Search Results (3,537)

This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of $-21$ dB and signal-to-noise ratio (SNR) of $-23$ dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article

This paper studies nonlinear time-fractional reaction–diffusion equations with Caputo memory and non-local integral boundary conditions on a bounded interval. The aim is to formulate a boundary-compatible well-posedness framework and to construct a high-order temporal approximation that can be coupled with a constraint-preserving spectral spatial discretization. The analytical part proves boundedness of the non-local boundary functionals, states compatibility assumptions, and introduces a finite-dimensional nondegeneracy condition for an explicit polynomial lifting. Under a sectorial non-local elliptic realization and a global Lipschitz reaction term, existence, uniqueness, stability, and continuous dependence of mild solutions are obtained by fractional resolvent estimates and fractional Gronwall inequalities. The main novelty is the combined construction of an explicit polynomial lifting for integral boundary constraints, a constraint-preserving Legendre–Galerkin basis, and a high-order Beta-window temporal quadrature together with a discrete stability condition that accounts for sign-changing weights. The numerical evidence shows high-order behavior for smooth Caputo benchmarks, accurate enforcement of the non-local boundary constraints, and improved accuracy over the classical $L1$ approximation in the reported tests. The stability discussion identifies the discrete coercivity condition required for the sign-changing Beta-window weights. Full article

This paper proposes the Continuous Operational Feedback Loop (COFL) architecture, a fully localized, event-driven operational monitoring and response system for Direct-to-Consumer (D2C) commerce. The architecture integrates the n8n workflow engine with on-premise large language model (LLM) inference via the Ollama framework, forming a containerized stack deployable on commodity CPU-only edge hardware (~USD 1640). Using a multi-source dataset of 1800 records constructed from publicly available e-commerce corpora and evaluated with a silver-standard automated labeling protocol, empirical validation demonstrates an end-to-end latency of 3.22 s and a macro-F1 sentiment classification score of 0.836—representing 98.2% of the full-precision baseline and 94.0% of cloud GPT-4o API generation quality measured by ROUGE-L—at approximately 1/200th of the per-request inference cost. A systematic quantization ablation study across six model-quantization configurations establishes LLaMA 3 8B Q4_K_M as the Pareto-optimal selection for the target hardware. An Analytic Hierarchy Process (AHP) multi-criteria framework with criterion weights derived from published literature confirms the COFL implementation achieves a higher composite score than cloud API deployment under the stated evaluation assumptions. Failure mode and effects analysis (FMEA) is summarized to characterize system reliability under identified failure scenarios. Full article

Background/Objectives: Mass spectrometry-based newborn screening for small-molecule biomarkers typically employs a rapid first-tier screen that omits chromatographic separations before mass spectrometric analysis, followed, only for a subset of samples and disorders, by a longer, more specific second-tier assay that includes liquid chromatographic separation prior to mass spectrometry. The second-tier screen is used when the primary biomarker lacks sufficient specificity and may result in higher false-positive rates. The throughput and specificity of first-tier newborn screening assays have been relatively stagnant over the past two decades despite significant improvements in mass spectrometry instrumentation. With the continuous expansion of disorders added to the Recommended Uniform Screening Panel in the United States, newborn screening laboratories have a need for higher-throughput assays and improved specificity. Methods: We developed and evaluated two first-tier tandem mass spectrometry approaches using a modern dual-needle, dual-loop LC-MS/MS platform: (1) a 30-s flow injection analysis tandem mass spectrometry (FIA-MS/MS) assay and (2) a rapid first-tier liquid chromatography tandem mass spectrometry (LC-MS/MS) assay using a hydrophilic interaction chromatography (HILIC) guard column (1TH). Analytical performance was assessed using dried blood spot quality control and linearity materials, including evaluations of recovery, precision, linearity, and matrix effects. Results: The 30-s FIA-MS/MS assay quadrupled the throughput of current 2-min FIA-MS/MS assays used routinely in newborn screening laboratories. The throughput improvement was achievable due to increased scan speeds of the mass spectrometer as well as the dual needle/loop design of the autosampler. In addition, these instrumentation improvements made it possible to employ liquid chromatographic separations prior to MS/MS analysis without sacrificing the approximately 2-min sample-to-sample throughput of conventional FIA-MS/MS workflows. The 1TH LC-MS/MS method separated critical isobaric and isomeric biomarkers, reduced matrix effects, improved specificity and quantification accuracy, and demonstrated acceptable recovery, precision, and linearity for newborn screening applications. Conclusions: Recent advances in LC-MS/MS instrumentation can be leveraged to either substantially increase first-tier newborn screening throughput or improve analytical specificity while maintaining current workflow timelines. First-tier LC-MS/MS using a HILIC guard column provides improved specificity that can reduce the need for second-tier testing, thereby improving overall throughput and turnaround time of the newborn screening workflow. These approaches provide flexible solutions for newborn screening laboratories seeking to accommodate expanding screening panels without compromising analytical quality or efficiency. Full article

