Bimodal Function Research Articles

Improved partitioning between matrix and macropore flow: Novel bimodal lognormal functions for water retention and hydraulic conductivity in pumice and non-pumice soils

Dual-porosity models have been shown to improve models of soil–water movement and enhance the water balance of structured soils. In this study we introduce novel, continuous, closed-form, bimodal, lognormal functions for soil–water retention, θ(ψ), and unsaturated hydraulic conductivity, K(ψ), enhancing traditional models and significantly improving predictions, particularly for pumice soils. These functions incorporate the thresholds for (a) water pressure, (b) soil water content, and (c) unsaturated conductivity, which accurately differentiates macropores from matrix pores. Validation using 313 observation points from laboratory data shows an increase in the Nash–Sutcliffe efficiency coefficient of θ(ψ) from 0.94 to 0.97, and for K(ψ) from 0.83 to 0.95. The model also requires six constant, semi-empirical tortuosity parameters and provides a physically constrained approach for the hydraulic parameters that reduces non-uniqueness risks. The derived functions yield improved predictions and enable the computation of macropore soil water content and flow contributions, with potential applications for the advancement of preferential flow modelling.

Bimodal functionality of highly conductive nanostructured silver film towards improved performance of photosystem I-based graphene photocathode

We present the novel design of photosystem I (PSI)-based biosolar cell, whereby conductive transparent electrode materials, such as ITO or FTO, are replaced with glass covered with silver island film. This nanostructured metallic layer combines high electric conductance with enhancing the absorption efficiency of the PSI biocatalyst via the plasmonic effect. We demonstrate strong enhancement of the photocurrent generated in the biohybrid electrode composed of oriented layers of PSI reaction centers due to plasmonic interactions of the PSI fluorophores and redox centres with the conductive silver island film.

Numerical investigation of bimodal tip clearance effect on the lift-drag and flow characteristics of a hydrofoil

Tip leakage flow has adverse effect on energy conversion of the hydraulic machinery. The new tip clearance structure based on Bimodal Gaussian function is designed to reduce tip leakage vortex. The effects of bimodal structure with different amplitudes and arrangement positions on lift-drag and flow characteristics are studied. The results show that the maximum rise of the lift-drag ratio reaches 5.88% when the bimodal structure amplitude is equal to the tip clearance size. The bimodal structure reduces the outflow velocity at the peak and forms a smooth streamline, which weakens the entrainment with the mainstream. The location of the bimodal structure far away from leading edge helps to produce smooth streamlines and make the flow field smoother, and reduces the area of high turbulent kinetic energy region near leading edge and in tip clearance outflow region significantly. The arrangement position of bimodal structure close to leading edge can produce a low-speed zone or a recirculation zone in the mainstream, which can block the mainstream flow and reduce the energy characteristics of the hydrofoil.

Analysis of the unsaturated hydraulic properties of rocks using multiple laboratory methods

Abstract Proper characterization of the unsaturated hydraulic properties in rocks is significant for predicting fluid flow in soil, hydrogeologic, and petroleum science and engineering problems. In this study, we contributed rigorous analysis of the unsaturated hydraulic properties of three reservoir rock samples (Berea Sandstone, Guelph Dolomite, and Indiana Limestone). An improved version of the standard evaporation method (HYPROP) was developed to cater specifically to rock samples. The improved HYPROP setup enables measurements of local water pressures within rock samples without disturbing the upper portion of the samples. The obtained results were compared with those obtained using the conventional pressure plate method and a state-of-the-art nuclear magnetic resonance (NMR) method. Observed data were analyzed in terms of four different unimodal and bimodal hydraulic functions. The HYPROP data were found to be relatively close to the pressure plate data of two carbonate rocks. The NMR-based data were reasonably consistent with the HYPROP data, with differences likely due in part to the fact that they were obtained using two different 5-cm long plugs taken from the same core. Heterogeneity along the rock cores from which the samples were taken could be a major reason for the observed differences, and hence should be considered in reservoir analyses.

Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector

AbstractThe Equatorial Electrojet (EEJ) is one of the important near‐earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi‐diurnal, and ter‐diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi‐diurnal, and ter‐diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).

