Determination Of Optimal Parameters Research Articles

Strength Simulator Codes for Different Age Groups Considering Physiological Processes of Adaptation in Conditions of Strength Fitness

Topicality. Determination of optimal load parameters in fitness, based on age-related physiological features and adaptive changes of the neuromuscular system, is one of the most debated issues among leading scientists in this field. The research purpose was to develop models of code combinations for strength training of different age groups, considering physiological processes of human body adaptation to a stressful physical stimulus in the conditions of strength fitness. Methods. A comparative analysis of the research results on the effectiveness of using the optimal combination between the parameters of an external physical stimulus and the physiological features of the human body adaptive reactions has been used over the research. The results. It was established that in the process of developing code combinations for different age groups` strength training, the nature of the physiological reactions of their neuromuscular system to a stressful stimulus depend on the features of the load regime and the structure of the exercise program. For adolescence, considering the peculiarities of the physiological process of adaptation, it is used of moderate-intensity activity in combination with a complex of isolation exercises that promotes the processes of intra-muscular and inter-muscular coordination. For youth, in the process of developing code combinations aimed at the development of the main types of strength, the entire range of load modes is actively used. Particular attention is paid to stimuli that contribute to increasing the number of active (FF) motor units. In adulthood, especially in the second period, the content of code combinations to increase strength capabilities changes significantly. In the vast majority, this applies to the parameters of the intensity of load modes, the use of which will selectively affect the hypertrophy of a certain type of muscle fibers within intermuscular coordination. For the elderly, in the process of developing code combinations for strength training, priority is given to the selective activity of agonists during moderate-intensity isolated exercises. Findings. Strength Simulator codes considering physiological processes of the neuromuscular system adaptation to a stressful physical stimulus, is one of the innovative mechanisms for improving the training program of different age groups in the conditions of strength fitness.

AutoATES v2.0: Automated Avalanche Terrain Exposure Scale mapping

Abstract. Avalanche risk assessment is complex and challenging, with terrain assessment as one of the most fundamental factors. To aid people's terrain assessment, Parks Canada developed the Avalanche Terrain Exposure Scale (ATES), a system that classifies the severity of avalanche terrain into five classes from non-avalanche terrain to extreme terrain. Manual classification is laborious and dependent on expert's assessments. To ease the process Larsen et al. (2020) developed an automated ATES model (AutoATES v1.0). Although the model allowed large-scale mapping, it had some significant limitations. This paper presents an improved AutoATES v2.0 model improving the potential release area (PRA) model, utilizing the new Flow-Py runout simulation package. Furthermore, it incorporates forest density data in the PRA, in Flow-Py, and in a newly developed post-forest-classification step. AutoATES v2.0 has also been rewritten in open-source software, making it more widely available. The paper includes a validation of the model measured against two consensus maps made by three experts at two different locations in western Canada. For Bow Summit, the F1 score (a measure of how well the model performs) improved from 64 % to 77 %. For Connaught Creek, the F1 score improved from 40 % to 71 %. The main challenge limiting large-scale ATES classification is the determination of optimal input parameters for different regions and climates. In areas where AutoATES v2.0 is applied, it can be a valuable tool for avalanche risk assessment and decision-making. Ultimately, our goal is for AutoATES v2.0 to enable efficient, regional-scale, and potentially global ATES mapping in a standardized manner rather than based solely on expert judgment.

SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.

Determination of Optimal Machining Parameters Based on Roughness and Vibration Measurements of Pieces Produced by Whirling on a Lathe Machine

Worms can be produced using special machines or standard lathes equipped with a whirling thread-cutting device. A blank is placed on the mandrel and tightened using the three-jawed chuck of the standard lathe. If the workpiece diameter is excessively large, passage through the driven pulley is not possible, and the workpiece cannot be supported. Therefore, a new tool holder for whirling devices is needed. During the whirling process, vibrations in the form of machine velocity amplitudes were measured. After whirling was complete, roughness values were calculated. Using numerical procedures of Wolfram Mathematica 10, vibration peaks were extracted, from which frequencies and maximum amplitudes were determined. The data were then inputted into Design Expert, and the rotational speed and amount of separated material were optimized. The results of the study showed that the quality of the processed surface did not improve with processing in two passes of the tool. The measured vibration amplitudes on the lathe carrier and thread whirling attachment increased with cutting speed at the same cutting depth, whereas the quality of the machined surface was best at the smallest and largest cutting depths.

