Accurate Profiles Research Articles

Pure rotational Raman lidar for accurate profiling of atmospheric temperature and aerosol/cloud backscatter coefficients

We have created a pure rotational Raman (PRR) lidar for round-the-clock and large-height-range quantitatively-physical measurements of atmospheric temperature profiles. A seeded frequency-doubled Nd:YAG laser is utilized as the light source. It has a typical power of ∼18 W and a pulse repetition rate of 30 Hz. The lidar uses a 0.3-m-diameter receiver and a 1.0-m-diameter receiver together. Each receiver contains a state-of-the-art three-channel polychromator that extracts respectively two individual Stokes N2 PRR lines with rotational quantum numbers J = 4 and 14, as well as a Rayleigh/Mie backscatter signal. The temperature profiles are derived from the strict analytical expression of temperature in terms of the ratio of the two PRR line signals without calibration. Furthermore, a strict analytical expression has been established to derive the aerosol/cloud backscatter coefficient profiles without assumptions on the lidar ratio. The lidar provides a rigorous and effective methodology for accurate profile measurements of the atmospheric temperature and optical properties of aerosol/cloud. Observational examples and statistics obtained at a subtropical site have demonstrated the high measurement accuracies, large measurement altitude range, good range/time resolution, and unprecedented daytime measurement capability of this single-line-extracted PRR lidar.

Improving the risk profile of Indonesian enterprise taxpayers using multilabel classification

Optimizing tax revenues is difficult in Indonesia due to obstacles such as tax evasion and tax avoidance. It is closely related to an organization's compliance with tax regulations, known as the taxpayers risk profile. However, this mechanism does not accurately detect tax avoidance and tax evasion risks. To overcome this limitation, we use a multilabel classification machine learning method in this study, which classifies a single observation into one or more labels at once. The approach involves problem transformation (binary relevance and label powerset), algorithm adaptation (multilabel k-nearest neighbor (ML-kNN) and multilabel-adaptive resonance associative map (ML ARAM)), and ensemble (label space partitioning and random k-label sets with disjoint (RAkELd)). Based on the model performance comparisons, we discovered that the ML-ARAM method based on deep learning is the best, with an average F1-score of 95.5% and a hamming loss of 7.4%. We also examine the feature importance of the best model to reduce the dimensions of features so that we can identify the dominant factors that encourage a taxpayer entity to engage in tax avoidance or tax evasion. The findings of this study improve the accuracy of tax avoidance risk detection and tax evasion risk profiles using machine learning methods, ensuring maximum tax revenues in Indonesia.

Estimating surface pressure on flexible structures subjected to flow-induced vibrations

A conventional flag is placed downstream of the inverted half cylinder to study pressure over the flag's surface due to fluid-flexible body interaction between the flag and the vortices generated by the half cylinder. An immersed boundary method provides a reliable and fast solution to the fluid-structure interaction problems, albeit with limitation. One such limitation is the accurate pressure profile over the Lagrangian grid, i.e., the structure boundary. A pressure prediction algorithm is proposed which couples with the improved immersed boundary method to predict pressure over flag's surface. The effect of flow patterns on flag's surface pressure is studied by varying flag's distance from the bluff body. The pressure results are analyzed as negative and positive pressure fluctuations from the assigned reference pressure in the far field. Results show high flag surface pressure at a smaller stream-wise distance. A subsequent decrease in flow pressure is observed, when the stream-wise distance is increased. Changing span-distance shows that the highest pressure occurs at Gy = 0.25 with a significant drop at span positions of 0 and 0.5. The average pressure over the flag's surface is also affected by the vibration modes and the vortex shedding from the surface of the flag. The deflected mode appears at a smaller stream-wise distance accompanied by additional vortex shedding from the flag surface resulting in a high average pressure. Whereas biased and conventional flapping modes appear as stream-wise distance is progressively decreased resulting in reduced average pressure. Hydrodynamic analysis shows that the vortices originating from the bluff body and their subsequent interaction with the flag serve as the primary source of pressure fluctuations resulting in a change in the pressure profile over the surface of the flag.

