Harmonic Model Research Articles

Strong decay widths and mass spectra of the 1D , 2P and 2S singly bottom baryons

We calculate the 1D, 2P, and 2S mass spectra of the singly bottom baryons and their strong decay widths. The calculations are performed within a harmonic oscillator quark model that incorporates the spin, spin-orbit, isospin, and flavor interactions. To obtain the model parameters, we conducted a fit using only 13 of the 22 experimentally observed states. Our predictions align well with the observed states, showing a root-mean-square deviation of 9.6 MeV. We calculate the three-quark strong decay widths within the P03 model, which has only one free parameter, the pair creation strength γ0; this is the first time that the Λbη, Σbρ, Σb*ρ, Λbη′, Λbω, ΞbK, Ξb′K, Ξb*K, ΞbK*, Ξb′K*, and Ξb*K* channels have been considered in the calculation of the strong decay widths of the excited Λb states; the Σbη, ΞbK, Σbρ, Σb*ρ, Λbρ, Σb*η, Σbη′, Σb*η′, Ξb′K,Ξb*K, ΞbK*, Ξb′K*, Ξb*K*, Σbω, Σb*ω, Σ8Bs, ΔB, N(1520)B, N(1535)B, N(1680)B, and N(1720)B channels in the calculation of the strong decay widths of the excited Σb states; the ΛbK*, Ξbρ, Ξb′ρ, Ξb*ρ, ΣbK*, Σb*K*, Ξbη′, Ξb′η′, Ξb*η′, Ξbω, Ξb′ω, Ξb*ω, Ξbϕ, Ξb′ϕ, Ξb*ϕ, Ξ8Bs, Σ8B*, and Σ10B channels in the calculation of the strong decay widths of the excited Ξb and Ξb′ states; the ΞbK*, Ξb′K*, Ξb*K*, Ωbη, Ωb*η, Ωbϕ, Ωb*ϕ, Ωbη′, Ωb*η′, Ξ8B, and Ξ10B channels in the calculation of the strong decay widths of the Ωb states. Moreover, in Appendix D, we give the flavor couplings that can be useful for other articles. In Appendix E, our partial decay widths are reported for each open flavor channel; these may be useful to the LHCb, ATLAS, and CMS experimentalists in order to plan in which particular channels to look for missing bottom baryons. The experimental masses and widths of the discovered Λb(6146)0 and Λb(6152)0 states are consistent with our mass and width predictions for the Dλ excitations with quantum numbers JP=32+ and JP=52+, respectively. Moreover, the masses and widths of the new Ξb(6327)0 and Ξb(6333)0 states agree with our calculations for the Dλ excitations with quantum numbers JP=32+ and JP=52+, respectively. Finally, we calculate the electromagnetic decay widths from P-wave states to ground states. We give the exact analytical expressions of the spin-flip and orbit-flip transition amplitudes, both of which are functions of the photon-transferred momentum. The electromagnetic decays are dominant when the strong decays are suppressed. A relevant case is the Ωb− missing spin excitation, with JP=32+, which cannot decay strongly, but has a nonvanishing predicted electromagnetic decay width in the Ωb−γ channel. Therefore, we suggest the Ωb−γ electromagnetic decay channel as a golden channel in which to search for this state. In all of our calculations, we report the uncertainties related to the experimental and model errors by means of the Monte Carlo bootstrap method. Published by the American Physical Society 2024

Assessment of time-series-derived no-flood references for sar-based Bayesian flood mapping