Incorporating flowpaths as an explicit measure of river-floodplain connectivity to improve estimates of floodplain sediment deposition

Variation in floodplain topography can lead to gradual flooding and increase river-floodplain connectivity. We show that incorporating flowpaths as an explicit measure of river-floodplain connectivity can improve estimates of floodplain sediment deposition. We focus on the floodplain of the South River, downstream of Waynesboro, Virginia, where measurements of mercury accumulation have been used to estimate decadal-scale sedimentation rates. We developed a two-dimensional Hydrologic Engineering Center's River Analysis System (2D HEC-RAS) hydrodynamic model and used simulated model results with sediment deposition data to create regression models describing sedimentation across the floodplain. All of our statistical models incorporated a flowpath length from the location on the floodplain downstream to the riverbank as an explicit measure of river-floodplain connectivity that improved our estimates of floodplain sediment deposition (r2 = 0.514). We applied our best regression model to our hydrodynamic model results to create a map of floodplain sedimentation rate and discuss differences of three separate sections of floodplain. We found that floodplains with variable topography had wider, bimodal probability distribution functions (PDFs) of sedimentation rate (aggregated spatially) than floodplains without this topographic relief (with narrower log-normal PDFs). Our work highlights how floodplain topography and river-floodplain connectivity affect sedimentation rates and can help inform the development of floodplain sediment budgets.

Modeling water flow and volumetric water content in a degraded peat comparing unimodal with bimodal porosity and flux with pressure head boundary condition

AbstractDegraded peatlands release large amounts of greenhouse gases. The development of effective mitigation and management measures requires an understanding of relevant site‐specific biogeochemical and hydraulic processes. However, the simulation of water fluxes and vadose zone state variables of degrading peatlands relies on proper process description, parameterization of hydraulic functions, and representation of the boundary conditions. The objective of this study was to analyze the effects of unimodal versus bimodal soil hydraulic functions and pressure head versus flux‐type lower boundary conditions (LBCs) on the calculated hydraulic characteristics of a degraded peat profile. HYDRUS‐1D was used to study the hydraulic flow dynamics parameterized with data from a weighable groundwater lysimeter for the period from May 1 to December 31, 2019. Simulations comparing uni‐ and bimodal hydraulic functions showed only minor differences. Simulations of soil water pressure at a depth of 30 cm using a flux‐type LBC (RMSE: 27 cm, where RMSE is root mean square error) performed better than simulations using a pressure head LBC (RMSE: 48 cm). The pressure head LBC performed better at simulating volumetric water contents in 30‐cm depth than the flux LBC variant (RMSE: 0.05 vs. 0.09 cm3 cm−3). For specific site conditions with a shallow, fluctuating groundwater table and temporary air entrapment, the choice of LBC was important for a more accurate simulation of soil water fluxes and volumetric water content.

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data.

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. We propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

Structure of the active Fokker-Planck equation.

This paper solves in one and two dimensions the steady noninteractive active Fokker-Planck (FP) equationand finds that its velocity distribution admits, under limiting cases, a dual behavior. Briefly, when the inertial relaxation time is smaller than the orientation time, the active FP equationadmits a bimodal shape, whereas the inverse condition is seen to admit a Gaussian one. Once the velocity distribution functions are available, they are used to find their effect on the system's transport properties, such as its mean-square speed. In the process, a useful mathematical identity for the first kind Bessel function as a sum of bimodal exponential functions is spotted.

Multiple linear regression based illumination normalization for non-uniform light image thresholding

Thresholding-based two-class image binarization is one of the simplest and most popular approaches. However, the performance of global thresholding degrades under non-uniform lighting conditions. Local thresholding methods are widely used for binarizing uneven light images. The appropriate choice of the initial size of the window and designing the bimodal criteria function are the most challenging tasks for the local thresholding approaches. Therefore, to make it simpler, in this work, a novel approach is developed to improve the efficacy of binarizing any uneven light images. To begin with, a two stage approach is developed to extract valid training sample points from the uneven light images for estimating the illumination surface. In addition, the Multiple-Linear-Regression (MLR) method is applied on the extracted training sample points to estimate the illumination surface. Furthermore, the estimated illumination surface is used to normalize the non-uniform light of the image to binarize the image using Otsu’s global thresholding. The proposed approach is validated on different variants of uneven light images and with six different states of art uneven light image binarization approaches. It is observed from the simulations that the performance of the proposed approach outperforms the other approaches in qualitative as well as quantitative measures. Further, the binarization of uneven document image methods are not effective on object background binarization of uneven images. The proposed approach has the average F-Measure (F1) score of 0.98, average Jaccard Index (JI) score of 0.97, average Percentage of Misclassification Error (PME) score of 1.10 and the computational complexity of 2.64 sec.

