Search Results (404)

In this paper, we focus on the optimization problem on the Stiefel manifold. Based on the inverse process of the QR-type retraction, we propose a new intrinsic vector transport. In combination with Dai’s nonmonotone conjugate gradient (CG) method, we present an intrinsic Riemannian nonmonotone CG method and its scaled version and establish the global convergence of the intrinsic scaled Riemannian nonmonotone CG method. Numerical results on a variety of optimization problems on Stiefel manifolds indicate the effectiveness of the proposed method. Full article

In this paper, we examine the inherent mathematical and physical inconsistencies of strain-gradient theories. It is shown that strain gradients are not proper measures of deformation, because their corresponding energetically conjugate stresses are non-physical and cannot represent the state of internal stresses in the continuum. Furthermore, the governing equations in these theories do not describe the equilibrium or motion of infinitesimal elements of matter properly. In the first strain-gradient theory (F-SGT), there are nine explicit governing equations of motion for infinitesimal elements of matter at each point: three force equations and six unsubstantiated artificial moment equations that violate Newton’s third law of action and reaction. This shows that F-SGT is not an extension of rigid-body mechanics, which is, therefore, recovered in the absence of deformation. Moreover, F-SGT would require the existence of six additional fictitious symmetries of space-time according to Noether’s theorem, and a complete revision of the well-established concept of static indeterminacy in introductory mechanics. The inconsistencies of F-SGT also manifest themselves in the appearance of strains as boundary conditions. Full article

Topology optimization using density-based approaches often requires high-resolution meshes to achieve reliable compliance evaluation and robustness against mesh dependency. However, increasing the problem sizes—especially in 3D—results in prohibitively expensive computation times. Coarse-mesh approaches significantly accelerate runtimes; however, they also introduce discretization errors that can guide the optimizer towards incorrect topology families if left unregulated. To address this issue, a multifidelity framework with acceptance control was developed that enables runtime verification and explicitly manages the optimizer state. The main idea is to use coarse discretizations to generate new design proposals and transfer candidate designs to fine discretizations at periodic intervals for verification. Proposals are then accepted or rejected using a best-referenced criterion; if verification fails, the optimizer reverts to the best verified state. The proposed framework balances fine-discretization accountability with coarse-discretization efficiency through configurable verification schedules and a cleanup phase. The framework is evaluated on standard 2D and 3D structural benchmark problems with deterministic load perturbations, and performance is assessed in terms of final verified compliance, wall-clock runtime, acceptance rate, and gray fraction. Full article

Eliminating poor scaling of variables of minimized functions is a pressing issue in solving high-dimensional minimization problems where it is impossible to use methods that change the metric of the space with full-scale metric matrices. In this paper, we propose an iterative method for scaling variables using a diagonal metric matrix and apply it to the gradient minimization method and the conjugate gradient method. In conjugate gradient methods, for quadratic functions, the descent directions are orthogonal to the previous gradient differences. In the proposed method, the transformation of diagonal metric matrices is based on the noted property. For the gradient method with a diagonal metric matrix, an estimate for the convergence rate on strongly convex functions with a Lipschitz gradient was obtained. A computational experiment was conducted, and the presented methods were compared with the Hestenes–Stiefel conjugate gradient method. On the given set of test functions, the gradient method with scaling is comparable in convergence rate to the Hestenes–Stiefel conjugate gradient method, while the conjugate gradient method with scaling matrices significantly outperforms the Hestenes–Stiefel conjugate gradient method. The obtained results confirm the acceleration properties of scaling methods in the case of poor scaling of the variables of the function being minimized. This allows us to conclude that the studied methods can be used alongside conjugate gradient methods to solve smooth, high-dimensional optimization problems with a high degree of conditionality. Full article