ABSTRACT The systematic mapping of flood events with Synthetic Aperture Radar (SAR) data is an area of growing importance. One global flood mapping algorithm utilized within the Copernicus Emergency Management Service is based upon a Bayesian Inference model that compares a SAR image to a simulated reference image representing no-flood conditions. This no-flood reference image is at present generated using a harmonic model trained using historic time series, thereby producing a backscatter image representing mean seasonal conditions. One known weakness of this approach is that it cannot account for changing environmental conditions from year to year, potentially causing an overestimation of flood extent during dry periods, snow and frost, or other effects causing lower-than normal backscatter. To minimize this detrimental effect, we introduce an exponential filter to estimate the no-flood reference image by weighting the most recent backscatter observations according to their time difference to the current SAR acquisition. We compare the performance of the new exponential filter model against the harmonic model using a novel time-series flood mapping assessment approach. First, we assess their predictions against the actual SAR image time series for the year 2023. Then, we analyze the false positive rate of the corresponding flood maps generated to ensure the robustness of the automated algorithm outside of flood events. Furthermore, we perform qualitative and quantitative analyses of flood maps matching with semi-automatic results from Copernicus Emergency Management Services and Sentinel Asia as a reference. Our time-series analysis confirms increased false positive rates due to well-known environmental drivers and highlights issues with agricultural overestimation. In this regard, the time-series comparisons of the no-flood reference models show a clear improvement in the TU Wien algorithm with the exponential filter, effectively reducing false positive rates on non-flooded scenes in most study sites. The exponential filter performed better than the harmonic model in most flooded scenes, where sites show generally improved Critical Success Index and User’s accuracy. However, the exponential filter model has difficulties with sites with prolonged floods in the time series, requiring further development. Overall, the exponential filter no-flood reference model shows great promise for improved global near-real-time flood mapping.

Harmonic Modeling and Ripple Suppression of Electromagnetic Torque in IPMSMs

Harmonic Modeling and Ripple Suppression of Electromagnetic Torque in IPMSMs

Harmonization for Parkinson’s Disease Multi-Dataset T1 MRI Morphometry Classification

Classification of disease and healthy volunteer cohorts provides a useful clinical alternative to traditional group statistics due to individualized, personalized predictions. Classifiers for neurodegenerative disease can be trained on structural MRI morphometry, but require large multi-scanner datasets, introducing confounding batch effects. We test ComBat, a common harmonization model, in an example application to classify subjects with Parkinson’s disease from healthy volunteers and identify common pitfalls, including data leakage. We used a multi-dataset cohort of 372 subjects (216 with Parkinson’s disease, 156 healthy volunteers) from 11 identified scanners. We extracted both FreeSurfer and the determinant of Jacobian morphometry to compare single-scanner and multi-scanner classification pipelines. We confirm the presence of batch effects by running single scanner classifiers which could achieve wildly divergent AUCs on scanner-specific datasets (mean:0.651 ± 0.144). Multi-scanner classifiers that considered neurobiological batch effects between sites could easily achieve a test AUC of 0.902, though pipelines that prevented data leakage could only achieve a test AUC of 0.550. We conclude that batch effects remain a major issue for classification problems, such that even impressive single-scanner classifiers are unlikely to generalize to multiple scanners, and that solving for batch effects in a classifier problem must avoid circularity and reporting overly optimistic results.

Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification

This study presents an automated noninvasive voice disorder detection and classification approach using an optimized fusion of modified glottal source estimation and deep transfer learning neural network descriptors. A new set of modified descriptors based on a glottal source estimator and pre-trained Inception-ResNet-v2 convolutional neural network-based features are proposed for the speech disorder detection and classification task. The modified feature set is obtained using mel-cepstral coefficients, harmonic model, phase discrimination means, distortion deviation descriptors, conventional wavelet, and glottal source estimation features. Early descriptor-level fusion is employed in this study for performance enhancement-however, the fusion results in higher feature vector dimensionality. A nature-inspired slime mould algorithm is utilized to remove redundant and select the best discriminating features. Finally, the classification is performed using the K-nearest neighbor (KNN) classifier. The proposed algorithm was evaluated using extensive experiments with different feature combinations, with and without feature selection, and with two popular datasets: the Arabic Voice Pathology Database (AVPD) and the Saarbrucken Voice Database (SVD). We show that the proposed optimized fusion method attained an enhanced voice pathology detection accuracy of 98.46%, encompassing a wide spectrum of voice disorders on the SVD database. Furthermore, compared to traditional handcrafted and deep neural network-based techniques, the proposed method demonstrates competitive performance with fewer features.

