Sinusoidal Model Research Articles

The constitutive modeling and processing map of homogenized Al-Mg-Si-Cu-Zn alloy

The true stress-strain curve of Al-Mg-Si-Cu-Zn alloy under the condition of temperature from 350 °C to 500 °C and strain rate from 0.01 s−1 to 10 s−1 was obtained by Gleeble-3500 thermal-mechanical simulator. Based on these data, the constitutive equation of aluminum alloy is established by using hyperbolic sinusoidal model, and the average hot deformation activation energy Q = 256 kJ/mol is obtained. The model established can well predict the flow behavior of Al-0.9Mg-0.7Si-0.3Cu-0.6Zn alloy, and the average absolute relative error (AARE) between the predicted value and the experimental value is 6.3 %. The processing map was developed according to dynamic material science model (DMM) and Prasad’s criterion and the optimum hot working window is found to be 470 °C -500 °C/0.1 s−1-0.01 s−1. The microstructure evolution of aluminum alloy during isothermal compression deformation was analyzed by transmission electron microscopy (TEM). It was found that DRV is the main dynamic softening mechanism under experimental deformation conditions.

Optical design and performance of a trifocal sinusoidal diffractive intraocular lens.

Two theoretical sinusoidal diffractive profile models to build up a trifocal intraocular lens (IOL) are analysed. Topographic features of the diffractive zones such as their shape, step height and radii, as well as the energy efficiency (EE) of the foci, depends on the particular model, and are compared to the ones experimentally measured in a trifocal lens that claims to be designed with a generic sinusoidal diffractive profile: the Acriva Trinova IOL (VSY Biotechnology, The Netherlands). The topography of the IOL is measured by confocal microscopy. The EE is experimentally obtained through-focus with the IOL placed in a model eye. The experimental results match very accurately with one of the theoretical models, the optimum triplicator, once that a spatial shift in the sinusoidal profile is introduced in the model.

Analysis of an adaptive short-time Fourier transform-based multicomponent signal separation method derived from linear chirp local approximation

Recently, a direct method of the time–frequency approach, called the signal separation operator (SSO), which is based on sinusoidal signal approximation, was introduced to solving the inverse problem of multicomponent signal separation. In a very recent paper “Direct signal separation via extraction of local frequencies with adaptive time-varying parameters”, the authors obtained a more accurate component recovery formula derived from the linear chirp (also called linear frequency modulation signal) approximation at any local time. However the theoretical analysis of the recovery formula derived from linear chirp local approximation has not been studied there. In this paper, we carry out the analysis of SSO based on the adaptive short-time Fourier transform (STFT). We study both the sinusoidal signal-based model and the linear chirp-based model, and obtain the error bounds for the instantaneous frequency estimation and component recovery. The error bounds are derived by studying the approximation to the STFT of each component and by the assumption of the decrease of the Fourier transform of the window function for STFT. These results provide a mathematical guarantee to the proposed adaptive STFT-based non-stationary multicomponent signal separation method. In addition, experiments are provided to illustrate the general theory.

The Use of Thermal Time to Describe and Predict the Growth and Nutritive Value of Lolium perenne L. and Bromus valdivianus Phil

The thermal time, expressed in accumulated growing degree-days (AGDD), was used as a predictor to describe and simulate the independent growth of two pasture crops, Lolium perenne L. and Bromus valdivianus Phil. Two sinusoidal models (four-parameter Logistic and Gompertz) were applied to the growth variables (total leaf blade length per tiller—LBL, and accumulated herbage mass—AHM). The nutritive value of pastures was predicted and modeled using regression equations (linear and quadratic), depending on each nutrient. Data for modeling were collected from a two-year study, in which LBL, AHM, and nutritive value variables for L. perenne and B. valdivianus pastures were measured at three-day intervals. Defoliation was determined according to the AGDD, such that the swards were defoliated at 90, 180, 270, 360, and 450 AGDD. The Logistic and Gompertz models presented similar values for the growth rate (GR) parameters, superior asymptote (Asup), inferior asymptote (Ainf), and point of maximum growth (Pmax). In both species, the maximum growth was 260 AGDD. The GR was similar for both species in different seasons of the year, but the maximum AHM varied, with B. valdivianus presenting a higher value (+1500 kg DM ha−1) than L. perenne during the spring. The regressions accurately described the nutritive value, demonstrating a positive linear relationship between the AGDD and concentrations of neutral and acid detergent fiber (NDF, ADF), an inverse linear relationship with crude protein (CP), and a quadratic relationship with metabolizable energy (ME) and water-soluble carbohydrate (WSC) concentration.