In this study, a three-dimensional Inverse Conjugate Heat Transfer Problem (ICHTP) is numerically and experimentally investigated to estimate the heat-source strength of multiple chips mounted on a printed circuit board (PCB) using the Conjugate Gradient Method (CGM) and infrared thermography. The interfaces between the PCB and the surrounding air domain are assumed to exhibit perfect thermal contact, establishing a fully coupled conjugate heat transfer framework for the inverse analysis. Unlike the conventional Inverse Heat Conduction Problem (IHCP), which typically only accounts for conduction within solid domains, the present ICHTP formulation requires the simultaneous solution of the governing continuity, momentum, and energy equations in the air domain, along with the heat conduction equation in the chips and PCB. This coupling introduces substantial computational complexity due to the nonlinear interaction between convective and conductive heat transfer mechanisms, as well as the sensitivity of the inverse solution to measurement uncertainties. The numerical simulations are conducted first with error-free measurement data and an inlet velocity of u_in = 4 m/s; the recovered heat-sources exhibit excellent agreement with the true values. The computed average errors for the estimated temperatures ERR1 and estimated heat sources ERR2 are as low as 0.0031% and 1.87%, respectively. The accuracy of the estimated heat sources is then experimentally validated under various prescribed inlet air velocities. During experimental verification at an inlet velocity of 4 m/s, the corresponding ERR1 and ERR2 values are obtained as 0.91% and 3.34%, while at 6 m/s, the values are 0.86% and 2.81%, respectively. Compared with the numerical results, the accuracy of the experimental estimations decreases noticeably. This discrepancy arises because the numerical simulations are free from measurement noise, whereas experimental data inherently include uncertainties due to thermal picture resolutions, environmental fluctuations, and other uncontrollable factors. These results highlight the inherent challenges associated with inverse problems and underscore the critical importance of obtaining precise and reliable temperature measurements to ensure accurate heat source estimation. Full article

The increasing demand for poultry meat, driven by its favorable nutritional profile, including low cholesterol and high protein content, has resulted in intensified production volumes and, consequently, elevated ammonia (NH₃) emissions. Artificial intelligence-based predictive approaches offer an effective alternative to conventional treatment-oriented methods by enabling faster and more accurate estimation of NH₃ removal performance. This study aimed to predict the ammonia removal efficiency of broiler litter generated during a production cycle under controlled laboratory-scale conditions using artificial neural networks (ANNs) trained with different learning algorithms. Four ANN models were developed based on the Levenberg–Marquardt (LM), Fletcher–Reeves (FR), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) algorithms. The results showed that the LM-based model with 12 hidden neurons achieved the highest predictive performance (R² = 0.9777; MSE = 0.0033; RMSE = 0.0574; MAPE = 0.0833), while the BR-based model with 10 neurons showed comparable accuracy. In comparison with the FR and SCG models, the LM algorithm demonstrated superior predictive accuracy and generalization capability. Overall, the findings suggest that ANN-based modeling is a reliable, data-informed approach for estimating NH₃ removal efficiency, providing a potential decision-support framework for ammonia mitigation strategies in poultry production systems. Full article

We propose Res-FormerNet, an improved inversion network that integrates a lightweight Transformer encoder into a ResNet50 backbone to enhance two-dimensional magnetotelluric (MT) inversion. The model is designed to jointly leverage residual convolutional structures for local feature extraction and global attention mechanisms for capturing long-range spatial dependencies in geoelectrical resistivity models. To evaluate the effectiveness of the proposed architecture, more than 100,000 synthetic models generated by a two-dimensional staggered-grid finite-difference forward solver are used to construct training and validation datasets for TE and TM apparent resistivity responses, with realistic noise levels applied to simulate field acquisition conditions. A smoothness-aware loss function is further introduced to improve inversion stability and structural continuity. Results from synthetic tests demonstrate that incorporating the Transformer encoder substantially enhances the recovery of large-scale anomalies, structural boundaries, and resistivity contrasts compared with the original ResNet50. The proposed method also exhibits strong generalization capability when applied to real MT field data from southern Africa, producing inversion results highly consistent with those obtained using the nonlinear conjugate gradient (NLCG) method. These findings confirm that the Res-FormerNet architecture provides an effective and robust framework for MT inversion and illustrate the potential of hybrid convolution–attention networks for advancing data-driven electromagnetic inversion. Full article