A clustering approach to integrative analyses of multiomic cancer data

Rapid technological advances have allowed for molecular profiling across multiple omics domains for clinical decision-making in many diseases, especially cancer. However, as tumor development and progression are biological processes involving composite genomic aberrations, key challenges are to effectively assimilate information from these domains to identify genomic signatures and druggable biological entities, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. We propose integrative frameworks for high-dimensional multiple-domain cancer data. These Bayesian mixture model-based approaches coherently incorporate dependence within and between domains to accurately detect tumor subtypes, thus providing a catalog of genomic aberrations associated with cancer taxonomy. The flexible and scalable Bayesian nonparametric strategy performs simultaneous bidirectional clustering of the tumor samples and genomic probes to achieve dimension reduction. We describe an efficient variable selection procedure that can identify relevant genomic aberrations and potentially reveal underlying drivers of disease. Although the work is motivated by lung cancer datasets, the proposed methods are broadly applicable in a variety of contexts involving high-dimensional data. The success of the methodology is demonstrated using artificial data and lung cancer omics profiles publicly available from The Cancer Genome Atlas.

Machine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles

Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial to assess the measurement accuracy of these solutions since residual errors may arise from environmental, methodological, or processing factors. This study aims to enhance validation by employing machine learning techniques to investigate the impact of such errors on the overall assessment of gait profiles. Two datasets of gait trials, collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system, were considered. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathologic gait peculiarities. Machine learning models show the equivalence and the high level of agreement and concordance between the measurement systems in assessing gait profiles (accuracy: 98.7%). In addition, they demonstrate data interchangeability and integrability despite residual errors identified by traditional statistical metrics. These findings suggest that validation procedures can extend beyond strict measurement differences to comprehensively assess gait performance.

RGB to hyperspectral: Spectral reconstruction for enhanced surgical imaging

AbstractThis study investigates the reconstruction of hyperspectral signatures from RGB data to enhance surgical imaging, utilizing the publicly available HeiPorSPECTRAL dataset from porcine surgery and an in‐house neurosurgery dataset. Various architectures based on convolutional neural networks (CNNs) and transformer models are evaluated using comprehensive metrics. Transformer models exhibit superior performance in terms of RMSE, SAM, PSNR and SSIM by effectively integrating spatial information to predict accurate spectral profiles, encompassing both visible and extended spectral ranges. Qualitative assessments demonstrate the capability to predict spectral profiles critical for informed surgical decision‐making during procedures. Challenges associated with capturing both the visible and extended hyperspectral ranges are highlighted using the MAE, emphasizing the complexities involved. The findings open up the new research direction of hyperspectral reconstruction for surgical applications and clinical use cases in real‐time surgical environments.

An Accurate Construction Method of E-commerce User Profile Based on Artificial Intelligence Algorithm and Big Data Analysis

User’s basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, psychological attributes, and other factors will lead to poor user experience, information overload, interference, and other negative effects. In order to develop more accurate marketing strategies, optimize user experience, and improve the conversion rate and user satisfaction of e-commerce platforms, an accurate construction method of e-commerce user profile based on artificial intelligence algorithm and big data analysis is proposed. Based on big data analysis technology, the basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, and psychological attributes of e-commerce users are collected and integrated from multiple dimensions. The improved sequential pattern mining algorithm (PBWL) is applied to mine the frequent sequential pattern in the e-commerce user behavior, and to reveal the user’s behavior habit. The comprehensive attribute representation of e-commerce users is obtained by combining the LINE network model and the convolutional neural network. The firefly K-means clustering algorithm is used to cluster the e-commerce users, group the users based on the similarity of user attribute information, create different types of user clusters, and achieve the accurate construction of an e-commerce user profile. The experimental results show that this method can build an accurate e-commerce user profile and provide strong support for personalized recommendation and precision marketing of e-commerce platforms. This method can dig deeply into the behavior habits of e-commerce users and accurately reflect their interest preferences and consumption characteristics. This method can quickly and stably cluster e-commerce users, and the clustering effect of user profiles is optimal. This method can also divide the data into meaningful groups according to the user’s consumption behavior, and reveal the characteristics and values of different groups.

