Classical Model Research Articles

Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data

AbstractWe present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the combinedd actuarial neural network (CANN) framework proposed by Wüthrich and Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.

Role of T Cells in Viral and Immune-mediated Myocarditis

Myocarditis is characterized by inflammatory cell infiltration into the myocardium and a high risk of deteriorating cardiac function with a heterogeneous etiology. Both viral- and myosin-induced myocarditis experimental models are used to mimic myocarditis in humans. Here, coxsackie virus B3-induced and non-virus-induced myocarditis models and data obtained in clinical studies were reviewed. Experimental murine myocarditis following immunization with α-myosin together with complete Freund adjuvant represents the classical immune-mediated model. T helper 1 (Th1) and Th2 pathways and important cytokines are involved in the autoimmunity of myocarditis, and the dynamic balance between Th17 and regulatory T cell seems to have an important role in the process of myocarditis. The purpose of this review is to summarize the existing understanding of the immunological mechanisms underlying myocarditis and exploring gaps in knowledge in both animal and human studies, since these mechanistic insights are a critical requirement for the development of novel therapeutic and vaccination strategies.

Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.

Empirical comparison of deep learning models for fNIRS pain decoding.

Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain. In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features. The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models. Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.

Finite-size versus finite-temperature effects in the critical long-range O(N) model

In this paper we consider classical and quantum versions of the critical long-range O(N) model, for which we study finite-size and finite-temperature effects, respectively, at large N. First, we consider the classical (isotropic) model, which is conformally invariant at criticality, and we introduce one compact spatial direction. We show that the finite size dynamically induces an effective mass and we compute the one-point functions for bilinear primary operators with arbitrary spin and twist. Second, we study the quantum model, mapped to a Euclidean anisotropic field theory, local in Euclidean time and long-range in space, which we dub fractional Lifshitz field theory. We show that this model admits a fixed point at zero temperature, where it displays anisotropic Lifshitz scaling, and show that at finite temperature a thermal mass is induced. We then compute the one-point functions for an infinite family of bilinear scaling operators.In both the classical and quantum model, we find that, as previously noted for the short-range O(N) model in [1], the large-N two-point function contains information about the one-point functions, not only of the bilinear operators, but also of operators that appear in the operator product expansion of two fundamental fields only at subleading order in 1/N, namely powers of the Hubbard-Stratonovich intermediate field.

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.

Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses. These provide empirical evidence of the strides made in this journal version over the initial work, solidifying our framework’s standing as a substantial contribution to the field of automated feature transformation. To improve the reproducibility, we have released the associated code and data by the Github link https://github.com/coco11563/TKDD2023_code.

Decision Tree based Data Modelling for First Detection of Thalassemia Major

Thalassemia is an inherited blood disease which lacks hemoglobin, the protein that is carrying oxygen to the body. The severe one is called Thalassemia Major which needs special care about blood transfusion. The use of rule-based method to create an inference as the first diagnosis of Thalassemia Major is not effective as rules have to be achieved from long interview with the medical personnel. This research aims to create a model based on decision tree for first detection of Thalassemia Major. The dataset is obtained by interview of Thalassemia symptoms and primary data of medical records from a hospital. Classical decision tree models used are ID3, C4.5 and CART. The models are evaluated by Train-Test Split consists of 70% training and 30% testing data and k-Fold Validation for checking model’s overfitting or underfitting. The output of this research is a final tree model from the best performance of decision tree models. The final result shows that C4.5 has the best performance with accuracy 100% and not overfitting or underfitting. Also, C4.5 performs feature selections to its tree modeling to simplify the inference. In brief, decision tree based modeling is effective to be used as first detection of Thalassemia Major by interview symptoms with generating automatic rules from its tree model.

Deciphering the effect of nucleation/growth kinetics on the morphology of Li plating/stripping on vertically aligned carbon nanofibers - A low-tortuosity 3-D nanostructured carbon host

