Search Results (10,738)

Kolmogorov–Arnold Networks employ learnable univariate activation functions on edges rather than fixed node nonlinearities. Standard B-spline implementations require $O\left(3KW\right)$ parameters per layer (K basis functions, W connections). We introduce shared Gaussian radial basis functions with learnable centers ${\mu }_k^{\left(l\right)}$ and widths ${\sigma }_k^{\left(l\right)}$ maintained globally per layer, reducing parameter complexity to $O(KW+2LK)$ for L layers—a threefold reduction, while preserving Sobolev convergence rates $O\left(h^{s-\Omega }\right)$. Width clamping at ${\sigma }_{min}={10}^{-6}$ and tripartite regularization ensure numerical stability. On MNIST with architecture $[784,128,10]$ and $K=5$, RBF-KAN achieves $87.8\%$ test accuracy versus $89.1\%$ for B-spline KAN with $1.4\times$ speedup and 33% memory reduction, though generalization gap increases from $1.1\%$ to $2.7\%$ due to global Gaussian support. Physics-informed neural networks demonstrate substantial improvements on partial differential equations: elliptic problems exhibit a $45\times$ reduction in PDE residual and maximum pointwise error, decreasing from $1.32$ to $0.18$; parabolic problems achieve a $2.1\times$ accuracy gain; hyperbolic wave equations show a $19.3\times$ improvement in maximum error and a $6.25\times$ reduction in $L^2$ norm. Superior hyperbolic performance derives from infinite differentiability of Gaussian bases, enabling accurate high-order derivatives without polynomial dissipation. Ablation studies confirm that coefficient regularization reduces mean error by 40%, while center diversity prevents basis collapse. Optimal basis count $K\in [3,5]$ balances expressiveness and overfitting. The architecture establishes Gaussian RBFs as efficient alternatives to B-splines for learnable activation networks with advantages in scientific computing. Full article

Designing provably safe controllers for high-dimensional nonlinear systems with formal guarantees represents a fundamental challenge in control theory. While control barrier functions (CBFs) provide safety certificates through forward invariance, manually crafting these barriers for complex systems becomes intractable. Neural network approximation offers expressiveness but traditionally lacks formal guarantees on approximation error and Lipschitz continuity essential for safety-critical applications. This work establishes rigorous theoretical foundations for learned barrier functions through explicit probabilistic bounds relating neural approximation error to safety failure probability. The framework integrates Lipschitz-constrained neural networks trained via PAC learning within multi-objective model predictive control. Three principal results emerge: a probabilistic forward invariance theorem establishing $\mathbb{P}\left(\mathrm{violation}\right)\le T{\delta }_{\mathrm{local}}+exp(-h_{min}^2/\left(2L^{2}T{\sigma }^2\right))$, explicitly connecting network parameters to failure probability; sample complexity analysis proving $\mathcal{O}\left(N^{-1/4}\right)$ safe set expansion; and computational complexity bounds of $\mathcal{O}\left(H^3{m}^3\right)$ enabling 50 Hz real-time control. An experimental validation across 648,000 time steps demonstrates a 99.8% success rate with zero violations, a measured approximation error of $\sigma =0.047$ m, a matching theoretical bound of $\sigma \le 0.05$ m, and a 16.2 ms average solution time. The framework achieves a 52% conservatism reduction compared to manual barriers and a 21% improvement in multi-objective Pareto hypervolume while maintaining formal safety guarantees. Full article

