Human induced pluripotent stem cells (hiPSC) are an invaluable resource for investigating the molecular mechanisms regulating cell fate specification during brain development. However, most directed differentiation methods exhibit significant cell fate heterogeneity and require several months to become functional. To address this challenge, we developed a green fluorescent protein (GFP) reporter system in hiPSC by targeting the genomic locus of Forebrain Enriched Zinc Finger 2 (FEZF2), which encodes a transcription factor essential for the fate specification of sub-cerebral projection neurons (SCPN) during forebrain development. Using this FEZF2-GFP reporter hiPSC line, we optimized a directed differentiation protocol to rapidly and efficiently generate pallial progenitors and glutamatergic neuronal subgroups after 3 weeks. Through fluorescence activated cell sorting for both GFP and CD200, isolated post-mitotic SCPN immediately displayed electrophysiological properties and formed glutamatergic synapses within 4 additional weeks of in vitro cell culture. Co-culture with hiPSC-derived spinal motor neurons further enhanced these electrophysiological characteristics, improved viability, and increased synapse formation in SCPN. This study presents a streamlined and effective strategy to generate, isolate, and characterize human motor neuron circuits, providing insights into the molecular determinants regulating synaptogenesis and functional maturation.

This paper proposes an energy-dissipative scheme for solving two- and three-dimensional time-fractional Navier-Stokes equations. The numerical scheme is constructed, using nonuniform $L2−1σ$ approximation in the temporal direction and the Fourier spectral method in the spatial direction. It is shown that the numerical scheme can keep discrete energy stable and the numerical solutions are uniformly bounded without any restriction on step sizes. Error estimates of the fully-discrete scheme are presented. Moreover, a fast algorithm is applied to accelerate the computation. Numerical results in long time intervals are presented to confirm the effectiveness and high efficiency of the scheme.

The aim of the present work is to investigate the efficiency of spectral element method for the numerical solution of the two-dimensional distributed-order fractional cable equation on regular and irregular domains. To this end, we first employ Gauss-Lobatto-Legendre quadrature to approximate the distributed-order integrals. The equation is then transformed into a multi-term fractional equation. Afterward, we use the finite difference method in the time direction and obtain a semi-discrete scheme of order O ( τ 2 ) . We prove that the semi-discrete method is unconditionally stable using the mathematical induction. Then we make the fully discrete scheme using the spectral element method in spatial directions and obtain an error bound for the proposed method. Finally, to show the versatility and applicability of the method, we implement it on both regular and irregular domains. The results of the proposed method are compared with other well-known methods in the literature, tested on both regular and irregular convex domains. Furthermore, we demonstrate the method's efficiency and high accuracy on non-convex domains in which has not been addressed in prior works.

Abstract The primary goal of this paper was to determine if there is a difference in the intensity of abrasive wear on the radial and tangential sections of sessile oak ( Quercus petraea ) in the direction that matches or opposes the primary growth direction of the tree. The resistance to abrasion was tested on the samples using the standard 'dry sand–rubber wheel' method in both directions and on both sections. Owing to the heterogeneity of the wood structure, the abraded mass was recalculated to the abraded volume. It was found that changing the direction of abrasion relative to the primary growth direction significantly affects the abraded volume on both sections, particularly on the radial section. Abrasion was more intense when the direction of wear opposed the primary growth direction in both sections. The smallest abraded volume was measured on the radial section in the primary growth direction, and the largest was measured on the same section in the opposite direction. The distribution of results was analyzed using the Weibull distribution, with less dispersion observed in the radial section compared to the tangential section in both wear directions. The total volume loss from abrasive wear in both directions was only 3.7% greater in the tangential section compared to the radial section. These results are attributed to the complex and highly oriented microstructure of oak wood, but still not fully understood.

