Search Results (131)

Bulk heterojunction (BHJ) active layers in organic photovoltaics (OPVs) are nanostructured composites in which electron-donating and electron-accepting semiconductors form interpenetrating phases for exciton dissociation and charge transport. The power conversion efficiency (PCE) of these organic-organic nanocomposites is governed by structural features spanning multiple length scales: molecular packing and energy-level alignment at donor/acceptor (D/A) interfaces, phase-separation morphology and crystallite connectivity, and thin-film optical and charge-transport characteristics. Rational design of high-performance OPV nanocomposites requires multiscale numerical modelling that bridges quantum chemistry, mesoscale morphology simulation, and device-scale optoelectronic modelling. This review surveys and critically compares recent advances in the structural design and numerical simulation of OPV BHJ nanocomposites. At the molecular scale, we examine density functional theory and non-adiabatic molecular dynamics approaches for resolving charge-separation driving forces, interfacial energy-level alignment, and exciton dynamics. At the mesoscale, we discuss molecular dynamics, kinetic Monte Carlo, and electronic coarse-graining methods for describing phase separation, crystallization kinetics, morphology evolution, and charge transport. At the device scale, we review exciton-diffusion, optical transfer-matrix, and drift-diffusion models that quantitatively link morphology to photovoltaic performance metrics. The review also evaluates how machine learning, high-throughput screening, surrogate models, and generative design accelerate donor–acceptor selection and morphology optimization, while distinguishing benchmark predictions from experimentally validated design rules. Across these scales, we compare the strengths, assumptions, and validation limits of the principal modelling approaches. Finally, we highlight emerging multiscale integration frameworks, including sequential parameter-passing pipelines and differentiable digital-twin concepts. By framing OPV BHJ layers as nanocomposites whose performance bottlenecks map onto composite-design challenges such as interface integrity, phase connectivity, multiscale charge transfer, and degradation-aware design, this review connects OPV modelling with broader structural-composites thinking for next-generation organic solar cells. Full article

High-resolution inverse synthetic aperture radar (ISAR) imaging under low signal-to-noise ratio (SNR) and sparse-aperture conditions remains challenging due to severe sidelobe artifacts, weak-scatterer loss, and high computational burden. Although sparse Bayesian learning (SBL) methods are robust to noise, most existing formulations assign pixel-wise independent hyperparameters to image coefficients, which limits their ability to characterize the spatial clustering of scattering centers. Moreover, conventional Bayesian inference often involves large-scale matrix inversion and iterative optimization, leading to high computational cost. To address these issues, this paper proposes a fast ISAR imaging method termed pattern-coupled (PC) two-dimensional (2D) fast inverse-free reconstruction (FIR) generalized expectation-maximization (GEM) network (PC-2D-FIR-GEM-Net), which integrates pattern-coupled hierarchical Bayesian modeling, inverse-free generalized expectation-maximization (GEM) inference, and model-driven deep unfolding. A pattern-coupled prior is first introduced to exploit local structural dependencies among neighboring scatterers, which improves the recovery of weak and clustered scattering structures. Then, an inverse-free GEM solver is developed by constructing surrogate objectives so that image updating can be performed without explicit matrix inversion. Finally, the iterative solver is unfolded into a finite-stage network, where a lightweight convolutional neural network (CNN) learns the coupled precision field and stage-wise update parameters while preserving the model-driven inverse-free update structure. Experimental results on both simulated and measured ISAR datasets demonstrate that the proposed method achieves improved focusing quality, better structural preservation, and significantly reduced computational time under challenging sparse-aperture and low-SNR conditions. Full article