Coupling particle image velocimetry and digital image analysis to characterize cluster dynamics in a fast fluidized bed

Particle clusters affect interphase interaction and dominate the overall performance of gas-solid fluidized beds. In this work, the particle image velocimetry, particle tracking velocimetry and digital image analysis were coupled to characterize clustering dynamics in a lab-scale riser. The clusters were identified by the Otsu algorithm based on grayscale distribution, and the cluster properties such as equivalent size, solids concentration and velocity distribution were extracted and analyzed under various operating conditions. It was found macroscopic operating conditions affect cluster characteristics significantly whereas the effect of particle composition is secondary. A bimodal or skewed probability density function was observed for not only the time-averaged hydrodynamic velocity but also the particle velocity around the cluster interface, indicating the coexistence of dilute and dense phases and the breaking of equilibrium assumption. The nonuniform distributions for laminar and Reynolds-stress-like granular temperatures were further discussed, which show remarkable anisotropy at both the microscopic and mesoscopic scales.

Study of the positional and orientational contributions to the Helmholtz free energy of a finite hard-disk system. A molecular dynamics analysis of its hexatic transition

Based on an equilibrium thermodynamic scheme, this work studies the separation of the Helmholtz free energy of a hard-disk system into its positional and orientational contributions. For the positional part, an accurate two dimensional version of the Carnahan-Starling equation of state is proposed, while a simple ansatz of the one particle partition function for the orientational part is proposed. The latter expression, which contains information about the probability of the norm of the six-fold orientational order parameter, is obtained through molecular dynamics simulations. Perhaps due to finite size effects, the ordering of the fluid is not a sudden phenomenon: the fluid-hexatic phase transition is preceded by a linear increase in the orientational contribution to the pressure of the fluid. A similar result is obtained for the hexatic-solid phase transition. Thus, the hexatic first-order phase transition is located between two linear ordering processes, consistent with the KTHNY theory. The proposed separation also predicts the existence of first order fluid-hexatic phase transition by using two procedures: the first, based on a purely bimodal probability density function, produces a pressure with a van del Waals-like first-order phase transition, while the second, considering density fluctuations, results in a flattened pressure phase transition.

Ghostly Galaxies: Accretion-dominated Stellar Systems in Low-mass Dark Matter Halos

Wide-area deep imaging surveys have discovered large numbers of extremely low surface brightness (LSB) dwarf galaxies, which challenge galaxy formation theory and, potentially, offer new constraints on the nature of dark matter. Here we discuss one as-yet-unexplored formation mechanism that may account for a fraction of LSB dwarfs. We call this the “ghost galaxy” scenario. In this scenario, inefficient radiative cooling prevents star formation in the “main branch” of the merger tree of a low-mass dark matter halo, such that almost all its stellar mass is acquired through mergers with less massive (but nevertheless star-forming) progenitors. Present-day systems formed in this way would be “ghostly” isolated stellar halos with no central galaxy. We use merger trees based on the extended Press–Schechter formalism and the Copernicus Complexio cosmological N-body simulation to demonstrate that mass assembly histories of this kind can occur for low-mass halos in ΛCDM, but they are rare. They are most probable in isolated halos of present-day mass ∼4 × 109 M ⊙, occurring for ∼5% of all halos of that mass under standard assumptions about the timing and effect of cosmic reionization. The stellar masses of star-forming progenitors in these systems are highly uncertain; abundance-matching arguments imply a bimodal present-day mass function having a brighter population (median M ⋆ ∼ 3 × 106 M ⊙) consistent with the tail of the observed luminosity function of ultradiffuse galaxies. This suggests that observable analogs of these systems may await discovery. We find that a stronger ionizing background (globally or locally) produces brighter and more extended ghost galaxies.