This paper presents a robust method for Direction of Arrival (DOA) estimation using Sparse Fourier Orthogonal Coding (FOC). High-precision DOA estimation in radar, sonar, and wireless systems often suffers from noise, interference, and limited resolution. To overcome these issues, this study proposes a framework that integrates Sparse FOC, Hermitian Propagator, and Sparse Toeplitz Covariance Matrix Projection, forming the Sparse Hermitian Propagator for DOA Estimation (SHP-DE). The proposed method improves estimation by decomposing received signals into orthogonal components, improving feature extraction and information diversity. The Hermitian Propagator eliminates the need for eigenvalue or singular value decomposition, reducing computational complexity without sacrificing accuracy. The Toeplitz Covariance Projection further refines the covariance structure, enhancing noise suppression and estimation stability. Simulation results demonstrate that SHP-DE achieves superior resolution and robustness, outperforming traditional algorithms, such as MUSIC and ESPRIT, in various noisy and closely spaced source scenarios. The ability of the proposed method to maintain performance under challenging conditions marks a significant step toward practical real-time DOA estimation. Thus, SHP-DE is well-suited for applications demanding reliable and accurate localization of signal sources in complex environments.

Abstract Due to a high specific impulse, electric propulsion systems are equalled with by an increasing number of geostationary (GEO) satellites. During the on-orbit operation of the satellite, the errors of direction of the thruster, center of mass and corresponding inertial matrix results in the saturation of angular momentum. In order to reduce the load of the angular momentum, a joint identification method with respect to the thrust direction, center of mass and the inertial matrix is developed. First, a framework for multi-parameter joint identification is constructed based on a multi-level series least square approach. Measurement methods for multiple parameters are developed using recursive least squares filter. Then, identification methods for direction of the thruster, center of mass and inertial matrix of GEO satellite are developed. The thrust direction, center of mass and the inertial matrix are identified and corrected on-orbit successively. Numerical simulation shows that the joint identification method achieves high identification accuracies. The error of the thrust direction is less than 0.1°, the center of mass is less than 0.2 mm, the relative error of principal inertia is less than 0.2%, and the relative error of inertia product is less than 1.5%.

This study investigates the residual stress patterns of welded box-section members constructed from high-strength steel (HSS). A finite element method (FEM) model developed in ANSYS is validated using experimental data from previous studies. Additionally, experimental data are directly utilized in the analysis to reinforce and contextualize numerical outcomes. A comprehensive parametric analysis explores the impact of plate thickness, width-to-thickness ratio, steel strength, welding sequence, and welding conditions on residual stress distributions. The results reveal that tensile residual stresses near weld regions consistently reach 82.6–97.8% of the yield strength and primarily depend on steel strength, with minimal sensitivity to section dimensions. In contrast, compressive residual stresses in mid-panel regions decrease by up to 72.2% with an increase in width-to-thickness ratio from 3.0 to 23.0, and the reduction rate is influenced by plate thickness. Additionally, welding sequences significantly affect residual stress magnitudes without altering their general distribution patterns. Diagonal welding method in the same direction effectively reduces mid-panel compressive stresses by up to 17.0%, and butt welds generate approximately 48.3% lower residual stresses than fillet welds. A residual stress distribution model for HSS welded box sections is developed. The model shows good agreement with experimental data with average deviation within 9.5% and can serve as a simplified yet reliable input for structural design, safety assessment, and advanced finite element modeling of welded steel members.

Abstract We propose a Bayesian approach to Local Projections (LPs) that optimally addresses the empirical bias-variance trade-off intrinsic in the choice between direct and iterative methods. Bayesian Local Projections (BLPs) regularize LP regressions via informative priors and estimate impulse response functions that capture the properties of the data more accurately than iterative VARs. BLPs preserve the flexibility of LPs while retaining a degree of estimation uncertainty comparable to Bayesian VARs with standard macroeconomic priors. As regularized direct forecasts, BLPs are also a valuable alternative to BVARs for multivariate out-of-sample projections.

