Dynamical Evolution Research Articles

Punctuated versus gradual shifts in the multivariate evolutionary process: a test with paired radiations of scincid lizards

Abstract As lineages become separated in time, they are expected to accumulate mutational (or developmental-genetic) differences that influence the macroevolutionary trajectories of those lineages even under similar environmental conditions. Here, we compare the dynamics of phenotypic evolution in radiations of scincid lizards from Australia and Madagascar that are separated by more than 100 million years of independent evolution and show rampant phenotypic parallelism. We collected linear measurements of the skull, limbs, and limb girdles from micro-CT scans of 94 Australian and 29 Malagasy species. Using multivariate comparative methods, we tested whether the underlying evolutionary covariance structure for this superficial parallelism was conserved and whether these patterns were consistent across distinct functional modules. Malagasy and most Australian skinks have similar covariance matrices for skull evolution. Results are ambiguous for limbs and limb girdles, as some trait subsets support different evolutionary processes and for other subsets, a shared covariance matrix could not be rejected. However, across most trait sets, the extremely speciose Australian genus Ctenotus exhibits a radically different covariance structure from all other lizards in these groups, including several closely related genera. The shift in Ctenotus demonstrates that the architecture of trait correlations can change at relatively shallow timescales and may explain the unique position of this clade in morphospace relative to other scincid lizards from both geographic regions. More generally, our results demonstrate that the multivariate evolutionary process can change dramatically in a relatively short period of time.

The Importance of Gas Starvation in Driving Satellite Quenching in Galaxy Groups at z ~ 0.8

Abstract We present results from a Keck/DEIMOS survey to study satellite quenching in group environments at z ~ 0.8 within the Extended Groth Strip (EGS). We target 11 groups in the EGS with extended X-ray emission. We obtain high-quality spectroscopic redshifts for group member candidates, extending to depths over 1 order of magnitude fainter than existing DEEP2/DEEP3 spectroscopy. This depth enables the first spectroscopic measurement of the satellite quiescent fraction down to stellar masses of ~109.5 M ⊙ at this redshift. By combining an infall-based environmental quenching model, constrained by the observed quiescent fractions, with infall histories of simulated groups from the IllustrisTNG100-1-Dark simulation, we estimate environmental quenching timescales (τ quench) for the observed group population. At high stellar masses (M⋆ = 1010.5 M ⊙) we find that τ quench = 2.4 + 0.2 − 0.2 Gyr, which is consistent with previous estimates at this epoch. At lower stellar masses (M⋆ = 109.5 M ⊙), we find that τ quench = 3.1 + 0.5 − 0.4 Gyr, which is shorter than prior estimates from photometry-based investigations. These timescales are consistent with satellite quenching via starvation, provided the hot gas envelope of infalling satellites is not stripped away. We find that the evolution in the quenching timescale between 0 <z <1 aligns with the evolution in the dynamical time of the host halo and the total cold gas depletion time. This suggests that the doubling of the quenching timescale in groups since z ~ 1 could be related to the dynamical evolution of groups or a decrease in quenching efficiency via starvation with decreasing redshift.

Emergency medicine : what's new in 2024

Emergency medicine plays a crucial vital role as the gateway to the Swiss healthcare system. Although it has not yet been officially recognized with a specialist title, unlike most European countries - emergency medicine in Switzerland is characterized by robust research activity. This scientific article demonstrates a dynamic and rigorous evolution. In this edition, we present six articles authored by Swiss emergency physicians, each examining different aspects of our field. Topics include gender stereotypes, prehospital cardiac arrest, the unique needs of the older patients in emergency department, the administration of antifibrinolytics in trauma cases, and a predictive model for infectious endocarditis.

