Model Parameters Research Articles

Normalization of hot deformation parameters for activation energy analysis in metallic alloys

Abstract Depending on melting point of a given alloy, the temperature range for
hot deformation of alloys varies widely. Consequently, the apparent activation energy
of hot deformation, Q, is greatly influenced by the temperature range as well as strain
rate applied during hot deformation. The purpose of this investigation is to normalize
hot deformation parameters – temperature and strain rate – to study their effects on
activation energy. Seventy published hot-deformation data sets were used to investigate
the effects of normalized hot-deformation variables on Arrhenius-type constitutive
model parameters. Flow stress, strain rate, and temperature from sample datasets
were normalized to the range [0, 1] and fitted with an Arrhenius-type model. The
results showed that, for normalized parameters, the activation energy has exponential
relationships with a partial derivative of flow stress with strain rate and temperature.

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for Multidevice Applications

Conventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. This study presents a novel machine learning–based method to accelerate and enhance the accuracy of compact model generation for multiple devices simultaneously. By integrating a Berkeley short‐channel Insulated‐Gate Field‐Effect Transistor (IGFET) model‐common multigate (BSIM) model with an error‐correction neural network, the proposed approach refines predictions for critical electrical behaviors such as drain current and gate capacitance. Extraction networks dynamically optimize parameter sets for both models, eliminating manual tuning and reducing the need for separate training for each device. The method was validated using TCAD‐simulated 3 nm nanosheet field‐effect transistors devices, achieving a mean absolute percentage error of 1.8% for drain current, 2.8% for transconductance, 8.5% for output conductance, and 0.4% for capacitances. Compared with the BSIM model alone, error reductions of 75, 70, 39, 85, and 81%, respectively, were achieved. This approach showed significant error reductions compared to the BSIM model alone and demonstrated robust performance across devices with variations, proving its effectiveness for large‐scale applications.

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model's accuracy, providing better support for doctors in disease diagnosis.

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma.

An Integrated Fuzzy Delphi and Fuzzy AHP Model for Evaluating Factors Affecting Human Errors in Manual Assembly Processes

Human errors (HEs) are prevalent issues in manual assembly, leading to product defects and increased costs. Understanding and knowing the factors influencing human errors in manual assembly processes is essential for improving product quality and efficiency. This study aims to determine and rank factors influencing HEs in manual assembly processes based on expert judgments. To achieve this objective, an integrated model was developed using two multi-criteria decision-making (MCDM) techniques—specifically, the fuzzy Delphi Method (FDM) and the fuzzy Analytic Hierarchy Process (FAHP). Firstly, two rounds of the FDM were conducted to identify and categorize the primary factors contributing to HEs in manual assembly. Expert consensus with at least 75% agreement determined that 27 factors with influence scores of 0.7 or higher significantly impact HEs in these processes. After that, the priorities of the 27 influencing factors in assembly HEs were determined using a third round of the FAHP method. Data analysis was performed using SPSS 22.0 to evaluate the reliability and normality of the survey responses. This study has divided the affecting factors on assembly HEs into two levels: level 1, called main factors, and level 2, called sub-factors. Based on the final measured weights for level 1, the proposed model estimation results revealed that the most influential factors on HEs in a manual assembly are the individual factor, followed by the tool factor and the task factor. For level 2, the model results showed a lack of experience, poor instructions and procedures, and misunderstanding as the most critical factors influencing manual assembly errors. Sensitivity analysis was performed to determine how changes in model inputs or parameters affect final decisions to ensure reliable and practical results. The findings of this study provide valuable insights to help organizations develop effective strategies for reducing worker errors in manual assembly. Identifying the key and root factors contributing to assembly errors, this research offers a solid foundation for enhancing the overall quality of final products.

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively.

Numerical simulation and validation of heavy rainfall flood inundation in small watersheds in undocumented mountainous areas

ABSTRACT With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the model-simulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00–01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km2, respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy.

