Nonlinear Prediction Research Articles

Nonlinear Levitation-Guidance Coupling Force Prediction for HTS Pinning Maglev Under Arbitrary Motion Based on Gated Recurrent Unit

Nonlinear Levitation-Guidance Coupling Force Prediction for HTS Pinning Maglev Under Arbitrary Motion Based on Gated Recurrent Unit

Nonlinear fatigue life prediction model based on isodamage curve considering two‐block loading

AbstractA non‐linear life prediction model considering the loading history is proposed based on isodamage curve. The stress‐controlled low‐high block loading of low cycle fatigue (LCF) at high temperature, with various previous cycles, was performed on a nickel‐based superalloy. The damage evolvement of the prior loading was revealed using scanning electron microscopy under the low‐high block loading, especially the failure mechanism of coaxing effect. In addition, load history correlation factor was introduced to describe the influence of prior cycle. Based on the Ni‐based superalloy, the life prediction model of two‐step loading, including the low‐high block (LH) and high‐low block loading (HL), was proposed, agreeing well with the experimental results for different metals. Compared with other life prediction models, the proposed model demonstrated the higher prediction accuracy and wider applicability.

A horizontal vibration prediction method of high-speed elevator based on transferred-digital twin model

The vibrations of high-speed-elevators (HSEs) significantly impact the comfort of elevator rides, with horizontal vibrations (HV) being the most sensitive to bodies. Customized elevator products are characterized by high-speed capabilities, customization options, and complex operational conditions. These characteristics often lead to challenges such as difficulties in predicting HV, low prediction accuracy, and limited reusability of prediction models and data. In order to mitigate HV in elevators, its crucial to implement HV prediction during the design phase. To address this need, this paper proposes a method for predicting HSEHV based on a Transferred Digital Twin (TLDT) model. The HSE’s design is physically extracted, and an elevator Digital Twin (DT) model with a digital layer is constructed. This model includes product geometry models, dynamics models, and simulation models. A transfer learning (TL) model is developed to integrate simulation data generated by the digital layer with measurement data acquired from the data interface layer, resulting in high-capacity and high-fidelity DT data. Using the support vector regression (SVR) method, a high-dimensional nonlinear HSEHV prediction model is established, and is trained and optimized using DT data to achieve data-driven HSEHV prediction. The validity of this approach is confirmed using measured data from an elevator test tower. Results indicate that the SVR-based TLDT model achieves the highest accuracy and the lowest absolute error values for both peak-to-peak and A95 vibration acceleration when compared to other models such as NN, KNN, decision tree, and lasso models. Furthermore, by incorporating DT data, the model accuracies improve by 16.9%, 14.5%, 8.6%, 14.1%, and 8.6%, and 12.3%, 12.4%, 19.7%, 7.6%, and 8.7%, respectively, for the mentioned models. Finally, the HV of KLK2 elevator car system is predicted. When compared with simulation analysis and the conventional SVR prediction model, obtained results through the proposed method closely align with the measured values.

Improving the statistical downscaling performance of climatic parameters with convolutional neural networks

Abstract This study examines two downscaling techniques, convolutional neural networks (CNNs) and feedforward neural networks for predicting precipitation and temperature, alongside statistical downscaling model as a benchmark model. The daily climate predictors were extracted from the European Center for Medium-range Weather Forecast (ECMWF) ERA5 dataset spanning from 1979 to 2010 for Tabriz city, located in the northwest of Iran. The biases in precipitation data of ERA5 predictors were corrected through the empirical quantile mapping method. Also, two nonlinear predictor screening methods, random forest and mutual information were employed, alongside linear correlation coefficient. While these methods facilitate identification of dominant regional climate change drivers, it is essential to consider their limitations, such as sensitivity to parameter settings, assumptions about data relationships, potential biases in handling redundancy and correlation, challenges in generalizability across datasets, and computational complexity. Evaluation results indicated that CNN, when applied without predictor screening, achieves coefficient of determination of 0.98 for temperature and 0.71 for precipitation. Ultimately, future projections were employed under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and concluded that the most increase in temperature by 2.9 °C and decrease in precipitation by 3.5 mm may occur under SSP5-8.5.

Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user’s and computer-intensive task. An attractive alternative is to leverage neural networks (NNs) bypassing the need of solving the governing fluid equations at all flight conditions of interest. NNs have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph NNs which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The test case is for the NASA common research model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal having used an existing database.

