Multi-objective topology optimization of piezoelectric stick-slip actuators for design and performance analysis

Energetics and dynamics of membrane necks in particle wrapping.

The object of the study is the process of pyrolysis of high-density polyethylene in the air environment. The aim of the work is to establish a qualitative and quantitative distribution of valuable and harmful gaseous products and to determine rational temperature regimes that ensure the maximum yield of valuable components and minimize the harmful impact on the environment. The study was carried out using reactive molecular dynamics (MD) modeling methods using the ReaxFF force field and specialized Materials Studio software for building and preparing an atomistic model and LAMMPS for productive modeling. The modeling methodology included the creation of a model of a HDPE polymer matrix with added air, geometric optimization, energy minimization and equilibration. Further modeling of HDPE pyrolysis was carried out in LAMMPS in a wide temperature range from 450°C to 2000°C. Based on the results of MD modeling, time dependences of the yield of key gases were constructed: H2, CO, NH3, CHN and others. Among the main components with the highest total yield, H2 (from 200 units for 450°C up to 600 for 2000°C), CHN (from 0 units for low temperatures up to 60 for 2000°C) and CO (from 30 up to 90 units depending on the temperature) were identified. Analysis of the MD modeling results showed that at 800°C there is a steady release of such gases as: H2, NH3, CHN and CO. At the same time, nitrogen-containing products are present in low concentrations (HN3 – near 0 units, H4N2 – 14, C2H7N – 10, HCN – 6), but at elevated temperatures their content increases noticeably. The temperature of 800°C is considered optimal for HDPE pyrolysis, as it ensures minimal formation of toxic molecules compared to higher temperatures, provides a higher reaction rate than at 450°C and 600°C, and reduces the energy consumption required to sustain the process

Accurate prediction of protein active-site structures remains a central challenge in structural biology, especially for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework designed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we cast structure prediction as a ground-state energy minimization task using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is represented on a tetrahedral lattice, and steric, geometric, and chirality constraints are encoded into a problem-specific Hamiltonian expressed as sparse Pauli operators. A two-stage architecture separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the method on 23 real protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, executing all experiments on the IBM-Cleveland Clinic quantum processor. Structural predictions are benchmarked against AlphaFold3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our quantum framework outperforms both AF3 and classical baselines in Root-Mean-Square Deviation (RMSD) and docking efficacy, demonstrating a practical end-to-end pipeline for biologically relevant structure prediction on real quantum hardware and highlighting its engineering feasibility for near-term quantum devices.

Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional solvents; however, our molecular-scale understanding of their intrinsic properties remains limited. In this study, we investigate menthol–fatty acid (valeric acid, enanthic acid, and pelargonic acid) DESs through a synergistic framework that combines molecular dynamics (MD) simulations, the conductor-like screening model (COSMO-RS) predictions, and Kirkwood–Buff integral (KBI) analysis. Our MD simulations reveal how the concentration and length of fatty acid chains influence the microscopic structure and dynamics within mixtures. COSMO-RS calculations provide predictive insights into activity coefficients and solvation tendencies, aligning closely with our atomistic results. Additionally, KBI analyses quantify preferential interactions, and isothermal compressibility, establishing a strong thermodynamic connection between microscopic ordering and macroscopic properties. Our findings demonstrate that subtle variations in fatty acid chain length affect the stability, heterogeneity, and transport behavior of these mixtures. This provides a rational approach for tuning their properties. Beyond these systems, the combined use of MD, COSMO-RS, and KBI methods presents a transferable and predictive protocol for designing DESs with tailored functionalities. This research enhances our understanding of the organization and thermodynamics of DESs, while also providing essential design principles to promote their application in bioprocessing and separation technologies. DES systems were designed using VMD and PACKMOL to arrange HBDs and HBAs. After performing energy minimization and equilibration at a temperature of 298.15 K, these systems were simulated under NPT conditions for 50 nanoseconds to allow for structural relaxation. Thermodynamic and solvation properties were estimated using COSMO-RS model, while electronic structure calculations were conducted using TURBOMOLE at the triple zeta valence potential (TZVP) basis set. This multiscale approach integrates molecular dynamics, and continuum solvation modelling to thoroughly characterize the structural and thermodynamic properties of menthol–fatty acid DESs.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-25866-9.

