Optimal Set Of Parameters Research Articles

Smoother: on-the-fly processing of interactome data using prefix sums.

Nucleic acid interactome data, such as chromosome conformation capture data and RNA-DNA interactome data, are currently analyzed via pipelines that must be rerun for each new parameter set. A more dynamic approach is desirable since the optimal parameter set is commonly unknown ahead of time and rerunning pipelines is a time-consuming process. We have developed an approach fast enough to process interactome data on-the-fly using a sparse prefix sum index. With this index, we created Smoother, a flexible, multifeatured visualization and analysis tool that allows interactive filtering, e.g. by mapping quality, almost instant comparisons between different normalization approaches, e.g. iterative correction, and ploidy correction. Further, Smoother can overlay other sequencing data or genomic annotations, compare different samples, and perform virtual 4C analysis. Smoother permits a novel way to interact with and explore interactome data, fostering comprehensive, high-quality data analysis. Smoother is available at https://github.com/Siegel-Lab/BioSmoother under the MIT license.

Research on production and testing of antifungal activity Phytophthora spp. causing fruit rot and Colletotrichum spp. causing anthracnose of silver nanoparticles, chitosan and turmeric resin bioproducts

In this paper, the research team made samples of bio-products (BN) of turmeric resin combined with silver nanoparticles and chitosan and investigated the factors affecting the production of BN preparations. Specifically, the results of research on the production of BN preparations including ingredients, additives, mixing ratios, and technological parameters have been calculated and tested to obtain nano-preparations with the highest durability and dispersion. The experimental results obtained the optimal set of parameters for production at a pilot scale of 5 litres/batch are the ratio of active ingredients/additives =3/1, the optimal temperature for the mixing process is 40-45oC, the stirring speed is 500 rpm, and the time is 60 minutes. From here, we have successfully fabricated 18 samples of BN1-BN18 preparations with colours ranging from light yellow to dark yellow. Antifungal activity was tested against Colletotrichum spp. causing anthracnose disease and fruit rot disease caused by Phytophthora spp., the results showed that BN1-BN18 products exhibited good antifungal activity, and antifungal activity increased gradually when the content of turmeric oil resin increased.

Investigating the properties of the water−vapor and water−graphite interfaces by PC-SAFT based density functional theory: A comparative study of different association functionals and PC-SAFT water parameter sets

In this work, we apply the perturbed-chain statistical associating fluid theory (PC-SAFT) based classical density functional theory to investigate the thermodynamics and structures of the water−vapor and water−graphite interfaces. For the water−vapor interface, we examine the surface tension as well as structural characteristics such as density profiles and hydrogen bonding distributions along the interface. For the water−graphite interface, we investigate these same structural features at a contact angle of 90° at 300 K. Specifically, we focus on comparing the results obtained from the three association functionals and six PC-SAFT water parameter sets from literature with experimental data and molecular simulation results. We find that the three association functionals exhibit qualitatively similar behavior in the calculation of surface tension, and it is more influenced by the parameter set rather than the association functional. The optimal functional or parameter set for the calculation of density profile of the vapor − liquid interface remains inconclusive, whereas preferable parameter sets and functionals have been identified for the water−graphite interface. Furthermore, the three functionals demonstrate a remarkably similar and qualitatively accurate depiction of the hydrogen bonding structures at both interfaces when compared with molecular simulation results. The level of quantitative accuracy, however, relies on whether the parameter set accurately reproduces the average number of hydrogen bonds of bulk water as observed in this particular set of molecular simulation results. The results of the present work offer valuable guidance for further investigation into the adsorption and wetting behavior of water on graphite-like surface, as well as the properties of water confined at the nanoscale.

Multi-objective optimization of a pipe energy pile with heat exchanger pipes subjected to chaotic advection