Reversible lithium metal anodes (LMAs) are the holy grail for future rechargeable lithium metal batteries. Three-dimensional (3-D) conductive hosts have been extensively explored as an effective approach to suppressing dendrite formation and enabling reversible Li plating/stripping. However, the microscopic morphologies of Li plating and their correlation with the cell performance are not clear. Herein we unravel these issues using the vertically aligned carbon nanofiber (VACNF) array as a model 3-D conductive carbon host which has a well-defined vertical low-tortuosity structure allowing observation of the intrinsic Li morphologies infiltrated into the 3-D host. The VACNF array indeed provides much higher stability and reversibility for Li plating/stripping due to its high surface area and lithiophilic properties. We found that Li plating on both VACNF array and planar Cu electrodes follows the classical nucleation and growth model. Though the low plating current density (≤0.10 mA/cm2) provides better cycling stability consistent with the Sand’s equation, it forms sparse irregular grains stacked with dendrite-like long Li fibers. In contrast, the moderate to high plating current densities (1.0 − 5.0 mA/cm2) produce more uniform Li morphologies consisting of smaller micro-columns or micro-spheres. By decoupling the plating and stripping current densities, we unravel that the more uniform micro-columnar Li infiltrated in the VACNF array obtained at the moderate plating current density (∼1.0 mA/cm2) indeed exhibits the highest cycling performance. This provides new insights into the relationship between macroscopic electrochemical tests and microscopic Li morphologies, aiding in optimizing the performance of LMAs based on 3-D conductive hosts.

Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay.

The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.

Design of birch plywood as gusset plates in timber-timber uniaxial tension connections: Influence of fastener pattern, face grain orientation, and discussions based on the Whitmore effective width theory

This study reveals timber-timber composite joints consisting of glulam pieces and birch plywood plates with three different load-to-face grain angles. Utilizing a similar number of fasteners and arranging the fastener array from narrow to wide, uniaxial tension specimens were manufactured with four different fastener patterns. The thickness of birch plywood was intentionally under-designed so that the failure modes for all connections were net tension failure of birch plywood plates. Thereafter, the influence of the fastener pattern and face grain orientation on the load-bearing capacity and stiffness of the investigated composite joints was studied. The load capacity and nominal strength generally increased when the nail patterns varied from narrow to wide. This observation was associated with the Whitmore effective width theory (load spread angle) in steel gusset plate design. Moreover, to derive valid analytical methods to predict the net-tension capacity of birch plywood plates, the classic spread angle model that assumes rectangular stress blocks and the modified spread angle model that considers the summation of stresses from each fastener row were discussed. Both models were adopted to predict the net tension capacity of investigated specimens at 0°. In addition, the stiffness of joints was measured and compared with slip modulus formulas in Eurocode 5. The measured local stiffness values were found to be independent of the fastener pattern and load-face grain angles. The analytical slip modulus assuming the case without predrilling exhibited a satisfactory prediction, while formulas assuming the case with predrilling tend to give overestimations.

Small-angle X-ray scattering unveils the internal structure of lipid nanoparticles

Lipid nanoparticles own a remarkable potential in nanomedicine, only partially disclosed. While the clinical use of liposomes and cationic lipid-nucleic acid complexes is well-established, liquid lipid nanoparticles (nanoemulsions), solid lipid nanoparticles, and nanostructured lipid carriers have even greater possibilities. However, they face obstacles in being used in clinics due to a lack of understanding about the molecular mechanisms controlling their drug loading and release, interactions with the biological environment (such as the protein corona), and shelf-life stability. To create effective drug delivery carriers and successfully translate bench research to clinical settings, it is crucial to have a thorough understanding of the internal structure of lipid nanoparticles. Through synchrotron small-angle X-ray scattering experiments, we determined the spatial distribution and internal structure of the nanoparticles' lipid, surfactant, and the bound water in them. The nanoparticles themselves have a barrel-like shape that consists of coplanar lipid platelets (specifically cetyl palmitate) that are covered by loosely spaced polysorbate 80 surfactant molecules, whose polar heads retain a large amount of bound water. To reduce the interface cost of bound water with unbound water without stacking, the platelets collapse onto each other. This internal structure challenges the classical core-shell model typically used to describe solid lipid nanoparticles and could play a significant role in drug loading and release, biological fluid interaction, and nanoparticle stability, making our findings valuable for the rational design of lipid-based nanoparticles.

Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

AbstractIn the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for Support Vector Classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.

Comparative Performance Analysis of Deep Learning, Classical, and Hybrid Time Series Models in Ecological Footprint Forecasting