Abstract Accurate and robust positioning systems are crucial for vehicular applications. In order to improve positioning performance in complex urban environments, such as urban canyons and dense forests, we propose a global navigation satellite system (GNSS) and inertial navigation system (INS) integrated approach based on weighted factor graph optimization with dynamic performance evaluation. This approach is designed to enhance the estimation of vehicle states, including position, velocity, and attitude. First, we introduce a dynamic performance evaluation framework to assess the observation quality of GNSS pseudorange measurements. Specifically, we use Fisher information and Kullback-Leibler divergence to comprehensively evaluate the observation performance of pseudorange measurements and the actual information gain that pseudorange measurements contribute to constraining the uncertainty of the system state. Finally, considering the different observational qualities of all pseudorange information, we use information weight assignment to handle the measurement accuracy of all pseudorange information, and then use weighted factor graph optimization with a sliding window for fusion estimation of GNSS and INS to achieve robust vehicle positioning. Simulation experiment and real scenario experiment demonstrate that our proposed method can provide more reliable positioning in complex urban environment. Compared with the existing adaptive weighted factor graph optimization (AFG) throughout the experiment, the overall positioning accuracy of the proposed method in the three-axis direction is improved by 13.6%. This shows that the proposed method can be widely applied to robust vehicle positioning.

The present paper proposes a three-dimensional (3D) spherical shell model for the magneto-electro-elastic (MEE) free vibration analysis of simply supported multilayered smart shells. A mixed curvilinear orthogonal reference system is used to write the unified 3D governing equations for cylinders, cylindrical panels and spherical shells. The closed-form solution of the problem is performed considering Navier harmonic forms in the in-plane directions and the exponential matrix method in the thickness direction. A layerwise approach is possible, considering the interlaminar continuity conditions for displacements, electric and magnetic potentials, transverse shear/normal stresses, transverse normal magnetic induction and transverse normal electric displacement. Some preliminary cases are proposed to validate the present 3D MEE free vibration model for several curvatures, materials, thickness values and vibration modes. Then, new benchmarks are proposed in order to discuss possible effects in multilayered MEE curved smart structures. In the new benchmarks, first, three circular frequencies for several half-wave number couples and for different thickness ratios are proposed. Thickness vibration modes are shown in terms of displacements, stresses, electric displacement and magnetic induction along the thickness direction. These new benchmarks are useful to understand the free vibration behavior of MEE curved smart structures, and they can be used as reference for researchers interested in the development of of 2D/3D MEE models.

This paper presents an analysis of control strategies applied to a deterministic compartmental model for conjunctivitis (viral/bacterial eye flu). The proposed model is novel in the way that it takes into account both the direct (human-to-human) and the indirect (environment-to-human) transmission of the disease by incorporating infectious density-induced force of infection and saturated incidence rate function, respectively. Alongside examining essential qualitative characteristics of the model like positivity and boundedness, we also obtain the basic reproductive threshold, [Formula: see text] and analyze the effect of various crucial parameters on [Formula: see text] using normalized sensitivity indices. The existence and stability (local and global) of conjunctivitis-free and endemic equilibrium are also discussed. Further, two time-dependent measures, prevention (to reduce direct transmission) and hygiene compliance (to reduce indirect transmission), are introduced as control within the model to formulate an optimal control problem (OCP). The existence and characterization of optimal controls are proved and the OCP is solved using the fourth-order Runge–Kutta method in forward and backward directions. The comparative study suggests that while the application of individual control measures is effective and complements each other, however, the most cost-efficient approach to rapidly reduce eye flu infection is the simultaneous implementation of both control measures. This paper provides valuable insights that can be useful in crafting precise and impactful synergy for managing conjunctivitis and disseminating awareness accordingly.

Inferring Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology. Most existing methods fail to consider the skewed degree distribution of genes, complicating the application of directed graph embedding methods. The Cross-Attention Complex Dual Graph Embedding Model (XATGRN) was proposed to address this issue. It employs a cross-attention mechanism and a dual complex graph embedding approach to manage the skewed degree distribution, ensuring precise prediction of regulatory relationships and their directionality. The model consistently outperforms existing state-of-the-art methods across various datasets. XATGRN provides an effective solution for inferring GRNs with skewed degree distribution, enhancing the understanding of complex gene regulatory mechanisms. The codes and detailed requirements have been released on Github: ( https://github.com/kikixiong/XATGRN ).