Recent Advances in the Performance and Mechanisms of High-Entropy Alloys Under Low- and High-Temperature Conditions

High-entropy alloys, since their development, have demonstrated great potential for applications in extreme temperatures. This article reviews recent progress in their mechanical performance, microstructural evolution, and deformation mechanisms at low and high temperatures. Under low-temperature conditions, the focus is on alloys with face-centered cubic, body-centered cubic, and multi-phase structures. Special attention is given to their strength, toughness, strain-hardening capacity, and plastic-toughening mechanisms in cold environments. The key roles of lattice distortion, nanoscale twin formation, and deformation-induced martensitic transformation in enhancing low-temperature performance are highlighted. Dynamic mechanical behavior, microstructural evolution, and deformation characteristics at various strain rates under cold conditions are also summarized. Research progress on transition metal-based and refractory high-entropy alloys is reviewed for high-temperature environments, emphasizing their thermal stability, oxidation resistance, and frictional properties. The discussion reveals the importance of precipitation strengthening and multi-phase microstructure design in improving high-temperature strength and elasticity. Advanced fabrication methods, including additive manufacturing and high-pressure torsion, are examined to optimize microstructures and improve service performance. Finally, this review suggests that future research should focus on understanding low-temperature toughening mechanisms and enhancing high-temperature creep resistance. Further work on cost-effective alloy design, dynamic mechanical behavior exploration, and innovative fabrication methods will be essential. These efforts will help meet engineering demands in extreme environments.

Dynamic simulation of landscape ecological risk in Gansu’s farming-pastoral ecotone based on topographic gradients

The farming-pastoral ecotone in Gansu Province, a crucial green barrier in northwestern China, faces a conflict between economic development and environmental fragility. Scientifically assessing the landscape ecological risks in this region is essential for developing an early warning system for ecological security and ensuring the sustainable management of regional ecosystems. This study systematically analyzes the dynamic evolution patterns and clustering characteristics of landscape ecological risk along the topographic gradient in the area, using Land-Use and Land-Cover Change (LUCC) data from five periods (each period representing 10 years) between 1980 and 2020, as well as Digital Elevation Model (DEM) data. Methods such as CA-Markov and the Topographic Position Index (TPI) were employed to assess these risks. Additionally, the study simulates the spatial distribution patterns of ecological risks in future years. The results reveal that from 1980 to 2020, the area of built-up land in the farming-pastoral ecotone of Gansu Province increased by approximately 82%, with roughly 21% of the expansion occurring at the expense of grasslands and 53% from arable land. Ecological risk in the study area exhibits a spatial distribution pattern characterized by lower risk in the southwest and higher risk in the northeast. Overall, ecological risk initially increased and then decreased. The area of high-risk zones has steadily decreased by approximately 50% since 2010, primarily in regions with low topographic gradients and high human activity. In other areas, high-risk levels are directly proportional to the topographic gradient, showing a high degree of spatial clustering. Consequently, land-use planning should be tailored to local conditions, and a long-term monitoring mechanism should be established. This study provides novel insights into the spatiotemporal changes in landscape ecological risk in ecologically vulnerable areas, focusing on topographic gradients.

Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach

A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states. Our proposed hybrid SEIRV-DNNs model can also be applied to other infectious diseases such as influenza and dengue, with some modifications to the compartments and parameters in the model to accommodate the related control measures. This approach will facilitate improving predictive modeling and optimizing public health intervention strategies.

Plastome data provides new insights into population differentiation and evolution of Ginkgo in the Sichuan Basin of China

BackgroundGinkgo biloba L., an iconic living fossil, challenges traditional views of evolutionary stasis. While nuclear genomic studies have revealed population structure across China, the evolutionary patterns reflected in maternally inherited plastomes remain unclear, particularly in the Sichuan Basin - a potential glacial refugium that may have played a crucial role in Ginkgo’s persistence.ResultsAnalysis of 227 complete plastomes, including 81 newly sampled individuals from the Sichuan Basin, revealed three distinct maternal lineages differing from known nuclear genome patterns. We identified 170 sequence variants and extensive RNA editing (235 sites) with a bias toward hydrophobic amino acid conversions, suggesting active molecular evolution. A previously undocumented haplotype (IIA2), predominant in western Sichuan Basin populations, showed close genetic affinity with rare refugial haplotypes. Western populations exhibited higher haplotypic diversity and distinctive genetic structure, supporting the basin’s role as both glacial refugium and corridor for population expansion. Ancient trees (314–784 years) provided evidence for interaction between natural processes and historical human dispersal in shaping current genetic patterns.ConclusionsOur findings demonstrate substantial genetic diversity within Sichuan Basin Ginkgo populations and reveal dynamic molecular evolution through plastome variation and RNA editing patterns, challenging the notion of evolutionary stasis in this living fossil. This study provides crucial genomic resources for understanding Ginkgo’s evolution and informs conservation strategies for this endangered species.