Shallow geothermal energy extraction through an energy pile system relies on the constant temperature available below the earth's surface at shallow depths. The energy pile system transfers the heat stored in the earth to a place of utility for space heating and cooling through fluid circulation, thus extracting geothermal energy. In this study, a 3D energy pile model comprising a helical heat exchanger pipe (HHEP) was developed and validated with the existing experimental data using a FEM-based software package. Further, it was hypothesized that replacing HHEP with a chaotic configured helical heat exchanger pipe (CHEP) would enhance thermal performance. The numerical analyses supported the hypothesis, where there was about a 15 % increase in heat release rate for energy piles containing CHEP. The above results form a basis for exploring the simulation results through laboratory scale model tests. Thus, an experimental setup was fabricated to conduct geothermal energy tests on a scaled pipe energy pile (PEP) model. The investigation was carried out by considering three design-input parameters: flow rate, pitch, and inlet temperature at three different levels with water and ethylene glycol as heat exchanger fluid (HEF) in a 4:1 ratio. The heat release rate of the PEP (Qp), heat flux per unit length of HEP (qpl), soil excess temperature (θs), total thermal resistance (TTR), and energy consumed (EC) were analyzed. Furthermore, multi-objective optimization using the MO-Jaya algorithm and multiple attribute decision-making (MADM) R-method were used to predict an optimal set of design-input parameters. Finally, a confirmation test was conducted to test the competence of the predicted parameters. The percentage variation between the predicted design input parameters and the experimental values was below 10 %.

Mapping Groundwater Potential (GWP) in the Al-Ahsa Oasis, Eastern Saudi Arabia Using Data-Driven GIS Techniques

Searching for new sources of water is becoming one of the most important aspects of scientific research, especially in areas prone to drought, like Saudi Arabia. The study aim was to delineate groundwater potential zones within the Oasis of Al-Ahsa, in Saudi Arabia’s eastern region, and to identify the optimum factors that control the availability of groundwater zones. This was achieved through examining the effect of ten environmental variables on groundwater recharge, namely: slope, topographic wetness index (TWI), land cover (LC), elevation, lineament density (Ld), drainage density (Dd), rainfall, geology, and soil texture. The variables were prepared from a variety of data sources, including spatial data (i.e., DEM and Landsat-8 image), in addition to other complementing data sources for appropriate parameters extraction. Two weighted overlay methods were used, namely the simple additive weight (SAW) as well as the optimum index factor (OIF) in order to categorize the optimal set of parameters for computing GWP and identifying its zones. Two GWP maps were obtained and validated through comparison with the locations of existing wells at GWP zones. The study findings have assured the cogency of the SAW map, where it was found that nearly 45–48% of the resultant zones were characterized as in the “moderate” class, whereas around 21–37% of the entire zones area were classified within the “high” class. The soil texture parameter was determined as being the most influencing parameter for GWP mapping followed by the “geology” parameter; however, the “lineament density” (Ld) was the least important factor. Furthermore, the OIF method has facilitated the identification of the optimal parameter combination for delineating groundwater potential (GWP) zones, which included “Ld”, “land cover”, and “TWI”. The study findings and methodology can serve as a potential model for other similar regions, supporting sustainable water resource management locally as well as globally.

Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures

Increasingly often, due to the high sensitivity level of diagnostic systems, they are also sensitive to the occurrence of a significant number of false alarms. In particular, in structural health monitoring (SHM), the problem of optimal sensor placement (OSP) is appearing due to the need to reach a balance between performance and cost of the diagnostic system. The applied approach of considering nondominated solutions allows for adaption of the system parameters to the user’s expectations, treating this optimization problem as multi-objective. For this purpose, the NSGA-II algorithm was selected for the determination of an optimal set of parameters in the OSP problem for the detection of delamination in composite structures. The objectives comprise minimization of type-I and type-II errors, and number of sensors to be placed. The advantage of the proposed approach is that it is based on experimental data from the healthy structure, whereas all cases with a presence of delamination were acquired from numerical experiments. This makes it possible to develop a customized SHM system for the arbitrary location of damage.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Automated parameter tuning with accuracy control for efficient reservoir simulations

Computer simulations of complex physical processes typically require sophisticated numerical schemes that internally involve many parameters. Different choices of such internal numerical parameters may lead to considerably different levels of computational efficiency, some may even result in wrong simulation results. The task of finding an optimal set of the numerical parameters (e.g. for the purpose of minimising the simulation time), while ensuring an accepted level of numerical accuracy, is therefore extremely important but challenging. In this paper, we propose a new automated search algorithm that is based on constrained stochastic searches within the parameter space. This iterative search scheme is also equipped with an accuracy check, which adopts several complementary measures for quantifying the similarities between time series from different simulations, such that parameter choices that lead to insufficiently accurate results will be automatically rejected. As a concrete scenario of usage, we have applied the automated parameter search scheme to the open-source reservoir simulation framework OPM. An empirical study shows that a suitable design of the optimisation objective function, together with an appropriate choice of the number of trials per search iteration and the perturbation scale per trial, can produce fast and convergent improvements with respect to the optimisation objective. For example, for a set of 12 numerical parameters, 30 trials from five search iterations are sufficient for reducing the objective function by 30% for the open Norne black-oil reservoir model. The robustness of the automated search scheme is also demonstrated for two other open reservoir models. Moreover, it is found that the parameter values automatically identified for the Norne model can also greatly improve the simulation efficiency of another proprietary reservoir model that has drastically different scale, resolution and geological properties.