Abstract. Wind speed and direction are spatial variables that vary over both time and space. These variables are crucial for urban and spatial planning, agriculture and crop management, sports activity planning, aerial navigation, air pollution modeling and fire management. This paper investigates the effectiveness of several interpolation methods for predicting wind speed and direction at unknown locations, using measurements from a network of weather stations. Four well-established methods were considered: Natural Neighbor, Inverse Distance Weighting (IDW), Kriging, and Ordinary Kriging.Data were collected from 28 weather stations distributed across Split and Dalmatia County. In two experiments, the four unknown stations were chosen to represent: 1) a station spatially surrounded by known measurements, and 2) stations representing typical geographical challenges, such as land, coast, canyon, and island locations. For each experiment, scenario, and interpolation method, we calculated and analyzed the Root Mean Squared Error (RMSE), Mean Absolute Error in the u-direction (MAE u), and Mean Absolute Error in the v-direction (MAE v). The analysis revealed that the highest errors occurred during Bora wind conditions. Among the methods, Ordinary Kriging demonstrated the lowest prediction error.

ABSTRACTA space‐time spectral method combined with mollification method is proposed for the inverse coefficient problem of the nonlinear Klein–Gordon equation. The spectral scheme is utilized to reconstruct an unknown time‐dependent coefficient and wave displacement in a nonlinear Klein–Gordon equation. We apply the Legendre–Galerkin method in spatial direction and the Legendre–Petrov–Galerkin method in temporal direction. We calculate the nonlinear term with the pseudospectral treatment by using Chebyshev‐Gauss‐Lobatto interpolation, which is efficiently computed via the fast Legendre transform. For the perturbed measurements, we apply the appropriate mollification method to obtain stable numerical differentiation and smooth boundary data. Using rigorous error estimates, we establish the convergence and stability of the iterative solution for the fully‐discrete algorithm. Especially, we also present, for the first time, the convergence and stability analysis of the iterative solution that combines spectral methods with regularization techniques. Numerical results show the efficiency and stability of this approach and agree well with the theoretical analysis.

Gastrointestinal parasitism in swine production is a world-wide problem especially in tropical resource-poor countries. These infections in animals result in significant economic losses. This study aimed to assess the prevalence and spectrum of gastrointestinal parasites of pigs reared in two research farms in Michael Okpara University of Agriculture, Umudike, South eastern Nigeria. From April, 2016 to July, 2016, 220 samples of pig faeces from two research farms (CASAP Research Farm and MOUAU Commercial Pig Farm) were analyzed using floatation and direct smear methods to identify varied parasitic stages present in the faeces. Data generated showed an overall prevalence of 64.6% (142/220) in the two farms. Five parasite species made up of four Nematode and one Cestode were observed namely Ascaris suum (26.4%), Trichuris suis (26.4%), Strongyloides spp (21.9%), Oesophagostomum dentatum (20.0%) and Pseudanoplocephala spp. (5.6%). Mixed infections were also observed. Those within the ages of 0-8months had the highest prevalence (74.12%). Further, 67.42% male pigs and 62.60% of female pigs were found to be infected with one or the other endoparasite revealing a high prevalence of parasitic problems within Umudike. It is recommended that pigs should be treated regularly to prevent or reduce infection to the barest minimal level.

A method for joint Direction of Arrival (DOA) and frequency estimation based on the Direct Data Domain (DDD-JDFE) is proposed. This algorithm, designed to work within an array antenna reception system, utilizes space-time adaptive processing techniques to acquire short-term samples through both spatial and temporal smoothing. It employs a broadband array direction-finding system to simultaneously determine the frequency and arrival direction of targets. Compared to traditional spatial-domain array signal processing methods, this approach provides improved direction-finding performance, particularly at low signal-to-noise ratios. Meanwhile, it shows great potential for long-distance signal detection, and simulation results have verified the effectiveness of the method.