Active vibration suppression for integrated electric drive systems based on the CNN-LSTM considering dead zone

To address the problems that the torque ripple and vibration of integrated electric drive system caused by dead-zone, which leads to the decrease of electric drive system reliability and vehicle comfort, this paper establishes a current harmonic model and nonlinear integrated electric drive system model considering the dead-zone. A predictive model for harmonic compensation voltage is constructed based on the conventional current harmonic injection method. Subsequently, an active vibration suppression method based on CNN-LSTM is proposed. The method reduces the vibration of the electric drive system by suppressing the current harmonics and suppresses the torque ripple at the same time. Simulation and experimental results show that the method effectively suppresses the vibration of the electric drive system, reduces current harmonics and control parameters, improves the system response speed, and decreases the torque ripple at high speed.

The Unidroit Principles of International Commercial Contracts: potential for greater use in the Middle East and North Africa

Abstract The International Institute for the Unification of Private Law’s (Unidroit) Principles of International Commercial Contracts (UPICC) provide a uniform international instrument setting out general rules for international commercial contracts as an alternative to the State law-centred approach to contracting and dispute resolution. While the UPICC are well known among academics and frequently referred to in practice in many regions of the world including Europe, Latin America, and Asia, they are applied far less often in the Middle East and North Africa (MENA). This is in spite of the fact that their use appears to be widely permissible under MENA States’ domestic legislation, and international trade involvement provides opportunities for them to be applied productively. The reasons for less usage in the MENA region are manifold and include, among others, a judicial tendency to apply domestic law, the hierarchy between the Islamic Sharia and lex mercatoria, the limited involvement of experts from the MENA region in the elaboration of the UPICC and their subsequent promotion, and limited linguistic accessibility. Potential advantages of greater usage include the promotion of fair and harmonized contract practices and facilitating the resolution of cross-border private legal conflicts as well as the UPICC’s use as a model and reference for modernization and harmonization of contract law across the region and beyond. This article examines the use of the UPICC in MENA and the potential for facilitating their future impact in the region, focusing on Arab jurisdictions influenced by Islamic law.

Collective nature of phonon energies beyond harmonic oscillators

Phonon quasi-particles have been monumental in microscopically understanding thermodynamics and transport properties in condensed matter for decades. Phonons have one-to-one correspondence with harmonic eigenstates and their energies are often described by simple independent harmonic oscillator models. Higher order terms in the potential energy lead to interactions among them, resulting in finite lifetimes and frequency shifts, even in perfect crystals. However, increasing evidence including constant volume heat capacity violating the Dulong-Petit law suggests the need for re-evaluation of phonons as having independent harmonic energies. In this work, we explicitly examine inter-mode dependence of phonon energies of a prototypical crystal, silicon, through energy covariance calculations and demonstrate the concerted nature of phonon energies even at 300 K, questioning independent harmonic oscillator assumptions commonly used for phonon energy descriptions of thermodynamics and transport.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

Reduced density matrix dynamics in multistate harmonic models via time-convolution and time-convolutionless quantum master equations with quantum-mechanical and semiclassical kernels.