Introduction Human pose estimation is a critical challenge in computer vision, with significant implications for robotics, augmented reality, and biomedical research. Current advancements in pose estimation face persistent obstacles, including occlusion, ambiguous spatial arrangements, and limited adaptability to diverse environments. Despite progress in deep learning, existing methods often struggle with integrating geometric priors and maintaining consistent performance across challenging datasets. Methods Addressing these gaps, we propose a novel framework that synergizes physics-inspired reasoning with deep learning. Our Spatially-Aware Pose Estimation Network (SAPENet) integrates principles of energy minimization to enforce geometric plausibility and spatiotemporal dynamics to maintain consistency across sequential frames. The framework leverages spatial attention mechanisms, multi-scale supervision, and structural priors to enhance feature representation and enforce physical constraints during training and inference. This is further augmented by the Pose Consistency_Aware Optimization Strategy (PCAOS), which incorporates adaptive confidence reweighting and multi-view consistency to mitigate domain-specific challenges like occlusion and articulated motion. Results and discussion Our experiments demonstrate that this interdisciplinary approach significantly improves pose estimation accuracy and robustness across standard benchmarks, achieving state-of-the-art results. The seamless integration of spatial reasoning and domain-informed physical priors establishes our methodology as a transformative advancement in the field of pose estimation.

Present study addresses water management in the gas diffusion layer (GDL) by investigating the propensity of liquid fingers formation. GDL configurations desirable for optimal water management are outlined. Applications of the model include quality assessment considering the impact of pinhole and coating defects, by evaluation of the breakthrough pressure which typically correlates with the balanced water removal function of the GDL. The wet-proofed GDL presents mixed wetting configurations, considered within the model together with the extent of coating defects on GDL fibers. The formation of liquid fingers is beneficial for water management in the GDL, by allowing the reactant gas to reach the proton exchange membrane while the water produced is being removed effectively.A surface energy minimization model of the water breakthrough process in the GDL is developed. The model simulates the water invasion through a pore inlet and into a pore space. The pore inlet is defined by fiber segments having varied wetting properties, capturing in this way the water transport behaviour in the presence of coating defects. This process is fundamentally different from the capillary invasion in a tube, and the effect of surface contact angle is reflected in the complex wetting morphology of water in simultaneous contact with multiple fibers. The surface energy, volume, and the Laplace pressure of evolving droplets are determined using the proposed model. In a GDL coated with a non-wetting coating such as PTFE, water makes contact with non-wetting fibers and with defects which pin the gas-liquid interface. As water invades the pore, mixed-non-wetting fingers form as the pressure follows a law depending on both geometry and wetting conditions, reaching a maximum at breakthrough. The model shows that the water fingers formation process is governed by the pore breakthrough pressure and by the energy barrier between different morphologies adopted by water droplets in contact with fibers. The breakthrough pressure and the energy barrier are determined for varied geometrical and wetting conditions. Ex-situ breakthrough pressure experiments align with the prediction of the model.

Abstract In recent years, C 4 F 7 N-CO 2 gas mixtures have emerged as a new type of environmentally friendly insulating medium, showing significant potential in replacing traditional SF 6 gases. This study focuses on the operation scenario of medium-voltage switchgear equipment at 0.1 MPa and investigates the dielectric breakdown properties of hot C 4 F 7 N-CO 2 gas mixtures with a C 4 F 7 N molar mixing ratio of 0-50% at 0.1MPa and 300-4000 K. Furthermore, this study can provide data support for research on the post-arc dielectric recovery process of C 4 F 7 N-CO 2 gas mixtures. Firstly, the equilibrium species composition of C 4 F 7 N-CO 2 gas mixtures with different mixing ratios was calculated using Gibbs free energy minimization. Secondly, the ionization cross sections of C 2 F 3 N, C 3 F 5 N and CN were calculated using the Binary Encounter Bethe (BEB) method, and the cross sections of some species were improved. Subsequently, the electron energy distribution function (EEDF), the reduced ionization coefficient α/N , the reduced attachment coefficient η/N and the critical reduced electric field strength (E/N) cr of the gas mixtures were derived by solving the two-term Boltzmann equation. The influence of temperature and mixing ratio on these parameters was analyzed. The results demonstrate that complex variations occur in the dielectric breakdown properties of C 4 F 7 N-CO 2 gas mixtures with temperature and mixing ratio. Specifically, with increasing mixing ratio, (E/N) cr increases continuously in the low temperature region (near 300 K), whereas at high temperatures (above 2000 K), it transitions from decreasing to forming a bulge. This indicates enhanced dielectric breakdown properties at high temperatures. Notably, high mixing ratio gas mixtures exhibit a hump-shaped profile across 300-4000 K.