Nominal Differences in Acute Symptom Presentation and Recovery Duration of Sport-Related Concussion Between Male and Female Collegiate Athletes in the PAC-12

BackgroundSport-related concussion (SRC) is a heterogenous injury that often presents with varied symptoms and impairment. Recently, research has focused on identifying subtypes, or clinical profiles of concussion to be used in assessing and treating athletes with SRC. The purpose of this study was to investigate sex differences in clinical profiles, recovery duration, and initial symptom severity after SRC in a cohort of collegiate athletes in the Pacific-12 Conference (Pac-12).MethodsThis prospective cohort study examined post-SRC symptoms, recovery, and return-to-play times using data from the Pac-12 CARE Affiliated Program and Pac-12 Health Analytics Program. Clinical profiles reported by student-athletes were defined by the number (> 50%) of specific symptoms frequently reported for each profile. Generalized linear mixed models were used to examine associations among sex, clinical profiles, time-to-recovery, and return-to-play times.Results479 concussion incidents met inclusion criteria. The probabilities of initial presentation of each clinical profile, initial injury severity scores, and recovery times within a profile did not differ between sexes (p = 0.33–0.98). However, both males and females had > 0.75 probabilities of exhibiting cognitive and ocular profiles. Initial injury severity score was a strong nonlinear predictor of initial number of clinical profiles (p < 0.0001), which did not differ between sexes. The number of clinical profiles was also a nonlinear predictor of time-to-recovery (p = 0.03) and return-to-play times (p < 0.0001).ConclusionsInitial symptom severity was strongly predictive of the number of acute clinical profiles experienced post-SRC. As the number of clinical profiles increased, time-to-recovery and time to return-to-play also increased. Factors other than sex may be better associated with acute symptom presentation post-concussion as no sex differences were found in reported clinical profiles or recovery. Understanding the number and type of clinical profiles experienced post-SRC may help inform concussion diagnostics and management.

Beyond sleepless nights: Unraveling the complexity of alexithymia and suicide risk among university students.

There is a solid relationship between alexithymia and suicide risk. Nonetheless, the specific impact of alexithymia's distinct subscales on suicide risk has received the attention it deserves. This article presents a comprehensive exploration of suicide risk among university students, focusing on the interconnections among alexithymia, insomnia, and suicidal behavior. Three components of alexithymia including difficulties in describing emotions or feelings (DDF), difficulties in identifying emotions or feelings (DIF), and the externally oriented thinking were considered. The study involved 208 participants from a Persian university sample, examining the significance of incorporating both alexithymia and insomnia in suicide risk assessment and intervention planning. Insomnia was positioned as a pivotal mediator. A secure electronic link in the Telegram application was employed to collect the data. Both linear and nonlinear prediction models were used to explore potential associations among alexithymia, insomnia, and suicide risk. The study revealed substantial positive correlations between alexithymia and suicide risk, as well as between insomnia and suicide risk. Additionally, specific components of alexithymia exhibited noteworthy links to suicide risk. The inclusion of insomnia scores in suicide risk predictions is critical, as it greatly enhances the precision of risk assessments and facilitates the design of targeted and effective therapeutic interventions. The association between alexithymia and suicide risk showed a significant relationship (r=.29, p<.01). Moreover, a significant correlation was observed between alexithymia and insomnia (r=.32, p<.01). Additionally, insomnia exhibited a significant positive correlation with suicide (r=.35, p<.01). Interestingly, DDF and DIF showed positive correlations with suicide (r=.28, p<.01; r=.33, p<.01). The findings carry profound implications for suicide prevention efforts, providing valuable insights to safeguard the well-being and resilience of university students facing suicide risk challenges.

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay

MotivationUnder contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. ObjectivesTo contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index. NoveltyIn line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid. MethodsThis study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. FindingsThe empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. ImplicationThe implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Nonlinear Acoustic Behavior Prediction and Analysis of Straight Through Perforated Tube Silencer With Flow

Straight through perforated tube silencers are widely used in intake and exhaust noise control, and their acoustic behaviors are influenced by the gas flow and high amplitude sound remarkably. To investigate the acoustic attenuation performance of straight through perforated tube silencers in the presence of flow and high amplitude sound, an approach based on the three-dimensional (3D) time-domain computational fluid dynamics (CFD) simulation is employed and validated by comparing predictions and measurements. Transmission loss of the straight through perforated tube silencer under pure tone and multi-tone sound excitations is predicted. Results show that, under the pure tone sound excitations, the acoustic attenuations are affected remarkably by the high amplitude sound, and the nonlinear effects turn to weaken as flow velocity increases. With the sound pressure level (SPL) decreasing along the axis of the silencer, the acoustic nonlinearity in the orifice weakens gradually. The shedding vortices are restricted to the region near perforations because of the transport of flow. When the multi-tone sound excitation is enforced on the inlet, transmission loss of the silencer is different from the pure tone excitation at the same SPL of every compositional frequency.