SeqLengthPlot v2.0: An All-in-one, Easy-to-Use Tool for Visualizing and Retrieving Sequence Lengths from FASTA Files

Abstract Motivation Accurate sequence length profiling is essential in bioinformatics, particularly in genomics and proteomics. Existing tools like SeqKit and the Trinity toolkit provide basic sequence statistics but often fall short in offering comprehensive analytics and plotting options. For instance, SeqKit is a very complete and fast tool for sequence analysis, delivering useful metrics (e.g., number of sequences, average, minimum, and maximum lengths) and can return sequences either shorter or longer (but not both at once) for a given length. Similarly, Trinity's Perl-based scripts provide detailed contig length distributions (e.g., N50, median, and average lengths) but do not include the total number of sequences or offer graphical representations of the data. Results Given that key sequence analysis tasks are often distributed across multiple tools, we introduce SeqLengthPlot v2.0, an all-in-one, easy-to-use Python-based tool. Through a simple command-line interface, this straightforward tool enables users to split input FASTA files (nucleotide and protein) into two distinct files based on a customizable sequence length cutoff. It also automatically retrieves the resulting FASTA files, generates length distribution plots, and provides comprehensive statistical summaries. Availability and implementation SeqLengthPlot_v2.0.2 can be accessed at https://github.com/danydguezperez/SeqLengthPlot/releases/tag/v2.0.2.

In-Situ visual reconstruction of strut profiles in pulsed wire arc additive manufacturing of lattice structures

ABSTRACT The wire arc additive manufacturing (WAAM) process often results in geometric inaccuracies in the produced struts, highlighting the need for in-situ monitoring. Industrial cameras struggle to capture accurate images due to interference from arc light and metal radiation. This study introduces a real-time visual reconstruction method that leverages regions of interest (ROI) to enhance profile accuracy. Initially, the melt pool is analyzed and segmented from a series of frames to establish an initial strut profile. This profile then serves as the ROI for segmenting the solidified weld bead regions, yielding a more precise strut profile. Comparative analysis shows that the proposed method achieves a high Intersection Over Union (IOU) score of 0.911 and an Average Symmetric Surface Distance (ASD) error of 0.355 mm, demonstrating both accuracy and robustness. Additionally, the inclination angle and diameter are extracted from the reconstructed profiles.

Geometric feature extraction and its profile accuracy evaluation for pockets on curved aircraft skin from scanned point clouds

Abstract Dimension inspection is crucial for aircraft skin manufacturing and assembly. To meet weight loss and other functional requirements, large numbers of shallow pockets are distributed on the surface of thin-walled aircraft skin. It is difficult to evaluate the profile accuracy of the pockets, as the aircraft skin is composed of curved surfaces and its actual geometry is inconsistent with the nominal model. This study proposes a method to evaluate the profile and position accuracy for the pockets on aircraft skin utilizing point clouds obtained by 3D scanners. Firstly, the feature contour lines are identified and extracted from the scanned aircraft skin surface point clouds by adopting the local curvature-aware method and minimum bounding rectangle algorithm. Then, an algorithm is proposed to mitigate the contour identification deviations of the segmented area by employing the shortest geodesic distance and transition zone section projection, so as to enhance the measurement accuracy. Finally, the viability of the proposed method for evaluating pockets on aircraft skin is verified by the simulation, and the reliability and robustness of the proposed method are verified by the simulation and experiment. Experimental results show that the measurement accuracy of the pocket can be improved from 0.5 mm to 0.0505 mm, compared with traditional specialized conformation fixture methods.

Prediction of aircraft fuel consumption based on machine learning algorithm

Abstract The shortcomings of traditional aircraft fuel consumption calculation methods include insufficient theoretical analysis, complex input parameters, and poor calculation accuracy. Through the analysis of aircraft motion equations and engine working characteristics, this paper screens out the key parameters affecting aircraft fuel consumption. Based on the real flight data, three machine learning methods, BP neural network, RBF neural network, and Support Vector Machine(SVM), are used for training, and a fuel consumption prediction model is established with key parameters as inputs. The prediction accuracy and characteristics of the three models are analyzed and compared. The aircraft fuel consumption prediction model achieves the operational requirements, and the accuracy in the flight profile reaches 5%. The trained model was applied to the planning of the transport aircraft mission profile, achieving a fuel consumption error of only 1.1% across the entire mission profile, resulting in a relatively accurate outcome.