Modeling and Simulation of Dynamic Recrystallization Microstructure Evolution for GCr15 Steel Using the Level Set Method

The microstructure of metallic materials plays a crucial role in determining their performance. In order to accurately predict the dynamic recrystallization (DRX) behavior and microstructural evolution during the hot deformation process of GCr15 bearing steel, a microstructural evolution model for the DRX process of GCr15 steel was established by combining the level set (LS) method with the Yoshie–Laasraoui–Jonas dislocation dynamics model. Firstly, hot compression tests were conducted on GCr15 steel using the Gleeble-1500D thermal simulator, and the hardening coefficient k1 and dynamic recovery coefficient k2 of the Yoshie–Laasraoui–Jonas model were derived from the experimental flow stress data. The effects of temperature, strain, and strain rate on DRX behavior and grain size during the hot working process of GCr15 steel were investigated. Through secondary development of the software, the established microstructural evolution model was integrated into the DIGIMU® software. Metallographic images were imported in situ to reconstruct its initial microstructure, enabling GCr15 steel DRX microstructure finite element simulation of the hot compression process. The predicted mean grain size and flow stress demonstrated a strong correlation and excellent agreement with the experimental results. The results demonstrate that the established DRX model effectively predicts the evolution of the DRX fraction and average grain size during the hot forging process and reliably forecasts DRX behavior.

Solar-driven production of renewable chemicals via biomass hydrogenation with green methanol

Solar-driven, selective biomass hydrogenation is recognized as a promising route to renewable chemicals production, but remains challenging. Here, we report a TiO2 supported Cu single-atom catalyst with a four-coordinated Cu1−O4 structure, which can be universally applied for solar-driven production of various renewable chemicals from lignocellulosic biomass-derived platform molecules with good yields using green methanol as a hydrogen donor, to address this challenge. It is significant that the biomass upgrading driven by natural sunlight on a gram scale demonstrates the great practical potential. By combining in situ soft X-ray absorption spectroscopy with theoretical calculations, we successfully identify the dynamic evolution of Cu sites along with the biomass hydrogenation and methanol oxidation, where the tandem process is enabled by the photogenerated electrons and holes to complete a chemical cycle. The concept of solar-driven biomass hydrogenation proposed here provides an efficient and sustainable methodology for the sustainable production of renewable chemicals.

Predicting the churn patterns of monetizers and non-monetizers: exploring the influence of behavioral variability in churn prediction

PurposeAlthough prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.Design/methodology/approachThis study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).FindingsThe findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.Originality/valueThis study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

Unveiling the Magnetized Nature of X‐Ray Binaries: Cyclotron Line Insights Title

ABSTRACTStudying the variations of measured cyclotron lines plays a crucial role in gaining insights into the physical processes of accretion in magnetized neutron stars. Our research focuses on the formation and distribution of magnetic fields in various high‐mass x‐ray binaries (HMXBs). Comparing these sources reveals valuable information about their origin and evolutionary history. It is worth noting that the presence of varying cyclotron lines can be attributed to the influence of accretion dynamics, which provides valuable information about the characteristics of the magnetic field. In addition, by visualizing data, we employ the Kernel Density Estimation method, to provide a robust framework for understanding the distribution of magnetic fields. We observed clear patterns of clustering in the energy range of (10–35) keV, which corresponds to magnetic fields of around G. This clustering indicates a shared magnetic field characteristics across these systems, suggesting a possible common origin or similar environmental and accretion condi tions. Finally, our research investigates the behavior of cyclotron lines in HMXBs, illuminating the accretion process and magnetic field properties to better understand their role as indicators of dynamical evolution.

Static and dynamic Raman excitation mapping of chirality-pure carbon nanotube films

Raman spectroscopy is a powerful method for probing electronic and vibrational properties of materials, particularly nanomaterials such as single-wall carbon nanotubes. Typically, Raman spectroscopy is conducted at a single, or few, excitation wavelengths, but that provides limited information about excitation resonance structure, and their dynamical evolution. Here, we extend a sensitive full-spectrum technique to rapidly obtain two-dimensional Raman excitation maps both statically and dynamically for chirality-pure single-wall carbon nanotube films. We demonstrate sensitive evaluation of structured resonance profiles even from weak vibrational modes, and sub-second time resolution of the dynamics of photo-driven defect production. Findings include the direct observation of bands and their profiles – including bands which could be missed in conventional Raman spectroscopy - and demonstration of differences for odd vs. even defect band combinations. This opens up possibilities to investigate the coupling of electronic states with vibrational modes in nanomaterials and track their dynamical evolution subject to intentional modulation.

