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The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton's method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom's method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness.
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http://dx.doi.org/10.3390/s23198193 | DOI Listing |
Traffic Inj Prev
September 2025
Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin.
Objective: Assessment of submarining occurrence in PMHS (Post-Mortem Human Subject) testing can be challenging, particularly for obese PMHS. This study investigates varied kinetic and kinematic response parameters as potential indicators of submarining. Data from 36 whole-body PMHS frontal sled tests conducted under varying boundary conditions were analyzed, incorporating three spring-controlled seat configurations, two extreme anthropometric profiles, two crash pulses, and two seatback angles.
View Article and Find Full Text PDFPLoS One
September 2025
Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.
Polar protic and aprotic solvents can effectively simulate the maturation of breast carcinoma cells. Herein, the influence of polar protic solvents (water and ethanol) and aprotic solvents (acetone and DMSO) on the properties of 3-(dimethylaminomethyl)-5-nitroindole (DAMNI) was investigated using density functional theory (DFT) computations. Thermodynamic parameters retrieved from the vibrational analysis indicated that the DAMNI's entropy, heat capacity, and enthalpy increased with rising temperature.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
California Institute of Technology, TAPIR, Division of Physics, Mathematics, and Astronomy, Pasadena, California 91125, USA.
In the gravitational-wave analysis of pulsar-timing-array datasets, parameter estimation is usually performed using Markov chain Monte Carlo methods to explore posterior probability densities. We introduce an alternative procedure that instead relies on stochastic gradient-descent Bayesian variational inference, whereby we obtain the weights of a neural-network-based approximation of the posterior by minimizing the Kullback-Leibler divergence of the approximation from the exact posterior. This technique is distinct from simulation-based inference with normalizing flows since we train the network for a single dataset, rather than the population of all possible datasets, and we require the computation of the data likelihood and its gradient.
View Article and Find Full Text PDFJ Appl Lab Med
September 2025
Department of Pathology, Moffitt Cancer Center, Tampa, FL, United States.
Background: Clonal plasma cell disorders, such as multiple myeloma (MM), often cause excretion of monoclonal free light chains (MFLC) into urine that serve as diagnostic markers and can cause renal injury.
Content: Measures of urinary protein excretion (PEx) and MFLC excretion are parameters for diagnosing and managing plasma cell disorders, although the roles are evolving as new diagnostic tools are applied. Current guidelines dictate measuring PEx and MFLC excretion using 24-hour urine specimens, which have multiple shortcomings that compromise the quality of testing, delay results, and are burdensome for patients.
Cereb Cortex
August 2025
Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
Over three decades, statistical parametric mapping has transformed neuroimaging from descriptive mapping to causal inference, placing generative models at the core of causal explanations for brain function. It inspired to a large degree The Virtual Brain, which builds subject-specific digital twins from multimodal data, enabling brain simulations and exploration. Both frameworks converge at parameter estimation, where model and data meet, providing the mathematical manifestation of cause-effect in pathophysiology.
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