EspalomaCharge: Machine Learning-Enabled Ultrafast Partial Charge Assignment.

Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge.

Effect of scour on the fatigue life of offshore wind turbines and its prevention through passive structural control

Abstract. Offshore wind turbine (OWT) support structures are exposed to the risk of fatigue damage and scour, and this risk can be effectively mitigated by installing structural control devices such as tuned mass dampers (TMDs). However, time-varying scour altering OWTs' dynamic characteristics has an impact on the TMD design and fatigue life, which has rarely been studied before. In this paper, a simplified modal model is used to investigate the influence of scour and a TMD on the fatigue life evaluation of a 5 MW OWT's support structure, and a traditional method and a newly developed optimization technique are both presented to obtain TMD parameters. This optimization technique aims at finding optimal parameters of the TMD which maximize the fatigue life of a hotspot at the mudline, and the effect of time-varying scour can be considered. This study assumes that the TMD operates in the fore–aft (FA) direction, while the vibration in the side–side (SS) direction is uncontrolled. Results show that scour can decrease the fatigue life by about 24.1 % and that the TMD can effectively suppress vibration and increase the fatigue life. When the scour depth reaches 1.3 times the pile diameter, the TMD with a mass ratio of 1 % can increase the fatigue life of an OWT's support structure by about 64.6 %. Further, it is found that the fatigue life can be extended by 25 % with the TMD optimized by the proposed optimization technique rather than using a traditional design method which does not take the change in dynamic characteristics into account.

Erosion Wear Analysis on Valve Cage of Cage-Typed Sleeve Control Valve for Coal Liquefaction

Abstract Cage-typed sleeve control valve (CSCV) is the key basic equipment in direct coal liquefaction projects. The working condition of CSCV has the characteristics of high-pressure difference, high velocity, and high solid content. There is a general issue of liquid–solid two-phase erosion wear in CSCV, which can easily lead to the failure of the internal structure in the valve cage. Therefore, it is necessary to study erosion wear characteristics of internal structures in the valve cage. Considering the real conditions of erosion wear in the valve cage, a simplified T-shaped flow path is designed, and the precision of both the liquid–solid two-phase flow model and the erosion prediction model is validated. The flow characteristics and erosion wear characteristics in the T-shaped flow path under different working conditions are studied. Based on the simulation results of different structural parameters and boundary conditions, the erosion wear of the T-shaped flow path is predicted and calculated by the response surface method. Subsequently, the prediction formula for the maximum erosion rate is derived. The formula enables the swift determination of optimal structural parameters for the flow path, aiming to mitigate damage to the valve caused by erosion wear. This work can quickly predict the erosion wear rate of the key areas in the valve cage, which can provide a certain reference value for the life prediction and structural optimization of CSCV, and it can also benefit the safety and maintenance of the coal liquefaction system.

Determination of Optimal Locations and Parameters of Passive Harmonic Filters in Unbalanced Systems Using the Multiobjective Genetic Algorithm

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed.

Exploring porous sulfur copolymers for efficient removal of heavy metal ions from wastewater: A computational study