Statistical-experimental modeling of the effect of process parameters on geometric characteristics of laser cladding of stellite 6 on SS316 using second-order regression

Applications of 316 stainless steel can be developed by laser cladding of the Co-based superalloys, which has multiple enhancement properties and creates a small heat affected zone. Quantitive investigation of the effect of process parameters on geometrical features and providing reliable models to achieve the desirable metallurgical bond, minimal structural defects, and predicting the dimensions of the geometry without conducting excessive experimental tests are an important issue. This study evaluate the effects of the main process parameters of laser cladding on geometric characteristics of stellite 6 using the response surface methodology. A quadratic model was developed for each of the geometric characteristics. Laser power, scanning speed, and powder feed rate ranged between 400 and 600 W, 6–10 mm/s and 12–20 gr/min, respectively, were considered as the input variables and height, width, penetration depth and dilution were considered as responses. The experiments were designed using the box-behnken method. The accuracy and fitness of the models were evaluated using analysis of variance method. The results indicated the R2 factors obtained for height, width, penetration depth and dilution are equal to 0.89, 0.98, 0.98 and 0.96 respectively. After obtaining the desirability factor of 0.795, the optimal set of process parameters were selected as laser power of 510 W, scanning speed of 10 mm/s and powder feed rate of 13 gr/min. Verification of the optimization was done by performing experimental test,which maximum 13 % difference between actual values and the predicted values was obtained.

Influence of Printing Parameters on Mechanical Behavior of 3D-Printed PLA Using Taguchi Based ANFIS Technique

This paper presents the problem of determining the optimal input process parameters of a Fused Deposition Modelling 3D printer for improved mechanical strength of the 3D printed objects. Polylactic Acid material tensile test and Izod impact test specimen are printed as per the ASTM standards. Various critical input parameters infill density, infill pattern, raster angle and number of contours are analysed on the material. The tensile and impact strengths were determined by conducting 16 experiments using a Tensometer for tensile test while a pendulum impact test is used for Izod impact test. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is used to train input and output data and optimal parameters are obtained for improved mechanical strength. The tensile strength and impact strength have been improved by 19.8% and 18.3% respectively with the optimal set of input parameters determined in the analysis.

Imitation Learning of Complex Behaviors for Multiple Drones with Limited Vision

Navigating multiple drones autonomously in complex and unpredictable environments, such as forests, poses a significant challenge typically addressed by wireless communication for coordination. However, this approach falls short in situations with limited central control or blocked communications. Addressing this gap, our paper explores the learning of complex behaviors by multiple drones with limited vision. Drones in a swarm rely on onboard sensors, primarily forward-facing stereo cameras, for environmental perception and neighbor detection. They learn complex maneuvers through the imitation of a privileged expert system, which involves finding the optimal set of neural network parameters to enable the most effective mapping from sensory perception to control commands. The training process adopts the Dagger algorithm, employing the framework of centralized training with decentralized execution. Using this technique, drones rapidly learn complex behaviors, such as avoiding obstacles, coordinating movements, and navigating to specified targets, all in the absence of wireless communication. This paper details the construction of a distributed multi-UAV cooperative motion model under limited vision, emphasizing the autonomy of each drone in achieving coordinated flight and obstacle avoidance. Our methodological approach and experimental results validate the effectiveness of the proposed vision-based end-to-end controller, paving the way for more sophisticated applications of multi-UAV systems in intricate, real-world scenarios.

From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation.

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.

Coupled effects of corrugation and rotation on the phase transition and thermal process in a vented cavity under MHD convection