The present study investigates the semantic structure, dominant themes, and temporal evolution of research on giftedness in early childhood through a comparative topic modeling approach. A final analytic sample (n = 518) of peer-reviewed journal articles indexed in the Scopus and Web of Science databases was analyzed. Three topic modeling methods, Latent Dirichlet Allocation (LDA), Structural Topic Modeling (STM), and BERTopic, were systematically compared using multiple evaluation metrics. BERTopic demonstrated the strongest overall performance, producing approximately 11% higher coherence than STM and approximately 34% higher coherence than LDA. In terms of diversity, it achieved 14% to 17% greater thematic variety and, according to the Gini coefficient, revealed a 58% to 60% more balanced thematic distribution. BERTopic-based analyses identified five major thematic axes: Socio-Linguistic Development and Family Context, Psychometric Intelligence, Identification, and Cognitive Differences, Program Access, Identification, and Educational Equity, Early Academic Skills and Cognitive Development, and Creativity, Higher-Order Thinking, and Enrichment Programs. Thematic mapping and topic similarity analysis were used to examine the semantic structure of the field, while linear regression-based trend analysis over the 1918–2026 publication period showed that family context, socio-linguistic development, and equity-related themes have gained increasing importance over time, whereas psychometric identification largely maintained its central position within the field. These findings indicate that the field is moving toward a more inclusive, semantically grounded, and equity-oriented perspective. However, they should be interpreted in light of the study’s reliance on article abstracts, the sensitivity of BERTopic clustering parameters, and the use of linear trend modeling. Full article

This work examines how well a Negative Derivative Feedback (NDF) controller suppresses vibration in a nonlinear electromechanical oscillator that is subjected to mixed excitations. Coupled nonlinear ordinary differential equations are used to model the system and show how mechanical and electrical components interact. The method of multiple scales (MMS) is used to develop analytical approximate solutions up to the second order, specifically for the primary resonance scenario. This study’s main contribution is a thorough bifurcation analysis and proof of the NDF controller’s high efficacy, which effectively lowers the first and second mode resonance amplitudes by roughly $99.8\%$ and $98\%$., respectively, with impressive reported effectiveness values of roughly 590 and 51.5. Additionally, the quantitative error analysis between the numerical simulation and the analytical approximation solution demonstrates a high degree of agreement, with a maximum error of less than ${10}^{-5}\%$ for the second mode and just $0.01\%$ for the first mode. Furthermore, we present the impact of parameters on FRCs. Frequency response curves (FRCs) are used in a thorough comparison analysis to assess the behavior of the system both before and after the controller is activated. A strong degree of connection between the analytical conclusions and numerical simulations carried out using the “fourth-order Runge–Kutta method” rigorously validates the accuracy of the perturbation analysis. Additionally, a performance benchmark between different control techniques, such as the NDF controller, Positive Position Feedback (PPF), and Linear Negative Position Feedback (LNPF), is shown in the paper. When compared to alternative approaches, the NDF controller shows the greatest reduction in oscillation amplitudes and higher robustness, as shown by transient response analysis (time history) at various time intervals. The outcomes validate the NDF approach’s dependability and efficiency in stabilizing intricate nonlinear electromechanical systems. The chaotic response and system periodicity were demonstrated through bifurcation diagrams and Poincaré maps. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