This study aims to evaluate the efficacy of porous sulfur copolymers (PSSD and NaSD) in adsorbing heavy metals from wastewater through an inverse vulcanization process. A computational fluid dynamics (CFD) model is developed and validated with experimental data to analyze the heavy metal removal by these materials. The results indicate that Arsenic ions exhibit the highest adsorption capacity compared to cadmium, mercury, and lead, indicating a stronger affinity of porous materials for arsenic ions. Lead ions show lower adsorption due to their larger ionic radius and lower charge density in PSSD. The CFD modeling assesses the adsorption process based on material position, revealing that in PSSD, maximum adsorption occurs at 1.5 cm thickness, while in NaSD, lead inhibits adsorption around 1 cm, with other metals continuing slow adsorption until 2 cm. The results also indicate that two parameter isothermal models indicate that the Langmuir model best fits the adsorption data and three isothermal models stipulate that the sips model is suitable for adsorption data. In the case of PSSD, the maximum adsorption trend is; Arsenic (289 µg/g) > cadmium (228 µg/g) > mercury (207 µg/g) > lead (188 µg/g) ions, respectively. The adsorption efficiency of adsorbents is 60–85 % for heavy metal ions removal. This study holds promise for industrial applications due to the availability of bulk sulfur for porous copolymer production. Leveraging CFD simulations enables rapid determination of optimal process parameters tailored to individual chemical plants, thereby enhancing operational efficiency. This groundbreaking approach enables accurate prediction of adsorption data for various heavy metals under consistent conditions, revolutionizing industrial applications.

Analysis of a Two-Step Gradient Method with Two Momentum Parameters for Strongly Convex Unconstrained Optimization

The paper is devoted to the theoretical and numerical analysis of the two-step method, constructed as a modification of Polyak’s heavy ball method with the inclusion of an additional momentum parameter. For the quadratic case, the convergence conditions are obtained with the use of the first Lyapunov method. For the non-quadratic case, sufficiently smooth strongly convex functions are obtained, and these conditions guarantee local convergence.An approach to finding optimal parameter values based on the solution of a constrained optimization problem is proposed. The effect of an additional parameter on the convergence rate is analyzed. With the use of an ordinary differential equation, equivalent to the method, the damping effect of this parameter on the oscillations, which is typical for the non-monotonic convergence of the heavy ball method, is demonstrated. In different numerical examples for non-quadratic convex and non-convex test functions and machine learning problems (regularized smoothed elastic net regression, logistic regression, and recurrent neural network training), the positive influence of an additional parameter value on the convergence process is demonstrated.

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators.

Soft robots that incorporate elastomeric matrices and flexible materials have gained attention for their unique capabilities, surpassing those of rigid robots, with increased degrees of freedom and movement. Research has highlighted the adaptability, agility, and sensitivity of soft robotic actuators in various applications, including industrial grippers, locomotive robots, wearable assistive devices, and more. It has been demonstrated that bellow-shaped actuators exhibit greater efficiency compared to uniformly shaped fiber-reinforced actuators as they require less input pressure to achieve a comparable range of motion (ROM). Nevertheless, the mathematical quantification of the performance of bellow-based soft fluidic actuators is not well established due to their inherent non-uniform and complex structure, particularly when compared to fiber-reinforced actuators. Furthermore, the design of bellow dimensions is mostly based on intuition without standardized guidance and criteria. This article presents a comprehensive description of the quasi-static analytical modeling process used to analyze bellow-based soft actuators with linear extension. The results of the models are validated through finite element method (FEM) simulations and experimental testing, considering elongation in free space under fluidic pressurization. This study facilitates the determination of optimal geometrical parameters for bellow-based actuators, allowing for effective biomimetic robot design optimization and performance prediction.

CaSSiDI: novel single-cell “Cluster Similarity Scoring and Distinction Index” reveals critical functions for PirB and context-dependent Cebpb repression

PirB is an inhibitory cell surface receptor particularly prominent on myeloid cells. PirB curtails the phenotypes of activated macrophages during inflammation or tumorigenesis, but its functions in macrophage homeostasis are obscure. To elucidate PirB-related functions in macrophages at steady-state, we generated and compared single-cell RNA-sequencing (scRNAseq) datasets obtained from myeloid cell subsets of wild type (WT) and PirB-deficient knockout (PirB KO) mice. To facilitate this analysis, we developed a novel approach to clustering parameter optimization called “Cluster Similarity Scoring and Distinction Index” (CaSSiDI). We demonstrate that CaSSiDI is an adaptable computational framework that facilitates tandem analysis of two scRNAseq datasets by optimizing clustering parameters. We further show that CaSSiDI offers more advantages than a standard Seurat analysis because it allows direct comparison of two or more independently clustered datasets, thereby alleviating the need for batch-correction while identifying the most similar and different clusters. Using CaSSiDI, we found that PirB is a novel regulator of Cebpb expression that controls the generation of Ly6Clo patrolling monocytes and the expansion properties of peritoneal macrophages. PirB’s effect on Cebpb is tissue-specific since it was not observed in splenic red pulp macrophages (RPMs). However, CaSSiDI revealed a segregation of the WT RPM population into a CD68loIrf8+ “neuronal-primed” subset and an CD68hiFtl1+ “iron-loaded” subset. Our results establish the utility of CaSSiDI for single-cell assay analyses and the determination of optimal clustering parameters. Our application of CaSSiDI in this study has revealed previously unknown roles for PirB in myeloid cell populations. In particular, we have discovered homeostatic functions for PirB that are related to Cebpb expression in distinct macrophage subsets.