Dynamic Resource Allocation: The Geometry and Robustness of Constant Regret

We study a family of dynamic resource allocation problems, wherein requests of different types arrive over time and are accepted or rejected. Each request type is characterized by its reward, arrival probability, and resource consumption. An upper bound for the collected reward is given by a linear optimization problem with a random right-hand side. This type of problem, known as packing linear program (LP), is ubiquitous in resource allocation. We provide a detailed characterization of the parametric structure of this packing LP. Relying on this geometric understanding, we revisit and expand on BudgetRatio algorithms that achieve constant regret by resolving this same packing LP in each period and accepting requests scored as sufficiently valuable. We illustrate the benefits of the geometric view in proving that (i) BudgetRatio achieves constant regret relative to the offline (full information) upper bound in the presence of inventory that is (slowly) restocked, and (ii) within explicitly identifiable bounds, the algorithm’s regret is robust to misspecification of the model parameters. This gives bounds for the bandits version of the problem in which the parameters have to be learned. (iii) The algorithm has an equivalent formulation as a generalized bid-price algorithm in which the bid prices can be adaptively and efficiently computed. Our analysis focuses on the evolution of the remaining inventory—in turn of the LP that drives BudgetRatio—as a stochastic process. We prove that it is attracted to sticky regions of the state space in which the online algorithm takes actions consistent with the optimal basis of the offline upper bound, a basis that is revealed only in hindsight at the horizon’s end. Funding: This work was supported by the U.S. Department of Defense [Grant W911NF-20-C-0008].

Early Sweet Potato Plant Detection Method Based on YOLOv8s (ESPPD-YOLO): A Model for Early Sweet Potato Plant Detection in a Complex Field Environment

Traditional methods of pest control for sweet potatoes cause the waste of pesticides and land pollution, but the target detection algorithm based on deep learning can control the precise spraying of pesticides on sweet potato plants and prevent most pesticides from entering the land. Aiming at the problems of low detection accuracy of sweet potato plants and the complex of target detection models in natural environments, an improved algorithm based on YOLOv8s is proposed, which can accurately identify early sweet potato plants. First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. Then, cross-scale feature fusion and the general efficient aggregation architecture are used to further enhance the network feature extraction capability. Finally, the loss function is replaced with InnerFocaler-IoU (IFIoU) to improve the convergence speed and robustness of the model. Experimental results showed that the mAP0.5 and model size of the improved network reached 96.3% and 7.6 MB. Compared with the YOLOv8s baseline network, the number of parameters was reduced by 67.8%, the amount of computation was reduced by 53.1%, and the mAP0.5:0.95 increased by 3.5%. The improved algorithm has higher detection accuracy and a lower parameter and calculation amount. This method realizes the accurate detection of sweet potato plants in the natural environment and provides technical support and guidance for reducing pesticide waste and pesticide pollution.

Fatigue characterization and life prediction of flexible lattice structures based on DLP processes

This study investigated the effects of cell configuration and relative density on the fatigue performance of truss-like lattice structures. Among the lattice structures examined, simple cube cells exhibited the highest fatigue strength across all relative densities. Numerical simulation results indicated that the S-N curves of the structures demonstrated pronounced power-law behavior as the volume fraction changed. The influence of various geometric factors on the power-law model parameters is discussed in detail. The power-law coefficient is influenced by the unit cell configuration and relative density; however, the power-law index is not affected by relative density and depends solely on the unit cell configuration. A fatigue life prediction model based on the Ashby-Gibson model was proposed. Finally, the reliability of the numerical simulation results was experimentally verified, with the predicted results of the model showing good agreement with the test data.

DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.

Bioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment. This study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model-evolutionary scale modeling (ESM-2)-to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models-CapsuleGAN, GRU, and CNN-while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach. In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides .

Fractional Derivative-based Burger Creep Model for Soft Rocks and its Verification Using Tunnel Monitoring Results and Experimental Data

AbstractConsidering the creep behavior of soft and weak rocks is critical for analyzing the long-term stability of underground constructions. This paper introduces a novel creep constitutive model to characterize the creep behavior of rocks under uniaxial and triaxial stress states. The fractional derivative Abel dashpot was used to improve the Burger model, and a viscoplastic component was added in series with the modified Burgers model to replicate the tertiary phase of rock creep. The effectiveness of the model was verified using creep test data from various soft rocks and monitoring measurements from a tunnel excavated in heavily jointed weak rock masses. Furthermore, a sensitivity analysis was carried out to assess the impact of the model parameters on creep deformation, and a comparative study was performed to evaluate the efficacy of the suggested model in modeling the accelerated stage of rock creep compared with some existing models. The strong agreement observed between the calculated results and both the creep test data and tunnel monitoring measurements underscores the accuracy and validity of the proposed model. The comparative analysis further revealed that the proposed model offers the highest fitting efficiency for describing the tertiary stage of rock creep. These findings suggest that the model effectively captures the creep behavior of rocks and precisely represents the entire creep process.