This paper evaluates neural-network approaches for lithium-ion battery state-of-charge (SoC) estimation under a unified pipeline, fixed data partitions, and identical preprocessing. We study FNNs trained with Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) across three hidden sizes (10, 20, 30) and three topologies: Fitting, Nonlinear Input–Output (Nonlinear I/O), and time-series NAR/NARX. Models are assessed using test MSE and RMSE, correlation (R), generalization gap, convergence indicators (final gradient, damping factor), wall time per epoch, and a relative compute-cost index. On the Fitting task, BR-Fitting-FNN with 20 neurons provides the best accuracy-efficiency balance, while LM-Fitting-FNN with 30 neurons reaches slightly lower error at a higher cost. For Nonlinear I/O, BR-Nonlinear I/O-FNN with 30 neurons achieves the lowest test MSE with clear evidence of effective weight shrinkage; LM-Nonlinear I/O-FNN with 20 neurons is a close alternative. In time-series settings, LM-NAR-FNN with 10 neurons attains the lowest test error and fastest epochs but shows a very negative gap that suggests test-split favorability; BR-NAR-FNN with 30 neurons is more costly yet consistently strong. For NARX, LM-NARX-FNN with 20 neurons yields the best test accuracy and robust convergence. Overall, BR delivers the most reliable accuracy–robustness trade-off as networks widen, LM often achieves the best raw accuracy with careful split validation, and SCG offers the lowest training cost when resources are limited. These results provide practical guidance for selecting SoC estimators to match accuracy targets, computing budgets, and deployment constraints in battery management systems. Full article

This paper considers the problem of unconstrained minimization of smooth functions. Despite the high efficiency of quasi-Newton methods such as BFGS, their performance degrades in ill-conditioned problems with unstable or rapidly varying Hessians—for example, in functions with curved ravine structures. This necessitates alternative approaches that rely not on second-derivative approximations but on the topological properties of level surfaces. As a new methodological framework, we propose using a procedure of incomplete orthogonalization in the directions of gradient differences, implemented through the iterative least-squares method (ILSM). Two new methods are constructed based on this approach: a gradient method with the ILSM metric (HY_g) and a modification of the Hestenes–Stiefel conjugate gradient method with the same metric (HY_XS). Both methods are shown to have linear convergence on strongly convex functions and finite convergence on quadratic functions. A numerical experiment was conducted on a set of test functions. The results show that the proposed methods significantly outperform BFGS (2 times for HY_g and 3.5 times for HY_XS in terms of iterations number) when solving ill-posed problems with varying Hessians or complex level topologies, while providing comparable or better performance even in high-dimensional problems. This confirms the potential of using topology-based metrics alongside classical quasi-Newton strategies. Full article

The lysosome is no longer viewed as a simple degradative “trash can” of the cell. The lysosome is not only degradative; its acidic, redox-active lumen also serves as a chemical “microreactor” that can modulate anticancer drug disposition and activation. This review examines how the distinctive chemical features of the lysosome, including its acidic pH (~4.5–5), strong redox gradients, limited thiol-reducing capacity, generation of reactive oxygen (ROS), diverse acid hydrolases, and reservoirs of metal ions, converge to influence the fate and activity of anticancer drugs. The acidic lumen promotes sequestration of weak-base drugs, which can reduce efficacy by trapping agents within a protective “safe house,” yet can also be harnessed for pH-responsive drug release. Lysosomal redox chemistry, driven by intralysosomal iron and copper, catalyzes Fenton-type ROS generation that contributes to oxidative damage and ferroptosis. The lysosome’s broad enzyme repertoire enables selective prodrug activation, such as through protease-cleavable linkers in antibody–drug conjugates, while its membrane transporters, particularly P-glycoprotein (Pgp), can sequester chemotherapies and promote multidrug resistance. Emerging therapeutic strategies exploit these processes by designing lysosomotropic drug conjugates, pH- and redox-sensitive delivery systems, and combinations that trigger lysosomal membrane permeabilization (LMP) to release trapped drugs. Acridine–thiosemicarbazone hybrids exemplify this approach by combining lysosomal accumulation with metal-based redox activity to overcome Pgp-mediated resistance. Advances in chemical biology, including fluorescent probes for pH, redox state, metals, and enzymes, are providing new insights into lysosomal function. Reframing the lysosome as a chemical reactor rather than a passive recycling compartment opens new opportunities to manipulate subcellular pharmacokinetics, improve drug targeting, and overcome therapeutic resistance in cancer. Overall, this review translates the chemical principles of the lysosome into design rules for next-generation, more selective anticancer strategies. Full article

