Two-step Model Research Articles

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Evaluating the influence of the initial high molecular weight level on monoclonal antibody particle formation kinetics using a short-term chemical stress study

Protein formulations may form proteinaceous particles that vary in size from nanometers to millimeters. Monitoring the kinetics of protein particle formation, e.g., through accelerated degradation studies, is an attempt to understand and assess the rate and progression of particle populations. Little is known about whether the initial level of high molecular weight (HMW) species, or initial HMW level (IHL), of a protein solution influences the propagation of protein particle formation, and thus affects the storage stability of proteins.In this study, we have established a method to generate protein solutions of different IHLs by thermal stress. We have evaluated a 16-week thermal stability study at 40 °C of two monoclonal antibodies (mAb-A and mAb-B) at different IHLs using size exclusion chromatography (SEC) and sub-visible particle analysis. We have performed an isothermal stress study with guanidinium hydrochloride (GuaHCl) at room temperature for 300-min to evaluate the formation of HMWs analysed by SEC. The application of the Finke-Watzky (F-W) two-step nucleation model allowed us to mathematically describe the kinetics of HMW formation and to extract kinetic parameters of this process.For mAb-A, the IHLs had a marginal influence on the loss of monomer rate; instead, mAb-A exhibited fragmentation at 40 °C, which was independent of the IHL. Nevertheless, above a threshold of ≥ 7 % IHL, existing trimers/tetramers undergo conversion into higher-order oligomers at 40 °C, which is not observed at lower IHLs. In contrast, mAb-B exhibited an increased HMW formation rate above a threshold of ≥ 4 % IHL, which was reflected in the monomer decay rates at 40 °C and the F-W kinetic parameters of the chemical stress study.This case study shows that the initial level of HMWs exerts a differential influence on the progression of HMW formation. In one instance, there is a discernible acceleration in the formation of HMWs with rising IHLs. Conversely, in another example, the IHL exerts only a slight influence on HMW formation. Moreover, the results of our short-term chemical stress study are in accordance with those of a classical storage stability study conducted at 40 °C, which evaluated different IHLs. The analysis of HMW formation kinetics will enhance our understanding of the protein particle formation process and facilitate the formulation development of biotherapeutics.

Numerical analysis on the fate and transport of TiO2 nanoparticles in fractured porous media: Addressing intrinsic heterogeneities in sorptive mass transfer

The aggregation and slow migration of nanoparticles in aqueous media have caused serious concerns about their fate and impacts in the subsurface environment. Anthropogenic release and distribution of TiO2 nanoparticles (TNP) have immense potential for surface adsorption, occlusion, impregnation, bioaccumulation, and phase partition into various environmental compartments, and the actual risks in their interactions are still unknown. In an attempt to realize the extent of source zone migration of TNP in a fracture-skin-matrix (F-S-M) medium, a numerical model is developed and analyzed for sensitivity of certain features of the flow field. In addition, the sorptive mass transfer is simulated under four characteristic scenarios with varying assumptions pertaining to the intrinsic heterogeneities. The simulation results highlight the non-selective regulatory role of the skin in providing temporary interfacial space for reversible adsorption between the fracture and the matrix as well as in retarding the desorption rate. A preferential detachment of TNP is observed to be favored by the enhanced properties of skin due to the similarity in diffusion and dispersion coefficients. Out of the four scenarios, the two-site model and two-step model simulated the dynamic pore-filling features of adsorption pertaining to the heterogeneities in TNP and F-S-M characteristics. The results demonstrate that the proposed numerical could fairly detect the transition between local equilibrium and dynamic adsorption-desorption cycles that would eventually determine the mass transfer limitations and the extent of elution of TNP along the flow.

Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms.