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation.

The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation. Second, an f-wave model is fitted to the f-wave signal to obtain a high resolution f-wave frequency trend and an index for signal quality control (). Third, respiratory modulation in the f-wave frequency trend is extracted by applying a narrow band-pass filter. The center frequency of the band-pass filter is determined by the respiration rate. Respiration rate is estimated from a surrogate respiration signal, obtained from the ECG using homomorphic filtering. Peak conditioned spectral averaging, where spectra of sufficient quality from different leads are averaged, is employed to obtain a robust estimate of the respiration rate. The envelope of the filtered f-wave frequency trend is used to quantify the magnitude of respiratory induced f-wave frequency modulation. The proposed methodology is evaluated using simulated f-wave signals obtained using a sinusoidal harmonic model. Results from simulated signals show that the magnitude of the respiratory modulation is accurately estimated, quantified by an error below 0.01 Hz, if the signal quality is sufficient (). The proposed method was applied to analyze ECG data from eight pacemaker patients with permanent AF recorded at baseline, during controlled respiration, and during controlled respiration after injection of atropine, respectively. The magnitude of the respiratory induce f-wave frequency modulation was 0.15 ± 0.01, 0.18 ± 0.02, and 0.17 ± 0.03 Hz during baseline, controlled respiration, and post-atropine, respectively. Our results suggest that parasympathetic regulation affects the magnitude of respiratory induced f-wave frequency modulation.

Hot Deformation Behaviors in Ti-6Al-4V/(TiB + TiC) Composites

Thermal compression testing was investigated using the Gleeble 3800 thermal simulator, and thermal deformation behavior of particle-reinforced titanium matrix composites (TMCs) was studied under deformation temperatures of 750–900 °C, strain rates of 0.001–1 s−1, and experimental deformation of 60%. According to obtained flow stress curves, the hot deformation characteristics were analyzed. Based on the Arrhenius hyperbolic sinusoidal model, the constitutive equation at high temperature was established. Based on the theory of dynamic material models, a hot processing map of TMCs at high temperature was established, and the peak region of power dissipation rate and the instability region in the hot processing map were both determined. At the same time, the corresponding microstructures in the peak power dissipation rate and rheological instability regions were observed. The results showed that flow stress decreased with increasing deformation temperature and increased with increasing strain rate. The thermal deformation activation energy of titanium matrix composites was 301.8 kJ/mol. The Ti-6Al-4V/(TiB + TiC) composites possessed only one instability zone under high-temperature compression at a strain of 0.5, with corresponding temperatures at 750–840 °C and strain rates at 0.1–1 s−1. The optimal thermal deformation parameters included corresponding temperatures of 830–880 °C and strain rates of 0.001–0.05 s−1. The microstructures corresponding to optimal hot working parameters in processing maps were more homogeneous than the microstructures in the instability zone, including the distribution uniformity of reinforcement and the degree of dynamic recrystallization, and no instability phenomena including abnormal grain growth, microcracks or intensive fracture of reinforcements were found, indicating that the hot processing map had a positive guiding effect on the option of desirable material thermal-working parameters.

Detection of period variations of eclipsing binaries in the Catalina Sky Survey

ABSTRACT We present 126 eclipsing binary candidates among 4683 Catalina Sky Surveys (CSS) detached and semi-detached eclipsing binary systems (EBs) showing cyclic or quadratic period variations over a 12 yr time span. By using inverse Gaussian profiles of the eclipses coupled with a Markov chain Monte Carlo procedure, times of minima (ToM) were calculated and diagrams with eclipse timing variations (ETVs) were constructed. Numerical tests were performed, involving synthetic EBs with period variations generated by the PHOEBE 2.0 engine and actual data for EBs with well-known period variations from the literature, to verify that the calculation of ToM variations for our CSS systems is reliable. A total of 63 out of the 126 EBs show likely cyclic ETVs, while the remainder present quadratic behaviour instead. Periods, amplitudes, period change rates, and associated errors were determined by using sinusoidal and parabolic models. 12 out of the 63 EBs (19 per cent) that appear to exhibit periodic ETVs are low-mass candidates. Additionally, four out of 126 also have maximum quadrature light variations. The possibility that the cyclic variations are caused by the light traveltime effect due to the presence of a tertiary companion is investigated. The possible nature of the quadratic ETVs is also discussed.

