Expansion Model Research Articles

Exploring Neobanks Adoption: A Study Among the Young Customers in Kerala Using Technology Acceptance Model

This research aims to explore the adoption and usage pattern of neobanks in Kerala using the Technology Acceptance Model (TAM) incorporated with trust and demographic characteristics. Data were collected from 210 young adults aged 17–35 through an online questionnaire. Hypotheses developed were tested using non-parametric tests like Mann–Whitney U test and Kruskal–Wallis H test in SPSS for demographic variables and partial least square-structural equation modelling (SEM) for the remaining variables. The results indicate that behavioural intention (BI) significantly influences actual neobank usage, with perceived usefulness (PU) and perceived ease of use (PEOU) that are crucial factors driving neobank acceptance. However, trust (T) does not significantly impact the BI to use neobanks among young customers but has a significant effect on PU. Additionally, gender emerges as significant demographic factor that affects neobank acceptance, while age, educational qualification, and occupation exhibit no significant effects. These insights help to identify key factors influencing neobank adoption, aiding in effective service expansion and business model development for neobanks. They also assist neobank managers in crafting marketing strategies that emphasize convenience and efficiency to attract and retain customers.

Rapid urbanization through cropland encroachment in the Jiangsu-Zhejiang-Shanghai region of China leads to substantial soil organic carbon loss

Abstract Soil organic carbon (SOC) is a major terrestrial carbon reservoir, crucial for the global carbon cycle and climate change. However, the impact of urbanization-induced cropland encroachment on SOC remains underexplored. This study quantified SOC loss in the top 20 cm (SOC20) and 100 cm (SOC100) soil layers in the Jiangsu-Zhejiang-Shanghai (JZH) region from 1985 to 2019 using high-resolution China Land Cover Dataset and multi-temporal SOC maps. Our results show that the cumulative cropland encroachment area in the study area reached 18,925.65 km2, approximately three times the area of Shanghai. The encroached areas of cropland in Jiangsu, Zhejiang, and Shanghai accounted for 59.72%, 31.49%, and 8.79% of the total, respectively. The cumulative SOC100 loss in the JZH region was approximately 65.31 ± 32.45 Tg C, with the SOC20 loss contributing about 32.97%, emphasizing the importance of deep SOC pool. The cumulative SOC20 (SOC100) losses in Jiangsu, Zhejiang, and Shanghai contributed approximately 55.36% (57.74%), 35.76% (31.96%), and 8.87% (10.3%) to the total SOC loss in the JZH region, respectively. Moreover, the annual average SOC100 loss accounted for about 8.6% to 25.59% of the terrestrial carbon sink flux (11.24 Tg C yr–1) in the JZH region, emphasizing that SOC loss due to cropland encroachment cannot be overlooked when evaluating the regional carbon sink capacity. Additionally, the positive correlation between SOC loss and regional Gross Domestic Product highlights the trade-off between economic development model of urban expansion through cropland encroachment and the resulting substantial SOC loss. This study emphasizes the importance of assessing the impacts of urbanization on regional SOC stocks, especially with regard to deep soil, and provides scientific insights for future urban planning and land management in this region.

A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery

Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha−1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from Gray Level Co-occurrence Matrix (GLCM) statistics, many spatial feature engineering techniques (e.g., morphological operations or edge detectors) have not been evaluated in the context of forest AGB estimation. Moreover, many prior investigations have been constrained by limited geographic domains and sample sizes. We utilize 176 lidar-derived AGB maps covering ∼9.3 million ha of forests in the Pacific Northwest of the United States to construct an expansive AGB modeling dataset that spans numerous biophysical gradients and contains AGB densities exceeding 1000 Mg ha−1. We conduct a large-scale inter-comparison of multiple spatial feature engineering techniques, including GLCMs, edge detectors, morphological operations, spatial buffers, neighborhood vectorization, and neighborhood similarity features. Our numerical experiments indicate that statistical features derived from GLCMs and spatial buffers yield the greatest improvement in AGB model performance out of the spatial feature engineering strategies considered. Including spatial features in Random Forest AGB models reduces the root mean squared error (RMSE) by 9.97 Mg ha−1. We contextualize this improvement model performance by comparing to AGB models developed with multi-temporal features derived from the LandTrendr and Continuous Change Detection and Classification algorithms. The inclusion of temporal features reduces the model RMSE by 18.41 Mg ha−1. When spatial and temporal features are both included in the model’s feature set, the RMSE decreases by 21.71 Mg ha−1. We conclude that spatial feature engineering strategies can yield nominal gains in model performance. However, this improvement came at the cost of increased model prediction bias.