This paper proposes a novel method for Direction of Arrival (DOA) estimation using a deep unfolded LISTA network in a non-uniform metasurface. Traditional DOA estimation methods often face challenges such as limited accuracy, high computational complexity, and poor adaptability to complex signal environments. To address these issues, we optimize a non-uniform metasurface array to reduce hardware costs and mutual coupling effects while enhancing resolution. Additionally, a deep unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) network is constructed by transforming Iterative Shrinkage Thresholding Algorithm (ISTA) iterative steps into trainable neural network layers, combining model-driven logic with data-driven parameter optimization. Simulation results prove that this method enhances higher precision and reduces computational complexity in comparison with traditional algorithms, especially under low SNR conditions. Furthermore, the method exhibits greater generalization ability, making it a reliable approach for high-precision DOA estimation in practical applications.

A simple, accurate and sensitive spectrophotometric method for the determinaion of epinephrine is described . The method is based on the coordination of Pr (III) with epinephrine at pH 6. Absorbance of the resulting orange yellow complex is measured at 482 nm . A graph of absorbance versus concentrations shows that beer 's low is obeyed over the concentration range (1-50)mg.ml-1 of epinephrine with molar absorpitivity of ( 2.180x103 L.mol-1.cm-1 ), a sandell sensitivity of (0.084 mg.cm-2 ), a relative error of (-2.83%) , a corrolation coffecient (r= 0.9989) and recovery % ( 97.03 ± 0.75 ) depending on the concentration.This method is applied to analyse EP in several commercially available pharmaceutical preparations using direct methods .All statistical calculations are implemented via a Minitab software version 11.

Background: Triple-Negative Breast Cancer (TNBC) presents a significant challenge due to its aggressive nature and lack of responsive hormone receptors, predominantly affecting younger premenopausal women. Ethyl ferulate (EF), a notable phytochemical, has demonstrated promising anti-cancer properties. This study aimed to enhance the efficacy of EF by synthesizing and characterizing ethyl ferulate gold nanoparticles (EF-AuNps) to passively target TNBC cells via the enhanced permeability and retention (EPR) effect. Methods: We synthesized EF-AuNps using a direct reduction method and characterized the NPs by employing various techniques, including UV-visible spectroscopy, DLS, XRD, EDX, TEM, and FT-IR. The anti-proliferative activity against MDA-MB-231 cells was assessed using MTT and colony formation assays, alongside evaluating cell viability with PI-FACS and live/dead assays. Furthermore, a Western blot was performed to determine the mechanism of action of EFAuNps in TNBC cells. Result: We successfully synthesized triangular EF-AuNps (< 100nm) and observed a substantial inhibition of cell proliferation (IC50 18μg/ml). Compared to EF alone, EF-AuNps significantly enhanced cell death in TNBC cells, as confirmed by flow cytometry and viability assays. Besides, Western blot analysis verified that the expression of apoptotic-related signal proteins, such as survivin, caspase 3, and caspase 9, were modulated by EF-AuNps. Conclusion: EF-AuNps showed higher anti-cancer efficacy than EF in the MDA-MB-231 cell line. These findings suggest the therapeutic potential of EF-AuNps for TNBC treatment, advocating for further preclinical and clinical investigations into this promising anti-cancer formulation.

Recently, the tensor nuclear norm (TNN)-based tensor robust principle component analysis (TRPCA) has achieved impressive performance in multidimensional data processing. The underlying assumption in TNN is the low-rankness of frontal slices of the tensor in the transformed domain (e.g., Fourier domain). However, the low-rankness assumption is usually violative for real-world multidimensional data (e.g., video and image) due to their intrinsically nonlinear structure. How to effectively and efficiently exploit the intrinsic structure of multidimensional data remains a challenge. In this article, we first suggest a kernelized TNN (KTNN) by leveraging the nonlinear kernel mapping in the transform domain, which faithfully captures the intrinsic structure (i.e., implicit low-rankness) of multidimensional data and is computed at a lower cost by introducing kernel trick. Armed with KTNN, we propose a tensor robust kernel PCA (TRKPCA) model for handling multidimensional data, which decomposes the observed tensor into an implicit low-rank component and a sparse component. To tackle the nonlinear and nonconvex model, we develop an efficient alternating direction method of multipliers (ADMM)-based algorithm. Extensive experiments on real-world applications collectively verify that TRKPCA achieves superiority over the state-of-the-art RPCA methods.