Experimental prototype of mesh antenna based on digital twin technology

The ring mesh antenna plays an important role in the field of aerospace communication owing to its excellent performance characteristics, but faces the challenge of decreasing profile accuracy due to environmental and structural failures during in-orbit operation. Digital twin, a promising technology, can effectively monitor and adjust the antenna surface accuracy. In other words, by creating a digital model of the physical entity, real-time monitoring and adjustment can be made to maintain the electrical performance of the antenna. In this paper, a digital twin system for an experimental prototype of a ring mesh antenna is proposed. Firstly, we introduce the preliminaries of digital twin technology. Secondly, we propose a system framework based on a five-dimensional digital twin model. Then, a digitization software and a visualization interface are developed. Finally, according to efficiency analysis and simulation results, the feasibility of the system was verified. We demonstrate its effectiveness in terms of real-time monitoring and automatic mesh adjustment functions, and significantly improve the efficiency of the adjustment process. It provides a reference with significant value for the application of digital twin technology in the field of aerospace communication, which significantly advances the development of this field.

Regadenoson stress echocardiography: a road ahead

Abstract Introduction Drugs used in stress echocardiography (SE) have important limitations due to side effects and contraindications. Since its approval in 2008, Regadenoson has seen a significant increase in usage. This is largely attributed to its good safety profile and diagnostic accuracy, making it the preferred drug for stress tests in many European centers. However, despite its expanding use and major advantages, current guidelines don't place it as the first choice for SE, probably because the reported experience with this drug remains limited. The aim of the present study was to assess the safety and short-medium term prognosis of patients who underwent Regadenoson SE. Methods Retrospective observational study. Patients who underwent Regadenoson SE between 2017 and 2023 were included. Safety was assessed by monitoring immediate adverse effects following Regadenoson infusion and the need for using an antidote (Aminophylline). A positive test result was defined by the occurrence of any of the following: wall motion abnormalities, left ventricle dilatation, electrocardiographic changes or symptoms of ischemia. During the follow up, we registered the incidence of ischaemic events (admission for unstable angina, acute myocardial infarction (AMI) or cardiovascular (CV) death) and other CV events such as admission for heart failure (HF) and stroke. Additionally, we evaluated patients in both groups who underwent coronary angiography (by computerized tomography or coronary catheterization) during the follow up period. The groups with negative SE (NSE) and positive SE (PSE) were compared. Results The total number of patients was 344, with a median follow up of 29 months. Basal population characteristics and events by group (NSE and PSE) are shown in Figure 1. We needed to use the antidote in only one case, because of ventricular bigeminism, with no other major adverse effects reported. SE was positive in 37 (11%) patients. In this group, patients were more likely to present ischaemic events (16% vs. 3%, p<0.01) and admissions for HF (24% vs. 5%, p <0.01), compared to the NSE one. In those who underwent coronary angiography, revascularization was needed in 14 patients of the PSE group, and only in 7 of the NSE group (38% vs 2%, p < 0.05). The indications for these last cases were persistent angina in 5 patients (2 of them with history of coronary artery disease) and revascularization following an AMI in 2 patients. (Figure 2). Conclusions The probability of an ischaemic event after a negative Regadenoson SE remains very low, unlike positive studies. Regadenoson SE showed to be both safe in assessing ischemic cardiomyopathy and very useful for its prognostic value. These findings encourage its expanding use in the stress echo lab.

Analysis of Geometrical Accuracy and Surface Quality of Threaded and Spline Connections Manufactured Using MEX, MJ and VAT Additive Technologies.