We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study. The best two-step model for predicting dementia incorporated Trail Making Test B (attention/executive function), Animal Fluency (verbal fluency), Mini-Mental State Examination (global cognition), and 10-word list recall (memory). The model's positive predictive value in ADNI was 85.8% and negative predictive value was 92.2% versus 62.5% and 95.6%, respectively, in BioFINDER-1. This two-step model accurately predicts individualized dementia risk. To our knowledge, this is the first algorithm for predicting all-cause dementia using a novel two-step model utilizing brief cognitive tests. Applying a validated model including the Trail Making Test B, Animal Fluency, MMSE, Alzheimer's Disease Assessment Scale delayed, and immediate recall can robustly and accurately categorize individuals into low, intermediate, or high risk of dementia progression and can facilitate clinical decision-making and personalized patient care. We created an app that is available for research and educational purposes at https://brainapps.shinyapps.io/PredictAllCauseDementia to provide an individualized risk score for dementia progression.

Numerical analysis of mixing performance in Y-junction mixers and its impact on yields from supercritical water hydrolysis

This study investigates the mixing behavior in a Y-junction mixer for supercritical water hydrolysis using large eddy simulation with a discrete phase model. Yield changes were simulated using a two-step reaction model with first-order kinetics, based on particles’ temporal temperature data. Effective mixing produced closely matched mass and particle flow temperature distributions, both exhibiting bell-shaped profiles near the mixed temperature. Although variations in flow rate within ±25 % and changes in the inlet temperatures of supercritical water from 350 °C to 430 °C and subcritical water from 100 °C to 170 °C did not significantly affect the overall mixing performance, they did alter the mixed temperature and, subsequently, yield changes. Additionally, backflow occurred when Richardson number for the subcritical inlet reached approximately 7. In effective mixing, simulated yields were approximately 15 % lower than the ideal theoretical yields, calculated using the reaction rate constant at the mixed temperature.

Reallocation of time after an exogenous reduction in mandatory travel: transport, work, and leisure in Chilean two-worker two-gender households

A two-step model framework is proposed and applied to analyze empirically the impact of a travel time reduction on labor supply in two-worker two-gender households, using a nationwide data set for time use collected in 2015 in Chile. First, a system of structural equations (SSE) is estimated to reveal which activities can be considered as committed, and to unveil the hierarchy of activities by gender undertaken by the household workers to help defining the labor supply model. Then a quadratic formulation including committed time for each individual and committed expenses for the family is applied to the time-use data to estimate a labor supply model, considering the findings with the SSE (hierarchy and committed activities). Results were obtained controlling for household size, region, and age, showing systematically that labor supply diminishes with the wage rates of either working member of the household (a forward falling shape), that committed expenses induces more work, and that diminishing mandatory travel time induces an increase in working hours that varies between 27 and 64 percent of the liberated time, in line with 2023 reports on the reallocation of liberated travel time due to remote working and confirming the theoretical findings by Jara-Diaz and Contreras (2024).

A Two-Step Dry Etching Model for Non-Uniform Etching Profile in Gate-All-Around Field-Effect Transistor Manufacturing.

The Gate-All-Around Field-Effect Transistor (GAAFET) is proposed as a successor to Fin Field-Effect Transistor (FinFET) technology to increase channel length and improve the device performance. The GAAFET features a complex multilayer structure, which complicates the manufacturing process. One of the most critical steps in GAAFET fabrication is the selective lateral etching of the SiGe layers, essential for forming the inner-spacer. Industry commonly encounters a non-uniform etching profile during this step. In this paper, a continuous two-step dry etching model is proposed to investigate the mechanism behind the formation of the non-uniform profiles. The model consists of four modules: anisotropic etching simulation, Ge atom diffusion simulation, Si/SiGe etch selectivity calculation and SiGe selective etching simulation. By calibrating and verifying this model with experimental data, the edge rounding and gradient etching rates along the sidewall surface are successfully simulated. Based on further examination of the influence of chamber pressure on the profile using this model, the inner-spacer shape is improved experimentally by appropriately reducing the chamber pressure. This work aims to provide valuable insights for etching process recipes in advanced GAAFETs manufacturing.

