Model Parameters Research Articles

Cardiac-sympathetic contractility and neural alpha-band power: cross-modal collaboration during approach-avoidance conflict.

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic (EEG) and cardiac-sympathetic signals. Participants were reward sensitive, but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward-sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.Significance statement Complex behavior likely involves coordination across multiple branches of the human nervous system. We know much of how cortical systems of the brain adapt to cognitive challenges. In parallel, we are beginning to understand that autonomic mediated responses in peripheral organ (cardiac-sympathetic) systems might also play an adaptive role in cognition, particularly complex decisions. We probed if such signals have separate or collaborative associations with behavior, using computational models of decision behavior, brain (electroencephalography) and cardiac-sympathetic (contractility) data. Our evidence suggests that these systems might work together, as humans attend to all available information when resolving particularly conflicting decisions. The cardiac-sympathetic system may be part of a coordinated response that helps balance the human tendency to overly focus on rewards.

Dopamine and Norepinephrine Differentially Mediate the Exploration-exploitation Tradeoff.

Dopamine (DA) and norepinephrine (NE) have been repeatedly implicated in neuropsychiatric vulnerability, in part via their roles in mediating the decision making processes. Although two neuromodulators share a synthesis pathway and are co-activated under states of arousal, they engage in distinct circuits and modulatory roles. However, the specific role of each neuromodulator in decision making, in particular the exploration-exploitation tradeoff, remains unclear. Revealing how each neuromodulator contributes to exploration-exploitation tradeoff is important in guiding mechanistic hypotheses emerging from computational psychiatric approaches. To understand the differences and overlaps of the roles of these two catecholamine systems in regulating exploration, a direct comparison using the same dynamic decision making task is needed. Here, we ran male and female mice in a restless two-armed bandit task, which encourages both exploration and exploitation. We systemically administered a nonselective DA antagonist (flupenthixol), a nonselective DA agonist (apomorphine), a NE beta-receptor antagonist (propranolol), and a NE beta-receptor agonist (isoproterenol), and examined changes in exploration within subjects across sessions. We found a bidirectional modulatory effect of dopamine on exploration. Increasing dopamine activity decreased exploration and decreasing dopamine activity increased exploration. The modulatory effect of beta-noradrenergic receptor activity on exploration was mediated by sex. Reinforcement learning model parameters suggested that dopamine modulation affected exploration via decision noise and norepinephrine modulation affected exploration via sensitivity to outcome. Together, these findings suggested that the mechanisms that govern the exploration-exploitation transition are sensitive to changes in both catecholamine functions and revealed differential roles for NE and DA in mediating exploration.Significance Statement Both dopamine (DA) and norepinephrine (NE) have been implicated in the decision making process. Although these two catecholamines have shared aspects of their biosynthetic pathways and projection targets, they are thought to exert many core functions via distinct neural targets and receptor subtypes. However, the computational neuroscience literature often ascribes similar roles to these catecholamines, despite the above evidence. Resolving this discrepancy is important in guiding mechanistic hypotheses emerging from computational psychiatric approaches. This study examines the role of dopamine and norepinephrine on the explore-exploit tradeoff. By testing mice, we were able to compare multiple pharmacological agents within subjects, and examine sources of individual differences, allowing direct comparison between the effects of these two catecholamines in modulating decision making.

Bayesian estimation of some reliability characteristics for Nakagami model using adaptive progressive censoring

The focus of this research is to clarify both conventional and Bayesian parametric estimation methods for the Nakagami distribution making use of adaptive progressive Type II censored data. From a classical estimation perspective, two estimation methods are considered: maximum likelihood and least squares estimations. Along with the model parameters, three reliability metrics are estimated using point and interval estimation. Bayes estimates with gamma and inverse gamma priors are investigated by employing the squared error loss function. The Bayes computations are created using the Markov Chain Monte Carlo technique. Moreover, the classical and Bayesian intervals are also taken into consideration. For evidence of the effectiveness of the given methodologies, a simulation study and three applications from the physics, chemistry, and engineering domains are explored. Lastly three optimality criteria are applied to the stated data sets to pick the best progressive censoring strategy.