This paper presents the process of manufacturing mechanical joint components using additive manufacturing (AM) techniques such as Material Extrusion (Fused Deposition Modelling (FDM)), Material Jetting (PolyJet), and Vat Photopolymerization (VAT)/Stereolithography (SLA). Using the PolyJet technique and a photopolymer resin, spline and threaded joint components were produced. For comparative analysis, the threaded joint was also fabricated using FDM and SLA techniques. PLA material was used for the FDM technique, while photopolymer resin was utilized for the SLA process. The components produced underwent a surface analysis to evaluate the accuracy of the dimensions in relation to the nominal dimensions. For the spline connection components, the dimensional deviations recorded by a 3D scanner ranged from -0.11 to +0.18 mm for the shaft and up to 0.24 mm for the sleeve. Measurements of screw and nut diameters showed the highest accuracy for screws produced using the PolyJet technique, while the nuts exhibited the best accuracy when fabricated with the SLA method. The profile of the screw threads using a contour gauge revealed the most accurate thread profile on the screw manufactured with the PolyJet technique.

PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks

Abstract. Effective observation of the ocean is vital for studying and assessing the state and evolution of the marine ecosystem and for evaluating the impact of human activities. However, obtaining comprehensive oceanic measurements across temporal and spatial scales and for different biogeochemical variables remains challenging. Autonomous oceanographic instruments, such as Biogeochemical (BGC)-Argo profiling floats, have helped expand our ability to obtain subsurface and deep-ocean measurements, but measuring biogeochemical variables, such as nutrient concentration, still remains more demanding and expensive than measuring physical variables. Therefore, developing methods to estimate marine biogeochemical variables from high-frequency measurements is very much needed. Current neural network (NN) models developed for this task are based on a multilayer perceptron (MLP) architecture, trained over point-wise pairs of input–output features. Although MLPs can produce smooth outputs if the inputs change smoothly, convolutional neural networks (CNNs) are inherently designed to handle profile data effectively. In this study, we present a novel one-dimensional (1D) CNN model to predict profiles leveraging the typical shape of vertical profiles of a variable as a prior constraint during training. In particular, the Predict Profiles Convolutional (PPCon) model predicts nitrate, chlorophyll, and backscattering (bbp700) starting from the date and geolocation and from temperature, salinity, and oxygen profiles. Its effectiveness is demonstrated using a robust BGC-Argo dataset collected in the Mediterranean Sea for training and validation. Results, which include quantitative metrics and visual representations, prove the capability of PPCon to produce smooth and accurate profile predictions improving upon previous MLP applications.

2-Nitro-4-Carboxyphenylhydrazine and 2,4-Dicarboxylphenylhydrazine as a Pair of Novel Reactive MALDI Matrices for Rapid and Accurate Profiling of N-Glycome in Dual Ion Modes.

N-glycosylation is closely linked to a wide range of biological functions in organisms. Owing to the constriction of awful crystals formed by conventional MALDI matrices and the extremely inferior ionization efficiency of N-glycans, the traditional direct detection of N-glycans by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has been gradually replaced by postderivatization detection using reactive matrices. Nevertheless, the laborious identification of complex spectral peaks remains the major difficulty in N-glycan profiling. Hence, we logically designed and synthesized two novel reactive matrices, 2-nitro-4-carboxylphenylhydrazine (NCPH) and 2,4-dicarboxylphenylhydrazine (DCPH), and separately combined them with the acidic matrix 2,5-dihydroxybenzoic acid (DHB) to constitute two composite matrices with high on-target derivatization efficiency and significant promotion of N-glycan ionization for productive MALDI analysis in dual ion modes. Using both composite matrices, we can actualize MALDI-MS and MS2 mass calibration in dual ion modes by postderivatization detection and fragmentation of dextrans and selectively enhance the ionization effect of oligosaccharides in mixed systems. Quite homogeneous cocrystals can ensure N-glycan quantification with decent linearity and reproducibility. A fixed mass difference derived from the identical N-glycan in two ion modes is available for rapid identification in complex biological samples. Ultimately, the developed strategy was triumphantly employed to identify and quantify the relative content and alteration tendency of peach N-glycans, which can be referable to the latent correlation between N-glycan expression and peach ripening.

Interior Profile Accuracy Assessment Method of Deep-Hole Parts Based on Servo Drive System.