Multi-scale contextual learning for medical image segmentation via dual distillation.

Recently, many studies have explored fusing features extracted from Convolutional neural networks (CNNs) and transformers to integrate multi-scale representations for better performance in medical image segmentation tasks. Although these hybrid models have achieved better results than previous CNN-based and transformer-based methods, they suffer from high computation and spacecomplexities. The purpose of this research is to address the prohibitive computation and space complexities of hybrid models, which limit their application in clinical practice where computational resources are usuallyconstrained. We propose a novel model equipped with a dual distillation scheme to sufficiently harness the complementary advantages of CNNs and transformers without compromising model efficiency. We further propose a multi-scale prior-knowledge distillation (MPD) module to effectively distill multi-scale knowledge from features extracted from transformers. In addition, to cooperate with the knowledge distillation scheme, we also propose an efficient and robust Selective Fusion module in the studentnetwork. We extensively evaluate the proposed model against fourteen different network frameworks on two representative datasets: SipakMed and ISIC 2017. In the SipakMed dataset, 3037 Pap smear images are used for training and 1012 for testing. In the ISIC 2017 dataset, 2000 dermoscopic images are used for training, 150 for validation, and 600 for testing. Experimental results demonstrate that our method not only surpasses existing methods by a considerable margin with respect to the evaluation metrics of mean Intersection over Union, mean Dice coefficient, mean average symmetric surface distance, but also requires fewer computational resources in terms of model parameters and floating-point operations persecond. Comprehensive comparisons in terms of segmentation accuracy and computational complexity unequivocally confirm that our method effectively and efficiently integrates the advantages of both CNNs and transformers, showing its suitability and significance for clinicalapplications.

Modeling Zika Virus Disease Dynamics with Control Strategies

In this research, we formulated a fractional-order model for the transmission dynamics of Zika virus, incorporating three control strategies: health education campaigns, the use of insecticides, and preventive measures. We conducted a theoretical analysis of the model, obtaining the disease-free equilibrium and the basic reproduction number, and analyzing the existence and uniqueness of the model. Additionally, we performed model parameter estimation using real data on Zika virus cases reported in Colombia. We found that the fractional-order model provided a better fit to the real data compared to the classical integer-order model. A sensitivity analysis of the basic reproduction number was conducted using computed partial rank correlation coefficients to assess the impact of each parameter on Zika virus transmission. Furthermore, we performed numerical simulations to determine the effect of memory on the spread of Zika virus. The simulation results showed that the order of derivatives significantly impacts the dynamics of the disease. We also assessed the effect of the control strategies through simulations, concluding that the proposed interventions have the potential to significantly reduce the spread of Zika virus in the population.

A Parameter Optimized Method for InVEST Model in Sub-Pixel Scale Integrating Machine Learning Algorithm and Vegetation–Impervious Surface–Soil Model

The InVEST model, with its ability to perform spatial visualization and quantification, is an important tool for mapping ecosystem services. However, the spatial accuracy and simulating performance of the model are deeply influenced by the land use parameter, which often relies on the accuracy of land use/cover data. To address this issue, we propose a novel method for optimizing the land use parameter of the InVEST model based on the vegetation–impervious surface–soil (V–I–S) model and a machine learning algorithm. The optimized model is called Sub-InVEST, and it improves the performance of assessing ecosystem services on a sub-pixel scale. The conceptual steps are (i) extracting the V–I–S fraction of remote sensing images based on the spectral unmixing method; (ii) determining the mapping relationship of the V–I–S fraction between land use/cover type using a machine learning algorithm and field observation data; (iii) inputting the V–I–S fraction into the original model instead of the land use/cover parameter of the InVEST model. To evaluate the performance and spatial accuracy of the Sub-InVEST model, we employed the habitat quality module of InVEST and multi-source remote sensing data, which were applied to acquire Sub-InVEST and estimate the habitat quality of central Guangzhou city from 2000 to 2020 with the help of the LSMA and ISODATA methods. The experimental results showed that the Sub-InVEST model is robust in assessing ecosystem services in sets of complex ground scenes. The spatial distribution of the habitat quality of both models revealed a consistent increasing trend from the southwest to the northeast. Meanwhile, linear regression analyses observed a robust correlation and consistent linear trends, with R2 values of 0.41, 0.35, 0.42, 0.39, and 0.47 for the years 2000, 2005, 2010, 2015, and 2020, respectively. Compared with the original model, Sub-InVEST had a more favorable performance in estimating habitat quality in central Guangzhou. The spatial depictions and numerical distribution of the results of the Sub-InVSET model manifest greater detail and better concordance with remote sensing imagery and show a more seamless density curve and a substantially enhanced probability distribution across interval ranges.