Angle Expansion Estimation and Correction Based on the Lindeberg–Feller Central Limit Theorem Under Multi-Pulse Integration

The radar monopulse angle measurement can obtain a target’s angle information within a single pulse, meaning that factors such as target motion and amplitude fluctuations, which vary over time, do not affect the angle measurement accuracy. However, in practical applications, when a target’s signal-to-noise ratio (SNR) is low, the single pulse signal is severely affected by noise, leading to a significant deterioration in angle measurement accuracy. Therefore, it is usually necessary to coherently integrate multiple pulses before estimating the angle. This paper constructs an angle expansion model for a multi-pulse angle measurement under coherent integration. The analysis reveals that even under noise-free conditions, after coherently integrating multiple pulses, the coupling of target amplitude fluctuations and motion state can still cause significant errors in the angle measurement. Subsequently, this paper conducts a detailed analysis of the impact of the amplitude fluctuations and target maneuvers on the random angle measurement error. It also derives approximate probability density functions of angle measurement errors under various fluctuation and motion scenarios based on the Lindeberg–Feller central limit theorem. In addition, based on the angle expansion model and the random error distribution, this paper proposes an angle correction algorithm based on multi-pulse integration and long-term estimation. Numerical experiments and radar data in the field verify the impact of target characteristics on the angle measurement under multi-pulse integration and the effectiveness of the angle correction algorithm.

HTXRD based lattice thermal expansion and specific heat capacity modelling of C15-Fe2Zr Laves phase

Crystallographic phase pure C15-Fe2Zr was obtained through arc melting technique. X-ray Diffraction (XRD), scanning electron microscope (FESEM) along with energy-dispersive X-ray analysis (EDS) and differential scanning calorimetry (DSC)were employed to study the single phase in Fe-33.3at.% Zr alloy. Rietveld refinement of the high-temperature XRD patterns (from 298K-873 K) revealed the average linear and volumetric coefficients of thermal expansion of the alloys as αai= 0.3643 × 10−5 K−1 and αVi = 1.0916 × 10−5 K−1, respectively. The experimental heat capacity and enthalpy increment of the C15-Fe2Zr phase from 310K to 890K was modelled employing Quasi-harmonic Debye Grüneisen theoretical modelling by identifying the vibrational, electronic, anharmonic and magnetic contributions. Curie temperature(~625K) was determined from the magnetic contribution of the heat capacity.

Genetic signature of demographic history explains the current sympatric distribution of sibling species of Craseomys (Rodentia: Arvicolinae)

Abstract Sequences of mitochondrial DNA were analysed in two vole species (Craseomys rex and Craseomys rufocanus), which are closely related based on phylogeny, morphology, and ecology. In Hokkaido, Japan, they occur sympatrically in forests, but Craseomys rufocanus also inhabits open fields. Capture records of 2532 specimens from 114 localities confirmed the widespread and fragmented distribution of Craseomys rufocanus and Craseomys rex and indicated that abundance of Craseomys rufocanus was 9.2–19.8 times higher than that of Craseomys rex. The phylogenetic tree illustrated four regional clades for Craseomys rex, but no distinct clades for Craseomys rufocanus. The observed mismatch distribution of Craseomys rufocanus matched with a spatial expansion model, indicating population expansion through range extension with frequent migration. Using 47 haplotypes from 151 Craseomys rex and 302 haplotypes from 1037 Craseomys rufocanus, a skyline plot revealed that Craseomys rufocanus increased during the Last Glacial Cycle (LGC) when grassy vegetation dominated forests. Craseomys rex expanded following forest recovery following LGC survival. The current effective population size (Ne) of Craseomys rufocanus was 15.9–33.8 times higher than that of Craseomys rex, in good agreement with capture records. Population expansion of Craseomys rufocanus during the LGC resulted in range extension and its current distribution, whereas the current range of Craseomys rex was derived from glacial refugia.