Minimizing distance between distribution functions: discrete counterparts to continuous random variables with applications in non-life insurance and stochastic reliability

In this work, we propose a novel family of procedures for deriving a discrete counterpart to a continuous probability distribution. They are based on a class of distances between cumulative distribution functions, including the Cramér, the Cramér-von Mises, and the Anderson-Darling distances as particular cases. The discrete counterpart is defined and derived as the random variable which minimizes its distance to the assigned continuous probability distribution among all the discrete random variables supported on the set of integers (or positive integers). Applications are provided with reference to the exponential and the normal distributions, among others; the discrete counterparts are derived, and their main properties are discussed, also in comparison with the one obtained through an existing discretization technique based on the preservation of the cumulative distribution function at integer values. Parameter estimation for these discrete analogs is discussed, along with an analysis of two real datasets, where they are compared in terms of goodness-of-fit with some popular discrete distributions. Furthermore, in order to highlight the effectiveness and the benefits derived from the proposed discretization procedures, we illustrate two practical applications in actuarial science and in reliability engineering. In the former case, the problem of determining the distribution of the total claims amount for a non-life insurance portfolio is considered, where the claim sizes can be modelled as iid random variables, and the number of claims is random as well. Actuaries use a recursive calculation method based on Panjer's formula, which requires an appropriate discretization of the individual claim distribution, and therefore the proposed procedures can be used. Since we consider two simple cases where the distribution of the total claims amount is analytically acquirable, the efficacy of the discretization procedures in the final approximation can be easily assessed and turns out to be satisfactory, especially when compared to the existing discretization. The latter case considers the determination of the reliability parameter for a complex stress-strength model. Here, the approximation by discretization is compared to Monte Carlo simulation and shown to be relevant: with a comparable if not smaller computational effort, discretization leads to similar results as simulation. Such discretizations can also naturally be applied to more complex problems such as scenario generation in stochastic programming. R code for this article is provided as supplementary material.

Research on pile bearing capacity prediction improved by grey wolf optimization in the SSA-LSSVM model

Abstract Pile foundations in soft soil often encounter challenges regarding abnormal bearing capacity, significantly impacting the safety of engineering projects. The bearing capacity of pile foundations is influenced by various factors, often characterized by complexity and unpredictability. Therefore, this study proposes a comprehensive model, termed GWOASSA-LSSVM, based on Grey Wolf Optimization (GWO)-enhanced Sparrow Search Algorithm (SSA) and Least Squares Support Vector Model (LSSVM). By introducing a grey wolf hierarchy to enhance the global search capability of the sparrow search algorithm and automating the optimization of parameters (γ, δ) in the LSSVM model using GWO, the GWOASSA-LSSVM predictive model is established. Evaluation metrics including correlation, and correlation between pile bearing capacity and the predictive target are considered, with pile bearing capacity as the predictive target. The GWOASSA-LSSVM model is compared with SSA-LSSVM, LSSVM, and Back Propagation Neural Network (BPNN). Results indicate that the GWOASSA-LSSVM model outperforms SSA-LSSVM, LSSVM, and BPNN across metrics such as Coefficient of Determination (R2), Variance Accounted For (VAF), Performance Index (PI), Index of Agreement (IOA), Index of Scatter (IOS), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and a20-index, demonstrating its capability for accurate prediction of pile bearing capacity.

Spontaneous anomalous Hall effect in magnetic and non-magnetic systems

Abstract We consider two cases of spontaneous anomalous Hall current. In a non-magnetic system with spin–orbit coupling (SOC), the applied bias results in the appearance of non-equilibrium magnetization and an anomalous Hall current. The latter demonstrates non-linear dependence on the applied bias. In a magnetic system with SOC, an anomalous current appears without applied bias. We perform the calculations in the framework of the free-electron model, whereas the common approach to this type of phenomenon is based on Berry connection. We demonstrate that anomalous currents acquire a measurable magnitude for reasonable model parameters.

A Novel Method for Determining Ventilatory and Gas Exchange Dynamics During Exercise: The "Chirp" Waveform.

