Optimal Model Parameters Research Articles

Enhancing requirements-to-code traceability with GA-XWCoDe: Integrating XGBoost, Node2Vec, and genetic algorithms for improving model performance and stability

This study addresses the challenge of requirements-to-code traceability by proposing a novel model, Genetic Algorithm-XGBoost With Code Dependency (GA-XWCoDe), which integrates eXtreme Gradient Boosting (XGBoost) with a Node2Vec model-weighted code dependency strategy and genetic algorithms for parameter optimisation. XGBoost mitigates overfitting and enhances model stability, while Node2Vec improves prediction accuracy for low-confidence links. Genetic algorithms are employed to optimise model parameters efficiently, reducing the resource intensity of traditional methods. Experimental results show that GA-XWCoDe outperforms the state-of-the-art method TRAceability lInk cLassifier (TRAIL) by 17.44% and Deep Forest for Requirement traceability (DF4RT) by 33.36% in terms of average F1 performance across four datasets. It is significantly superior to all baseline methods at a confidence level of α¡0.01 and demonstrates exceptional performance and stability across various training data scales.

Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction

Nuclear receptors (NRs) play a crucial role as biological targets in drug discovery. However, determining which compounds can act as endocrine disruptors and modulate the function of NRs with a reduced amount of candidate drugs is a challenging task. Moreover, the computational methods for NR-binding activity prediction mostly focus on a single receptor at a time, which may limit their effectiveness. Hence, the transfer of learned knowledge among multiple NRs can improve the performance of molecular predictors and lead to the development of more effective drugs. In this research, we integrate graph neural networks (GNNs) and Transformers to introduce a few-shot GNN-Transformer, Meta-GTNRP to predict the binding activity of compounds using the combined information of different NRs and identify potential NR-modulators with limited data. The Meta-GTNRP model captures the local information in graph-structured data and preserves the global-semantic structure of molecular graph embeddings for NR-binding activity prediction. Furthermore, a few-shot meta-learning approach is proposed to optimize model parameters for different NR-binding tasks and leverage the complementarity among multiple NR-specific tasks to predict binding activity of compounds for each NR with just a few labeled molecules. Experiments with a compound database containing annotations on the binding activity for 11 NRs shows that Meta-GTNRP outperforms other graph-based approaches. The data and code are available at: https://github.com/ltorres97/Meta-GTNRP.Scientific contributionThe proposed few-shot GNN-Transformer model, Meta-GTNRP captures the local structure of molecular graphs and preserves the global-semantic information of graph embeddings to predict the NR-binding activity of compounds with limited available data; A few-shot meta-learning framework adapts model parameters across NR-specific tasks for different NRs in a joint learning procedure to predict the binding activity of compounds for each NR with just a few labeled molecules in highly imbalanced data scenarios; Meta-GTNRP is a data-efficient approach that combines the strengths of GNNs and Transformers to predict the NR-binding properties of compounds through an optimized meta-learning procedure and deliver robust results valuable to identify potential NR-based drug candidates.

Potentially suitable geographical area for Colletotrichum acutatum under current and future climatic scenarios based on optimized MaxEnt model.

Global climate warming has led to changes in the suitable habitats for fungi. Colletotrichum acutatum, a common fungus causing anthracnose disease, is widely distributed in southern China. Currently, research on the relationship between C. acutatum and environmental warming was limited. In this study, MaxEnt and ArcGIS software were used to predict the suitable habitats of C. acutatum under current and future climate conditions based on its occurrence records and environmental factors. The optimal MaxEnt model parameters were set as feature combination (FC) = lp and regularization multiplier (RM) = 2.6. Bio15, Bio12, Bio09, and Bio19 were identified as the main environmental factors influencing the distribution of C. acutatum. Under current climate conditions, C. acutatum was distributed across all continents globally, except Antarctica. In China, C. acutatum was primarily distributed south of the Qinling-Huaihe Line, with a total suitable area of 259.52 × 104 km2. Under future climate conditions, the potential suitable habitat area for C. acutatum was expected to increase and spread towards inland China. The results of this study provided timely risk assessment for the distribution and spread of C. acutatum in China and offer scientific guidance for monitoring and timely controlled of its distribution areas.

The Protection and Management of Wapiti in Desert Oases: Bare Land Poses a Limitation to Wapiti Conservation.