Establishing best practices for modeling multi-day energy storage in deeply decarbonized energy systems

Abstract Multi-day energy storage (MDS, a subset of long-duration energy storage or LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale capacity expansion model. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: 1) modeling MDS with linked representative periods is crucial to capturing its full value, 2) MDS value is highly sensitive to the cost and availability of other resources, and 3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

An Integrated Model for Dam Break Flood Including Reservoir Area, Breach Evolution, and Downstream Flood Propagation

The reasonable and efficient prediction of dam failure events is of great significance to the emergency rescue operations and the reduction in dam failure losses. This work presents a model that is based on the physical mechanism. It is coupled with a multi-architecture (multi-CPU and GPU) open-source two-dimensional flood model, which is based on high-precision terrain and land use data. The aim is to enhance the accuracy of dam break flood process simulations. The model uses DEM data as a computational grid and updates it at each time step to reflect breach evolution. Simultaneously, the breach evolution model incorporates an analysis of stress on sediment particles, establishing the initial erosion state and lateral expansion model while accounting for seepage. The determination of the overflow of the breach is resolved through the application of a two-dimensional hydrodynamic model. This approach achieves a robust connection between the upstream reservoir, the dam structure, and the downstream inundation area. The coupled model is utilized to calculate the failure of earth-rock dams and landslide dams, and a sensitivity analysis is conducted. Taum Sauk Dam and Tangjiashan landslide dam were selected to represent earth dam break and barrier lake break, respectively, which are the main types of dam breaks. The obtained results demonstrate strong concurrence with the measured data, the relative errors of the four important parameters of the application case, the peak discharge of the breach, the top width of the final breach, the depth of the breach and the arrival time of the maximum peak discharge are all within ±10%. Although the relative error of the completion time of the final breach is greater than 10%, it is about 30% less than the relative error of the physical model.

Mechanical properties of a novel fiber-bridging interface for connecting FRP profile and concrete

The connection between FRP profile and concrete is critical for structural performance. This study introduces a novel fiber-bridging interface to enhance the FRP-concrete interfacial behavior. The interface comprises an epoxy resin adhesive layer, a carbon fabric layer, a mixture of adhesive and sand layer, and U-shaped steel fibers. Central pull-out tests were conducted to investigate the mechanical performance of this novel interface. The investigated variables included bond length and fiber volume fraction. Test results indicate that all specimens failed in a brittle mode at the adhesive layer, with load plateauing after reaching the peak. The number of steel fibers had limited influence on the interfacial behavior. Based on the load-slip curves, a bond stress-slip model for the tangential behavior was developed. An interfacial expansion model was further developed by means of FE analysis and machine learning. The three most widely used machine learning models, i.e., the BP neural network model, the random forest model, and the XGBoost model were selected. Comparisons show that all three models provide reasonable predictions, with the XGBoost model demonstrating the best performance. These models for the tangential and normal behavior of FRP-concrete interface were implemented into FE models for numerical analysis. Comparisons between numerical and experimental results show that the proposed models accurately describe the interfacial behavior of the fiber-bridging interface under brittle failure mode. The innovative interface proposed in this paper can be used for connecting concrete and FRP in various scenarios, and the proposed methodology of calibrating local bond behavior parameters from global response offers a new approach for establishing interfacial bond models.

Moiré plane wave expansion model for scanning tunneling microscopy simulations of incommensurate two-dimensional materials

Moiré plane wave expansion model for scanning tunneling microscopy simulations of incommensurate two-dimensional materials

Bidirectional soft-linking of a Capacity Expansion Model with a Production Cost Model to evaluate the feasibility of transition pathways towards carbon neutrality in the power sector