On a Chirp-Like Model and Its Parameter Estimation Using Periodogram-Type Estimators

Parametric modelling of physical phenomena has received a great deal of attention in the signal processing literature. Different models like ARMA models, sinusoidal models, harmonic models, models with amplitude modulation, models with frequency modulation and their different versions and combinations have been used to describe natural and synthetic signals in a wide range of applications. Two of the classical models that were considered by Professor C. R. Rao were one-dimensional superimposed exponential model and two-dimensional superimposed exponential model. In this paper, we consider parameter estimation of a newly introduced but related model, called a chirp-like model. This model was devised as an alternative to the more popular chirp model. A chirp-like model overcomes the problem of computational difficulty involved in fitting a chirp model to data to a large extent and at the same time provides visually indistinguishable results. We search the peaks of a periodogram-type function to estimate the frequencies and chirp rates of a chirp-like model. The obtained estimators are called approximate least squares estimators (ALSEs). We also put forward a sequential algorithm for the parameter estimation problem which reduces the computational load of finding the ALSEs significantly. Large-sample properties of the proposed estimators are investigated and the results indicate strong consistency and asymptotic normality of the ALSEs as well as the sequential ALSEs. The performance of the estimators is analysed in an extensive manner both on synthetic as well as real world signals and the results indicate that the proposed methods of estimation provide reasonably accurate estimates of frequencies and frequency rates.

Predicting potential future reduction in shark bites on people.

Despite the low chance of a person being bitten by a shark, there are serious associated costs. Electronic deterrents are currently the only types of personal deterrent with empirical evidence of a substantial reduction in the probability of being bitten by a shark. We aimed to predict the number of people who could potentially avoid being bitten by sharks in Australia if they wear personal electronic deterrents. We used the Australian Shark Attack File from 1900 to 2020 to develop sinusoidal time-series models of per capita incidents, and then stochastically projected these to 2066. We predicted that up to 1063 people (range: 185–2118) could potentially avoid being bitten across Australia by 2066 if all people used the devices. Avoiding death and injury of people over the next half-century is of course highly desirable, especially when considering the additional costs associated with the loss of recreational, commercial and tourism revenue potentially in the tens to hundreds of millions of dollars following clusters of shark-bite events.

Distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times using a novel biogeography-based optimization algorithm

This article proposes a novel biogeography-based optimization (NBBO) algorithm to solve the distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times (DAPFSP-SDST). The optimization objective of this problem is minimizing the maximum completion time (makespan). In the initialization phase, NBBO generates two kinds of feasible solutions. Secondly, the linear migration model is replaced with the sinusoidal migration model and a modified product insertion method is performed in the migration phase. Then, in the mutation phase, a job insertion method is used to adjust the processing order of jobs in each product. A local search method based on SDST is combined to jump out of local optima. Finally, simulation experiments based on 540 test instances and comparisons with seven existing algorithms as well as one simple biogeography-based optimization algorithm verify the superiority of NBBO.

Signal separation based on adaptive continuous wavelet-like transform and analysis

In nature and the technology world, acquired signals and time series are usually affected by multiple complicated factors and appear as multi-component non-stationary modes. In many situations it is necessary to separate these signals or time series to a finite number of mono-components to represent the intrinsic modes and underlying dynamics implicated in the source signals. Recently the synchrosqueezed transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multi-component non-stationary signal and provides more accurate component recovery. To recover individual components, the SST method consists of two steps. First the instantaneous frequency (IF) of a component is estimated from the SST plane. Secondly, after IF is recovered, the associated component is computed by a definite integral along the estimated IF curve on the SST plane. The reconstruction accuracy for a component depends heavily on the accuracy of the IFs estimation carried out in the first step. More recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced for multi-component signal separation. While both SST and SSO are mathematically rigorous on IF estimation, SSO avoids the second step of the two-step SST method in component recovery (mode retrieval). The SSO method is based on some variant of the short-time Fourier transform. In the present paper, we propose a direct method of signal separation based on the adaptive continuous wavelet-like transform (CWLT) by introducing two models of the adaptive CWLT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any time instant. A more accurate component recovery formula is derived from linear chirp local approximation. We present the theoretical analysis of our approach. For each model, we establish the error bounds for IF estimation and component recovery.