An Evaluation Method of Gas-Bearing Properties Based on Gaussian Bimodal Function Pore Structure Characterization: A Case Study of Tight Sandstone in the East China Sea Basin

The Xihu Sag in the East China Sea Basin is located at the edge of the eastern Chinese continent and has great exploration potential. In recent years, the development of low-permeability and tight sandstone gas has become an important area of exploration and development in the Huagang Formation (E3h) of the Xihu Sag. The tight sandstone reservoir in the Xihu Sag is characterized by serious heterogeneity, high water saturation, low resistivity, and a complex gas–water relationship. Because of these characteristics of tight sandstone reservoirs, it is difficult to perform an evaluation of them. In this work, a bimodal Gaussian density function was constructed using the data of high-pressure mercury intrusion (HPMI) and nuclear magnetic resonance (NMR); this approach was used to analyze the pore structure parameters. The reservoirs were divided into four types using the fitting parameter η, and the rock electric parameters that correspond to different pore structures were quite different. When combined with the log response equation of η with acoustic interval transit time (AC), density (DEN), and natural gamma (GR) logging curves, an evaluation method of gas-bearing properties that was based on the characteristics of the pore structure was established. Compared with the water saturation test of the sealing core, it was found that the water saturation calculated by the classification of the pore structure was more accurate than that obtained by the conventional method, and the error was less than 8.35%, which proves that this method is feasible and effective. The findings of this study can help provide a better understanding of the distribution characteristics of gas and water in tight sandstone and provide help for tight gas exploration and development.

Hells (LSH) Controls Gene Expression in T-Cell Lymphomas By Regulating RNA Polymerase II with a Bimodal Function

Chromatin modifiers are emerging as determinants of many types of cancer, including anaplastic large cell lymphoma (ALCL), a family of highly heterogeneous T-cell lymphomas for which therapeutic options are still limited. We recently identified the DNA helicase HELLS - a member of the SWI/SNF2 family- as a genetic vulnerability of ALK - ALCLs. HELLS is a multifunctional chromatin remodeler that affects genomic instability. Although its transcriptional function has been suggested, no clues on how HELLS controls transcription are currently available. To explore its transcriptional function in ALCLs, we integrated HELLSChromatin immunoprecipitation-sequencing (ChIP-seq) analysis in the TLBR-2 cell line with RNA-sequencing data TLBR-2 HELLS KD and control cells. Out of 729 genes significantly affected by HELLS KD, 467 genes (64% of the total) resulted directly bound by HELLS. We termed these genes HELLS-direct genes (HDGs). Analysis of HELLS binding profile at HDGs site showed that 67% of HELLS binding sites at HDGs has intragenic localization being associated with promoters, introns, and exons. Gene-set enrichment analysis of HDGs revealed several pathogenic processes associated with deregulated genes including JAK/STAT signaling pathway, inflammation mediated by chemokines, and cytokines signaling pathway. To assess the contribution of HELLS to the transcription of its HDGs, the distribution of RNA-PolymeraseII (RNAPII) was investigated by ChIP-seq in TLBR-2 HELLS KD and control cells. We observed that HELLSdepletion caused a bimodal alteration of RNAPII. On genes belonging to biological processes involved in the dysregulation of circuitries that controls T-cells dependent host interactions (n=195, 40% of HDG promoters), the depletion of HELLS caused a significant impairment of RNAPII recruitment suggesting that HELLS partakes in transcription regulation of these pathways acting as a co-transcriptional factor. Whereas, on the remaining 60% of HDGs (n=272), which are enriched for selective biological functions including cytoskeleton regulation and DNA metabolic processes, we observed a selective loss of RNAPII signal in the regions immediately downstream of the TSS coherent with a potential proximal promoter pausing of RNAPII. Target analysis of phospho-CTD modification ser2P of RNAPII confirmed the RNAPII pausing after HELLS depletion. Stacked RNAPII during elongation drives DNA-RNA hybrids (R-loops) formation and it is a primary source of DNA break accumulation. We hypothesized that HELLS could promote RNAPII elongation by relieving R-loops, thereby decreasing potential obstacles to the transcription machinery. We investigated the effect of HELLS depletion on R-loops accumulation by performing immunofluorescences using the S9.6 antibody to detect R-loops in control and HELLS KD cells. We observed a significant increase in the nuclear intensity of R-loops in several models of ALCL HELLS KD cells. The increased S9.6 signal was accompanied by a different pattern of nuclear Ser2P-RNAPII with clusters adjacent to R-loop structures suggesting that R-loops are associated with stalled transcription. After HELLS KD, we also observed an overall increase in the intensity of the nuclear γH2AX signal - used as a readout of DNA damage accumulation- and a significant increase in the formation of γH2AX foci in all cell lines tested. The increase in γH2AX foci was found in S9.6 positive cells andwasassociated with a distinctive pattern of ser2P-RNAPII. By using reporter-GFP-based assays, we investigated which pathway mediates HELLS ability to repair DNA damage discovering that the loss of HELLS results in decreased NHEJ and MMEJ efficiency (37% and 36% respectively over time). To validate the clinical impact of HELLS in vivo, we interrogated a cohort of 44 ALCL patients using the NCounter platform. Gene expression analysis showed that HELLS was significantly associated with its HDGs showing a positive correlation with 68% of genes and a negative correlation with 32% of genes in ALK -ALCL samples. Our data indicate that i) HELLS works with a bi-modal function in controlling gene expression by regulating both the initiation and the elongation of RNAPII and ii) the different work modalities underline different biological consequences. At the crossroads between transcription and DNA repair, HELLS is as an attractive target for developing novel treatments in hematological malignancies