This study proposes a sequential Hybrid GP-ANN architecture based on the concept of autonomous feature engineering for multi-output performance prediction of single-phase induction motors, serving as a high-fidelity surrogate model ready for digital twin integration. The required high-resolution motor dataset (88,375 samples) was generated using a parametric sweep approach with Ansys RMxprt. In the proposed architecture, Genetic Programming (GP) is not positioned as a final predictor but as an analytical filter that discovers hidden physical relationships in raw input data and converts them into super features, thereby structurally eliminating the polynomial explosion risk and scale sensitivity that arise when raw data are modelled directly. The nonlinear physical relationships discovered autonomously by GP are added to the network input matrix as mathematical vectors, breaking the black-box structure of standard artificial neural networks and transforming it into a physics-inspired grey-box model. The nonlinear terms discovered by GP are fed into the network after independent Z-score normalisation to preserve gradient stability. To comprehensively evaluate this framework, its predictive performance is benchmarked against industry-standard machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Results on variation-based unseen test data show that the hybrid model achieves competitive prediction accuracy relative to XGBoost—matching or exceeding it for Output Torque and Input Current, and remaining broadly comparable for Output Power, though XGBoost achieves substantially lower RMSE for Efficiency and Power Factor—while additionally offering transparent mathematical traceability, reducing error rates (RMSE) by 42.1% to 66.1% per parameter compared to the standard ANN. The model achieves an $R^2$ score of 0.9879 for the power factor parameter, where conventional approaches struggle. To rigorously validate the interpretability of this grey-box architecture, a global Permutation Feature Importance (PFI) analysis was conducted, showing that the GP-derived super features collectively account for 61.46% of the decision logic, outweighing the combined contribution of the raw inputs (38.54%). Furthermore, the autonomous feature engineering layer is found to reduce the learning burden on the ANN, allowing it to converge to a substantially lower error floor within the same fixed epoch budget. With an online inference time below 0.02 ms, the proposed architecture offers a robust methodological infrastructure for sustainable motor digital twins through a balanced trade-off between prediction accuracy and computational efficiency. Full article

Hybrid renewable energy systems (HRESs) represent a promising strategy for reducing carbon emissions across multiple sectors by integrating complementary resources such as solar, wind, hydropower, and energy storage technologies. Identifying the most suitable location for a pilot installation requires a comprehensive evaluation that balances technical performance, environmental benefits, social considerations, and economic feasibility. This study employs an enhanced multi-criteria decision analysis (MCDA) framework, supported by machine learning (ML) techniques, to assess four pilot sites developed within the HY4RES project: a rural community, an aquaculture facility, a port installation, and an agriculture network. A comprehensive set of key performance indicators (KPIs) was established to capture technical, environmental, social, and economic dimensions. These include the degree of hybridization, carbon intensity, community benefit scores, net present value, levelized cost of energy, and payback period. After collecting and normalizing the site-specific data, ML EL-SVM, decision tree, and logistic regression models as computational surrogates designed to bypass the multi-step, matrix inversion mathematical requirements of the AHP when screening massive numbers of future scenario outputs supporting consistency checks and sensitivity exploration were used, along with criterion adjustments, to refine the relative importance of each KPI. The Analytical Hierarchy Process (AHP) was employed to assess potential factors and rank the sites, with the rural site achieving the highest overall score in the system, driven by its complex four-source hybrid configuration and strong community-level benefits. The agriculture scheme ranked second, demonstrating significant potential for carbon emission reductions. The port pilot placed third, distinguished by high technical innovation but more limited social impact. The aquaculture site ranked fourth, primarily due to environmental scores, despite its economic self-sufficiency. Full article

k-entanglement, including entanglement relative to full separability and genuinely multipartite entanglement, is a fundamental quantum resource in multipartite quantum systems. Its identification and quantification play essential roles in quantum information processing, quantum simulation, and quantum metrology. However, the practical computation of rigorous k-entanglement measures remains highly challenging due to the need for high-dimensional optimization. In this work, we propose a machine-learning-based surrogate framework for approximating the witness-based k-entanglement measure $E_w^{(k,n)}$. The numerical evaluation of the computationally realized quantity ${\tilde{E}}_w^{(k,n)}\left(\rho \right)$ is reformulated as a supervised regression problem, where the input is the density matrix $\rho$ and the labels are obtained from finite witness databases. The framework combines multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and light gradient boosting machine (LightGBM) through a stacking ensemble. Numerical experiments are performed for 3- and 4-qubit systems as representative demonstrations of the proposed workflow. The results show that the learned models achieve high predictive accuracy in terms of MAE, MSE, and $R^2$, while providing millisecond-level inference for single-state evaluation. Werner state tests serve as symmetric benchmark checks, and an additional four-qubit noisy circuit-generated state family, obtained from finite-depth circuit preparation followed by local amplitude-damping noise, is used as a structured physical test beyond random density matrices. Compared with the optimization-based evaluation, the trained surrogate model significantly reduces the computational time while maintaining accuracy within the tested system sizes and data distributions. These results show that the proposed framework provides an efficient numerical surrogate for rapid approximation of witness-based k-entanglement measures, while extensions to larger systems and experimental data require further validation. Full article