Energy models have been a valuable tool in support of well-informed decision-making towards the transition to climate neutrality in the European Union. However, considering the extra levels of detail required when designing a system based on intermittent renewables, modelling approaches in the field often lack the necessary time resolution, or are not open source, raising concerns of transparency and scientific reproducibility. This article addresses this gap by introducing a novel bidirectional soft-linking approach between two open-source energy models to generate long-term scenarios in the power sector and evaluate their feasibility, allowing for the optimisation of investments over a 30-year period and the sector's hourly operation at different snapshots. To demonstrate the applicability of this modelling approach, the Greek power sector is used as a testing ground in order to study the capacity and flexibility requirements of different transition pathways by 2050. Simulation outcomes show that a more ambitious variable renewable energy and storage capacity expansion than the one projected by the National Energy and Climate Plan is required to achieve the targets of 2050, while also highlighting a path dependency on gas at least until 2033. The latter could either result in a lock-in effect or to stranded assets if the decision to phase out gas is not taken rapidly. On the other hand, there is the potential to achieve carbon neutrality by 2035, if significant investments take place in time. Finally, switching from natural gas to hydrogen could be an effective solution for new gas power plants to avoid becoming stranded assets.

Interspecific competition reduces seed dispersal in an annual plant and slows simulated range expansions

Understanding the factors influencing how fast populations can spread across the landscape will be crucial as species ranges shift due to climate change. While the role of abiotic factors in determining expansion speed has been well studied in theory and empirical research, how competition impacts speed has received far less attention. Here, we investigated how seed dispersal distances change in response to competition and how these changes to distributions of dispersed seeds impact expansion speed. We dispersed four genotypes of the annual plant Arabidopsis thaliana with variation in life history traits into greenhouse mesocosms of either empty habitat or habitat containing the annual grass competitor, Lolium multiflorum. We found that competition decreased both mean and maximum dispersal distance. We then built a simulation model of range expansion with experimental data from this and a prior experiment to explore whether competition slows species expansions primarily through decreasing dispersal or fecundity. We found that competition primarily slows expansion speed through decreases in dispersal, but that when competition impacts both dispersal and fecundity, expansions slow more than with dispersal alone. The genotype with traits associated with longer distance dispersal was the most affected by competition in both experimental dispersal and simulations. This research suggests that not only does competition slow range expansions through decreases in both fecundity and dispersal, but that there may be consequences for evolutionary processes at the leading edge.

TMOD-08. DETAILED GENERATION OF AN ORTHOTOPIC INTRACRANIAL GLIOBLASTOMA (GBM) PATIENT-DERIVED XENOGRAFT (PDX) MODEL FOR PRECLINICAL IMMUNOTHERAPEUTIC STUDIES

Abstract PURPOSE This study aimed to establish an orthotopic intracranial PDX mouse model using cryopreserved GBM tissues expanded in a heterotopic subcutaneous PDX model for immunotherapeutic studies. The model is designed to closely replicate human GBM, enabling the exploration of its pathophysiology and the development of effective immunotherapies for this type of cancer. METHODS Glioblastoma tissues from 16 fresh and cryopreserved samples were used to establish a heterotopic subcutaneous PDX mouse model for tumor expansion. Successful engraftments were then used to create an orthotopic intracranial PDX mouse model. The tumor formation rates and durations were estimated in both models. Tumor fidelity to the original GBM tumors was confirmed through immunohistology (GFAP, P53, and Ki67) and genetic analysis (whole exome sequencing, RNA sequencing, and methylome analysis). The therapeutic effects of immunotherapy (allogenic NK cells) in combination with Avastin and Irinotecan were tested in the orthotopic intracranial PDX model. RESULTS The orthotopic intracranial GBM mouse model achieved 100% engraftment from successful tumor growth in the subcutaneous PDX model. The overall success rate was 81.25% (85.71% for cryopreserved tissues), with a median process duration of 26.5 weeks (19 weeks for the subcutaneous and 7.5 weeks for intracranial establishment). No significant differences in tumor formation were observed within one year for cryopreserved tissue in the subcutaneous model. Molecular and genetic analysis confirmed similar tumor characteristics between the intracranial PDX tumors and the original patient tumors. Additionally, NK cells combined with Avastin and Irinotecan demonstrated efficacy in this model. CONCLUSIONS We successfully established an orthotopic intracranial PDX mouse model using cryopreserved GBM tissues, with no observed differences within one year. These models accurately mirror patients’ tumor characteristics, promising advancements in glioblastoma research and treatment, particularly for immunotherapy studies. Keywords: Glioblastoma (GBM), cryopreserved GBM tissues, patient-derived xenograft (PDX) model, NOD/SCID IL-12Rγnull (NSG) mice

Speed and Shape of Population Fronts with Density-Dependent Diffusion

There is growing empirical evidence that animal movement patterns depend on population density. We investigate travelling wave solutions in reaction-diffusion models of animal range expansion in the case that population diffusion is density-dependent. We find that the speed of the selected wave depends critically on the strength of diffusion at low density. For sufficiently large low-density diffusion, the wave propagates at a speed predicted by a simple linear analysis. For small or zero low-density diffusion, the linear analysis is not sufficient, but a variational approach yields exact or approximate expressions for the speed and shape of population fronts.