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements.

Rapid species-level metagenome profiling and containment estimation with sylph.

Profiling metagenomes against databases allows for the detection and quantification of microorganisms, even at low abundances where assembly is not possible. We introduce sylph, a species-level metagenome profiler that estimates genome-to-metagenome containment average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. On the Critical Assessment of Metagenome Interpretation II (CAMI2) Marine dataset, sylph was the most accurate profiling method of seven tested. For multisample profiling, sylph took >10-fold less central processing unit time compared to Kraken2 and used 30-fold less memory. Sylph's ANI estimates provided an orthogonal signal to abundance, allowing for an ANI-based metagenome-wide association study for Parkinson disease (PD) against 289,232 genomes while confirming known butyrate-PD associations at the strain level. Sylph took <1 min and 16 GB of random-access memory to profile metagenomes against 85,205 prokaryotic and 2,917,516 viral genomes, detecting 30-fold more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate containment ANI estimation even for low-coverage genomes.

Phaseless inverse scattering with a parametrized spatial spectral volume integral equation for finite scatterers in the soft x-ray regime

Soft x-ray wafer-metrology experiments are characterized by low signal-to-noise ratios and lack phase information, which both cause difficulties with the accurate three-dimensional profiling of small geometrical features of structures on a wafer. To this end, we extend an existing phase-based inverse-scattering method to demonstrate a sub-nanometer and noise-robust reconstruction of the targets by synthetic soft x-ray scatterometry experiments. The targets are modeled as three-dimensional finite dielectric scatterers embedded in a planarly layered medium, where a scatterer’s geometry and spatial permittivity distribution are described by a uniform polygonal cross section along its height. Each cross section is continuously parametrized by its vertices and homogeneous permittivity. The combination of this parametrization of the scatterers and the employed Gabor frames ensures that the underlying linear system of the spatial spectral Maxwell solver is continuously differentiable with respect to the parameters for phaseless inverse-scattering problems. In synthetic demonstrations, we demonstrate the accurate and noise-robust reconstruction of the parameters without any regularization term. Most of the vertex parameters are retrieved with an error of less than λ/13 with λ=13.5nm, when the ideal sensor model with shot noise detects at least five photons per sensor pixel. This corresponds to a signal-to-noise ratio of 3.5 dB. These vertex parameters are retrieved with an accuracy of λ/90 when the signal-to-noise ratio is increased to 10 dB, or approximately 100 photons per pixel. The material parameters are retrieved with errors ranging from 0.05% to 5% for signal-to-noise ratios between 10 dB and 3.5 dB.

Multidimensional RF pulse design with consideration of concomitant field effects.

To develop a small-tip multidimensional RF pulse design procedure that incorporates linear time-invariant gradient imperfections and concomitant field effects. This could be particularly important for contemporary low-field MRI systems with high-performance gradients. We developed an extension of the small-tip excitation k-space formalism, where concomitant fields were approximated as a Bloch-Siegert shift in the rotating frame. This was evaluated using realistic simulations of 2D selective excitation at various field strengths (0.2T, 0.55T, 1.5T, 3T, and 7T) with single and parallel transmit. Simulated excitation profiles from the original and extended k-space formalisms were compared. Experimental validations were performed at 0.55T with a single-channel transmit. The extended formalism provides improved 2D excitation profiles in all scenarios simulated, compared against the original formalism. The proposed method corrects the concomitant field effects on 2D selective excitations for B0 > 0.2T when the magnitude of the B0 is far larger than that of nonrotating concomitant fields. Simulation and phantom experiments at 0.55T match well for both original and proposed methods, with the proposed method providing sharper and more accurate excitation profiles at off-isocenter distances up to 15 cm. The impact of the proposed method is greatest in scenarios where concomitant fields are substantial, such as low field strengths and off-isocenter. Concomitant fields can be modeled as a Bloch-Siegert shift in the rotating frame during multidimensional RF pulse design, resulting in improved excitation profiles with sharp edges. This is important to consider for off-isocenter excitations and imaging at low field strengths with strong gradients.