In a globalized world, factors such as increasing population, rising production rates, changing consumption habits, and continuous economic growth contribute significantly to climate change. Therefore, successfully forecasting the Ecological Footprint (EF) effectively indicates global sustainable development. Despite the significant role of the EF as one of the indicators of sustainable development, there is a gap in the literature regarding time series methods and forward-looking predictions. To address this gap, Ecological Footprint (EF) forecasting was performed using deep learning methods such as LSTMs, classical time series methods like ARIMA and Holt–Winters, and the developed hybrid ARIMA-SVR model. In the scope of the study, first, a spreadsheet was created using the total Ecological Footprint (EF) worldwide between 1961 and 2022, obtained from the Global Footprint Network database. Second, the forecasting performances of the ARIMA, Holt–Winters, LSTM, and the hybrid ARIMA-SVR models were compared using MAPE and RMSE metrics. Finally, the forecasting performances of the time series models were statistically validated through Wilcoxon Signed-Rank and Friedman tests. The study findings indicate that the proposed ARIMA (1,1,0) model demonstrated better performance with an average MAPE of 2.12%, compared to Holt–Winters (MAPE of 2.27%), LSTM (MAPE of 3.19%), and ARIMA-SVR (MAPE of 2.68%) methods in the test dataset. Additionally, it was observed that the ARIMA model forecasted the EF, which experienced a sudden decrease due to the COVID-19 lockdown, with a lower error compared to other models. These findings highlight the adaptability of the ARIMA model to variable and uncertain conditions.

Energy dissipation law of the variable time-step fractional BDF2 scheme for the time fractional molecular beam epitaxial model

In this work, we take a consideration of the time fractional molecular beam epitaxial(MBE) models. The variable time-step BDF2 methods are proposed for time fractional MBE models in order to obtain the high-order accuracy in time since the low regularity occurring in the initial state. By virtue of discrete gradient structures, energy dissipation laws of the schemes for time fractional MBE model with and without slope selection are proved, respectively. Besides, the unique solvabilities of two schemes are also guaranteed. The discrete modified energy and the corresponding energy dissipation law are asymptotically compatible with the associated discrete energy and the energy dissipation law of the variable time-step BDF2 method for the classical MBE model. Numerical experiments with the time adaptive strategies are provided to verify the effectiveness of the schemes.

Hemeroby Mapping of the Belém Landscape in Eastern Amazon and Impact Study of Urbanization on the Local Climate

This work contributes to the studies on landscape mapping induced by human pressure directly related to the urbanization process, whose approach is based on the concept of hemeroby adapted to the metropolitan area of Belém in the eastern Amazon. The mapping results using 1985 and 2021 Landsat satellite data indicated that the artificial coverage characterized as medium to the high urban system (polyhemerobic and metahemerobic degrees) has increased by almost 17% toward northern districts and Outeiro/Mosqueiro islands, while the natural vegetation cover suppression (ahemerobic degree) was around 11%. In addition, we investigated the impacts of urban expansion on seasonal (WET from January to April and DRY from July to November) surface air temperature (minimum TN and maximum TX) patterns. From statistical comparisons between 18-year samples (2004/2021 to 1985/2002), we find evidence of a current significantly warmer climate, with a notable indication of higher surface temperature over densely urbanized areas compared to lower values over natural areas. In the TX climatology, particularly for the DRY regime, we identified a pattern similar to the classic heat island model with concentric isotherms reaching a maximum center over the more urbanized continental region of Belém and a thermal decrease at the edges. Therefore, the findings of this work are convincing that Belém already faces the direct impacts of urbanization on the local climate, so it is crucial to develop government strategies aimed at taking action to mitigate socioenvironmental risks and threats to the well-being of urban populations.

Identification of Distribution Network Topology and Line Parameter Based on Smart Meter Measurements

Accurate line parameters are the basis for the optimal control and safety analysis of distribution networks. The lack of real-time monitoring equipment in grids has meant that data-driven identification methods have become the main tool to estimate line parameters. However, frequent network reconfigurations increase the uncertainty of distribution network topologies, creating challenges in the data-driven identification of line parameters. In this paper, a line parameter identification method compatible with an uncertain topology is proposed, which simplifies the model complexity of the joint identification of topology and line parameters by removing the unconnected branches through noise reduction. In order to improve the solving accuracy and efficiency of the identification model, a two-stage identification method is proposed. First, the initial values of the topology and line parameters are quickly obtained using a linear power flow model. Then, the identification results are modified iteratively based on the classical power flow model to achieve a more accurate estimation of the grid topology and line parameters. Finally, a simulation analysis based on IEEE 33- and 118-bus distribution systems demonstrated that the proposed method can effectively realize the estimation of topology and line parameters, and is robust with regard to both measurement errors and grid structures.

Improving U-net network for semantic segmentation of corns and weeds during corn seedling stage in field.