Monthly variation in diagnosis of acute lymphoblastic leukemia and survival outcome in children and adults: 15-year trends at a single center

BackgroundThe date of acute lymphoblastic leukemia (ALL) diagnosis has been studied regarding potential etiologic roles with contrasting results and the issue remains controversial. The principal aim of this study was to analyze monthly variation of ALL diagnosis in a large homogenous Hispanic Latin American cohort over 15 years; its association with survival rates was also assessed. MethodsClinical files and electronic records of 501 consecutive patients of all ages with ALL in northeastern Mexico over the years of 2004–2018 were scrutinized. Patients were divided into children ≤18 and adults >18 years. The Chi–square heterogeneity analysis was used to test for non-uniform variation. The Poisson regression analysis was used to fit sinusoidal (harmonic) models to the data, using the month of diagnosis as a covariate in a separate model. ResultsDuring the study period 363 children (72.5%) and 138 adults (27.5%) (p < 0.001) were diagnosed with ALL. Heterogeneity across the months of diagnosis was confirmed (p = 0.019) and the Poisson regression analysis confirmed a significant monthly variation (p < 0.001) (95% CI, 3.024–3.745), a higher annual peak being observed in the month of March (p = 0.002), followed by a second peak in October (p = 0.026). The five-year OS for children was 68.2% (95% CI, 67.64–68.74) and for adults, 43.7% (95% CI, 42.67–44.71) (p < 0.001). No significant association between the month of diagnosis and OS was found (p = 0.789). ConclusionThe monthly variation of ALL diagnosis was documented; these results confirm the heterogeneous behavior of the disease and appear to be consistent with an interplay of environmental and biologic factors. Further studies are needed to examine putative candidate agents.

Waveform characterisation and comparison of nystagmus eye-tracking signals

Objective. Pathological nystagmus is a symptom of oculomotor disease where the eyes oscillate involuntarily. The underlying cause of the nystagmus and the characteristics of the oscillatory eye movements are patient specific. An important part of clinical assessment in nystagmus patients is therefore to characterise different recorded eye-tracking signals, i.e. waveforms. Approach. A method for characterisation of the nystagmus waveform morphology is proposed. The method extracts local morphologic characteristics based on a sinusoidal model, and clusters these into a description of the complete signal. The clusters are used to characterise and compare recordings within and between patients and tasks. New metrics are proposed that can measure waveform similarity at different scales; from short signal segments up to entire signals, both within and between patients. Main results. The results show that the proposed method robustly can find the most prominent nystagmus waveforms in a recording. The method accurately identifies different eye movement patterns within and between patients and across different tasks. Significance. In conclusion, by allowing characterisation and comparison of nystagmus waveform patterns, the proposed method opens up for investigation and identification of the underlying condition in the individual patient, and for quantifying eye movements during tasks.

Comparison of non-linear models to describe the growth in the Andalusian turkey breed