Spatiotemporal Changes in Evapotranspiration and Its Influencing Factors in the Jiziwan Region of the Yellow River from 1982 to 2018

Evapotranspiration (ET) is a critical process in the interaction between the terrestrial climate system and vegetation. In recent years, ET has undergone significant changes in the Jiziwan region of the Yellow River Basin, primarily due to the implementation of ecological restoration programs and the dual impacts of climate change. As a result, hydrological cycle processes have been profoundly affected, making it crucial to accurately capture trends in ET and its components, as well as to identify the key drivers of these changes. In this study, we first systematically analyzed the dynamic evolution of ET and its components in the Jiziwan of the Yellow River area between 1982 and 2018 from the perspective of land use change. To achieve accurate ET simulations, we introduced a multiple linear regression algorithm and quantitatively evaluated the specific contributions of five climate factors, including precipitation, temperature, wind speed, specific humidity, and radiation, as well as the normalized difference vegetation index (NDVI), a vegetation factor, to ET and its components. On this basis, we explored the combined influence mechanism of climate change and vegetation change on ET in detail. The results revealed that the structure of ET in the Jiziwan of the Yellow River area has changed significantly and that vegetation evapotranspiration has gradually replaced soil evaporation, occupies a dominant position, and has become the main component of ET in this area. Among the many factors affecting ET, the contribution of climate change is the most significant, with an average contribution rate of approximately 59%. Moreover, the influence of human activities on total ET and its components is also high. The factors that had the greatest impact on total ET, soil evaporation, and vegetation transpiration were precipitation, radiation, and the NDVI, respectively. In terms of spatial distribution, the eastern part of Jiziwan was more significantly affected by environmental changes, and the trends of the ET changes were more dramatic. This study not only enhances our scientific understanding of the changes in ET and their driving mechanisms in the Jiziwan area of the Yellow River but also provides a solid scientific foundation for the development of water resource management and ecological restoration strategies in the region.

Implications of Pyrolytic Gas Dynamic Evolution on Dissolved Black Carbon Formed During Production of Biochar from Nitrogen-Rich Feedstock.

Gases and dissolved black carbon (DBC) formed during pyrolysis of nitrogen-rich feedstock would affect atmospheric and aquatic environments. Yet, the mechanisms driving biomass gas evolution and DBC formation are poorly understood. Using thermogravimetric-Fourier transform infrared spectrometry and two-dimensional correlation spectroscopy, we correlated the temperature-dependent primary noncondensable gas release sequence (H2O → CO2 → HCN, NH3 → CH4 → CO) with specific defunctionalization stages in the order: dehydration, decarboxylation, denitrogenation, demethylation, and decarbonylation. Our results revealed that proteins in feedstock mainly contributed to gas releases, and low-volatile pyrolytic products contributed to DBC. Combining mass difference analysis with Fourier transform ion cyclotron resonance mass spectrometry, we showed that 44-60% of DBC molecular compositions were correlated with primary gas-releasing reactions. Dehydration (-H2O), with lower reaction energy barrier, contributed to DBC formation most at 350 and 450 °C, whereas decarboxylation (-CO2) and deamidization (-HCNO) prevailed in contributing to DBC formation at 550 °C. The aromaticity changes of DBC products formed via gas emissions were deduced. Compared to their precursors, dehydration increased DBC aromaticity, while deamidization reduced the aromaticity of DBC products. These insights on pyrolytic byproducts help predict and tune DBC properties via changing gas formed during biochar production, minimizing their negative environmental impacts.

Ecology of prophage-like elements in Bacillus subtilis at global and local geographical scales.