Quantitating exercise ventilatory and gas exchange dynamics affords insights into physiological control processes and cardiopulmonary dysfunction. We designed a novel waveform, the chirp waveform, to efficiently extract moderate intensity exercise response dynamics. In the chirp waveform, work rate fluctuates sinusoidally with constant amplitude as sinusoidal period decreases progressively from approximately 8.5 to 1.4 minutes over 30 minutes of cycle ergometry. We hypothesized that response dynamics of pulmonary ventilation (V̇E) and gas exchange (V̇O2 and V̇CO2) extracted from chirp waveform are similar to those obtained from step-wise transitions. Thirty-one participants (14 young-healthy, 7 older-healthy, 10 COPD patients) exercised on three occasions. Participants first performed ramp-incremental exercise for gas exchange threshold (GET) determination. In randomized order, the next two visits involved either chirp or step-wise waveforms. Work rate amplitude (20W to ∼95% GET work rate) and exercise duration (30 min) were the same for both waveforms. A first-order linear transfer function with system gain (G) and time constant (τ) characterized response dynamics. Agreement between model parameters extracted from chirp and step-wise waveforms was established using Bland-Altman analysis and Rothery's Concordance Coefficient (RCC). V̇E, V̇O2, and V̇CO2 Gs showed no systematic bias (p>0.178) and moderate-to-good agreement (RCC>0.772, p<0.01) between waveforms. Similarly, no systematic bias (p=0.815) and good agreement (RCC=0.837, p<0.001) was found for τV̇O2. Despite moderate agreement for τV̇CO2 (RCC=0.794, p<0.001) and τV̇E (RCC=0.722, p=0.083), chirp τ was less (-6.9(11.7)s and -12.2(22.5)s, respectively). We conclude that the chirp waveform is a promising method for measuring exercise response dynamics and investigating physiological control mechanisms.

Impact of charge and non-minimal fluid-geometry coupling on anisotropic interiors

This paper explores the interior configuration of various charged anisotropic compact models within the framework of f(R,T,RληTλη) gravity. The chosen model in this theory is represented by R+γRληTλη , where γ is a real-valued parameter. We adopt a static spherical metric to describe the interior of compact strange stars and derive the corresponding field equations. The solution to these equations is then obtained using the Finch-Skea metric and a linear equation of state. Taking the radius and mass of the compact star model 4U 1820-30 as a case study, we investigate the impact of charge and modified corrections on its internal distribution. The stability of the resulting model is also determined within a specific range of γ. Additionally, we determine the values of the model parameter through the vanishing radial pressure constraint, aligning with the experimental data of eight different stars. Our findings indicate that the resulting model adheres to the conditions necessary for physically relevant interiors, particularly in the case of lower charge.

Modelling and Characterisation of Orthotropic Damage in Aluminium Alloy 2024

The aim of the work presented in this paper was development of a thermodynamically consistent constitutive model for orthotopic metals and determination of its parameters based on standard characterisation methods used in the aerospace industry. The model was derived with additive decomposition of the strain tensor and consisted of an elastic part, derived from Helmholtz free energy, Hill’s thermodynamic potential, which controls evolution of plastic deformation, and damage orthotopic potential, which controls evolution of damage in material. Damage effects were incorporated using the continuum damage mechanics approach, with the effective stress and energy equivalence principle. Material characterisation and derivation of model parameters was conducted with standard specimens with a uniform cross-section, although a number of tests with non-uniform cross-sections were also conducted here. The tests were designed to assess the extent of damage in material over a range of plastic deformation values, where displacement was measured locally using digital image correlation. The new model was implemented as a user material subroutine in Abaqus and verified and validated against the experimental results for aerospace-grade aluminium alloy 2024-T3. Verification was conducted in a series of single element tests, designed to separately validate elasticity, plasticity and damage-related parts of the model. Validation at this stage of the development was based on comparison of the numerical results with experimental data obtained in the quasistatic characterisation tests, which illustrated the ability of the modelling approach to predict experimentally observed behaviour. A validated user material subroutine allows for efficient simulation-led design improvements of aluminium components, such as stiffened panels and the other thin-wall structures used in the aerospace industry.