Flutter performance simulation on streamlined bridge deck with active aerodynamic flaps

AbstractActive aerodynamic flaps can effectively improve the aerodynamic stability of bridges; however, the determination of optimal control parameters often requires a large number of experiments. This study proposes a method for determining the optimal control parameters of active flaps based on the surrogate model and computational fluid dynamics (CFD) simulation technology. The computational fluid dynamics method is used to calculate the flutter derivatives of the deck–flap system under a small number of control parameters, and then the surrogate model is used to extend the flutter derivatives to the design domain of entire control parameters. Furthermore, the changing trend of flutter performance under flap control parameters can be obtained. This method can avoid complex and expensive active control wind tunnel tests and obtain more accurate optimal control parameters at less cost, which is helpful for the convenient application of active control technology in actual engineering.

A simple heuristic for computing non-stationary inventory policies based on function approximation

We consider a finite-horizon periodic-review inventory system with fixed replenishment costs that faces non-stationary demands. The structure of the optimal control policy for this system has long been known. However, finding optimal policy parameters requires solving a large-scale stochastic dynamic program. To circumvent this, we devise a recursion-free approximation for the cost function of the problem. This translates into an efficient and effective heuristic to compute policy parameters that significantly outperforms earlier heuristics. Our approach is easy-to-understand and easy-to-use as it follows by elementary methods of shortest paths and convex minimization.

Determining optimal process means in a multi-stage production system with inspection errors in 100% inspection

ABSTRACT The problem of determining optimal process parameters for multiple processes has not been widely addressed in the literature. Another important consideration that is frequently overlooked is the presense of inspection error during any inspection process due to factors related to human inspectors and measuring instuments. This paper investigates the optimal determination of process parameters in a multi-stage production system in the presence of inspection error. A mathematical model was developed for two processes producing a single product with two quality characteristics. The first quality characteristic was determined by the first process, and the second quality characteristic was determined by both processes. A 100% inspection was used as a means for product quality control and assumed to be error prone. A real case was used to test the developed model. Inspection error was found to have an impact on machine settings and hence the expected profit. The developed model provides quality managers with a mechanism to optimize their processes.

Investigation and optimization of multiple objectives by hybrid evolutionary algorithms for turning of Nimonic 80A under nano MQL environment

ABSTRACT Nimonic80A is one of the hard-to-cut superalloy materials. However, machining the same remains difficult. The present study’s objective is attained by taking surface quality and tool wear as output core attributes of machining by deploying conventional statistical approaches and meta-heuristic optimisation algorithms to find optimal input attributes. Optimal input parameters were found using ANN hybridised with meta-heuristic optimisation algorithms, considering cutting acceleration as one of the response attributes along with cutting force (Fz), flank wear (Vb) and surface roughness (Ra). In this process, nanofluids were formulated by suspensions of graphene oxide into rice bran oil as the minimum quantity lubrication (MQL). Experiments were conducted based on L27 Taguchi experimental trials, and the experimental results revealed that minimum feed rate and cutting velocity reduced the cutting force, surface roughness, and tool flank wear up to 51% during nano-MQL turning conditions compared to dry machining. Thus, an artificial neural network (ANN) was used to generate the model for each machining response, which was further interfaced with multiobjective particle swarm optimisation (MOPSO) and multiobjective mayfly algorithms (MOMA) as a hybrid algorithm for finding optimal parameters. The optimal results were compared, and ANN-MOMA was found to be better.