Inspiring Contributions: Fan Leaders’ Directives and Encouragement in the Chinese Online Celebrity Fan Community

Abstract In the neoliberal era, there is a new phenomenon where numerous fans follow fan leaders’ instructions to make tangible or intangible contributions to support celebrities, which has exacerbated economic polarization. Although past studies have explored fan empowerment in the Internet era, fan leaders’ promotion effect has remained unstudied. This study aimed to discover fan leaders’ role in guiding fans’ contributions in the Chinese fan community. By integrating a two-step flow model and participatory culture theory, it used qualitative content analysis to analyze 410 posts and 972 comments in the Chinese singer Song Yaxuan’s fan community from February 3 to 10, 2023. Through an inductive approach, four main themes were derived from results: (1) task delegation and empowerment; (2) user-friendly instructions and seamless communication; (3) highlighting a sense of crisis and putting pressure on fans; and (4) fostering a collective mindset. The findings revealed fan leaders use different techniques and the top-down structure in the community to encourage fans to give voluntary contributions of money or time to the celebrity’s development. Theoretically, this study showed internal operation logic in Chinese fan community. Practically, the study shed light on how to lessen the harmful consequences of neoliberalism’s features within the Chinese entertainment industry and realize healthy development.

Sulfur fixation performance of calcium-bearing carbonates in iron ore roasting process: Thermodynamics, phase transition, microstructure evolution, and kinetics

Roasting technology is increasingly used in the beneficiation of refractory iron ores. During the roasting process, the presence of pyrite in some iron ores leads to the formation of SO2, requiring effective sulfur fixation techniques. This study proposed an innovative in-situ sulfur fixation method by pre-oxidation roasting, in which sulfur was fixed during the oxidation roasting process before reduction roasting. The effects of the main iron mineral (hematite), primary gangue mineral (quartz), and common calcium-containing carbonate minerals (calcite and dolomite) on sulfur migration during pyrite roasting were investigated, with particular emphasis on the sulfur fixation capabilities of calcite and dolomite. The study included thermodynamic equilibrium analysis, phase transitions and microstructural evolution in different reaction systems. The results showed that calcite and dolomite effectively reduced SO2 emissions by forming CaSO4 and MgSO4, with calcite exhibiting a stronger sulfur fixation capacity for the same mass. Kinetic analysis indicated that pyrite pyrolysis followed a two-step random nucleation and growth model. Furthermore, the addition of calcite increased the apparent activation energy of SO2 formation and altered the reaction pathway, providing insight into the sulfur fixation mechanism of calcite from a kinetic perspective.

Determinants of livelihood diversification in an integrated agricultural and non-agricultural livelihood system in Ghana

The diversification of livelihoods by households has been widely acknowledged as a way to overcome food insecurity and poverty challenges in developing countries. Diversification of livelihoods helps spread the risk among multiple livelihood earning activities to provide households with a range of their food needs all year round. By examining the integrated livelihood systems of 405 rural farm households in the Upper East Region of Ghana, empirical evidence is provided in this study using the Sustainable Livelihoods Framework to advance arguments in the literature for the creation of sustainable strategies that improve diversified livelihood systems. The mean diversification indices estimated were 0.45 for agricultural diversification systems, 0.32 for non-agricultural diversification systems and 0.59 for integrated agricultural and non-agricultural diversification systems. With the use of the Cragg two-step regression model, we demonstrate that the decision to diversify and the extent of diversification of rural livelihoods are distinct decisions and are influenced by distinct sets of factors. Similarly, for the three categories of livelihood diversification studied, the effect of these factors also differed. The results emphasize the significance of access to good road network, credit and market information in encouraging rural farm households to diversify their livelihoods. In the short term, improving access to credit and market information through community-based initiatives can provide immediate support to rural households. Communities should also organize local markets and cooperatives to strengthen their economic resilience. While government and stakeholders should focus on long-term infrastructure projects, these community actions can complement such efforts and contribute to achieving global and regional goals targeting food insecurity and poverty eradication.