Cancer is one of the leading causes of mortality worldwide, with breast and colon cancers being among the most common neoplasms in men and women, respectively. Despite significant advancements in treatment, there is a pressing need to enhance specificity and reduce systemic side effects. Importantly, a distinctive feature of cancer cells is their acidic extracellular environment, which profoundly influences cancer progression. In this study, we evaluated the anticancer activity of a pH-sensitive nanocomposite based on silver nanoparticles and pegylated carboxymethyl chitosan (AgNPs-CMC-PEG) in breast cancer (MCF-7) and colon cancer (HCT 116) cell lines. To achieve this, we synthesized and characterized the nanocomposite using UV-Vis spectroscopy, Dynamic Light Scattering (DLS), Fourier-Transform Infrared Spectroscopy (FT-IR), and Scanning Electron Microscopy (STEM-in-SEM). Furthermore, we assessed cytotoxic effects, apoptosis, and reactive oxygen species (ROS) generation using MTT, DAPI, and H2DCFDA assays. Additionally, we analyzed the expression of DNA methyltransferases (DNMT3a) and histone acetyltransferases (MYST4, GCN5) at the mRNA level using RT-qPCR, along with the acetylation and methylation of H3K9ac and H3K9me2 through Western blot analysis. The synthesized nanocomposite demonstrated an average hydrodynamic diameter of approximately 175.4 nm. In contrast, STEM-in-SEM analyses revealed well-dispersed nanoparticles with an average core size of about 14 nm. Additionally, Fourier-transform infrared (FTIR) spectroscopy verified the successful surface functionalization of the nanocomposite with polyethylene glycol (PEG), indicating effective conjugation and structural stability. The nanocomposite exhibited a pH and concentration dependent cytotoxic effect, with enhanced activity observed at an acidic pH 6.5 and at concentrations of 150 µg/ml, 75 µg/ml, and 37.5 µg/ml for both cell lines. Notably, the nanocomposite preferentially induced apoptosis accompanied by ROS generation. Moreover, expression analysis revealed a decrease in H3K9me2 and H3K9ac in both cell lines, with a more pronounced effect in MCF-7 at an acidic pH. Furthermore, the expression of DNMT3a at the mRNA level significantly decreased, particularly at acidic pH. Regarding histone acetyltransferases, GCN5 expression decreased in the HCT 116 line, while MYST4 expression increased in the MCF-7 line. These findings demonstrate that the AgNPs-CMC-PEG nanocomposite has therapeutic potential as a pH-responsive nanocomposite, capable of inducing significant cytotoxic effects and altering epigenetic markers, particularly under the acidic conditions of the tumor microenvironment. Overall, this study highlights the advantages of utilizing pH-sensitive materials in cancer therapy, paving the way for more effective and targeted treatment strategies. Full article

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional vector of relative offsets, rotations, and impact velocity, is formulated as an inverse problem in which a Sum of Squared Relative Deviations (SSRD) between measured and simulated residual deformations serves as the objective function. A reduced two-vehicle finite element (FE) model is developed to capture the dominant impact dynamics, an Optimal Latin Hypercube Design is used to sample the parameter space, and a Kriging surrogate model is constructed to approximate the response. A simulated annealing algorithm is applied to search for the global minimum. The framework is demonstrated on a real high-speed rear-end collision of electric multiple units. The Kriging model achieves a coefficient of determination of about 0.85, and the optimized kinematic state yields FE-predicted residual deformations that agree with field measurements at key locations to within about 5%. The results show that the method can efficiently reconstruct physically plausible collision scenarios and provide insight into parameter sensitivity and identifiability for railway safety analysis. Full article

Sustainable soil management is increasingly recognized as essential for crop health, productivity, and resilience, especially in vineyard ecosystems. Within the B-Wine project, biochar was evaluated as a soil amendment to improve physicochemical properties, water availability, plant eco-physiological functions, and yield. The trial was carried out in one growing season, one year after biochar application (16 t ha⁻¹ fresh weight ≈ 10.4 t ha⁻¹ dry weight) on three organically managed vineyards in the Chianti Classico region (Tuscany, Italy), integrating soil parameters (e.g., organic carbon content, soil moisture, saturated hydraulic conductivity, bulk density) and eco-physiological measurement (e.g., leaf water content, photosynthetic performance) with remote-sensing analysis of multispectral Sentinel-2 level-2A imagery from the Copernicus program and soil spectral measurements. Results indicated that biochar significantly improved key soil properties, although the magnitude of these improvements varied according to soil characteristics. Bulk density decreased by 5–16%, while soil organic carbon increase differed in the three sites, being nearly 50% in the medium-to-fine textured soils and exceeding 200% in the coarse-textured soil. The impact of biochar on saturated hydraulic conductivity varied depending on the soil, the type of biochar, and the moisture conditions. However, it improved the water balance of the vines and yield. Considering all three vineyard sites, the average yield increase was approximately 42%. However, this result was largely driven by pronounced responses at two sites, while the third showed no measurable increase, likely due to site-specific differences in soil properties and climatic conditions. Overall, biochar proved to be an effective, soil-dependent strategy for enhancing vineyard resilience, plant performance, and productivity under challenging conditions. Full article