The adaptive markets hypothesis: Insights into small stock market efficiency

ABSTRACT In this paper, we explore whether the adaptive markets hypothesis (AMH) describes the efficiency of the Finnish stock market better than the efficient markets hypothesis (EMH) does. Building on this, we also test how small market size and market liberalization impact the efficiency of the Finnish stock market and examine the relationship between market volatility and return in this market. We conduct this study by applying the subsample analysis and the rolling window analysis to the daily returns of the OMXH25 index and by measuring the efficiency through three linear and two nonlinear predictability tests. The results of our study strongly support the AMH. They also suggest that small market size alone does not make a market less efficient; opening a market to foreign investors improves its efficiency after a delay; and the correlation between market volatility and return varies over time in the Finnish stock market, being usually negative. These findings mostly contradict the traditional investment paradigm.

Matching Job Demands and Job Resources as Linear and Non-linear Predictors of Employee Vigor and Sustainable Performance

ABSTRACT The present study studied two prominent job stress models as predictors of employee vigor and employee sustainable performance. First, based on the matching principle of the Demand-Induced Strain Compensation (DISC) Model, we predicted that job resources moderate (i.e. strengthen) the linear relation of job demands with employee vigor and sustainable performance, particularly when the type of demands corresponds well with the type of resources. Second, based on the non-linearity principle of the Vitamin Model, we predicted that non-linear (i.e. curvilinear) relations between job demands or job resources with employee vigor and sustainable performance exist, particularly when the type of demands or resources matches well with the outcome. An online cross-sectional study was conducted among 1,175 Chinese employees working in different industries. Multivariate regression analyses revealed that (1) although we did not find moderating effects of job resources, “conceptual matches” were found in line with the matching principle of the DISC Model, and (2) curvilinear patterns were detected (although with a non-expected shape) in which so-called “non-linear matching” relations between either demands or resources and outcomes were present. This study highlights the importance of matching demands and resources in both linear and non-linear ways to predict employee vigor and employee sustainable performance. Finally, theoretical and practical implications of the findings were addressed.

Predictive Model of Restart-Up Pressure Drop after Shutdown for Heavy Oil-Water Ring Transportation Pipeline.

Aiming at the problem of restart-up for a heavy oil-water ring transportation pipeline due to instability and damage of the water ring, based on the self-developed design of a small indoor loop simulation experimental device and taking four kinds of ordinary heavy oil in the Lvda oilfield as the research object, the change trend of restart-up pressure drop with time is experimentally studied when the pipeline is restarted-up after shutdown at a constant water flow. On the basis of the regression analysis of the orthogonal restart-up experimental data of four factors (oil holdup, oil viscosity, standstill period, and water cleaning superficial velocity) and mixed levels by the statistical product and service solutions statistical analysis software, a multivariate nonlinear restart-up maximum pressure drop prediction model is established. Through analysis of the characteristics of each stage of the restart-up process, an exponential decay model of restart-up pressure drop with time is created. The research results show that the variations in restart-up pressure drop with time can be divided into two stages: the attenuation stage and the equilibrium stage. The predicted value of restart-up pressure drop with time is in good agreement with the measured one, and the goodness of fit is very close to 1. The maximum restart-up pressure drop rises along with the increase in oil holdup, oil viscosity, standstill period, and water cleaning superficial velocity. The restart-up time prolongs with the increase in oil holdup, oil viscosity, and standstill period but shortens with the increase in water cleaning superficial velocity.

A numerical research on transient transport of high-temperature particles associated with air gouging process

The high-temperature particles produced by air gouging processes are common pollutants in cleaning workshops, which could endanger workers’ health. In this research, a two-way coupled Eulerian-Lagrangian method based on discrete phase modelling (DPM) was used to investigate the transport characteristics of high-temperature particles generated from air gouging processes. The particle group in the movement was divided into core zone and boundary zone, and the kinetic mechanism of particles in different zones was analysed. The results show particles in the boundary zone are more susceptible to the vortex entrainment effect than particles in the core zone, and some particles could be moved from the vortex diffusion zone to the vortex recirculation zone. The smaller the particle size, the better the followability to the hot airflow. The slower the temperature decay of particles, the more significant the thermophoresis effect, which could enhance the diffusion behaviour of the small particles. By analysing the average residence time of particles in the breathing zone, the influence of different factors on the average vertical diffusion height [Formula: see text] of particles is summarized, and a nonlinear regression prediction model of [Formula: see text] is established. The results can contribute to health risk assessment and industrial ventilation design.