Seven non-linear growth models were compared in the Andalusian turkey, an endangered native breed. To this aim, turkeys were weekly weighted until they reached 35 weeks. The goodness-of-fit and flexibility criteria of Brody, Von Bertalanffy, Verhulst, Logistic, Gompertz, Richards, and Sinusoidal growth models were evaluated to quantify their ability to describe the biological growth curve. Goodness-of-fit criteria were assessed comparing the mean squared error (MSE) and adjusted determination coefficient (Pseudo-R2), while the flexibility criteria of Akaike (AIC) and Bayesian information criteria (BIC) were evaluated to quantify the explanatory and predictive ability of the models tested. Afterward, all criteria were considered in a combined index to determine the most efficient model to describe and predict growth patterns. The best-fitting model for males growth was Logistic (MSE: 250,349.87; Pseudo-R2: 0.97) which also reported the best explanatory and predictive properties (AIC: 18,949.25; BIC: 18,963.24), while best goodness-of-fit criteria, explanatory and predictive capacity in females were reported for the Richards model (MSE: 144,432.45; Pseudo-R2: 0.95; AIC: 17,529.83; BIC: 17,549.02, respectively). Von Bertalanffy and Richards models underestimated the weight at early age stages, contrary to Logistic and Verhulst models. The asymptotic weight was higher in males than in females at all evaluated models, being 11,085.37 g for Logistic and 5,706.38 g for Richards, respectively. In conclusion, a marked sexual dimorphism is evident, with females reaching maturity earlier than males. The higher inflection point in males may enable their relatively easier commercial standardisation than in turkey hens. Highlights Logistic was the best fitting model for males' growth and Richards for females. Females reach maturity earlier than males with higher rates of maturity. Andalusian turkey breed shows an intense sexual dimorphism.

Microstructure and texture evolutions in AZ80A magnesium alloy during high-temperature compression

In the process of thermal deformation, the microstructure and texture evolutions of AZ80A magnesium alloy have an important influence on its properties. In order to reveal the evolutions of microstructure and texture of AZ80A magnesium alloy during hot deformation, isothermal compression tests were performed on a Gleeble-3800 thermal simulator at a temperature range of 623 K∼723 K, a strain rates range of 0.001 s−1∼1 s−1, and a deformation rate of 20%∼60%. Based on the analysis of the true stress-true strain curves under the given deformation conditions, the hyperbolic sinusoidal constitutive model and dynamic recrystallization (DRX) kinetics model were established by regression method, respectively. The DRX behavior, microstructure and texture evolutions during the thermal compression were then analyzed by optical microscopy (OM) and electron backscattered diffraction (EBSD). The results show that an increasing deformation temperature and decreasing strain rate greatly support the development of DRX of AZ80A magnesium alloy. As the deformation rate increases, DRX of AZ80A alloy is gradually and more sufficiently developed, and the dominated deformation mechanism is gradually transformed from the {0001} base slip to {} cylinder slip. Moreover, the low-angle grain boundaries (between 5° and 75° in deformation rate of 20%) gradually transform into large-angle grain boundaries (between 50° and 90° in deformation rate of 60%). Besides, the texture is gradually transformed from a base texture paralleled to compression direction to a texture perpendicular to compression direction, where twinning, rather than slip, is the main deformation mechanism. The results in this study provides a guidance in the hot forming process of AZ80A magnesium alloy.

Flipped halfwave: improved modeling of spontaneous breathing effort

Abstract Spontaneous breathing effort of a mechanically ventilated patient can seriously deteriorate the treatment outcome if it is not taken into account when choosing the appropriate settings. This paper presents an improved spontaneous breathing effort model that can be used to develop improved mechanical ventilation algorithms. Through an indicative experimental study it is shown that this model accurately describes the spontaneous breathing effort of a healthy human test subject. In comparison to the commonly used sinusoidal halfwave effort model it is shown that the proposed model improved fitting quality by a factor two.

The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation

Many speech and music analysis and processing schemes rely on an estimate of the fundamental frequency f0 of periodic signal components. Most established schemes apply rather unspecific signal models such as sinusoidal models to the estimation problem, which may limit time resolution and estimation accuracy. This study proposes a novel time-domain locked-loop algorithm with low computational effort and low memory footprint for f0 estimation. The loop control signal is directly derived from the input time signal, using a harmonic signal model. Theoretically, this allows for a noise-robust and rapid f0 estimation for periodic signals of arbitrary waveform, and without the requirement of a prior frequency analysis. Several simulations with short signals employing different types of periodicity and with added wide-band noise were performed to demonstrate and evaluate the basic properties of the proposed algorithm. Depending on the Signal-to-Noise Ratio (SNR), the estimator was found to converge within 3–4 signal repetitions, even at SNR close to or below 0 dB. Furthermore, it was found to follow fundamental frequency sweeps with a delay of less than one period and to track all tones of a three-tone musical chord signal simultaneously. Quasi-periodic sounds with shifted harmonics as well as signals with stochastic periodicity were robustly tracked. Mean and standard deviation of the estimation error, i.e., the difference between true and estimated f0, were at or below 1 Hz in most cases. The results suggest that the proposed algorithm may be applicable to low-delay speech and music analysis and processing.