Navigating through Complexity: Optimizing Cathodes for Organic Electrohydrogenation through Coherent Workflows

AbstractElectrochemical hydrogenation reactions offer a green and sustainable production pathway for both bulk and fine chemicals employed in the modern chemical industry. However, optimizing such systems can be tremendously challenging due to the number of variables that potentially influence the overall performance. The tailored and scalable electrode fabrication via catalytic inks can be especially difficult due to the complex interplay of material and process variables during the formulation of the inks and their deposition on suitable substrates. As a result, the significance of each variable must be systematically investigated to reveal the high‐impact and low‐impact variables, enabling rapid progression towards finding optimal conditions and parameters. In this work, we present an adaptable, coherent workflow to proficiently optimize electrode fabrication for electrochemical hydrogenation reactions with well‐adjustable experimental effort. Using the hydrogenation of phenylacetylene as a model reaction in a scalable zero‐gap reactor, we demonstrate the influence of deposition techniques, substrates, and catalyst loadings as well as the interactions of binders and additives on the electrochemical performance. Future works can greatly benefit from this coherent workflow as it enables direct comparability between datasets and functional, multidimensional optimization, hastening the rate at which new material systems are understood, reach maturity, and become industrially relevant.

Determination of Optimal Technological Parameters for Sorting Wheat Grains in Chambers of Different Constructions

In order to extend grain’s storage time and ensure its quality, it is necessary to sort and clean it. The aim of this study was to justify the rational shape of the sorting chamber and the optimal technological parameters for the sorting of wheat grains in airflow. This study used newly designed grain sorting chambers with constant, widening, and narrowing cross-sections for the airflow sorting of “Skagen” wheat grain. The aerodynamic properties of wheat grains were investigated when moisture was at 14 ± 2.0%. The grain flow rate in the chambers varied from 4 to 12 kg min−1 every 2 kg min−1. In addition, the airflow velocity varied from 8 to 12 m s−1 every 1 m s−1. The tilt angle of the constant cross-section camera was increased to 5°. Experimental studies have determined a terminal airflow velocity of 11.53 m s−1 for wheat grains. At the terminal airflow velocity, the grain flight coefficient was obtained to be about 0.074. These studies showed that the narrowing chamber is preferable for lower grain flow rates compared to the constant cross-section of the chamber. The widening chamber requires a lower airflow velocity to achieve the same performance and quality as the other chambers.

Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq

This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight.

Optimization of Butt-joint laser welding parameters for elimination of angular distortion using High-fidelity simulations and Machine learning

The high temperature gradient and high cooling rate induced in the butt-joint laser welding process generate a high residual stress, which can result in significant distortion of the final weld. Previous researches usually employed experimental approach to search for optimal parameters which is costly and time consuming. Additionally, there was several studies that used simulation to compute the distortion and to find optimal parameters, however, these studies neglect the effect of solid cooling rate and size of the heat-affected zone (HAZ) in the process of finding optimal parameters. Accordingly, the present study proposes a new systematic framework based on finite element (FE) simulations, regression analysis, and two artificial neural networks (ANNs) for determining the laser welding parameters (i.e., the laser power and welding speed) which minimize the angular distortion of the laser-welded butt joint and enhance the mechanical properties. In the proposed approach, to determine the variables of the selected double-conical heat source model for any combination of the laser power and welding speed is studied within the feasible design space. The variables are then imported into a three-dimensional COMSOL Multiphysics FE model to predict the melt pool dimensions, solid cooling rate, and size of the HAZ as a function of the laser power and welding speed. Finally, the processing map produced by the ANN is screened by four quality criteria based on the melt pool depth, angular distortion, cooling rate, and HAZ width, respectively, to determine the optimal laser welding parameters which minimize the angular distortion and maximize the mechanical properties. It shows that SS316L tensile test specimens fabricated using the optimal parameters have a tensile strength (662 MPa) which is higher than that of the base metal (660 MPa). Furthermore, the angular distortion of the butt-joint samples is less than 564 µm. Thus, the effectiveness of the proposed optimization procedure is confirmed.