The Helan Mountains, situated in the heart of the desert, act as a dividing line between China's arid and semi-arid zones. Often referred to as a "desert oasis", they create an ecological island with a uniquely distinctive geographical location, making this area a focal point of contemporary research. Ungulates play a critical role in this ecosystem. The Alashan wapiti (Cervus canadensis alashanicus), an isolated population of China's smallest wapiti (Cervus canadensis) subspecies, is found exclusively within the Helan Mountains Nature Reserve. The conservation of this isolated population is fraught with challenges, particularly during winter, the harshest season for northern ungulates. Winter habitats are crucial for ensuring population stability. Therefore, we used certain methods, such as factor screening and model parameter optimization to assess habitat suitability using multi-scale species distribution models. The optimized results show that suitable habitats overlap with areas of high vegetation coverage in the Helan Mountains, covering just 588.32 km2, which is less than a quarter of the reserve's total area. The bare land area and winter NDVI are the two primary factors influencing habitat suitability, with other factors having minimal impact, underscoring the critical importance of food resources for the Alashan wapiti. The limited availability of these resources poses significant conservation challenges. Our findings provide a more precise foundation for targeted habitat protection and restoration efforts. We recommend enhancing the protection and restoration of food resources, effectively conserving vegetated areas, and preventing desertification.

Climate covariate selection influences MaxEnt model predictions and predictive accuracy under current and future climates

The performance and transferability of species distribution models (SDMs) depends on a number of ecological, biological, and methodological factors. There is a growing body of literature that explores how the choice of climate covariate combinations and model parameters can affect predictive performance, but relatively few that delve into covariate reduction methods and the optimisation of model parameters, and the resulting spatial and temporal transferability of those models. The present work used the citrus pest, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), to illustrate how MaxEnt models trained on the insect’s native range in Asia varied in their predictions of climatic suitability across the introduced range when eight different covariate reduction methods were applied during model building. Additionally, it showed how model sensitivity varied across these different covariate combinations using three sets of independently validated occurrence points in the invaded range of the psyllid. Climatically suitable areas for D. citri differed by as much as two-fold between the best and worst-performing models in selected areas. Great care should be taken in the selection of the highest-performing predictor combinations and model parameter settings for SDMs, particularly in the case of invasive species where the assumption of environmental equilibrium is likely violated in the introduced range. Understanding how the predictive ability of SDMs can be influenced by the methodological choices made during the model building phase is vital to ensuring that ecological and invasion management programmes do not over- or underestimate climatic suitability and subsequent invasion risk.

Automatic optimization design of laser triangulation ranging sensors using an improved genetic algorithm

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and 10−4 relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods.

Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework

To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.

Improved exponential smoothing grey-holt models for electricity price forecasting using whale optimization

This study introduces a ground-breaking approach, the Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in <1.3 s. Three key aspects of customization distinguish this novel approach:•The WOA algorithm dynamically adjusts model parameters based on evolving electricity market dynamics.•The model employs a sophisticated GMHES approach, considering multiple factors for a comprehensive understanding of price trends.•The WOA-GMHES(1,N) model stands out for its computational efficiency, providing rapid and precise forecasts, making it a valuable tool for time-sensitive decision-making in the energy sector.

Advancing Adversarial Training by Injecting Booster Signal.

Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have been proposed, among which adversarial training (AT) has been demonstrated to be the most effective strategy. However, it has been known that AT sometimes hurts natural accuracy. Then, many works focus on optimizing model parameters to handle the problem. Different from the previous approaches, in this article, we propose a new approach to improve the adversarial robustness using an external signal rather than model parameters. In the proposed method, a well-optimized universal external signal called a booster signal is injected into the outside of the image which does not overlap with the original content. Then, it boosts both adversarial robustness and natural accuracy. The booster signal is optimized in parallel to model parameters step by step collaboratively. Experimental results show that the booster signal can improve both the natural and robust accuracies over the recent state-of-the-art AT methods. Also, optimizing the booster signal is general and flexible enough to be adopted on any existing AT methods.

Participation of wind-storage combined system in the secondary frequency regulation control strategy of power grid

Abstract The secondary frequency modulation of the unit, that is, the automatic generation control (AGC), is the process of increasing or decreasing the power of the power generation unit by the power plant unit according to the dispatching command issued by the power grid dispatching center, and then using the frequency modulator to restore the frequency. Based on the principle of AGC, this paper establishes a frequency dynamic response model of the secondary frequency modulation of the traditional unit and the wind storage participation system including load frequency control (LFC) and traditional proportional-integral-derivative (PID) controllers, determines the frequency modulation control strategy of wind storage, and uses the distribution mode based on Area Control Error (ACE) signal to assist in the secondary frequency modulation. In order to solve the parameter optimization problem of the classical PID controller when the wind-storage combined system participates in the secondary frequency modulation under the large load disturbance, a parameter optimization model of the controller with PID was established, and the ITAE evaluation index was finally selected as the fitness function by comparing different fitness evaluation indexes. To overcome the problem that the conventional Particle Swarm Optimization (PSO) is prone to local extremum, the inertia weights and learning factors are adjusted nonlinearly to balance and optimize the global and local optimization capabilities and improve the search efficiency, and the parameters of the PID controller are optimized by the improved PSO algorithm. The simulation results show that the proposed algorithm improves the secondary frequency modulation effect under the background of a high proportion of renewable energy connected to the power grid.