A new parametric quantile regression model based on an Owen distribution

Quantile regression is a versatile statistical tool for modeling conditional quantiles, offering a comprehensive view of the relationship between explanatory and response variables. This technique is widely used in fields like economics, finance, environmental science, and psychology. This paper introduces a new quantile regression model based on the Owen distribution, which provides flexibility in capturing quantile-specific relationships. The model parameters are estimated using maximum likelihood estimation, with a detailed discussion on the estimation process. Diagnostic measures, including a pseudo- R 2 statistic, quantile residuals, and generalized Cook’s distance, are proposed to assess model adequacy and identify influential data points. Monte Carlo simulations are employed to evaluate the estimators' performance. The model’s effectiveness is illustrated through an application to Chilean household income data, highlighting its advantages over conventional quantile regression models. Implemented in the R programming language, this model is accessible for practical use, offering a valuable addition to the quantile regression field and suggesting promising directions for further research and application across disciplines.

Numerical Optimization of Drucker-Prager-Cap Model Parameters in Powder Compaction Employing Particle Swarm Algorithms

A growing number of scholars are drawn to using numerical approaches powered by computer simulations as a potential solution to industrial problems. Replicating the compaction process in powder metallurgy with accuracy is one such issue. The Drucker-Prager-Cap model requires parameter calibration as the most used method for simulating powder compaction. This paper addresses this issue and presents a new technique for doing so. Utilizing Abaqus software 2020, the compaction process was simulated for the benchmark powder, which is the alloy Ag57.6-Cu22.4-Sn10-In10. The difference between simulation results and experimental data was reduced by applying the Particle Swarm Optimization technique in Python. The suggested approach may accurately forecast the Drucker-Prager-Cap model parameters, as demonstrated by comparing the optimized parameters utilizing the research’s method with their experimental values. The findings revealed how well the suggested approach in this study calibrated the DPC model, yielding three parameters—Young’s modulus, material cohesion, and hydrostatic pressure yield stress—with respective RMSEs of 1.95, 0.12, and 324.64 concerning their experimental values.

A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Inference of Some Macroeconomic Variables on Nigeria Unemployment Rate

This research investigates the impact of foreign direct investment (FDI), government expenditure (GOE), and inflation rate (IFR) on the unemployment rate (UPR) in Nigeria from 1985 to 2021 through Autoregressive Distributed Lag (ARDL) modeling approach. An initial assessment to test the significance of signals between each independent variable and UPR was performed using rolling correlation analysis. Subsequently, the bounds test methodology to examine cointegration among between the FDI, GOE, IFR, and UPR was performed. Additionally, the causal relationship between these economic variables was performed through the Error Correction Model (ECM) approach. The estimated ARDL model parameters stability was determined using the cumulative sum (CUSUM) of squares chart. The Augmented Dickey Fuller unit root test suggests that the variables are stationary at first differences (I(1)). The bounds test revealed that the variables are cointegrated at 1%, 5%, and 10% indicating a long run relationship between UPR and FDI, GOE, and IFR. The ARDL results indicates that at 5%, a unit increase in FDI at lag one have a long run significant decreasing impact on UPR by 19.96%. But in the short run, the FDI at lag one has a significant increasing effect on UPR by 7.23%. However, the CUSUM of square chart shows unstable parameter estimation based on the Akaike Information Criteria selected model, ARDL (1, 3, 0, 0). The study concludes that UPR is being influenced by FDI in reducing UPR in the long run. Recommendation based on the findings is that FDI should be considered most important when policies are drafted for tackling the issue bordering UPR in Nigeria.