Automatic Parallel Tempering Markov Chain Monte Carlo with Nii-C

Due to the high dimensionality or multimodality that is common in modern astronomy, sampling Bayesian posteriors can be challenging. Several publicly available codes based on different sampling algorithms can solve these complex models, but the execution of the code is not always efficient or fast enough. The article introduces a C language general-purpose code, Nii-C, that implements a framework of automatic parallel tempering Markov Chain Monte Carlo. Automatic in this context means that the parameters that ensure an efficient parallel tempering process can be set by a control system during the inital stages of a sampling process. The autotuned parameters consist of two parts, the temperature ladders of all parallel tempering Markov Chains and the proposal distributions for all model parameters across all parallel tempering chains. In order to reduce dependencies in the compilation process and increase the code’s execution speed, Nii-C code is constructed entirely in the C language and parallelized using the message-passing interface protocol to optimize the efficiency of parallel sampling. These implementations facilitate rapid convergence in the sampling of high-dimensional and multimodal distributions, as well as the expeditious code execution time. The Nii-C code can be used in various research areas to trace complex distributions due to its high sampling efficiency and quick execution speed. This article presents a few applications of the Nii-C code.

Liquid–Liquid Equilibrium with Ab Initio Molecular Dynamics Simulation and NRTL Parameter Estimation

ABSTRACTIn this study, we propose a method for estimating liquid–liquid equilibrium with ab initio molecular dynamics (MD) simulation. Additionally, we determined the non‐random two‐liquid model parameters for the systems applicable to the extraction design using a commercial software. The minimized energy of separate liquid phases yielded equilibrium data from a more versatile procedure than the existing MD simulation. Although many experimental measurements with the parameters have been published, the parameters are not always compatible with the software. The performances are graphically illustrated for example systems including ionic liquid and deep eutectic solvent, and the results indicated that the proposed procedures of the estimation and the model parameter determination were satisfactory.

CIS: A Coral Instance Segmentation Network Model with Novel Upsampling, Downsampling, and Fusion Attention Mechanism

Coral segmentation poses unique challenges due to its irregular morphology and camouflage-like characteristics. These factors often result in low precision, large model parameters, and poor real-time performance. To address these issues, this paper proposes a novel coral instance segmentation (CIS) network model. Initially, we designed a novel downsampling module, ADown_HWD, which operates at multiple resolution levels to extract image features, thereby preserving crucial information about coral edges and textures. Subsequently, we integrated the bi-level routing attention (BRA) mechanism into the C2f module to form the C2f_BRA module within the neck network. This module effectively removes redundant information, enhancing the ability to distinguish coral features and reducing computational redundancy. Finally, dynamic upsampling, Dysample, was introduced into the CIS to better retain the rich semantic and key feature information of corals. Validation on our self-built dataset demonstrated that the CIS network model significantly outperforms the baseline YOLOv8n model, with improvements of 6.3% and 10.5% in PB and PM and 2.3% and 2.4% in mAP50B and mAP50M, respectively. Furthermore, the reduction in model parameters by 10.1% correlates with a notable 10.7% increase in frames per second (FPS) to 178.6, thus effectively meeting real-time operational requirements.

Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation

Abstract. Through the Bayesian lens of four-dimensional variational (4D-Var) data assimilation, uncertainty in model parameters is traditionally quantified through the posterior covariance matrix. However, in modern settings involving high-dimensional and computationally expensive forward models, posterior covariance knowledge must be relaxed to deterministic or stochastic approximations. In the carbon flux inversion literature, (Chevallier et al., 2007) proposed a stochastic method capable of approximating posterior variances of linear functionals of the model parameters that is particularly well suited for large-scale Earth-system 4D-Var data assimilation tasks. This note formalizes this algorithm and clarifies its properties. We provide a formal statement of the algorithm, demonstrate why it converges to the desired posterior variance quantity of interest, and provide additional uncertainty quantification allowing incorporation of the Monte Carlo sampling uncertainty into the method's Bayesian credible intervals. The methodology is demonstrated using toy simulations and a realistic carbon flux inversion observing system simulation experiment.

A new multivariate gamma process model for degradation analysis

AbstractIn reliability engineering, it is frequently encountered that multiple performance characteristics (PCs) deteriorate simultaneously. The associated degradation processes are usually dependent and exhibit some heterogeneity from unit to unit, which makes the multivariate degradation modeling and reliability evaluation more challenging. To this end, we propose a new multivariate gamma process model. This model introduces a multivariate random vector, whose joint distribution is constructed by marginal gamma distributions and a copula function, to describe the unit‐to‐unit variability and the dependence among PCs. Meanwhile, it does not require all PCs to be inspected at the same time points in contrast to the traditional copula‐based degradation models. In addition, two reliability evaluation methods are developed. Model parameters are estimated by the stochastic expectation maximization algorithm, and a three‐step procedure is provided to initialize this algorithm. Subsequently, numerical simulations are implemented to verify the proposed methods. Finally, two examples are provided for illustration, and it is shown that the proposed model and methods scale well to the degradation data with different numbers of PCs. What is more, comparisons with several benchmark models are performed, and the superiority of the proposed model is well demonstrated.