Optical inverse design faces a dilemma: neural surrogates enable fast, differentiable search but can yield physically unreliable pseudo-optima, whereas solver-in-the-loop optimization is reliable yet costly. Most surrogate methods also trust the surrogate throughout the search, train separate models for performance prediction and structure optimization, and remain largely black-box. We propose Physics-Verified Spectral Dreaming (PVSD), a unified framework for forward prediction, inverse design, and physical interpretability: a frozen differentiable spectral surrogate “dreams” structural mutations by input-gradient ascent to explore the design space, while a physical solver adjudicates every accepted update—the surrogate proposes, physics decides. We instantiate it as PVSD-TMM for one-dimensional multilayer radiative coolers. The forward predictor attains $R^2=0.9936/0.9964/0.9828$ for net cooling power, solar reflectance, and primary-window emissivity; neural dreaming lifts the population-mean net cooling power of 1000 random seeds from $-466.7$ to $65.8$ W m⁻² ($91.4\%$ reaching net cooling), and continuous-thickness refinement with 5 nm rounding yields a 14-layer manufacturable final design. Independent COMSOL finite-element and analytic TMM cross-validation converge to $P_{\mathrm{cool}}\approx 172$ W m⁻², $R_{\mathrm{solar}}\approx 0.970$, and ${\epsilon }_{\mathrm{win}}=0.9252$. This is a full-spectrum radiative-balance result for an idealized radiative-only case ($h_{\mathrm{conv}}=0$), not a window-emittance-only metric; PVSD thus achieves high simulated broadband radiative-cooling performance under the stated assumptions, without claiming global optimality. Full article

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and progressive respiratory disorder characterized by airflow limitation, chronic inflammation, and structural lung alterations. Despite advances in clinical monitoring, current approaches such as spirometry, symptom scores, and imaging remain limited in capturing disease complexity and early pathophysiological changes. In this context, microRNAs (miRNAs) have emerged as promising molecular biomarkers due to their involvement in key inflammatory and immune pathway regulation. Sputum is gaining attention as a non-invasive and informative matrix for studying COPD. As a surrogate of airway mucus, sputum reflects local pathological processes, including inflammation and tissue damage, and contains biomarkers such as miRNAs. Compared to circulating miRNAs, sputum-derived miRNAs appear to more accurately represent lung-specific alterations. In this review, we focus on works published so far about the identification of miRNAs in COPD sputum. Several miRNAs have been associated with COPD diagnosis, severity, and exacerbations. These findings, including the possible correlation of some miRNAs with sputum biophysical properties, support the value of integrated biomarker approaches. While further large-scale and standardized studies are required to validate the role of COPD miRNAs assessed in sputum, their evaluation in sputum represents a promising tool for improving the diagnosis, phenotyping and monitoring of COPD. Full article

This paper presents a decentralized adaptive generalized-minimum-variance (GMV) control framework for large-scale stochastic nonlinear systems composed of interconnected multi-input multi-output (MIMO) Hammerstein subsystems with unknown time-varying parameters. Each subsystem consists of a coupled multivariable static nonlinearity represented on a known invertible basis, followed by a matrix-polynomial dynamic block affected by colored noise and delayed input–output interconnections. The proposed scheme estimates only identifiable composite Hammerstein parameters through a decentralized recursive extended least-squares algorithm with forgetting, thereby avoiding the non-unique separation of nonlinear and linear gains. A constructive matrix Diophantine identity is established to derive an optimal multi-step predictor, leading to a GMV control law expressed as a multivariable polynomial equation in the current input. Sufficient conditions for real solvability, mean-square boundedness, and near-optimal adaptive tracking are provided using Hadamard–Lévy global-diffeomorphism, minimum-phase, small-gain, persistent-excitation, strict-positive-realness, and convex-projection arguments, and the implemented controller—inexact Newton solver with fallback and persistent dither—is itself covered by the analysis. The analysis further shows that delayed interconnections become measurable and can be exactly compensated, while robustness to basis under-modeling is explicitly quantified. Simulation results on an interconnected two-subsystem MIMO Hammerstein process with coupled cubic nonlinearities, colored noise, delayed interactions, and time-varying parameters—run in the forgetting-factor regime required by the theory, with measured persistent excitation and complete solver diagnostics—demonstrate operational-noise-floor tracking and a $2.3$-fold mean-RMSE reduction relative to the strongest linear-MIMO surrogate, while a channel-wise SISO Hammerstein design fails structurally and a feedback-linearization controller with exactly known nonlinearity offers no advantage. The study further demonstrates scalability on a chain of four subsystems with size-independent per-subsystem computational cost, validates a physically motivated interconnected coupled-tank network with progressive-valve nonlinearities, and confirms agreement between the observed stability limits and the predicted small-gain boundary. Full article