Prophages constitute a substantial portion of bacterial genomes, yet their effects on hosts remain poorly understood. We examine the abundance, distribution, and activity of prophages in Bacillus subtilis using computational and laboratory analyses. Genome sequences from the NCBI database and riverbank soil isolates reveal prophages primarily related to mobile genetic elements in laboratory strains. Distinct and previously unknown prophages in local isolates prompt an investigation into factors shaping prophage presence, with phylogenetic relatedness predicting the prophage repertoire slightly better than geographical origin. Data also show that prophages exhibit strong co-occurrence and exclusion patterns within genomes. Laboratory experiments indicate that most predicted prophages are cryptic, as they are not induced under DNA-damaging conditions. Importantly, stress responses increase with the number of predicted prophages, suggesting their influence on host physiology. This study highlights the diversity, integration patterns, and potential roles of prophages in B.subtilis, shedding light on bacterial genome evolution and phage-host dynamics.

Impedance mapping with high-density microelectrode array chips reveals dynamic heterogeneity of in vitro epithelial barriers

Epithelial tissues in vitro undergo dynamic changes while differentiating heterogeneously on the culture substrate. This gives rise to diverse cellular arrangements which are undistinguished by conventional analysis approaches, such as transepithelial electrical resistance measurement or permeability assays. In this context, solid substrate-based systems with integrated electrodes and electrochemical impedance monitoring capability can address the limited spatiotemporal resolution of traditional porous membrane-based methods. This label-free technique facilitates local, continuous, long-term analysis of tissue barrier properties for organ-on-chip applications. Increasing spatial resolution requires small electrodes arranged in a dense array, known as high-density microelectrode arrays (HD-MEAs). Integrated with Complementary Metal Oxide Semiconductor (CMOS) technology for multiplexing and rapid impedance measurements, HD-MEAs can enable high spatiotemporal resolution assessments of epithelial tissues. Here, we used 16,384 CMOS-integrated HD-MEA chip with subcellular-sized electrodes (8 μm diameter, 15 μm pitch, patterned in 16 clusters each consisting of 1024 electrodes in a 32 × 32 matrix) and impedance sensing capability to monitor dynamic evolution of Caco-2 cells, such as their proliferation, barrier formation, and 3D structure development on the chip. Changes in impedance at the selected frequency of 1 kHz (|Z|1kHz) enabled monitoring and analyzing the life cycle of Caco-2 cells grown on the HD-MEA chips (up to + 453% change after 7 days in culture). The |Z|1kHz maps of proliferating Caco-2 cells and the differentiating epithelial tissue developing 3D domes aligned with the corresponding optical images at cellular resolution, which demonstrates the capability of the chip in tracking the dynamic heterogeneity of Caco-2 tissues in a label free and real-time fashion. Importantly, |Z|1kHz maps acquired during chemically induced barrier disruption showed electrodes covered with 3D cell domes experienced a stronger decrease in impedance than those covered with adherent cells (-41% ± sd 10% against -16% ± sd 10%, respectively). This method could thus, in principle, enable detection of tissue barrier disrupting and modifying agents with higher specificity. Epithelial barrier function assays benefit from using HD-MEA impedance sensors due to their increased informativity and resolution, which will be of great value in future organ-on-chip platforms.

Risk dynamic evolution model of pension financial system based on partial differential equation from the perspective of complex network

Risk dynamic evolution model of pension financial system based on partial differential equation from the perspective of complex network

Ecosystem assessment of semi-natural tidal flat in the Yangtze estuary by pressure-state-response model and fuzzy comprehensive evaluation method

With the development of hydraulic engineering such as harbor, channel and reclamation, more and more natural tidal flat turn into the semi-natural tidal flat (SNTF). How to evaluate ecosystem of semi-natural tidal flat is a scientific problem that has not been fully solved. The study tries to assess ecosystem health change of SNTF adjoining Hengsha East Shoal training dike in the Yangtze estuary. The study found SNTF area > 0m isobaths increased from 30.33 km2 in 2010–2014 to 46.66 km2 in 2014–2017 and to 56.84 km2 in 2017–2020, and the Hengsha Shoal region.0m, -2m and -5m isobaths increased from 174.78 km2, 303.82 km2, and 556.77 km2 in 2010–2014 to 233.34 km2, 365.59 km2, and 596.83 km2 in 2014–2017 and to 243.81 km2, 363.27 km2 and 567.45 km2 in 2017–2020. Using database of 3 pressure indicators, 16 state indicators and 3 response indicators, the study combined pressure-state-response (PSR) model and fuzzy comprehensive evaluation (FCE), constructed a systematic evaluation method of ecosystem health of semi-natural tidal flat. The evaluation result found the ecosystem health of SNTF of Hengsha East Shoal was rated as "Fair" level during 2010–2020, and the summation of comprehensive evaluation index (CEI) increased from 0.4009 in 2010–2014 to 0.4703 in 2014–2017 due to the rapid expansion of tidal flat vegetation, and then decreased to 0.4450 in 2017–2020 due to a reduction in vegetation area caused by erosion. The area of salt marsh vegetation plays an important role in the health of wetland ecosystems. The Hengsha East Shoal ecosystem is undergoing a quickly dynamic evolution processes, long-term series monitoring and further research for this area are necessary to guide its future development according to the tidal flat utilization patterns.