Do Reactive Oxygen and Nitrogen Species Have a Similar Effect on Digestive Processes in Carnivorous Nepenthes Plants and Humans?

Carnivorous plants attract animals, trap and kill them, and absorb nutrients from the digested bodies. This unusual (for autotrophs) type of nutrient acquisition evolved through the conversion of photosynthetically active leaves into specialised organs commonly called traps. The genus Nepenthes (pitcher plants) consists of approximately 169 species belonging to the group of carnivorous plants. Pitcher plants are characterised by specialised passive traps filled with a digestive fluid. The digestion that occurs inside the traps of carnivorous plants depends on the activities of many enzymes. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) also participate in the digestive process, but their action is poorly recognised. ROS and RNS, named together as RONS, exhibit concentration-dependent bimodal functions (toxic or signalling). They act as antimicrobial agents, participate in protein modification, and are components of signal transduction cascades. In the human stomach, ROS are considered as the cause of different diseases. RNS have multifaceted functions in the gastrointestinal tract, with both positive and negative impacts on digestion. This review describes the documented and potential impacts of RONS on the digestion in pitcher plant traps, which may be considered as an external stomach.

Particle Size Distributions and Extinction Coefficients of Aerosol Particles in Land Battlefield Environments

In land battlefield environments, aerosol particles can cause laser beams to undergo attenuation, thus deteriorating the operational performance of military laser devices. The particle size distribution (PSD) and extinction coefficient are key optical properties for assessing the attenuation characteristics of laser beams caused by aerosol particles. In this study, we employed the laser diffraction method to measure the PSDs of graphite smoke screen, copper powder smoke screen, iron powder smoke screen, ground dust, and soil explosion dust. We evaluated the goodness of fit of six common unimodal PSD functions and a bimodal lognormal PSD function employed for fitting these aerosol particles using the root mean square error (RMSE) and adjusted R2, and selected the optimal PSD function to evaluate their extinction coefficients in the laser wavelength range of 0.249~12 μm. The results showed that smoke screens, ground dust, and soil explosion dust exhibited particle size ranges of 0.7~50 µm, 1~400 µm, and 1.7~800 μm, respectively. The lognormal distribution had the best goodness of fit for fitting the PSDs of these aerosol particles in the six unimodal PSD functions, followed by the gamma and Rosin–Rammler distributions. For the bimodal aerosol particles with a lower span, the bimodal lognormal PSD functions exhibited the best goodness of fit. The graphite smoke screen exhibited the highest extinction coefficient, followed by the copper and iron powder smoke screens. In contrast, the ground dust and soil explosion dust exhibited the lowest extinction coefficients, reaching their minimum values at a wavelength of approximately 8.2 μm. This study provides a basis for analyzing and improving the detection and recognition performance of lasers in land battlefield environments.