Insulated concrete sandwich panels (ICSPs) are widely utilized in modern building structures due to their excellent combination of energy efficiency and structural load-bearing capacity. However, compared to their mechanical and thermal properties, the sound insulation characteristics of ICSPs remain insufficiently studied, presenting a scientific deficit. In practical engineering, insufficient consideration of these acoustic properties—particularly the “acoustic bridging” induced by connectors—often leads to unpredictable noise transmission, making it difficult for building envelopes to meet stringent modern acoustic codes. To further investigate their acoustic characteristics, this paper extends existing theories on infinite periodic ICSPs to study the airborne sound insulation performance of finite-sized ICSPs. First, analytical models for ICSPs under simply supported on all edges (SS) and clamped on all edges (CC) boundary conditions are derived, wherein the connectors are equivalently modeled as elastic media and discrete elastic springs, respectively. Subsequently, the accuracy and applicability of the analytical models are verified through finite element (FE) models and an airborne sound insulation experiment. Finally, based on the analytical models, a parametric study is conducted to explore the effects of the stiffness of connectors, boundary conditions, and the thickness of the core layer on the sound insulation performance of the ICSPs. The results indicate that connector stiffness has a non-monotonic influence on the sound insulation performance of ICSPs. As the connector stiffness increases, the R_w first decreases and then increases, and the sound insulation performance gradually stabilizes when the connector stiffness becomes sufficiently high. Boundary conditions have a significant effect on the acoustic response. For the reference ICSPs, changing the boundary condition from SS to CC increases the R_w from 49 dB to 62 dB, corresponding to an increment of 13 dB and an approximately 95.0% reduction in the equivalent sound transmission coefficient. When the total panel thickness is kept constant, reducing the core layer thickness from 80 mm to 40 mm increases the R_w from 49 dB to 55 dB under SS boundary conditions and from 62 dB to 66 dB under CC boundary conditions, corresponding to increments of 6 dB and 4 dB, respectively. These improvements are equivalent to reductions of approximately 74.9% and 60.2% in the sound transmission coefficient, though this must be weighed against the inevitable reduction in thermal insulation capacity. Although the sound insulation performance of ICSPs is inferior to that of solid concrete panels (SCPs) of equivalent thickness, with reasonable parameter optimization, their sound insulation indices can significantly exceed the latest requirements of current building codes. By fully accounting for boundary effects in practical engineering, this study provides an analytical basis for the acoustic performance prediction and engineering-oriented optimization of finite-sized ICSPs. Full article

Stevia (Stevia rebaudiana Bertoni) is an important source of natural sweeteners. Since its commercial value depends on steviol glycosides, quality assessment primarily involves quantifying these compounds in leaves and shoots. While chromatography is the standard analytical method, it is labor-intensive and time-consuming; it involves multiple processing steps that may cumulatively introduce errors and remains relatively expensive. Although chromatography remains the most accurate method, this exploratory study evaluates the potential of using spectroscopy as an auxiliary method for the approximate assessment of steviol glycoside content. Leaf reflectance spectroscopy could be a simpler and more cost-effective approach. However, relationships between leaf reflectance and steviol glycoside content are indirect and mediated by physiological processes. To account for these indirect dependencies, cumulative UVB exposure was included as an additional feature because it influences both leaf optical properties and plant metabolic processes. A low-cost spectrometer was utilized as the measuring instrument. The study was conducted over a period of three months on 77 S. rebaudiana clones, divided into four groups based on their level of UVB irradiance (control without irradiation, 400, 600, and 800 μW m⁻²). Based on the collected data, linear and polynomial regression, Random Forest, XGBoost, PLSR, and ElasticNetCV models were trained. Cumulative UVB exposure was found to be the most important feature. Of the spectral features, the most informative for assessing the content of steviol glycosides were spectral indicators in the far-red and near-infrared (NIR) ranges. Our results indicate a detectable relationship, with Random Forest being the best-performing model and achieving a moderate predictive performance (R² = 0.66). Despite their limited predictive performance, the models demonstrate that leaf reflectance spectra combined with cumulative UVB exposure contain information related to steviol glycoside content. These findings support further investigation of remote sensing approaches for crop quality assessment. Full article

Excessive concentrations of carbon dioxide (CO₂) in the atmosphere lead to adverse environmental effects. Biologically assisted processes that rely on organisms such as microalgae (i.e., Chlorella vulgaris) are common in capturing CO₂ from the atmosphere. Microalgae are rich in proteins, vitamins, minerals, and omega-3 fatty acids. Thus, microalgae production serves both health and environmental sectors. Varying light intensity and temperature are shown to influence algae growth. To quantify algae production under different light intensity and temperature conditions, and monitoring or scaling-up of biological reactors, reliable mathematical models are required. In this work, mathematical models that incorporate light intensity and temperature effects on algae growth in batch and continuous bioreactors are developed. Based on the modeling, the growth rate is maximum at T_opt = 25 °C, reaching the value of μ_max = 0.14 day⁻¹. The growth rate exponentially increases until light intensity (I) reaches around 150 ${\displaystyle \frac{\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l}}{{\mathrm{m}}^2\mathrm{s}}}$, which is approximately the optimal light intensity for Chlorella vulgaris. The effect of T on growth rate is found to be more sensitive than light intensity (I) in both batch and continuous reactor systems. When there are too many parameters in models, uncertainties exist and parameter estimation and model predictions become cumbersome. For these reasons analytical solutions to the models are presented in simplified forms and these models are more practical and easier to implement. The novelty of the work is also the presentation of the models in analytical forms. Analytical solutions to the two reactor models (batch and continuous) will help quantify biomass production as a function of time under the varying light intensity and temperature conditions encountered. Full article