Graph neural network incorporating time-varying frequency domain features with application in spatial wind speed field prediction

Precise forecasting of wind speed is vital to guarantee safe travel on bridges. Nevertheless, current research primarily concentrates on single-point prediction. When it comes to forecasting spatial wind fields, graph neural networks prove to be powerful methods, but many of them have yet to take into account the impact of frequency domain attributes. To address this, a novel model called the graph neural network with time-varying frequency features (GTF) is proposed. Its core layer employs wavelet transforms in matrix form to extract various frequency sub-series, followed by the use of adaptive adjacency matrices to identify their spatial characteristics. Furthermore, aggregating all frequency band information and exploring spatial features further through multi-kernel convolutions. The predictive performance of different models for wind speed forecasting is assessed by utilizing spatial wind field data collected on a large-span bridge. Experimental results demonstrate that spatiotemporal model GTF outperforms the other models in terms of predictive accuracy. The quantity of wind observation points and their level of correlation exert a certain impact on the GTF model, while hyperparameters such as stacking depth and wavelet decomposition parameters have minimal effects. Finally, recommended parameters for the GTF model are provided.

An experimental and theoretical study on the creep behavior of silt soil in the Yellow River flood area of Zhengzhou City.

We took the silt soil in the Yellow River flood area of Zhengzhou City as the research object and carried out triaxial shear and triaxial creep tests on silt soil with different moisture contents (8%, 10%, 12%, 14%) to analyze the effect of moisture content on silt soil. In addition, the influence of moisture contents on soil creep characteristics and long-term strength was analyzed. Based on the fractional derivative theory, we established a fractional derivative model that can effectively describe the creep characteristics of silt soil in all stages, and used the Levenberg-Marquardt algorithm to inversely identify the relevant parameters of the fractional derivative creep model. The results show that the shear strengths of silt soil samples with moisture contents of 8%, 10%, 12% and 14% are 294kPa, 236kPa, 179kPa and 161kPa, respectively. The shear strength of silt soil decreases with increasing moisture content. When the moisture content increases, the cohesion of the silt soil decreases. Under the same deviatoric stress, the higher the moisture content of the silt soil, the greater the deformation will be. The long-term strength of silt soil decreases exponentially with the increase of moisture content. If the moisture content is 12%, the long-term strength loss rate of silt soil is the smallest, with a value of 32.96%. The calculated values of our creep model based on fractional derivatives have a high goodness of fit with the experimental results. This indicates that our model can better simulate the creep characteristics of silt soil. This study can provide a theoretical basis for engineering construction and geological disaster prevention in silt soil areas in the Yellow River flood area.

Addressing Autocorrelation, Multicollinearity, and Heavy-Tail Errors in the Linear Regression Model

The most popular estimator for estimating parameters of linear regression models is the Ordinary Least Squares (OLS) Estimator. The OLS is considered the best linear unbiased estimator when certain assumptions are not violated. However, when autocorrelation, multicollinearity, and heavy-tail error are jointly present in the dataset, the OLS estimator is inefficient and imprecise. In this paper, we developed an estimator of linear regression model parameters that jointly handle multicollinearity, autocorrelation, and heavy tail errors. The new estimator, LADHLKL, was derived by combining the Hildreth-Lu (HL), the Kibria Lukman (KL), and the Least Absolute Deviation (LAD) estimators. The LADHLKL poses both the characteristics of the LAD, HL, and KL estimators which makes it resistant to both problems. We examined the properties of the proposed estimator and compared its performance with other existing estimators in terms of mean square error. An application to real-life data and simulation study revealed that the proposed estimator dominates other estimators in all the considered conditions in terms of mean square error.

