Lyapunov exponent spectrum. These tools verify the existence of chaotic dynamics in the fractional-order case; however, the corresponding integer-order counterpart exhibits quasi-periodic dynamics when using the same choice of initial conditions and parameter set. Projective synchronization via non-linear controllers is also achieved between drive and response states of the hidden chaotic attractors of the fractional Matouk’s system. ResultsDynamical analysis and computer simulation results verify that the chaotic attractors exist only in the fractional-order case when using the specific selection of parameter values in the Matouk’s hyperchaotic system. ConclusionsAn example of the existence of hidden and self-excited chaotic attractors that appears only in the fractional-order case is discussed. So, the obtained results give the first example that shows chaotic states are not necessarily transmitted between fractional- and integer-order dynamical systems when using a specific selection of parameter values. Chaos synchronization using the hidden attractors’ manifolds provides new challenges in chaos-based applications to technology and industrial fields.