Optimization of regional power low-carbon transition pathways in China under differentiated tradable green certificates trading models

In line with China's carbon dioxide peaking and carbon neutrality goals, the widespread adoption of renewable energy has become a key global and national trend. Tradable Green Certificates (TGC), used as a policy tool to implement the Renewable Portfolio Standards (RPS) system, stimulate the supply and demand for renewable energy. China's current TGC trading model comprises Electricity Certificate Combination (ECC) and Electricity Certificate Separation (ECS). These models lead to varying low-carbon transition pathways in the regional power sector. This study investigates the development trends of low-carbon power under different TGC models, aiming to identify the optimal transition path. By formulating policy parameters from a global optimization perspective, this study considers factors such as power balance, natural resource allocation, technological constraints, and carbon reduction policies. Using a bottom-up optimization model for energy expansion, the study analyzes the impacts of different market models and carbon emission reduction pressures on China's low-carbon power transition from 2023 to 2050. The recommendation is first to use the ECC model to promote renewable energy development by 2030, then transition to the ECS model to optimize TGC benefits, achieving a balanced approach to carbon reduction, economic costs, and social costs for the sustainable development of electricity.

Optimal replacement of coal with wind and battery energy storage

Abstract Electric utilities are considering replacing their coal power plants with renewables and energy storage to reduce emissions. However, they have also expressed concerns about operational changes and system reliability brought by these replacements. Utilities in remote rural areas face more challenges as they also face energy insecurity while having limited interconnections to wider systems and reliance on imported fuels. Therefore, it is critical for remote utilities to understand different coal replacement approaches and their impacts on system expansion, operation and energy security. In this paper, we define and investigate three approaches to replace coal using wind and batteries: (1) replacing exact coal generation, (2) replacing at least coal generation, and (3) replacing total energy provided by coal. We develop a case study inspired by the small remote grid in Fairbanks, Alaska, which has a single limited interconnection with the grid south of it. We utilize a power system expansion and economic dispatch model that co-optimizes the capacities of wind and batteries required for each approach and the hourly dispatch of energy and reserves for one year. We further analyze the operational cost variability under fixed and fluctuating fuel prices. We find that replacing the exact coal generation requires minimal operational changes, but also significantly more wind and battery capacities. In contrast, replacing total energy provided by coal induces more cycling in other resources, challenging grids with limited flexibility-providing resources. However, replacing total energy provided by coal allows more generation variability in response to fuel price fluctuations, enhancing energy security.

The value of long-duration energy storage under various grid conditions in a zero-emissions future

Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions Western Interconnect with high geographical resolution to understand the value of LDES under 39 scenarios with different generation mixes, transmission expansion, storage costs, and storage mandates. We find that a) LDES is particularly valuable in majority wind-powered regions and regions with diminishing hydropower generation, b) seasonal operation of storage becomes cost-effective if storage capital costs fall below US$5 kWh−1, and c) mandating the installation of enough LDES to enable year-long storage cycles would reduce electricity prices during times of high demand by over 70%. Given the asset and resource diversity of the Western Interconnect, our results can provide grid planners in many regions with guidance on how LDES impacts and is impacted by energy storage mandates, investments in LDES research and development, and generation mix and transmission expansion decisions.