Weeds are one of the main factors affecting crop growth, making weed control a pressing global problem. In recent years, interest in intelligent mechanical weed-control equipment has been growing. We propose a semantic segmentation network, RDS_Unet, based on corn seedling fields built upon an improved U-net network. This network accurately recognizes weeds even under complex environmental conditions, facilitating the use of mechanical weeding equipment for reducing weed density. Our research utilized field-grown maize seedlings and accompanying weeds in expansive fields. We integrated the U-net semantic segmentation network, employing ResNeXt-50 for feature extraction in the encoder stage. In the decoder phase, Layer 1 uses deformable convolution with adaptive offsets, replacing traditional convolution. Furthermore, concurrent spatial and channel squeeze and excitation is incorporated after ordinary convolutional layers in Layers 2, 3, and 4. Compared with existing classical semantic segmentation models such as U-net, Pspnet, and DeeplabV3, our model demonstrated superior performance on our specially constructed seedling grass semantic segmentation dataset, CGSSD, during the maize seedling stage. The Q6mean intersection over union (MIoU), precision, and recall of this network are 82.36%, 91.36%, and 89.45%, respectively. Compared to those of the original network, the proposed network achieves improvements of 5.91, 3.50, and 5.49 percentage points in the MIoU, precision, and recall, respectively. The detection speed is 12.6 frames per second. In addition, ablation experiments further confirmed the impactful contribution of each improvement component on the overall semantic segmentation performance. This study provides theoretical and technical support for the automated operation of intelligent mechanical weeding devices.

Individual variations in motives for nicotine self-administration in male rats: evidence in support for a precision psychopharmacology

The significant heterogeneity in smoking behavior among smokers, coupled with the inconsistent efficacy of approved smoking cessation therapies, supports the presence of individual variations in the mechanisms underlying smoking. This emphasizes the need to shift from standardized to personalized smoking cessation therapies. However, informed precision medicine demands precision fundamental research. Tobacco smoking is influenced and sustained by diverse psychopharmacological interactions between nicotine and environmental stimuli. In the classical experimental rodent model for studying tobacco dependence, namely intravenous self-administration of nicotine, seeking behavior is reinforced by the combined delivery of nicotine and a discrete cue (nicotine+cue). Whether self-administration behavior is driven by the same psychopharmacological mechanisms across individual rats remains unknown and unexplored. To address this, we employed behavioral pharmacology and unbiased cluster analysis to investigate individual differences in the mechanisms supporting classical intravenous nicotine self-administration (0.04 mg/kg/infusion) in male outbred Sprague–Dawley rats. Our analysis identified two clusters: one subset of rats sought nicotine primarily for its reinforcing effects, while the second subset sought nicotine to enhance the reinforcing effects of the discrete cue. Varenicline (1 mg/kg i.p.) reduced seeking behavior in the former group, whereas it tended to increase in the latter group. Crucially, despite this fundamental qualitative difference revealed by behavioral manipulation, the two clusters exhibited quantitatively identical nicotine+cue self-administration behavior. The traditional application of rodent models to study the reinforcing and addictive effects of nicotine may mask individual variability in the underlying motivational mechanisms. Accounting for this variability could significantly enhance the predictive validity of translational research.

Accurate solution of the SAIL model by leaf inclination angle calculation based on laser point clouds

ABSTRACT The canopy radiative transfer model enables quantitative inversion of vegetation biophysical parameters by rapidly simulating spectral information. However, structural input parameters, such as the leaf inclination angle distribution (LAD), are often set to empirical values in classic models, leading to simulation errors. Laser point cloud technology has developed rapidly in recent years, and the 3D structure of vegetation can be detailed using laser sensors and processing software, providing the necessary conditions for accurate solutions of radiative transfer models of the canopy. However, the mechanisms for improving existing models of canopy radiative transfer by introducing laser point clouds have not yet been theoretically analyzed in detail. In this study, a precise solution was proposed by incorporating the accurate LAD obtained from laser point clouds into the intermediate function of the traditional Scattering by Arbitrarily Inclined Leaves (SAIL) model, which is a widely used multiangle radiative transfer model. The calculation of the leaf inclination angle and the derivation of the improved model were detailed in this paper. Experiments from different observation angles and bands compared the similarity between the actual measured spectra and the simulated spectra for both the traditional SAIL model and the proposed model with accurate structural parameters. The overall similarity of the proposed model with respect to the conventional SAIL model was improved, indicating that introducing laser point clouds improved the simulation accuracy. The proposed model achieved more significant improvements in the blue 450 nm and red 670 nm bands. Moreover, the similarity improvements differed between different angles. Finally, we analyzed the sensitivity of the structural parameters of the SAIL model and the proposed model using local and global sensitivity analysis methods.