One of the challenges in the utilization of phase change materials (PCMs) is their low thermal conductivity. Therefore, for effective usage of PCMs in thermal systems, novel methods for phase change process control such as geometry modification and incorporating different boundary conditions can be adopted. In this study, phase transition and thermal process in a triangular shaped wavy enclosure equipped with spherical shaped encapsulated PCM are explored under magneto-convection of nanofluid. The inner walls of region where PCM is embedded is rotating and sinusoidal form of corrugation is considered for the wavy wall of the triangular cavity. Numerical calculations are done for different values of Re (200≤Re≤1000), Rew (−2500≤Rew≤2500), Ha (0≤Ha≤60), wave amplitude (0≤Ap≤0.1H) and wave frequency (2≤Np≤8). It is observed that when rotations are active at Rew=100, phase transition time (TF) is reduced by 12.5% while it is increased by 16.6% at Rew=0. Magnetic field is effective when used without rotation of the inner part. The amount of reduction of TF with magnetic field at the highest strength is obtained as 15.8%. The heat transfer is reduced with higher Ha for stationary and rotating cases while reduction amount up to 14% can be achieved. Amplitude of the wavy form of cavity wall is influential on the phase change process at Rew=0 and TF is reduced by about 14.5%. The contribution of amplitude and frequency of the wave form on the heat transfer is very slight. Optimum parameter set for lowest TF is obtained at (Re, Rew, Ha, Ap, Np) = (600, 2500, 60, 0.075H, 2) while for highest heat transfer rate it becomes (Re, Rew, Ha, Ap, Np) = (1000, 2500, 0, 0.1H, 8). Generalized neural network based modeling is found to be an efficient way of predicting the time dependent behavior of phase change process with respect to changes in operating parameters such as Re, Rew and Ha.

Estimation of weight window parameters based on recursive Monte Carlo approach for reactor shielding problems

Weight window, a variance reduction tool, is often used to improve the performance of radiation shielding calculations. One common issue with the weight window is determining the optimal set of weight window parameters for solving deep penetration shielding problems. To address this issue, the recursive Monte-Carlo methodology has been used with the in-house TRAWEI code. The program is responsible for generating the optimal weight parameters in a single run with minimum computational time. This paper presents the results of a numerical test conducted using a simple reactor model to evaluate the performance of the developed weight generator program. The findings reveal that MCNP simulations utilizing TRAWEI-generated weight values exhibit significantly higher calculation efficiency compared to both analog simulation and MCNP simulation using weights generated by the existing MCNP weight window generator. Overall, the utilization of the RMC methodology has shown its potential to significantly contribute to deep penetration shielding calculations.

Calibration of constitutive models using genetic algorithms

Constitutive models, describing material response to load, are an essential part of computational materials engineering. Semi-empirical constitutive laws including the Johnson–Cook and Zerilli–Armstrong models are widely used in finite element simulation for easy computability and rapid run time. The reliability of these models depends on accurate and reproducible fitting of parameters. This work presents a genetic algorithm (GA) based tool to fit parameters in constitutive models. The GA approach is capable of finding the global optimum parameter set in a robust, repeatable, and computationally efficient manner. It has been demonstrated that the obtained fits are better than those using traditional term-wise optimisation. Allowed to fit freely, the GA method will be likely to produce non-physical parameter values. However, by constraining the fit, the GA method can produce parameters that are physically reasonable and minimise the error when extrapolating to unseen data. Finally, the GA method may be used to choose between a variety of possible constitutive models based on a transparent best fit approach. The model has been demonstrated by using datasets from the literature for DH–36 steel and Ti-6Al-4V. This includes data from different studies, in which there are both random and systematic variations. The framework developed here is made freely available and modifiable, and may be extended to include other constitutive models as required.

Multi-objective optimization of fiber laser cutting quality characteristics of glass fiber reinforced plastic (GFRP) materials

Laser cutting can achieve ultra-precision cutting of materials by using appropriate parameters. In order to optimize processing parameters and obtain better processing quality, conducted experimental research on laser cutting of Glass fiber reinforced plastic (GFRP). In this study, the impact of four process parameters (laser power, cutting speed, assistant gas pressure and focus position) on quality characteristics (kerf width, kerf taper and kerf section roughness) was evaluated through analysis of variance (ANOVA), the regression relationship between laser processing parameters and quality characteristics was analyzed. And an integrative model for predicting and optimizing the quality characteristics of fiber laser cutting was constructed. In the proposed integrative model, Back-Propagation Neural Network (BPNN) was used to build a prediction model for quality characteristics. Through Non-dominated Sorting Genetic Algorithm (NSGAII) optimizes and outputs the complete optimal solution set of processing parameters, and finally realizes the nonlinear optimization of multi-objective parameters. The fitness value of BPNN model can reach 97.814%, and the maximum prediction relative error is 8.614%. And the set of suggested optimal solutions can be used as most of them are showing certain improvements in the quality characteristics. The results indicate that the integrative modeling strategy has the ability to predict and optimize laser cutting of composite materials.