In this work, we explore the electronic reduced density matrix (RDM) dynamics using time-convolution (TC) and time-convolutionless (TCL) quantum master equations (QMEs) that are based on perturbative electronic couplings within the framework of multistate harmonic (MSH) models. The MSH model Hamiltonian consistently incorporates the electronic-vibrational correlations between all pairs of states by satisfying the pairwise reorganization energies directly obtained from all-atom simulations, representing the globally heterogeneous environments that couple to the multiple states differently. We derive the exact quantum-mechanical and a hierarchy of semiclassical approximate expressions for the kernels in TC and TCL QMEs that project the full RDM for general shifted harmonic systems, including the MSH model. These QMEs are applied to simulate RDM dynamics of photoinduced charge transfer (PICT) in organic photovoltaic carotenoid-porphyrin-fullerene triad solvated in tetrahydrofuran solution and the excitation energy transfer (EET) dynamics in photosynthetic Fenna-Matthews-Olson complexes from C. tepidum and P. aestuarii. Our results show that while both TC and TCL QMEs capture similar phenomena in PICT and EET processes, TC QME generally provides more accurate results than TCL QME, particularly in the initial oscillation of EET population dynamics. This study highlights the effectiveness of the TC and TCL QMEs in modeling RDM dynamics of nonadiabatic processes, offering insights for realistic condensed phase systems.

Evaluation of ComBat Harmonization for Reducing Across-Tracer Differences in Regional Amyloid PET Analyses.

Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh compound-B (PiB). One hundred thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.

Regional spherical harmonic model for large telescope pointing error correction

Regional spherical harmonic model for large telescope pointing error correction

Implementing the procedure of measurement phase difference of the model signals using a virtual laboratory practicum

As this virtual laboratory practicum, which is a tool for mastering theoretical principles and practical skills in digital signal processing, was developed, users (mainly students) were given the possibility to parameterize harmonic signals. It includes the determination of the number of samples, sampling rate, amplitudes, initial phases, and other characteristics necessary for accurate metrological measurements. This publication presents the structure of the virtual workshop and the mathematical model underlying it, with an emphasis on aspects of analyzing the procedures for measuring the phase difference of harmonic signal models using the Discrete Fourier Transform. Particular attention is focused on the phase correction algorithm, which is aimed at improving the accuracy of measurements of the phase difference of signals. The presented methodology describes in detail the measurement process, estimation of errors arising from limited measurement time due to non-synchronization, the influence of the initial phase values of signals, and shows how the synchronization process contributes to a significant increase in the accuracy of measuring informative parameters.

Structural design and biomechanical analysis of a combined titanium and polyetheretherketone cage based on PE-PLIF fusion.

In lumbar spinal fusion, the titanium cage tends to cause stress shielding due to their high elastic modulus, which can lead to degenerative lesions in adjacent spinal segments. Furthermore, polyetheretherketone (PEEK) cages have certain material characteristics that do not promote bone ingrowth and fusion stability. In this study, a new cage was designed, and its biomechanical performance in percutaneous endoscopic posterior lumbar interbody fusion (PE-PLIF) was analyzed using the finite element (FE) method. A complete model of the L4-L5 lumbar spine was established, and static and harmonic vibration FE analysis models were developed based on it. The biomechanical properties of titanium, PEEK, and combined cage in PE-PLIF fusion were compared. The strain capacity of the combined fusion increased by 9.5% when compared to the titanium fusion. The surgical model under the combined fusion reduces the L5 endplate stress by 12% in the forward flexion condition and the fusion stress by 17% in the vibration condition compared to the model supported by the titanium fusion, and the differences in screw stress and mobility among the three models are not significant in multiple conditions. Consequently, the combined cage demonstrates a certain reduction in the stress-shielding effect when compared to the titanium cage; it has better fusion effect and provides theoretical support and guidance for the design of the clinical fusion cage.