Comparison of Trapezoidal and Sinusoidal PWM Techniques for Speed and Position Control of PMSM

In this article, speed and position controls of Permanent Magnet Synchronous Motor (PMSM) are performed by using a genetic algorithm-based controller. Hall Effect sensors have been used to obtain position data of the motor. Since Hall Effect sensors have been mounted, PMSM has been driven as a Brushless DC BLDC) motor. Sinusoidal and trapezoidal current reference models have been used in the control system. The proposed control system has been operated for speed control as well as position control of the motor. The developed genetic-based speed and position control method has been tested for both trapezoidal and sinusoidal PWM commutation techniques. The results obtained from these commutation techniques have been compared. Speed and position results of the motor have been obtained under the different load and operating conditions. The results reveal that the proposed control system is reliable, robust and effective.

242. Seasonality in antibiotic use and resistance across multiple bacterial pathogens and antibiotic classes

BackgroundAntibiotic use is an important driver of antibiotic resistance. Previous studies have shown that this relationship is detectable on a seasonal timescale, where seasonally cycling antibiotic prescribing corresponds with cycling levels of resistance. Here, we evaluated the seasonal relationship between antibiotic use and resistance across multiple species and antibiotic classes in Boston, Massachusetts.MethodsTo conduct our study, we analyzed antibiotic claims data from the Massachusetts All Payers Claims Database for five antibiotic classes, beta-lactams, macrolides, quinolones, tetracyclines, and nitrofurans, which together account for 74% of the total outpatient antibiotic claims in this dataset. In addition, we analyzed antibiotic susceptibility data for clinical isolates collected from two tertiary care hospitals, focusing on three clinically important bacterial pathogens. Our analysis included a total of 131,815 Escherichia coli, 47,208 Staphylococcus aureus, and 27,237 Klebsiella pneumoniae isolates collected over the years 2007–2019, with susceptibility testing data for 5–6 antibiotics per species. To determine the extent of seasonality in use and resistance, we fit the antibiotic claims data for each antibiotic class and the minimum inhibitory concentration (MIC) data for each species-antibiotic combination to a sinusoidal model with either a 6 or 12-month period.ResultsWe found that use of all 5 antibiotic classes and resistance in 9 out of 15 species-antibiotic combinations showed a significant amplitude of seasonality (p-value < 0.05). Despite different seasonal peaks in antibiotic use across classes, resistance peaked in the winter for almost all species-antibiotic combinations and was most highly correlated with use of winter-peaking antibiotic classes, beta-lactams and macrolides.ConclusionOverall, these results suggest that co-resistance and “bystander” selection – selection for resistance in asymptomatically colonizing and potentially pathogenic bacteria that are not the direct target of treatment – mediate the effects of antibiotic prescribing on the landscape of resistance.Disclosures All Authors: No reported disclosures

Cell size influences inorganic carbon acquisition in artificially selected phytoplankton.

Cell size influences the rate at which phytoplankton assimilate dissolved inorganic carbon (DIC), but it is unclear whether volume-specific carbon uptake should be greater in smaller or larger cells. On the one hand, Fick's Law predicts smaller cells to have a superior diffusive CO2 supply. On the other, larger cells may have greater scope to invest metabolic energy to upregulate active transport per unit area through CO2 -concentrating mechanisms (CCMs). Previous studies have focused on among-species comparisons, which complicates disentangling the role of cell size from other covarying traits. In this study, we investigated the DIC assimilation of the green alga Dunaliella tertiolecta after using artificial selection to evolve a 9.3-fold difference in cell volume. We compared CO2 affinity, external carbonic anhydrase (CAext ), isotopic signatures (δ13 C) and growth among size-selected lineages. Evolving cells to larger sizes led to an upregulation of CCMs that improved the DIC uptake of this species, with higher CO2 affinity, higher CAext and higher δ13 C. Larger cells also achieved faster growth and higher maximum biovolume densities. We showed that evolutionary shifts in cell size can alter the efficiency of DIC uptake systems to influence the fitness of a phytoplankton species.