Optimization of Process Parameters to Minimize the Surface Roughness of Abrasive Water Jet Machined Jute/Epoxy Composites for Different Fiber Inclinations

Composites materials like jute/epoxy exhibit high hardness and are considered as difficult-to-machine materials. As a result, alternatives to conventional machining become essential to post-process the composites. Accordingly, due to its non-thermal nature, abrasive water jet machining has recently come to be seen as one of the most promising machining methods for composite materials. In the current study, the impact of machining parameters such as traverse speed (TS), standoff distance (SOD) and abrasive mass flow rate (MFR) on machined surface roughness (Ra) has been investigated. In addition, the optimum combination of process parameters to machine a jute fiber-reinforced polymer composite with minimum Ra is predicted. The experimental results are analyzed using Taguchi and Response Surface Methodology (RSM) approaches to determine the optimum set of process parameters to achieve the lowest roughness values. Without making any changes in the machining conditions, the optimum set of values is determined for two conditions by reinforcing the fiber with 45° inclination and 90° inclination. The results reflect the different optimum combinations for each fiber inclination. For 45° fiber inclination, to achieve the minimum Ra value, the predicted combination is TS = 30 mm/min, SOD = 2 mm and MFR = 0.35 kg/min. When the fiber inclination is 90°, the predicted optimum combination is TS = 25 mm/min, SOD = 2 mm, and MFR = 0.35 kg/min. It is evident from the results that the optimum combination will be changed according to the machining conditions as well as material properties. The results confirm the effect of fiber orientation on surface roughness. The specimen with 45° fiber inclination produces a lower Ra with an average of 4.116 µm, and the specimen with 90° fiber inclination generates a higher Ra with an average of 4.961 µm.

Deep Learning Is Singular, and That's Good.

In singular models, the optimal set of parameters forms an analytic set with singularities, and a classical statistical inference cannot be applied to such models. This is significant for deep learning as neural networks are singular, and thus, "dividing" by the determinant of the Hessian or employing the Laplace approximation is not appropriate. Despite its potential for addressing fundamental issues in deep learning, a singular learning theory appears to have made little inroads into the developing canon of a deep learning theory. Via a mix of theory and experiment, we present an invitation to the singular learning theory as a vehicle for understanding deep learning and suggest an important future work to make the singular learning theory directly applicable to how deep learning is performed in practice.

Parametric Optimizing Green Sand-Casting Process Parameters using hybrid Taguchi Grey Relational Analyses and Principal Component Analyses

The Green Sand-casting technique is a very ancient method of casting that has many different uses. The increased rate of errors and rejection in this process is a key drawback that reduces output and profits. It’s challenging to develop a good link between the many different parameters and defects since the process is so complicated. This article describes a hybrid approach to find the co-relation for sand casting process’s variables. This approach mixes the Taguchi method (TM) with Grey Relational Analysis (GRA) paired with Principal Component Analysis (PCA). Moisture content, Permeability, Loss of Ignition, Pouring Time & Pouring Temperature selected as input parameters while types of defects (Shrinkage, Blow holes, Cracks, Porosity) as responses for proposed study. The L27 OA from Taguchi is used to plan the tests. TM implemented to analyse individual responses. GRA is applied to find optimal solutions for a set of replies, whereas PCA is used to determine how much weight each response should be given. Using proposed methodology, 4% moisture content, 160% permeability, 5% loss of ignition, 60 seconds of pouring time, and 1400°C found as optimum set of parameters. The findings demonstrate that the hybrid approach, which makes use of both a cost-effective and efficient experimental design strategy, was successful in resolving the complexity trade-off experienced throughout the judgment process of multi-response optimization.

A framework for dynamical distributed flocking control in dense environments

The bio-inspired flocking model has been widely utilized in self-organized swarms. However, existing potential field methods fail to guarantee safe and orderly swarm movement in dense environments featuring both static and dynamic obstacles. In this study, we propose an innovative, dynamically optimized flocking control framework that integrates an improved and tunable self-propelled flocking model with an enhanced dynamic particle swarm optimization (DPSO) algorithm. Our model incorporates pigeon-inspired obstacle gap selection based on a probability approach to optimize steering decisions. Moreover, we introduce the DPSO algorithm, which combines a collaboration-based particle swarm optimizer with fractional order velocity incorporated history-guided estimation. This hybrid algorithm dynamically determines the optimal set of control parameters to maintain the desired swarm state. Through numerical comparison experiments with state-of-the-art models, we demonstrate that our approach enhances the safety, synchronization, and efficiency of the swarm even within a limited visual field, without prior environmental information. These features bring the simulations closer to real-world experiments.