We introduce Lehmer Activation Units (LAUs), a class of aggregation-based neural activations derived from the Lehmer transform that unify feature weighting and nonlinearity within a single differentiable operator. Unlike conventional pointwise activations, LAUs operate on collections of features and adapt their aggregation behavior through learnable parameters, yielding intrinsically interpretable representations. We develop both real-valued and complex-valued formulations, with the complex extension enabling phase-sensitive interactions and enhanced expressive capacity. We establish a universal approximation theorem for LAU-based networks, providing formal guarantees of expressive completeness. Empirically, we show that LAUs enable highly compact architectures to achieve strong predictive performance under tightly controlled experimental settings, demonstrating that expressive power can be concentrated within individual neurons rather than architectural depth. These results position LAUs as a principled, interpretable, and efficient alternative to conventional activation functions. Full article

With the increasing penetration of renewable energy in China’s power system, wide-band oscillations with multiple modes have emerged, posing new challenges to the assessment of renewable energy oscillation hosting capacity. At present, the construction of artificial intelligence-based assessment models still relies heavily on researchers’ subjective experience when selecting input features, which lacks theoretical justification. Moreover, the expansion of system scale increases data dimensionality and introduces a higher risk of model overfitting. To address these issues, this paper proposes a two-stage key feature selection method based on participation factors and XGBoost. First, the participation factor theory is utilized to establish the functional mapping between system electrical quantities and oscillatory characteristics, enabling an initial identification of the electrical variables most relevant to renewable energy oscillation hosting capacity. Second, to mitigate the curse of dimensionality brought by large-scale systems, a variational autoencoder is employed to compress the initial feature set and extract its latent representations. Finally, XGBoost is applied to these latent representations to further identify the most critical features that accurately reflect the oscillation hosting capacity of renewable energy. Experimental results on a wide-band oscillation dataset show that active power achieves the highest importance score among all features; moreover, a model using only active-power data attains an accuracy of approximately 97%, demonstrating its effectiveness as the most strongly correlated and least redundant key feature subset. Full article

Accurate genotyping of small insertions and deletions (InDels; <5 bp) remains technically challenging in routine molecular breeding, largely due to the limited resolution of agarose gel electrophoresis and the labor-intensive nature of polyacrylamide-based assays. Here, we present the Tri-Primer Amplification Refractory Mutation System (TP-ARMS), a simple and cost-effective PCR-based strategy that enables high-resolution genotyping of small InDels using standard agarose gels. The TP-ARMS employs a universal reverse primer in combination with two allele-specific forward primers targeting insertion and deletion alleles, respectively. This design allows clear discrimination of homozygous and heterozygous genotypes using a two-tube PCR workflow. The method showed complete concordance with Sanger sequencing in detecting 1–5 bp InDels across multiple crop species, including rice (Oryza sativa) and quinoa (Chenopodium quinoa). In addition, using a TP-ARMS reduced experimental time by approximately 90% compared with PAGE-based approaches and avoided the high equipment and DNA quality requirements of fluorescence-based assays. The practical applicability of the TP-ARMS was demonstrated in breeding populations, including efficient genotyping of a 3-bp InDel in OsNRAMP5 associated with cadmium accumulation and a 6-bp promoter InDel in OsSPL10 underlying natural variation in rice trichome density across 370 accessions. Collectively, the TP-ARMS provides a robust, scalable, and low-cost solution for precise small InDel genotyping, with broad applicability in marker-assisted breeding and functional genetic studies. Full article