Enhancing Effort-Moderated Item Response Theory Models by Evaluating a Two-Step Estimation Method and Multidimensional Variations on the Model

Rapid-guessing behavior in data can compromise our ability to estimate item and person parameters accurately. Consequently, it is crucial to model data with rapid-guessing patterns in a way that can produce unbiased ability estimates. This study proposes and evaluates three alternative modeling approaches that follow the logic of the effort-moderated item response theory model (EM-IRT) to analyze response data with rapid-guessing responses. One is the two-step EM-IRT model, which utilizes the item parameters estimated by respondents without rapid-guessing behavior and was initially proposed by Rios and Soland without further investigation. The other two models are effort-moderated multidimensional models (EM-MIRT), which we introduce in this study and vary as both between-item and within-item structures. The advantage of the EM-MIRT model is to account for the underlying relationship between rapid-guessing propensity and ability. The three models were compared with the traditional EM-IRT model regarding the accuracy of parameter recovery in various simulated conditions. Results demonstrated that the two-step EM-IRT and between-item EM-MIRT model consistently outperformed the traditional EM-IRT model under various conditions, with the two-step EM-IRT estimation generally delivering the best performance, especially for ability and item difficulty parameters estimation. In addition, different rapid-guessing patterns (i.e., difficulty-based, changing state, and decreasing effort) did not affect the performance of the two-step EM-IRT model. Overall, the findings suggest that the EM-IRT model with the two-step parameter estimation method can be applied in practice for estimating ability in the presence of rapid-guessing responses due to its accuracy and efficiency. The between-item EM-MIRT model can be used as an alternative model when there is no significant mean difference in the ability estimates between examinees who exhibit rapid-guessing behavior and those who do not.

Predicting aeration time and nitrite accumulation rate variations for Partial Nitritation: A model incorporating nitrogen oxidation rate dynamics

This study aimed to develop a two-step nitrification model to predict variations in aeration time and nitrite accumulation rate (NAR) under fluctuating operational conditions in mainstream partial nitritation (PN) processes. Lab-scale sequencing batch reactors (SBRs) were used to evaluate the ammonia oxidation rate (AOR) and nitrite oxidation rate (NOR) under different solids retention times (SRT) (10, 15, 20, 30, and 50 days) and total volumetric nitrogen loadings (TVNL) (20–60 mg N/L per cycle). A static model was developed to predict consistent AOR and NOR values in the steady state, whereas a dynamic model was established to capture the growth dynamics of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) under unsteady-state conditions. The static model accurately predicted the AOR, NOR, and aeration time at steady state. The dynamic model quantified the relationship between specific growth rates (μ) and food-to-microorganism ratios (F/M) through exponential fitting, successfully capturing AOB and NOB growth dynamics. Validation experiments (SRT = 10 d, TVNL = 60 mg/L per cycle) demonstrated the ability of the dynamic model to predict trends in NAR and aeration time accurately. This study emphasizes the importance of accurately modeling AOR and NOR variations to predict aeration time and NAR, thereby providing valuable insights for aeration control and precise management of AOB and NOB populations in mainstream PN processes.

Non-linear Kinetic Model of Coke Deactivation in Methanol-to-Gasoline Conversion: Effects of Nano and Micro ZSM-5 Crystals

Methanol-to-gasoline conversion over ZSM-5 catalysts, a non-petroleum carbon fuel process, is significantly impacted by coke deposition, which deactivates the catalyst by blocking zeolite pores during the reaction and diffusion process. This study investigated the differences in coke species development and deactivation using a non-linear kinetic model, comparing microcrystalline ZSM-5 with longer pore lengths to nanocrystalline ZSM-5 with shorter pores, both tested at 350°C. Catalyst and coke were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), temperature-programmed oxidation (TPO), thermogravimetric analysis (TGA), and an elemental analyzer. Micro ZSM-5 initially achieved 99% conversion but rapidly dropped to 61% due to coke buildup, while nano ZSM-5 maintained over 98% conversion for 24hours before declining, highlighting the superior catalytic durability of shorter pores. Micro ZSM-5 showed faster coking, accumulating 7.19% coke with higher temperature-resistant graphitic coke, whereas nano ZSM-5 exhibited a lower coke buildup of 4.56%, forming primarily amorphous coke. A two-step non-linear kinetic model for soft and hard coke oxidation was applied, with the second-order model providing the highest correlation coefficient. The activation energy for coke decomposition was influenced by pore length, revealing distinct kinetic behaviors for hydrogen-deficient graphitic coke and amorphous coke. This study underscores the importance of zeolite pore structure on coke formation, deposition patterns, and catalytic durability, offering deeper insights into the methanol-to-gasoline conversion process.