Numerical characterization on fracture evolution behavior of pre-flawed sandstone under monotonic and multilevel cyclic loading

This study employed an improved stress corrosion model within a three-dimensional particle flow program to investigate the mechanical and cracking behavior of fractured sandstone samples with varying rock bridge angles under both monotonic loading and multilevel cyclic loading. Firstly, using the experimental results from intact sandstone under uniaxial compression, we calibrated the microscopic mechanical parameters of the parallel bond model. Then, the model was validated using the experimental results of fractured sandstone under monotonic and cyclic loading. Finally, the initiation, propagation, and coalescence behavior of cracks in fractured sandstone samples under the above two loading paths, as well as the evolution process of the stress field, displacement field, and force chain distribution, were discussed in detail. The results show that the stress–strain curves, strength, deformation parameters, and macroscopic failure modes obtained from numerical simulations are consistent with those from laboratory mechanical tests. The mechanical behavior of fractured sandstone is influenced by both the rock bridge angle and the loading path. As the rock bridge angle decreases, the mechanical parameters of the sample show an increasing trend, and the influence of rock bridge angle on the failure mode is reduced. Cracks in the samples all initiate at the tips of pre-existing flaws, and the rock bridge angle affects the crack propagation and coalescence process: for sandstones with smaller rock bridge angles, cracks gradually extend to the sample’s ends; for sandstones with larger rock bridge angles, direct coalescence of cracks at the rock bridge is observed, then extending to the sample’s ends. Compared with monotonic loading, the damage degree of sandstone samples under multilevel cyclic loading is more severe, and even obvious block spalling is observed. The experimental and numerical simulation results are expected to improve the understanding of the mechanical behavior and fracture behavior of fractured rocks under monotonic and multilevel cyclic loading.

Thermomechanical characterisation and plane stress linear viscoelastic modelling of ethylene-tetra-fluoroethylene foils

AbstractEthylene-tetra-fluoroethylene (ETFE) is a polymer employed in tension membrane structures with mechanical properties that strongly depend on time and temperature effects. A comprehensive understanding of the mutual influence of these variables and a unified viscoelastic constitutive model design can enable wider exploitation of ETFE in sustainable lightweight construction. This study presents a thermomechanical characterisation of ETFE foils through quasi-static tensile experiments spanning two orders of magnitude of strain rates, creep, relaxation, shear and dynamic cyclic tests in a wide range of temperatures suitable for building applications, from $-20^{\circ }\text{ C}$ − 20 ∘ C to $60^{\circ }\text{ C}$ 60 ∘ C . The experimental results in different material orientations are used to identify the limits of the linear viscoelastic domain, define the direction-dependent creep compliance master curves and calibrate the parameters of a plane stress orthotropic linear viscoelastic model, employing the Boltzmann superposition and the time-temperature superposition principles. The model has been numerically implemented using a recursive integration algorithm and its code is provided open source. A validation on independently acquired data shows the accuracy of the constitutive model in predicting ETFE behaviour within the linear viscoelastic regime usually adopted during structural design, with excellent extrapolation capabilities outside the range of the calibration data.

Research on Lightweight Rice False Smut Disease Identification Method Based on Improved YOLOv8n Model

In order to detect rice false smut quickly and accurately, a lightweight false smut detection model, YOLOv8n-MBS, was proposed in this study. The model introduces the C2f_MSEC module to replace C2f in the backbone network for better extraction of key features of false smut, enhances the feature fusion capability of the neck network for different sizes of false smut by using a weighted bidirectional feature pyramid network, and designs a group-normalized shared convolution lightweight detection head to reduce the number of parameters in the model head to achieve model lightweight. The experimental results show that YOLOv8n-MBS has an average accuracy of 93.9%, a parameter count of 1.4 M, and a model size of 3.3 MB. Compared with the SSD model, the average accuracy of the model in this study increased by 4%, the number of parameters decreased by 89.8%, and the model size decreased by 86.9%; compared with the YOLO series of YOLOv7-tiny, YOLOv5n, YOLOv5s, and YOLOv8n models, the YOLOv8n-MBS model showed outstanding performance in terms of model accuracy and model performance detection; compared to the latest YOLOv9t and YOLOv10n models, the average model accuracy increased by 2.8% and 2.2%, the number of model parameters decreased by 30% and 39.1%, and the model size decreased by 29.8% and 43.1%, respectively. This method enables more accurate and lighter-weight detection of false smut, which provides the basis for intelligent management of rice blast disease in the field and thus promotes food security.