Semi-analytical solution of pore-water pressure in unsaturated ground and infinite slope considering highly nonlinear soil hydraulic properties

A multi-exponential function (ME) is proposed to depict the highly nonlinear soil water retention curve and hydraulic conductivity function. Then, semi-analytical solution of pore-water pressure in an unsaturated infinite slope at steady state is derived using ME to depict soil hydraulic properties. The solution is verified with experimental and numerical results. It is found that ME can better describe the highly nonlinear hydraulic properties of soil than the single exponential function (SE), which is commonly-used previously. At steady state, the adoption of SE results in overestimating negative pore-water pressure (PWP; i.e., matric suction) by about 25 kPa and slope’s factor of safety (FOS) by 100% under rainfall, but underestimating negative PWP by about 50 kPa under evaporation. The difference between PWP and FOS calculated by using SE and ME becomes more significant for soil with bimodal hydraulic conductivity function than that with unimodal one. This difference reduces at a larger slope angle and deeper depth. Moreover, this difference generally increases at a lower SME/SSE, where SME and SSE represent the suction at which the unsaturated hydraulic conductivity expressed by ME and SE equals the applied rainfall intensity or evaporation rate, respectively. When SME/SSE is greater than 0.1, the difference basically diminishes.

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Identifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of natural secondary forests (NSFs) by fitting a bimodal Gaussian function. Three machine learning models (Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (Xgboost)) and three deep learning models (One-dimensional Convolutional Neural Network (1D-CNN4), 1D Visual Geometry Group Network (1D-VGG16), and 1D Residual Network (1D-Resnet34)) were applied. A completely randomized design was utilized to investigate the effects of four feature sets (original CHD features, original LiDAR features, the proposed CHD features fitted by the bimodal Gaussian function, and the LiDAR features selected by the recursive feature elimination algorithm) and models on estimating the AGB of NSFs. Results revealed that the models were the most important factor for AGB estimation, followed by the features. The fitted CHD features significantly outperformed the other three feature sets in most cases. When employing the fitted CHD features, the 1D-Renset34 model demonstrates optimal performance (R2 = 0.80, RMSE = 9.58 Mg/ha, rRMSE = 0.09), surpassing not only other deep learning models (e.g.,1D-VGG16: R2 = 0.65, RMSE = 18.55 Mg/ha, rRMSE = 0.17) but also the best machine learning model (RF: R2 = 0.50, RMSE = 19.42 Mg/ha, rRMSE = 0.16). This study highlights the significant role of the new CHD features fitting a bimodal Gaussian function and the effects between the models and the CHD features, which provide the sound foundations for effective estimation of AGB in NSFs.

A Metaheuristic Approach to Parameter Estimation of a Flexible Parametric Mixture Cure Rate Model with Interval-Censored Data

A flexible parametric mixture cure model, called bi-lognormal cure rate model or simply BLN model, is defined and studied. The BLN model can be effectively used to analyze survival dataset in the presence of long-term survivors, especially when the dataset presents the underlying phenomenon of latent competing risks or when there is evidence that a bimodal hazard function is appropriated to described it, which are advantages over other cure rate models found in the literature. We discuss the maximum likelihood estimation for the model parameters considering interval-censored data through the differential evolution algorithm that is a nature-inspired computing metaheuristic used for global optimization of functions defined in multidimensional spaces. This approach is also used because the likelihood function of the model is multimodal and the direct application of gradient methods in this case is not ideal, since such methods are local search methods with a high chance of getting stuck at a local maximum when the starting point is chosen outside the basin of attraction of a global maximum. In addition, a simulation study was implemented to compare the performance of differential evolution algorithm with the performance of the Newton-Raphson algorithm in terms of bias, root mean square error, and the coverage probability of the asymptotic confidence intervals for the parameters. Finally, an application of the BLN model to real data is presented to illustrate that it can provide a better fit than other mixture cure rate models.