Oocysts of Toxoplasma gondii exhibit remarkable resistance to environmental stressors and most conventional disinfectants. Despite its ability to infect a wide variety of host species, sexual reproduction and oocyst formation occur exclusively within felid definitive hosts. Despite the epidemiological significance of oocyst-mediated transmission, the molecular mechanisms governing oocyst production and sporulation remain incompletely understood. Glutaredoxin, serving as a central regulator of cellular redox homeostasis and multiple vital cellular processes in cells, is a potential regulator for oocyst sporulation. Here, we investigated the role of TGME49_227100 (glutaredoxin 5, Grx5) in the T. gondii Pru strain-a type II strain capable of oocyst formation, with a particular focus on its functions during oocyst formation and sporulation. We found that Grx5-knockout tachyzoites exhibited no defects in growth or virulence. Neither in vitro nor in vivo tachyzoite-to-bradyzoite differentiation was affected compared to wild-type parasites. Notably, Grx5 deletion significantly reduced oocyst production in cats by approximately 70%. Additionally, the collected oocysts showed a 50% decrease in sporulation rate. These results indicate that Grx5 plays a predominant role within feline host and the external environmental stage of sporulation, which of these is likely to provide a crucial molecular target for developing a transmission-blocking vaccine. Full article

We develop a novel Steffensen-type iterative solver to solve nonlinear scalar equations without requiring derivatives. A two-parameter one-step scheme without memory is first introduced and analyzed. Its optimal quadratic convergence is then established. To enhance the convergence rate without additional functional evaluations, we extend the scheme by incorporating memory through adaptively updated accelerator parameters. These parameters are approximated by Newton interpolation polynomials constructed from previously computed values, yielding a derivative-free method with R-rate of convergence of approximately 3.56155. A dynamical system analysis based on attraction basins demonstrates enlarged convergence regions compared to Steffensen-type methods without memory. Numerical experiments further confirm the accuracy of the proposed scheme for solving nonlinear equations. Full article

In response to the deterioration of path-tracking accuracy and driving stability encountered by intelligent vehicles under dynamically varying operating conditions, a multi-objective optimization strategy integrating soft actor-critic (SAC) reinforcement learning with variable-parameter Model Predictive Control (MPC) is proposed in this paper to achieve online adaptive adjustment of path-tracking controller parameters. Based on a three-degree-of-freedom vehicle dynamics model, a linear time-varying (LTV) MPC controller is constructed to jointly optimize the front wheel steering angle. An SAC agent is developed utilizing the actor-critic framework, with a comprehensive reward function designed around tracking accuracy and control smoothness to enable online tuning of the MPC weighting matrices (lateral error weight, heading error weight, and steering control weight) as well as the prediction horizon parameter, thereby realizing adaptive balance between tracking accuracy and stability under different operating conditions. Based on the simulation results, it can be concluded that under normal operating conditions, the proposed integrated SAC-MPC control scheme demonstrates superior tracking performance, with the maximum absolute lateral error and mean lateral error reduced by 44.9% and 67.2%, respectively, and the maximum absolute heading error reduced by 23.5%. When the system operates under nonlinear conditions during the transitional phase, the proposed control scheme not only enhances tracking accuracy—evidenced by reductions of 43.4% and 23.8% in the maximum absolute lateral error and maximum absolute heading error, respectively—but also significantly improves system stability, as indicated by a 20.7% reduction in the sideslip angle at the center of gravity. Experimental validation further confirms these findings. The experimental results reveal that, compared with the fixed-parameter MPC, the maximum absolute value and mean value of the lateral error are reduced by approximately 36.2% and 78.1%, respectively; the maximum absolute heading angle error is decreased by 24.3%; the maximum absolute yaw rate is diminished by 19.6%; and the maximum absolute sideslip angle at the center of gravity is reduced by 30.8%. Full article