Feline Infectious Peritonitis (FIP) has been transformed from a fatal disease to a treatable condition following the introduction of GS-441524, a nucleoside analogue targeting feline coronavirus replication. However, the widespread use of unregulated compounded formulations and the absence of validated analytical tools for therapeutic drug monitoring (TDM) represent critical gaps in clinical FIP management. This study describes the development and full ICH M10-compliant validation of a high-throughput LC-MS/MS method for the quantification of GS-441524 in feline serum, incorporating an automated protein precipitation protocol and a PBS-BSA surrogate matrix in accordance with 3Rs principles. The method met all acceptance criteria across validated parameters, including linearity (0.1–50 µg/mL), accuracy (bias within ±12.5%), precision (CV ≤ 10.9%), selectivity, extraction recovery (87.5–107.9%), and stability under clinically relevant storage conditions. Matrix equivalence between PBS-BSA and authentic feline serum was confirmed, enabling routine calibration without animal-derived materials. The validated method was applied to clinical TDM in cats undergoing GS-441524 treatment for FIP, providing preliminary evidence of inter-individual pharmacokinetic variability. The compounded formulations administered to the TDM cohort were independently verified by LC-MS/MS, confirming drug content within ±15% of labelled claims and excluding pharmaceutical quality as a confounding factor in the interpretation of serum drug concentrations. Full article

Viruses cause a great number of infectious diseases with medical, veterinary, agricultural, social and economic impact. Their unique mechanisms to spread, overcome and resist the existing countermeasures require innovative and smart antiviral strategies such as the effective disinfection of enclosed environments with ensured broad-spectrum efficacy and minimized risks associated with handling liquid biocides. Formic acid (FA) is a well-established natural acaricide used in beehives with an antiviral potential; however, its application in a liquid form is hindered by severe corrosiveness and rapid, uncontrolled evaporation. This study describes a novel formulation of FA, using a cryogel carrier for achieving a vapor-phase inactivation of viruses, thus eliminating the need for direct contact between the disinfectant and the pathogen. Firstly, a poly(N-isopropylacrylamide) (PNIPAm) cryogel was synthesized by a procedure involving cryogenic treatment, photochemical crosslinking, and freeze-drying, and then the cryogel was swollen with 65% FA or _ddH₂O as a control. After an exposure of a panel of animal and human viruses to FA, evaporated by the polymer carrier for time intervals between 15 min and 12 h, they were neutralized completely as follows: Poliovirus (PV) as a surrogate for major bee viral pathogens for 60 min by 5.1 ∆lg; Feline calicivirus (FCV) for 60 min by 5.3 ∆lg; Adenovirus 5 (AdV5) for 12 h by 4.0 ∆lg; and Influenza virus A (IAV) for 15 min by 5.1 ∆lg. Results were recorded after titration, 48–72 h incubation, cytopathic effect estimation and NR uptake assay. Our results suggest that 65% FA, when delivered via the PNIPAm cryogel matrix, acts as a powerful agent for fumigation-like disinfection. This “dry” delivery strategy offers significant practical advantages: it eliminates the need for open liquid containers, prevents spill-related hazards, and provides an alternative for controlled, long-term release of active vapors. Full article

In this work, we applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) to predict key performance characteristics of quantum cascade lasers (QCLs), including material gain, current density, and emission frequency. By developing a machine learning-based surrogate modeling framework that replaces computationally expensive simulations of QCLs, we enable orders-of-magnitude-faster evaluation and optimization of a high-dimensional configuration space. The training dataset was generated using a numerical simulator based on the density-matrix transport model. By combining physics simulations with machine learning, we achieved reliable predictions of device characteristics, with standardized RMSE values ranging from 0.21 to 0.55 for RF, 0.16 to 0.51 for XGBoost, and 0.04 to 0.22 for the ANN model, demonstrating the superior predictive performance of the ANN across all investigated performance characteristics. The ANN was subsequently used to analyze the full configuration space defined by possible layer thicknesses and electric fields. Approximately 44 million configurations were evaluated in about five minutes, achieving a speedup of approximately 90,000 times over the numerical simulator for a single configuration. This approach allowed the identification of designs with improved material gain and facilitated the efficient optimization of key parameters while maintaining high prediction reliability. Full article