Synthesizing Landsat images using time series model-fitting methods for China’s coastal areas against sparse and irregular observations

ABSTRACT Long historical records and free accessibility have made Landsat data valuable for time-series analysis. However, Landsat time-series analysis is restricted for coastal areas due to the lack of sufficient numbers of clear images. The generation of synthetic Landsat images using model-fitting methods is accepted as an effective means of overcoming this problem. But for coastal areas, available observations are typically sparse and irregular, leading to ill-fitted models that distort synthetic Landsat images. In this study, we propose a linear harmonic model with different orders and implement it on an annual basis to generate synthetic Landsat images based on the Google Earth Engine (GEE) platform. First, we incorporated the available observations from adjacent years and filtered them by season to address the problems of data sparsity and irregularity. Then, we combined the threshold segmentation and ridge regression approaches to address the fake cloud problem, which sometimes contaminates synthetic Landsat images in summer. All cloud-free real Landsat images from five typical sites along China’s coasts during 1986–2020 were used to evaluate the accuracy and robustness of the synthetic Landsat images generated using our proposed methods. The principal results are as follows: (1) the R2 value of the linear harmonic model averaged across different Landsat bands and land cover types was 0.640, and the model was especially successful in simulating the surface reflectance in near NIR bands in forest and grassland areas; (2) 81.1% of the synthetic Landsat images covered by fake clouds were effectively restored, and there was the particular need to remove fake clouds for the synthetic Landsat images at low latitudes; (3) The mean absolute error for our synthetic Landsat images was 0.015, with the rate of 0.124; this was achieved under the clear-sky probability of only 36% and the average number of annual observations below 9, indicating a good performance. Compared with the continuous change detection and classification (CCDC) and seasonal median composite (SMC) methods, our proposed method offers advantages in both the accuracy and integrity of synthetic Landsat images. Our proposed method for synthesizing images also has potential for application to global coastal areas and other satellite datasets.

Detection of a Transient Quasiperiodic Oscillation in γ-Rays from Blazar PKS 2255-282

We conducted a comprehensive variability analysis of the blazar PKS 2255-282 using Fermi-LAT observations spanning over 4 yr, from MJD 57783.5 to 59358.5. Our analysis revealed a transient quasiperiodic oscillation (QPO) with a period of 93 ± 2.6 days. We employed a variety of Fourier-based methods, including the Lomb–Scargle periodogram (LSP) and weighted wavelet Z-transform (WWZ), as well as time domain analysis techniques such as seasonal and nonseasonal autoregressive Integrated moving average models and the Stochastic modeling with stochastically driven damped harmonic oscillator models. Consistently, the QPO with a period of 93 days was detected across all methods used. The observed peak in LSP and time-averaged WWZ plots has a significance level of 4.06σ and 3.96σ, respectively. To understand the source of flux modulations in the light curve, we explored various physical models. A plausible scenario involves the precession of the jet with a high Lorentz factor or the movement of a plasma blob along a helical trajectory within the relativistic jet.

All Deforestation Matters: Deforestation Alert System for the Caatinga Biome in South America’s Tropical Dry Forest

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome.

A Halpern fixed-point iteration-based approach to harmonic model predictive control problem

A Halpern fixed-point iteration-based approach to harmonic model predictive control problem

The Electro-Excitation Form Factors for Low-Lying States of 7Li Nucleus

The transverse electron scattering form factors have been studied for low –lying excited states of 7Li nucleus. These states are specified by J? T= (0.478MeV), (4.63MeV) and (6.68MeV). The transitions to these states are taking place by both isoscalar and isovector components. These form factors have been analyzed in the framework of the multi-nucleon configuration mixing of harmonic oscillator shell model with size parameter brms=1.74fm. The universal two-body of Cohen-Kurath is used to generate the 1p-shell wave functions. The core polarization effects are included in the calculations through effective g-factors and resolved many discrepancies with experiments. A higher configuration effect outside the 1p-shell model space, such as the 2p-shell, enhances the form factors for q-values and reproduces the data. The present results are compared with other theoretical models. PACS: 25.30.Bf Elastic electron scattering - 25.30.Dh Inelastic electron scattering to specific states – 21.60.Cs Shell model – 27.20. +n 5? A ?19

Evaluation of ComBat harmonization for reducing across-tracer differences in regional amyloid PET analyses.

Introduction Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. Conclusion ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.