The Leaf Area Index (LAI) is a key vegetation biophysical variable extensively studied using various remote sensing platforms and applications. Most studies focused on retrieving LAI using remote sensing data have primarily applied visible to shortwave infrared (0.3–2.5 µm) data. While we have previously retrieved LAI using thermal infrared (TIR 2.5–14 µm) hyperspectral data under controlled laboratory conditions, this study aims to evaluate the reliability of our earlier findings using in situ and airborne TIR hyperspectral data. In this study, 36 plots, each 30 × 30 m in size, were randomly selected in the Bavarian Forest National Park in southeastern Germany. The EUFAR-TIR flight campaign, conducted on 6 July 2017, aligned with field data collection using an AISA Owl TIR hyperspectral sensor at 3 m spatial resolution. Statistical univariate and multivariate approaches have been applied to predict LAI using emissivity data. The LAI was derived using six narrowband indices, computed from all possible combinations of wavebands between 8 µm and 12.3 µm, via partial least squares regression (PLSR) and artificial neural network (ANN) models, applying the Levenberg–Marquardt and Scaled Conjugate Gradient algorithms. The results indicated that compared to LAI estimation under controlled conditions, TIR narrowband indices demonstrated poor performance in estimating in situ LAI (R² = 0.28 and RMSE_CV = 0.02). Instead, it was observed that the PLSR model unexpectedly achieved higher prediction accuracy (R² = 0.86 and RMSE_CV = 0.36) in retrieving LAI compared to the ANN approach using the Levenberg–Marquardt algorithm (R² = 0.56, RMSE_CV = 0.71); however, it was outperformed by the Scaled Conjugate Gradient algorithm (R² = 0.83, RMSE_CV = 0.18). The results revealed that wavebands located at 8.1 µm, 9.1 µm, 9.85–9.95 µm, and 9.99–10.27 µm are equally effective in predicting LAI, regardless of sensor or measurement/environmental conditions. Our findings have important implications for upscaling LAI predictions, as the identified wavebands are effective across varying conditions and align with the capabilities of upcoming thermal satellite missions such as Landsat Next and Copernicus LSTM. Full article

The information and communication technology revolution has provided researchers with new opportunities to enhance traffic prediction methods. Accurate long-term traffic forecasts are essential for sustainable infrastructure planning, supporting proactive maintenance and efficient resource allocation. They also enable environmental impact assessments and help reduce carbon footprints through optimized traffic flow, minimized idling, and better planning for low-emission infrastructure. Most traffic prediction studies focus on short-term urban traffic, but there remains a gap in methods for long-term planning of rural highways, which pose significant challenges for intelligent transportation systems. This paper assesses and compares six prediction models for long-term daily traffic volume prediction, including two traditional time series methods (ARIMA and SARIMA) and four artificial neural networks (ANNs): three feedforward networks trained with Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), and Levenberg–Marquardt (LM), along with a nonlinear autoregressive (NAR) network. Applying mean absolute percentage error (MAPE) as the performance metric, the results showed that all models effectively captured the data’s nonlinearity, though their accuracy varied significantly. The NAR model proved to be the most accurate, with a minimum average MAPE of 2%. The Bayesian Regularization (BR) algorithm achieved superior performance (average MAPE: 4.50%) among the feedforward ANNs. Notably, the ARIMA, SARIMA, and ANN-LM models exhibited similar performance. Accordingly, the NAR model is recommended as the optimal choice for long-term traffic prediction. Implementing these models with optimal design will enhance long-term traffic volume forecasting, supporting sustainable transportation and improving intelligent highway operation systems. Full article