Next-generation non-linear and collapse prediction models for short- to long-period systems via machine learning methods

Since the 1960 s, a cornerstone of earthquake engineering has been estimating the non-linear response of structures based just on lateral strength, modal properties, and the anticipated seismic demand. Over the years, several studies have quantified this empirical relationship and integrated it within seismic design and assessment methodologies. These have been widely accepted for practical application and adopted in building codes worldwide. While these models work reasonably well, there are still areas in which improvements can be made, especially concerning their robust quantification of uncertainty. This is mainly due to the amount of data used to quantify these empirical relationships, the choice of functional forms during the fitting, the model fitting and testing process, and how the ground motion shaking intensity is characterised. This study tackles these issues via the non-linear analysis of single-degree-of-freedom oscillators to train several machine learning (ML) models. This was to examine the accuracy and applicability of such models within a seismic engineering context and explore potential gains in quantifying the non-linear response of structures via next-generation intensity measures, namely average spectral acceleration, Saavg. The results show that the Decision Tree and XGBoost models worked well across a broad range of periods, accurately predicting collapse and non-collapse responses. Appraising these with existing models showed a notable improvement all around. It indicates that the models based on data-driven ML approaches represent a positive step and can be seamlessly integrated with seismic analysis methodologies utilised worldwide. Subsequently, a Python-based library of these XGBoost ML models was developed and made available online to foster practical application.

Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion

In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

Bioaerosols are biologically originated particles in the atmosphere, which is mainly composed of bacteria, fungi, viruses, pollen, spores, and the fragmentation and disintegration of plants and animals. Bioaerosols are easy to be spread in the lower atmosphere and cause various epidemic diseases, which is harmful to human health. The forecasting and alert of bioaerosols have important scientific significance and reality needs. In this paper, a method is proposed for estimating and predicting the concentration profile of atmospheric bioaerosols using fluorescence lidar observational data. Using the powerful nonlinear prediction ability of artificial neural networks and through repeated training, a mathematical model can be established for the relationship among atmospheric environment, meteorological parameters, and bioaerosol concentration profiles. The input parameters are temperature and humidity, aerosol extinction coefficient, backscatter coefficient, PM2.5, PM10, SO2, NO2, CO, O3, and wind speed, and outputs the concentration profile of bioaerosols. The prediction results with the measurement relative deviation of genetic algorithm back propagation (GA-BP) neural network and adaptive genetic algorithm back propagation (AGA-BP) neural network were analyzed. The results indicate that the AGA-BP neural network can effectively predict the concentration distribution of bioaerosols, and the predicted concentrations of bioaerosols are 1793 particles × m−3, 3088 particles × m−3, 5261 particles × m−3, 7410 particles × m−3 and 9133 particles × m−3 for air quality with superior, fine, mild contamination, middle level pollution and heavy pollution at an altitude of 0.315 km, respectively. We found that the predicted concentration of pollution weather is much higher than that of good weather. Furthermore, the AGA-BP neural network was used to predict the concentration profiles of atmospheric bioaerosols under different weather conditions, which provided a new research method for forecasting and alert of atmospheric bioaerosols.

Word Frequency and Predictability Dissociate in Naturalistic Reading.

Many studies of human language processing have shown that readers slow down at less frequent or less predictable words, but there is debate about whether frequency and predictability effects reflect separable cognitive phenomena: are cognitive operations that retrieve words from the mental lexicon based on sensory cues distinct from those that predict upcoming words based on context? Previous evidence for a frequency-predictability dissociation is mostly based on small samples (both for estimating predictability and frequency and for testing their effects on human behavior), artificial materials (e.g., isolated constructed sentences), and implausible modeling assumptions (discrete-time dynamics, linearity, additivity, constant variance, and invariance over time), which raises the question: do frequency and predictability dissociate in ordinary language comprehension, such as story reading? This study leverages recent progress in open data and computational modeling to address this question at scale. A large collection of naturalistic reading data (six datasets, >2.2 M datapoints) is analyzed using nonlinear continuous-time regression, and frequency and predictability are estimated using statistical language models trained on more data than is currently typical in psycholinguistics. Despite the use of naturalistic data, strong predictability estimates, and flexible regression models, results converge with earlier experimental studies in supporting dissociable and additive frequency and predictability effects.

Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage

Lithium-ion batteries behave nonlinear degradation during long-term usage. Prediction of the nonlinear degradation is of guiding significance in taking proactive measures to prolong battery life and ensure battery safety. In this study, a new nonlinear degradation knee-point prediction method is proposed utilizing relaxation voltage as the feature sequence, and it is the first attempt with the joint prediction of the knee-point and remaining useful life. A remaining useful life prediction framework integrating degradation features of the knee-point is established, which leads to stable improvements in the accuracy of remaining useful life prediction. Through transfer learning, the proposed joint prediction method is validated on different battery datasets, obtaining mean absolute errors within 26 cycles for the knee-point and remaining useful life prediction, with root mean square errors below 28 cycles. The predicted results can serve as evaluation indicators for various application scenarios, including battery design, ability evaluation, and functionality enhancement.

A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximation

Fuzzy Neural Networks (FNN) have the ability of decision-making based on constructing semi-ellipsoidal clusters in the input space as the antecedent parts of their fuzzy rules. To determine the output value for each input instance, FNNs consider its membership degree to different sub-regions of the input space. However, forming such meaningful sub-regions is not possible in all applications due to the nonlinear interactions among input variables and their low information gain. Indeed, the samples could be distributed on a manifold in the input space. Therefore, to cover the input space, we need lots of rules, each representing a small region of input space. This issue decreases the generalization ability of the model along with its explainability. Consequently, to efficiently form fuzzy rules, first, it is necessary to unfold the manifold by mapping the samples to an appropriate embedding space. Next, the fuzzy rules in the form of semi-ellipsoidal regions should be constructed in this extracted feature space. Deep Fuzzy Neural Networks address this problem by representation learning through stacking multiple cascade mapping layers. In this paper, we propose a novel approach for nonlinear function approximation and time-series prediction problems, based on using the kernel trick to implicitly learn the mapping function to the new feature space. Moreover, to initialize the fuzzy rules, a KNN-based method using the kernel trick is proposed. A hierarchical Levenberg–Marquardt approach is applied to learn the model’s parameters. The performance and structure of the proposed method are studied and compared with some other relevant methods in synthetic and real-world benchmarks. Based on these experiments, the proposed method has the best performance with the most parsimonious architecture. According to these experiments, the test RMSE of the proposed method is 0.002 for Mc-Glass chaotic time-series prediction, 0.015 for a Nonlinear dynamic system identification, 0.0345 for Box–Jenkins nonlinear system identification, 0.0609 for Fuel consumption prediction of automobiles, 10.24 for Sydney stock price tracking, and 0.595 for California housing.

Longitudinal antimicrobial susceptibility trends of canine Staphylococcus pseudintermedius

Antimicrobial resistance within Staphylococcus pseudintermedius poses a significant risk for the treatment of canine pyoderma and as a reservoir for resistance and potential zoonoses, but few studies examine long-term temporal trends of resistance. This study assesses the antimicrobial resistance prevalence and minimum inhibitory concentration (MIC) trends in S. pseudintermedius (n=1804) isolated from canine skin samples at the Cornell University Animal Health Diagnostic Center (AHDC) between 2007 and 2020. Not susceptible (NS) prevalence, Cochran-Armitage tests, logrank tests, MIC50 and MIC90 quantiles, and survival analysis models were used to evaluate resistance prevalence and temporal trends to 23 antimicrobials. We use splines as predictors in accelerated failure time (AFT) models to model non-linear temporal trends in MICs. Multidrug resistance was common among isolates (47%), and isolates had moderate to high NS prevalence to the beta-lactams, chloramphenicol, the fluoroquinolones, gentamicin, the macrolides/lincosamides, the tetracyclines, and trimethoprim-sulfamethoxazole. However, low levels of NS to amikacin, rifampin, and vancomycin were observed. Around one third of isolates (38%) were found to be methicillin resistant S. pseudintermedius (MRSP), and these isolates had a higher prevalence of NS to all tested antimicrobials than methicillin susceptible isolates. Amongst the MRSP isolates, one phenotypically vancomycin resistant isolate (MIC >16 µg/mL) was identified, but genomic sequence data was unavailable. AFT models showed increasing MICs across time to the beta-lactams, chloramphenicol, the fluoroquinolones, gentamicin, and the macrolides/lincosamides, and decreasing temporal resistance (decreasing MICs) to doxycycline was observed amongst isolates. Notably, ATF modeling showed changes in MIC distributions that were not identified using Cochran-Armitage tests on prevalence, MIC quantiles, and logrank tests. Increasing resistance amongst these S. pseudintermedius isolates highlights the need for rational, empirical prescribing practices and increased antimicrobial resistance (AMR) surveillance to maintain the efficacy of current therapeutic agents. AFT models with non-linear predictors may be a useful, breakpoint-independent, surveillance tool alongside other modeling methods and antibiograms.