Empirical formula model and process parameter optimization of two-dimensional ultrasonic-assisted grinding force based on 2.5D-Cf/SiC fiber orientation

Empirical formula model and process parameter optimization of two-dimensional ultrasonic-assisted grinding force based on 2.5D-Cf/SiC fiber orientation

Process Parameters Optimization and Numerical Simulation of AlCoCrFeNi High-Entropy Alloy Coating via Laser Cladding.

The use of laser cladding technology to prepare coatings of AlCoCrFeNi high-entropy alloy holds enormous potential for application. However, the cladding quality will have a considerable effect on the properties of the coatings. In this study, considering the complex coupling relationship between cladding quality and the process parameters, an orthogonal experimental design was employed, with laser power, scanning speed, and powder feed rate as correlation factor variables, and microhardness, dilution rate, and aspect ratio as characteristic variables. The experimental data underwent gray correlation analysis to determine the effect of various process parameters on the quality of cladding. Then, the NSGA-II algorithm was used to establish a multi-objective optimization model of process parameters. Finally, the ANSYS Workbench simulation model was employed to conduct numerical simulations on a group of optimized process parameters and analyze the change rule of the temperature field. The results demonstrate that the laser cladding coating of AlCoCrFeNi high-entropy alloy with the single pass is of high quality within the determined orthogonal experimental parameters. The powder feed rate exerts the most significant influence on microhardness, while laser power has the greatest impact on dilution rate, and scanning speed predominantly affects aspect ratio. The designed third-order polynomial nonlinear regression model exhibits a high fitting accuracy, and the NSGA-II algorithm can be used for multi-objective optimization to obtain the Pareto front solution set. The numerical simulation results demonstrate that the temperature field of AlCoCrFeNi high-entropy alloy laser cladding exhibits a "comet tail" phenomenon, where the highest temperature of the molten pool is close to 3000 °C. The temperature variations in the molten pool align with the features of laser cladding technology. This study lays the groundwork for the widespread application of laser cladding AlCoCrFeNi high-entropy alloy in surface engineering, additive manufacturing, and remanufacturing. Researchers and engineering practitioners can utilize the findings from this research to judiciously manage processing parameters based on the results of gray correlation analysis. Furthermore, the outcomes of multi-objective optimization can assist in the selection of appropriate process parameters aligned with specific application requirements. Additionally, the methodological approach adopted in this study offers valuable insights applicable to the exploration of various materials and diverse additive manufacturing techniques.

Predicting the Population Size and Potential Habitat Distribution of Moschus berezovskii in Chongqing Based on the MaxEnt Model

The forest musk deer (Moschus berezovskii) is a national Class I protected wild animal in China, and the IUCN Red list classifies it as globally endangered. It has significant value in traditional Chinese medicine and spices. However, wild M. berezovskii has faced a severe population decline due to human hunting, habitat loss, and fragmentation. Thus, studying its population size and distribution pattern is of great importance to develop effective conservation measures. Here, we determined the optimal MaxEnt model and used stratified sampling and the fecal pile counting method to predict the population size and potential habitat distribution of wild M. berezovskii in Chongqing using 133 species distribution points and 28 environmental variables. The results were as follows: (1) When the optimal model parameters were RM = 3.5 and FC = LQHPT, it had high model prediction accuracy (AUC = 0.909 ± 0.010, TSS = 0.663). (2) Under various climatic, topographic, vegetation, and anthropogenic disturbance scenarios, M. berezovskii was primarily distributed in northern, eastern, southwestern regions of Chongqing, covering an area of approximately 5562.80 km2. (3) The key environmental factors affecting the potential habitat distribution of M. berezovskii were elevation (36.5%), normalized difference vegetation index (NDVI, 16.6%), slope (11.8%), and land-use type (7.6%), whereas climate and anthropogenic disturbance factors had relatively little influence. (4) A population estimation for M. berezovskii identified approximately 928 ± 109 individuals in Chongqing. We recommend prioritizing the preservation of high-altitude habitats and native vegetation to mitigate human interference and minimize road damage. In summary, our results can enhance the understanding of M. berezovskii distribution and provide a basis for effective conservation and management initiatives.