Surface erosion of loess slopes in arid and semi-arid regions of China remains a critical geotechnical issue, requiring green and low-carbon stabilization techniques. This study investigated the effectiveness of enzyme-induced carbonate precipitation (EICP) for the surface spraying reinforcement of Q3 loess collected from a high-fill engineering site at Yan’an University. Single-factor tests, response surface methodology (RSM), surface strength tests, CT-based three-dimensional pore reconstruction, and scanning electron microscopy (SEM) were conducted to evaluate the effects of cementation solution concentration and spraying dosage. The cementation solution was prepared by mixing analytical-grade urea and anhydrous calcium chloride at a 1:1 molar ratio, and the specimens were compacted to a dry density of 1.4 g/cm³. The results showed that surface strength first increased and then decreased with increasing cementation solution concentration and spraying dosage. Spraying dosage had a more pronounced influence than cementation solution concentration; excessive spraying above 9 L/m² reduced surface strength because of the high water sensitivity of loess. Five replicate tests at the central point were conducted to evaluate experimental error. The optimal parameters were 1.5 mol/L for cementation solution concentration and 9 L/m² for spraying dosage. CT and SEM results showed that CaCO₃ precipitation filled large pores and cemented soil particles, reducing total porosity from 6.7% to approximately 4.0%. These findings indicate that EICP improves loess surface strength mainly through pore filling and particle cementation, providing guidance for the ecological protection of loess slopes. Full article

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

Estimation of population proportion is a significant problem in survey sampling and has wide application in social sciences, economics, agriculture, medicine, and public health. The accuracy of estimators can be significantly improved by effectively using auxiliary information. This study proposes an improved generalized class of estimators for estimating the population proportion using two auxiliary attributes. First-order approximation of the mathematical property is obtained for the proposed class, including the expressions for the bias and mean square error (MSE). Theoretical comparisons are made with the traditional sample proportion estimator and some existing estimators that are available in the literature. Analytical conditions under which the proposed generalized class performs better than the other estimators are also determined. In order to analyze the practical performance of the proposed methodology, numerical and simulation studies are carried out on the real and artificially generated population. The results of the experiments confirm that the proposed generalized class consistently yields lower MSE and higher PRE than the traditional estimators. It is concluded that the proposed generalized class is a reliable and efficient alternative to the population proportion estimation for practical survey sampling applications having appropriate auxiliary attributes. Full article

This paper presents a time-domain analysis of an N-bit pulse-width-modulated digital-to-analog converter (PWM DAC) employing a first-order passive resistor-capacitor (RC) filter. Exact analytical expressions are derived for the output settling time in both rising and decreasing digital-code transition modes. The worst-case condition is identified, and the settling-time criterion is expressed as a function of the DAC resolution N, tolerance ε, and normalized filter parameter k = T/RC. The derived criterion is compared with a commonly used first-order RC settling approximation. For N = 8 and ε = 1/4, the proposed worst-case criterion gives a discrete settling interval of 823 PWM periods, whereas the literature-based estimate gives 355 periods. The analytical results are confirmed by numerical evaluation and LTspice transient simulations and are further supported by experimental measurements obtained using a microcontroller-based PWM generator and a passive RC filter. The results confirm the duty-cycle dependence of the steady-state ripple and demonstrate that the proposed criterion provides a conservative design rule for selecting PWM DAC parameters while balancing accuracy, ripple, and settling speed. Full article

Acoustic propagation in stratified shallow seas is governed by finite-depth waveguiding, impedance contrasts at the seawater–seabed interface, and coupled space–time wave dynamics. Conventional numerical solvers are accurate but often require detailed environmental priors, mesh generation, and explicit time marching, increasing the cost of simulations involving complex boundaries or repeated evaluations. This study proposes a physics-informed residual network (ResNet-PINN) for continuous simulation of two-dimensional acoustic fields in shallow-sea stratified media. The framework embeds a variable-density, variable-sound-speed acoustic pressure wave equation, initial and boundary constraints, and interface-focused collocation into network training. A Gaussian initial wave packet and temporal gating are incorporated through the output transformation to improve early-time physical consistency. The model is validated against SPECFEM2D simulations and a stratified semi-analytical modal benchmark. The results show that it captures source-region spreading, main wavefront evolution, and transmission–reflection structures near the seawater–seabed interface at an equivalent frequency of approximately 477 Hz. Supplementary tests with sloping and arched interfaces and modified boundary conditions indicate adaptability to smooth interface variations. Overall, the framework provides a physically consistent neural network strategy for continuous shallow-sea acoustic field simulation and a complementary basis for future extensions to higher-frequency propagation, more complex environments, and dynamically varying ocean conditions. Full article