The aim of the study was to modify polylactide with zinc oxide nanoparticles (ZnO), raspberry leaf extract (E), and a combined ZnO/extract system (EZnO) in order to prepare novel packaging materials via a solvent-free method, namely cast extrusion. Physicochemical properties: Morphology (GPC, SEM, FTIR), mechanical (tensile tests, puncture), barrier (WVTR, OTR, UV-Vis) and water contact angle for PLA-based films with two thickness ranges were investigated. Additionally, antimicrobial (antibacterial, antifungal and antiviral) tests were performed. GPC results revealed that the presence of the extract counteracted biopolyester degradation during hot melt processing. The best mechanical properties (TS ca. 50 MPa, EB ca. 18%) were obtained for PLA modified with raspberry leaf extract (PLA/E). EZnO addition led to the highest increase in oxygen (with 25%) and water vapor (up to ca. 28%) barrier properties. The material with EZnO addition was also found to be the only one to demonstrate antibacterial effectiveness, although the activity was insignificant. However, the incorporation of EZnO into the biopolymer matrix enhanced its antiviral properties, resulting in the complete inactivation of Φ6 bacteriophage particles used as a surrogate of SARS-CoV-2 virus. Full article

This study presents a surrogate-assisted evolutionary optimization framework for parametric design under limited data conditions, integrating computational fluid dynamics (CFD), machine learning, and evolutionary algorithms to optimize spiked blunt body geometries in supersonic flow. A dataset of CFD simulations covering a range of Mach numbers and geometric ratios, including spike length ($L/D$) and diameter ($d/D$), was used to train regression-based surrogate models.Among the evaluated models, the Gradient Boosting Regressor (GBR) achieved the highest predictive accuracy ($R^2=0.8909$, RMSE = 0.00775), effectively capturing the nonlinear relationship between flow conditions, geometry, and drag coefficient ($C_d$). The trained surrogate model was coupled with three evolutionary algorithms—Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Genetic Algorithm (GA)—to identify optimal geometric configurations across different Mach regimes. To validate the proposed framework, the optimal solutions obtained from the surrogate-based optimization were re-evaluated using CFD simulations. A strong agreement between predicted and simulated drag coefficients was observed, confirming the reliability of the surrogate model for guiding optimization within the explored design space. The results reveal consistent geometric trends, with the optimal spike length ratio decreasing as Mach number increases, while the diameter ratio converges to a narrow range around $d/D\approx 0.17$. Additionally, SHapley Additive exPlanations (SHAP) analysis identified $L/D$ as the most influential parameter affecting drag, followed by Mach number and $d/D$, supporting the physical interpretation of the flow behavior. Overall, the proposed framework demonstrates that the integration of CFD, machine learning, and evolutionary algorithms provides an efficient and reliable approach for geometric optimization in supersonic applications, enabling accurate design exploration with a limited number of high-fidelity simulations. Full article

Traditional wood identification models are vulnerable to out-of-distribution (OOD) substitution in the global timber trade. In response to this issue, this study presents a dual-stage cascade authentication architecture using in situ Raman spectroscopy and machine learning. First, a physically informed preprocessing strategy, integrating adaptive truncation (>1749 cm⁻¹) and first-derivative filtering, is developed to extract a 1309-dimensional composite feature matrix. This step effectively decouples non-linear fluorescence and converts physical detector saturation into highly discriminative features. To mitigate data leakage, the system utilizes a cross-validated Random Forest engine for Stage-1 closed-set discriminative screening. Subsequently, it cascades a high-dimensional One-Class Support Vector Machine (OCSVM) for Stage-2 open-set non-linear boundary verification in the Reproducing Kernel Hilbert Space. This design avoids the “variance trap” of traditional linear dimensionality reduction (e.g., PCA), preserving weak but critical secondary metabolite signals. Under a controlled OOD benchmarking scenario involving three taxonomically and chemically similar substitute species, the optimized Stage-1 engine maintains a 91.67% closed-set accuracy on known species. Crucially, Stage-2 verification achieves an open-set detection AUROC of 0.9722 and limits the FPR95 to 3.33%. Feature importance mapping indicates that the model effectively incorporates macroscopicoptical surrogate features (e.g., fluorescence decay boundaries) for decision-making. Overall, this study offers a robust, controlled non-destructive approach for real-world wood authenticity verification. Full article

This paper aims to address the signal coupling problem of a shared-aperture dual-band dual-circularly polarized microstrip antenna. A decoupling optimization method that combined a convolutional neural network (CNN) with binary particle swarm optimization (BPSO) was proposed. The method introduced pixelated decoupling branches near the antenna feeds, constructed a surrogate model to capture the nonlinear mapping between the branch topology and the electromagnetic performance using a CNN, and adopted BPSO to perform global optimization on the binary pixel matrix, thereby alleviating the time-consuming optimization in a complex, high-dimensional parameter space. Simulation results showed that the optimized S₂₁ was reduced by an average of 20 dB over 1.607–1.620 GHz and by an average of 25 dB over 2.487–2.502 GHz, effectively improving the port isolation. These findings demonstrate that the proposed intelligent optimization strategy is effective and practically applicable for solving antenna decoupling problems. Full article