Forecasting energy consumption with a novel ensemble deep learning framework

Energy baseline development is a central and significant component of an energy management system. In commercial buildings where a wide variety of internal and external variables might impact the electricity usage, having a robust forecasting method is elemental to evaluate and enhance the energy performance over time. Due to extensive progress in data analytics, a number of single prediction methods have been developed recently. Nevertheless, either development of the theoretical study or application of ensemble learning methods to enhance prediction performance and stability find lacking in energy management of commercial sectors. This research proposes a novel heterogeneous ensemble model framework for short-term electricity consumption prediction under the hourly and daily granularity to bridge this research gap. The framework is composed of two levels. The first uses five stand-alone deep learning models including Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) as base models. In contrast, the second level applies the predicted outputs of the base models as feature inputs of k nearest neighbors (KNN), which is a strong model. The Grid Search is also used to search for the key optimal hyper parameters of the base models and the strong model. To demonstrate the developed model's effectiveness, four different space types of real-world educational buildings are used for verification. The extensive database has been setup for this study to collect data from Jan. 2018 to Dec. 2022. The quantitative analysis of the model was conducted in contrast with the base models and prevalent machine learning methods, including artificial neural network (ANN), KNN, support vector regression (SVR), AdaBoost, and linear regression (LR) during the testing phase. The prediction accuracy has improved substantially, with roughly a 95 % reduction in CV-RMSE compared to that of AdaBoost and SVR. Moreover, the results signify the introduced model outperforms the base models and the ML methods in all studied buildings, thereby providing a foundation for developing effective management policies.

Computer modeling of a new type galactic cosmic rays simulator

A new type of a galactic cosmic rays (GCR) simulator, provided at the JINR Laboratory of Radiation Biology, is potentially capable of generating a complex radiation field with inclusions of a variety of ions with a wide energy range and with required abundance at the charged particle accelerators. This complex multicomponent radiation field simulates radiation environment inside a spacecraft during an interplanetary flight, for example, to Mars. The article provides an analytical description of the GCR simulator as well as a description of a specially developed software that enables selection of necessary parameters of a simulator model for creating relevant mixed radiation conditions. The software implements processing of data obtained with Monte Carlo-based FLUKA and PHITS programs, fitting and optimization of model parameters as well as data visualization tools.

Evaluation of translation quality of English literary works based on big data

With globalization and technological progress, the demand for language translation is increasing. Especially in the fields of education and research, accurate and efficient translation is considered essential. However, most existing translation models still have many limitations, such as inadequacies in dealing with cultural and contextual differences. This study aims to solve this problem by combining big data analysis, machine learning and translation theory, and proposes a comprehensive translation quality evaluation model. On the basis of screening and constructing a representative sample database, pre-processing and standardization, feature selection is carried out by combining multi-dimensional features such as grammatical complexity and cultural adaptability factors, and different machine learning algorithms are used for model construction and parameter optimization. Finally, by training and testing the model, the performance and effectiveness of the model are evaluated, and a comprehensive evaluation standard is constructed. The results show that this model can not only effectively improve the translation quality, but also has a high system application and universality.

FLRW cosmology in metric-affine F(R,Q) gravity* *Supported by the Ministry of Science and Higher Education of the Republic of Kazakhstan (AP14870191)

We investigated some Friedmann-Lemaître-Robertson-Walker (FLRW) cosmological models in the context of metric-affine gravity, as proposed in [arXiv: 1205.5266v6]. Here, R and Q are the curvature and nonmetricity scalars using non-special connections, respectively. We obtained the modified field equations using a flat FLRW metric. We then found a connection between the Hubble constant , density parameter , and other model parameters in two different situations involving scalars u and w. Next, we used new observational datasets, such as the cosmic chronometer (CC) Hubble and Pantheon SNe Ia datasets, to determine the optimal model parameter values through a Markov chain Monte Carlo (MCMC) analysis. Using these best-fit values of the model parameters, we discussed the results and behavior of the derived models. Further, we discussed the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for the derived models in the context of the Lambda cold dark matter (ΛCDM). We found that the geometrical sector dark equation of state parameter behaves just like a dark energy candidate. We also found that both models are transit phase models. Model-I approaches the ΛCDM model in the late-time universe, whereas Model-II approaches quintessence scenarios.

Discussion on “Speed optimization for maximizing the ship's economic benefits considering the Carbon Intensity Indicator (CII) ” by R Hua, J Yin, S Wang, Y Han, and X Wang (https://doi.org/10.1016/j.oceaneng.2024.116712)

The inconsistencies in the optimization data presented in the original paper by Hua et al. (2024) (hereafter identified as “the original paper”, or, “the original authors”) will be thoroughly examined in this study. The necessary parameters in the optimization model will be identified from a data consistency standpoint, and any insufficiencies in the original conclusions will be highlighted. An optimization model with consistently reliable data will be developed, based on the available information in the original paper. An illustrated example is solved by a rigorous meta-heuristic method. The original paper could potentially enhance its clarity by providing a more detailed explanation of the optimization model's parameters to further reinforce its findings and conclusions. This not only adds credibility to the study but also enables readers to replicate the analysis and validate the outcomes.

Research on the Optimal Trajectory Planning Method for the Dual-Attitude Adjustment Mechanism Based on an Improved Multi-Objective Salp Swarm Algorithm

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.