Two-step machine learning-aided two-stage hydrothermal liquefaction of biomass for bio-oil upgrading to lower nitrogen content: Experimental verification and parameter optimization

The two-stage hydrothermal liquefaction (HTL) is a promising process for converting biomass into low-nitrogen bio-oil. However, for two-stage HTL, there is a considerable lack of application of artificial intelligence tool. In this study, a two-step machine learning (ML) model was developed to optimizing bio-oil production of two-stage HTL. A total of 644 data points was collected, and a novel feature NH2-C (the mass ratio of amino to carbon) was used to enhancing ML models using Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGB) algorithms. The XGB multi-task model demonstrated the best performance (train R2 of 0.98, test R2 of 0.94 and 0.83). The nitrogen content in feedstock is the decisive factor for total nitrogen and nitrogen recovery rate of aqueous phase in HTL stage Ⅰ, while NH2-C for yield and nitrogen content of bio-oil in stage Ⅱ. The two-step ML model performed excellently in 22 validation experiments (with all errors ≤13.68%), and guided the bio-oil preparation with lower nitrogen content (5.46%) from Chlorella. This study provides a new approach for the application of multi-step ML in biomass HTL.

Deriving the Interaction Point between a Coronal Mass Ejection and High-speed Stream: A Case Study

We analyze the interaction between an interplanetary coronal mass ejection (ICME) detected in situ at the L1 Lagrange point on 2016 October 12 with a trailing high-speed stream (HSS). We aim to estimate the region in the interplanetary (IP) space where the interaction happened/started using a combined observational-modeling approach. We use minimum variance analysis (MVA) and the Walen test to analyze possible reconnection exhaust at the interface of ICME and HSS. We perform a graduated cylindrical shell reconstruction of the CME to estimate the geometry and source location of the CME. Finally, we use a two-step drag-based model (DBM) model to estimate the region in IP space where the interaction took place. The magnetic obstacle observed in situ shows a fairly symmetric and undisturbed structure and shows the magnetic flux, helicity, and expansion profile/speed of a typical ICME. The MVA together with the Walen test, however, confirms reconnection exhaust at the ICME–HSS boundary. Thus, in situ signatures are in favor of a scenario where the interaction is fairly recent. The trailing HSS shows a distinct velocity profile which first reaches a semi-saturated plateau with an average velocity of 500 km s−1 and then saturates at a maximum speed of 710 km s−1. We find that the HSS's interaction with the ICME is influenced only by this initial plateau. The results of the two-step DBM suggest that the ICME has started interacting with the HSS close to Earth (∼0.81 au), which compares well with the deductions from in situ signatures.

16O2 – 18O2 interface exchange study between gas phase and the BaFeO3–δ oxide

Studies of oxygen surface exchange kinetics for BaFeO3–δ oxide were performed using the oxygen isotope exchange method with pulsed supply of an isotopically enriched mixture (PIE) at the partial oxygen pressure 21.3 kPa in the temperature range of 350–600 °С. Oxygen surface exchange kinetics was considered in the framework of two-step model including two consecutive steps: dissociative adsorption of oxygen and incorporation of oxygen adatoms into the crystal lattice of the oxide. The rates of oxygen heterogeneous exchange (rH), as well as the rates of dissociative adsorption (ra) and oxygen incorporation (ri), have been calculated. The process of oxygen dissociative adsorption at the surface of BaFeO3–δ oxide was found to be the rate-determining step of the surface exchange. The appropriate models describing the oxygen exchange kinetics and possible mechanisms occurring in the system “gaseous oxygen – BaFeO3–δ oxide” were discussed.