Integrated additional cooling channels offer precise thermal management for solid-oxide fuel cells (SOFCs), mitigating temperature gradients. This research studies the thermal–hydraulic performance of cooling channels integrated between SOFC interconnectors, including a Diamond-type triply periodic minimal surface (TPMS), a conventional topology-optimized structure, and a topology-optimized lattice-filled structure. A conjugate heat transfer analysis is employed to investigate the influences of flow rate within the range of Reynolds numbers from 300 to 5000, and the effects of coolant type, including air and liquid metals, as well as the impacts of structural material. The results demonstrate that the topology-optimized lattice-filled structure, generating high turbulence mixing, achieves superior temperature uniformity, especially at high flow rates, despite having higher thermal resistance and pressure loss than the conventional topology-optimized design. The coolant types show the largest influence on thermal–hydraulic performance, and the use of liquid gallium in the conventional optimized design obtains the best temperature uniformity, yielding differences between the maximum and minimum temperatures of less than 5 K. Moreover, the higher-thermal-conductivity material improves temperature uniformity, even at low flow rates. Overall, the optimized-baffle designs in the conventional topology-optimized model, utilizing high-conductivity coolant and structural materials, could be the most suitable for thermal management of the SOFC. Full article

In the present work, we aim to study the inverse problem of recovering an unknown spatial source term in a multi-term time-fractional diffusion equation involving the fractional Laplacian. The forward problem is first analyzed in appropriate fractional Sobolev spaces, establishing the existence, uniqueness, and regularity of solutions. Exploiting the spectral representation of the solution and properties of multinomial Mittag–Leffler functions, we prove uniqueness and derive a stability estimate for the spatial source term from finaltime observations. The inverse problem is then formulated as a Tikhonov regularized optimization problem, for which existence, uniqueness, and strong convergence of the regularized minimizer are rigorously established. On the computational side, we propose an efficient reconstruction algorithm based on the conjugate gradient method, with temporal discretization via an L¹-type scheme for Caputo derivatives and spatial discretization using a Galerkin approach adapted to the nonlocal fractional Laplacian. Numerical experiments confirm the accuracy and robustness of the proposed method in reconstructing the unknown source term. Full article

Objectives: To develop a proof-of-concept machine learning (ML) neural network model to predict post-operative visual outcomes in children with congenital cataracts undergoing intraocular lens (IOL) implantation, thereby guiding the optimal timing for IOL insertion. Determining the ideal timing and predicting outcomes for IOL implantation in children remains clinically complex due to variability in eye development and measurement accuracy. Methods: Retrospective analysis using a publicly available dataset from 110 children diagnosed with congenital cataracts, who underwent IOL implantation at the Eye and ENT Hospital of Fudan University. A neural network model with a hidden layer of 10 nodes was developed in MATLAB 2024a using the scaled conjugate gradient algorithm. Input variables included demographic and clinical features; the target was achieving visual acuity greater than 20/40. Performance metrics were evaluated using cross-entropy loss, sensitivity, specificity, and accuracy. Results: Training completed after 14 epochs with the test set reaching the highest performance metrics: 88.2% accuracy, 88.9% sensitivity, and 87.5% specificity. ROC curve analysis showed AUC values of 0.942 (training), 0.920 (validation), 0.885 (test), and 0.917 (overall). Conclusions: The neural network effectively predicted post-operative visual outcomes, offering potential clinical utility in guiding IOL implantation decisions. Despite limitations in dataset diversity, this study lays the foundation for future development of personalized strategies in pediatric cataract care. Full article