We consider a network of a residential heating system (RHS) composed of two types of agents: a prosumer and a consumer. Both are connected to a community heating system (CHS), which supplies non-intermittent thermal energy for space heating and domestic hot water. The prosumer utilizes a combination of solar thermal collectors and CHS heat, whereas the consumer depends entirely on the CHS. Any excess heat generated by the prosumer can either be stored on-site or fed back into the CHS. Weather conditions, modeled as a common noise term, affect both agents simultaneously. The prosumer’s objective is to minimize the expected discounted total cost, taking into account storage charging and discharging losses as well as uncertainties in future heat production and demand. This leads to a stochastic optimal control problem addressed through dynamic programming techniques. Scenario-based analyses are then performed to examine how different parameters influence both the value function and the resulting optimal control strategies. For a common noise coefficient ${\sigma }_0=0.4$, the prosumer incurs an approximate $16.08\%$ increase in the aggregated discounted cost from the case of no common noise. For a discharging efficiency ${\eta }_E={\displaystyle \frac{1}{0.9}}$, the maximum aggregated discounted cost increases by approximately $1.85\%$ as compared to the perfect discharging efficiency. Similarly, for a charging efficiency ${\eta }_E=0.9$, we observe an approximate $1.94\%$ increase in the aggregated discounted cost as compared to a perfect charging efficiency. Furthermore, we derive insights into the maximum expected discounted investment that a consumer would need to make in renewable technologies in order to transition into a prosumer. Full article

Accurate channel estimation is critical for enabling effective directional beamforming and spectrally efficient transmission in beamspace massive multiple-input multiple-output (MIMO) systems. However, conventional model-driven algorithms are derived from idealized mathematical models and typically suffer severe performance degradation under model mismatches caused by complex and nonideal propagation environments. Although data-driven deep learning (DL) approaches can learn channel characteristics from data, they typically require large-scale training datasets and demonstrate limited generalization capability. To overcome these limitations, we propose a model-data hybrid-driven network (MD-HDN) scheme to address the wideband beamspace channel estimation problem. In the MD-HDN scheme, we unfold the vector approximate message passing (VAMP) algorithm into a trainable network, where a novel shrinkage function is introduced to enhance the estimation accuracy. Extensive numerical results confirm that the proposed MD-HDN scheme can significantly outperform existing schemes under various signal-to-noise ratio (SNR), and achieve substantial improvements in both estimation accuracy and robustness. Full article

Accurate evaluation of the standard normal cumulative distribution function is fundamental in many areas of mathematics, statistics, and applied computation, yet no closed-form expression in elementary functions exists. We present a simple analytic approximation based on a logistic function with a cubic argument, designed to preserve symmetry, monotonicity, and analytic invertibility. The parameters of the approximation are obtained through numerical optimization over a wide domain, targeting both maximum absolute error and root-mean-square error. The resulting function achieves uniformly low approximation error and significantly reduces error relative to the classical logistic approximation, while remaining competitive with commonly used high-accuracy numerical methods. Unlike rational or high-degree polynomial approximations, the proposed form admits an explicit inverse, making it convenient for applications requiring analytic quantile evaluation or inverse transform sampling. Numerical error analysis and illustrative examples demonstrate that the approximation provides a practical balance between accuracy, simplicity, and analytic tractability. Full article

Light-emitting diodes (LEDs) are becoming increasingly important across various sectors of the lighting industry and are being used more frequently. In the field of symbolic projection, research is increasingly focusing on implementing light modulation using energy-efficient, incoherent LEDs rather than lasers. Since light modulation in micro- and nano-optics is typically achieved through phase modulation, Finite-Difference Time-Domain (FDTD) simulations are employed for analysis. The objective of this article is to investigate different approaches for approximating incoherent monochromatic light sources within FDTD simulations. To this end, two approaches based on dipole sources are considered, as well as a method involving plane waves with modulated wavefronts based on Cosine–Fourier functions and a method based on the superposition of Gaussian beams. These methods are evaluated in terms of their accuracy using a two-dimensional double-slit configuration and are compared against a fully incoherent analytical reference. Full article