The impact of smart city construction on achieving peak carbon neutrality: Evidence from 31 provinces in China

Amidst the intensification of global warming concerns, addressing climate change has become an urgent imperative, with carbon dioxide emissions standing as the primary driver of the greenhouse effect. This study delves into the relationship between smart city development and carbon emissions in China, leveraging panel data spanning 2012–2021 across 31 provincial units for empirical analysis. The study posits a negative correlation between smart city construction and carbon emissions. To comprehensively gauge the level of smart city development, a weighted evaluation index was constructed using the panel entropy weighting-topsis method. Employing fixed effects, random effects, high-dimensional fixed effects, and two-step system GMM models, the study aims to comprehensively assess the impact of smart city construction levels on carbon emissions. Results reveal that heightened smart city construction levels coincide with reduced carbon emissions, thus substantiating the research hypothesis.

Corrosion behavior and mechanism of Cr10Mo1 steel subjected to different corrosive ions in simulated concrete pore solution

The corrosion of pre-passivated Cr10Mo1 steels in simulated concrete pore solution of saturated Ca(OH)2 induced by different corrosive ions were investigated, namely Cl−, SO2- 4, and combination of Cl− and SO2- 4. Electrochemical methods, Pourbaix analysis of Fe–Cr–H2O system and DFT calculation were adopted to characterize corrosion behavior and reveal underlying mechanism. Results showed that while sulfate may induce corrosion at higher threshold value, the scenario of chloride alone leads to lowest corrosion potential, linear polarization resistance and highest corrosion rate, and the presence of SO2- 4 ions mitigate corrosion risk under the coexistence condition of chloride and sulfate. A two-step reaction model of competitive adsorption-catalytic corrosion was proposed based on theoretical analysis and DFT calculation results.

An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings

Early bearing fault diagnosis plays a crucial role in ensuring the reliability and safety of wind turbines. This process encounters two primary challenges, that is, weak fault characteristics and strong background noise. Consequently, they restrict the effectiveness of conventional diagnosis methods. To address these difficulties, a two-step early fault diagnosis model is proposed based on an anomaly monitoring index [Formula: see text] and improved successive variational mode decomposition-fast spectral correlation (ISVMD-FSC). Initially, [Formula: see text] enables early anomaly detection. Subsequently, the fault type is further identified using ISVMD-FSC. The ISVMD adaptive decomposition of early warning signals can effectively reduce the noise and isolate the fault impact characteristics. Then, the fault characteristic frequency is extracted using spectral coherence analysis to achieve the purpose of fault diagnosis. This model is tested and validated on simulated data, laboratory accelerated bearing life span data, and real high-speed bearing fault data of wind turbines. Notably, the comparative study shows that [Formula: see text] is capable of detecting the fault earlier than the spectral L2/L1 norm, the sum of the weighted normalized square envelope, index 8, and index 9. Additionally, the fault feature extraction effect of ISVMD-FSC is better than many more advanced time-frequency analysis methods. These results emphasize the model’s excellent performance in early bearing fault diagnosis and its good generalization ability.

Host preference explains the high endemism of ectomycorrhizal fungi in a dipterocarp rainforest.

Ectomycorrhizal (ECM) fungi are important tree symbionts within forests. The biogeography of ECM fungi remains to be investigated because it is challenging to observe and identify species. Because most ECM plant taxa have a Holarctic distribution, it is difficult to evaluate the extent to which host preference restricts the global distribution of ECM fungi. To address this issue, we aimed to assess whether host preference enhances the endemism of ECM fungi that inhabit dipterocarp rainforests. Highly similar sequences of 175 operational taxonomic units (OTUs) for ECM fungi that were obtained from Lambir Hill's National Park, Sarawak, Malaysia, were searched for in a nucleotide sequence database. Using a two-step binomial model, the probability of presence for the query OTUs and the registration rate of barcode sequences in each country were simultaneously estimated. The results revealed that the probability of presence in the respective countries increased with increasing species richness of Dipterocarpaceae and decreasing geographical distance from the study site (i.e. Lambir). Furthermore, most of the ECM fungi were shown to be endemic to Malaysia and neighbouring countries. These findings suggest that not only dispersal limitation but also host preference are responsible for the high endemism of ECM fungi in dipterocarp rainforests. Moreover, host preference likely determines the areas where ECM fungi potentially expand and dispersal limitation creates distance-decay patterns within suitable habitats. Although host preference has received less attention than dispersal limitation, our findings support that host preference has a profound influence on the global distribution of ECM fungi.