A New Model for Steam Cavity Expansion in Vertical Wells of Heavy Oil Reservoirs

Summary Steamflooding is one of the most effective and mature methods for developing heavy oil. However, existing research has not fully explained the phenomena of steam overburden and heat loss, particularly overlooking the steam velocity loss at the steam cavity front. Therefore, the condensation heat transfer coefficient (CHTC) was introduced to describe the decrease in steam velocity at the steam cavity front. A steam cavity front expansion model, considering shape factor, pseudofluidity ratio, and CHTC ratio (CHTCR), was established and validated by using mine monitoring data. The research results show that the steam cavity of the revised model is in the shape of a “funnel,” and the steam overburden phenomenon is more pronounced at the steam cavity front. Through comparison with the field data of the P6 and P612-1 well groups, it is observed that the average displacement error of the steam cavity front of the two is only 6.58%. As steam injection progresses, the steam cavity is more degraded, and the steam overburden phenomenon is more intensified compared with the initial stage. Taking into account the phenomenon of kinetic energy loss, a higher formation heat loss rate is calculated during the middle and late stages of steam injection in the modified model. The pressure gradient at the steam cavity front is analyzed to determine an optimal displacement radius of 40 m for vertical well steamflooding in heavy oil reservoirs. When parameters such as shape factor, steam injection rate, and CHTC are increased, the development shape of the steam cavity is more ideal, but the convexity of the steam cavity front is increasingly pronounced as the pseudomobility ratio increases. The CHTC was incorporated into the steam cavity expansion model, elucidating the steam overburden phenomenon and advancing the theoretical understanding of steamflooding. By improving the existing model, the analytical formula can be used to rapidly predict the position of the steam cavity front and estimate of the steam-saturated zone, even in the absence of some field data or numerical models. Practical guidance for optimizing steam injection strategies is provided, with the potential to significantly enhance the recovery of heavy oil reservoirs.

Estimating the minimum ignition energy of spark-ignited fuel/air mixtures: preliminary steps towards a novel modelling approach

Abstract In spark-ignition (SI) engines, the achievement of a fast combustion with low cycle-to-cycle variation is highly dependent on the successful initiation of a flame kernel from the spark plug. Its growth can be sped up by increasing the electrical energy supply, but at the cost of higher plug wear, whereas too little energy may result in an ignition failure. Therefore, knowledge of the minimum ignition energy (MIE) of a fuel/air mixture is of key importance to guarantee a proper combustion process at minimal cost. To model the MIE several approaches have been proposed in literature, primarily derived from the experiments conducted by Lewis and Von Elbe and their resulting theory of quenching distances. However, these approaches appear in conflict with more recent experimental outcomes, and the impact of the ignition device is neglected. This work proposes a novel approach for modelling the MIE, which is based on a flame kernel expansion model recently proposed in another paper. In this approach, the proposed model, which has general validity, is specialized to the particular case of the estimation of the MIE, supplied via an electrical breakdown. A model advancement is also included that consists in the quantification, albeit at a preliminary level, of the impact of different gap distances and spark plug quenching effects on the flame kernel development. The results are validated against literature models and experimental data for two fuels, propane and hydrogen, and multiple equivalence ratios. In contrast with the noticeable MIE overestimation of literature models, for propane the proposed approach leads to better results compared to the experiments. Instead, for hydrogen a tendency towards a MIE underestimation is observed, especially for lean mixtures. The model is also tested for SI-engine-relevant conditions, showing satisfactory overall trends. The key source of error seems related to the very complex kernel-electrode interaction, the modelling of which will be improved in future developments.

Continual compression model for online continual learning

Task-Free Continual Learning (TFCL) presents a notably demanding but realistic ongoing learning concept, aiming to address catastrophic forgetting in sequential learning systems. In this paper, we tackle catastrophic forgetting by introducing an innovative dynamic expansion framework designed to adaptively enhance the model’s capacity for novel data learning while also remembering the information learnt in the past, by using a minimal-size processing architecture. Our proposed framework incorporates three key mechanisms to mitigate model’ forgetting: (1) by employing a Maximum Mean Discrepancy (MMD)-based expansion mechanism that assesses the disparity between previously acquired knowledge and that from the new training data, serving as a signal for the model’s architecture expansion; (2) a component discarding mechanism that eliminates components characterized by redundant information, thereby optimizing the model size while fostering knowledge diversity; (3) a novel training sample selection strategy that leads to the diversity of the training data for each task. We conduct a series of TFCL experiments that demonstrate the superiority of the proposed framework over all baselines while utilizing fewer components than alternative dynamic expansion models. The results on the Split Mini ImageNet dataset, after splitting the original dataset into multiple tasks, are improved by more than 2% when compared to the closest baseline.