MKER: multi-modal knowledge extraction and reasoning for future event prediction

Humans can predict what will happen shortly, which is essential for survival, but machines cannot. To equip machines with the ability, we introduce the innovative multi-modal knowledge extraction and reasoning (MKER) framework. This framework combines external commonsense knowledge, internal visual relation knowledge, and basic information to make inference. This framework is built on an encoder-decoder structure with three essential components: a visual language reasoning module, an adaptive cross-modality feature fusion module, and a future event description generation module. The visual language reasoning module extracts the object relationships among the most informative objects and the dynamic evolution of the relationship, which comes from the sequence scene graphs and commonsense graphs. The long short-term memory model is employed to explore changes in the object relationships at different times to form a dynamic object relationship. Furthermore, the adaptive cross-modality feature fusion module aligns video and language information by using object relationship knowledge as guidance to learn vision-language representation. Finally, the future event description generation module decodes the fused information and generates the language description of the next event. Experimental results demonstrate that MKER outperforms existing methods. Ablation studies further illustrate the effectiveness of the designed module. This work advances the field by providing a way to predict future events, enhance machine understanding, and interact with dynamic environments.

Drift Correction Method of Wastewater Treatment Model Based on Transfer Learning Across Time Scales

Data is the core foundation of intelligent operation and maintenance, but currently, there is generally insufficient data for wastewater treatment plants, and the status of wastewater treatment systems dynamically evolves with the changes in the internal and external environment. The intelligent operation and maintenance of wastewater plants face difficulties in modeling and model drift caused by system evolution. In response to this issue, the summer and winter seasons with significant differences in wastewater temperature, wastewater quality, and microbial status were selected as typical comparison scenarios. The mechanism model was combined with neural networks to establish a wastewater treatment model drift correction method based on cross-time scale transfer learning. Firstly, in response to the problem of insufficient data, an activated sludge model （ASM） was established and calibrated. Summer operating data was used as input to simulate and calculate operating parameters and effluent data, generating a simulated operating dataset to achieve data augmentation and quality improvement. This was used to train a multi-layer perceptron neural network （MLP） model. The results showed that the average simulation accuracy of the MLP model for summer effluent COD, ammonia nitrogen, total phosphorus, etc., was all over 95%. This indicates the feasibility of training MLP models based on ASM-generated data. Then, the MLP model was used to guide the operation of the pilot A2O project. Experimental data analysis showed that the model drift phenomenon was significant in the field of wastewater treatment. During the operation of the pilot plant guided by the summer model, the accuracy of the predicted values gradually decreased, and the average prediction accuracy of the model for effluent COD gradually decreased from 98.14% to 75.18%. The phenomenon of model drift required effective correction to maximize the effectiveness of the model. In response to the problem of model drift caused by a significant decrease in simulation accuracy in winter operating conditions, a transfer learning approach was introduced. The winter measured data was used as the target domain dataset, and the MLP model was used as the pre-trained model for transfer learning. The experimental results showed that transfer learning methods can significantly improve model performance. After transfer learning, the average simulation accuracy of the MLP model for effluent COD, ammonia nitrogen, total nitrogen, and total phosphorus was relatively improved by 28.58%, 184.44%, 207.56%, and 100.51%, with absolute improvement values of 21.49%, 60.79%, 58.14%, and 46.74%, respectively. This indicates that the cross-time scale transfer learning method proposed in this study can significantly improve model performance, effectively solve model drift problems, and achieve a model-following response to the dynamic evolution of wastewater treatment systems. This study indicates that transfer learning based on pre-trained models only requires a small amount of engineering data and computational complexity to achieve model updating and correction. Compared to model retraining, this method reduces computational complexity and reduces the dependence on engineering data during data-driven model updates.