Foams and dense emulsions display complex mechanical behavior, including intermittent rearrangement dynamics, power-law rheology, and slow recovery after perturbation. These effects have long been considered evidence for glassy physics in these and other materials having similar mechanics, such as the cytoskeleton. Here, we study such anomalous mechanics in a simulated wet foam driven by ripening and find behavior that has a different physical origin than that in glasses. Rather, the dynamics is due to a balance of forces from the system's self-similar potential energy landscape and viscous stress. At the lowest viscosities, bubbles move intermittently, with the system shifting abruptly between shallow potential energy minima. For higher viscosities, in contrast, the bubbles move continuously and the system follows a tortuous, fractal path through high-dimensional configuration space, at higher mean energy than the lower viscosity case. The long-time dynamics and power-law rheology are the direct consequence of the potential energy landscape's self-similar geometry. Last, we find that the slow recovery of perturbed foams is due to the foam being kinetically rather than energetically trapped in high-energy portions of the energy landscape. Overall, viscous ripening foams follow a biased energy minimization pathway that explores regions of the energy landscape that are qualitatively different (flatter and smoother) than those corresponding to well-annealed glasses.

We present a detailed derivation and discussion of cavity Born-Oppenheimer coupled cluster (CBO-CC) theory and address cavity-modified electron correlation in the vibrational strong coupling regime. Methodologically, we combine the recently proposed cavity reaction potential (CRP) approach with the Lagrangian formulation of CC theory and derive a self-consistent CRP-CC method at the singles and doubles excitations level (CRP-CCSD). The CRP-CC approach is formally similar to implicit solvation CC models and provides access to the CBO-CC electronic ground state energy minimized in cavity coordinate space on a CC level of theory. A hierarchy of linearization schemes (lCRP-CCSD) similar to canonical CC theory systematically lifts the self-consistent nature of the CRP-CCSD approach and mitigates numerical cost by approximating electron correlation effects in energy minimization. We provide a thorough comparison of CRP-CCSD, lCRP-CCSD, and CRP-Hartee-Fock methods for a cavity-modified Menshutkin reaction, pyridine+CH3Br, and cavity-induced collective electronic effects in microsolvation energies of selected methanol-water clusters. We find lCRP-CCSD methods to provide excellent results compared to the self-consistent CRP-CCSD approach in the few-molecule limit. We furthermore observe significant differences between mean-field and correlated results in both reactive and collective scenarios. Our work emphasizes the nontrivial character of electron correlation under vibrational strong coupling and provides a starting point for further developments in ab initio vibro-polaritonic chemistry beyond the mean-field approximation.

Objective: Polycystic Ovary Syndrome (PCOS) is a multifactorial endocrine disorder that affects women of reproductive age and is frequently associated with insulin resistance, hyperandrogenism, and metabolic abnormalities. Peroxisome Proliferator-Activated Receptor Gamma (PPARG) is a nuclear receptor involved in glucose and lipid metabolism and plays a pivotal role in the pathogenesis of PCOS. Amorphophallus muelleri is known to contain various fatty acid derivatives that may influence metabolic pathways through PPARG modulation. Methods: This study aimed to evaluate the binding affinity and interaction profiles of nine phytochemicals derived from A. muelleri toward PPARG using molecular docking analysis. The three-dimensional structure of PPARG (PDB ID: 3DZY) was retrieved from the Protein Data Bank, and docking simulations were conducted using the Molecular Operating Environment (MOE) software. Ligand preparation was performed through energy minimization using the MMFF94x force field, and the docking site was defined based on the co-crystallized ligand binding domain. Results: Docking results showed that all tested compounds had negative binding free energy values, indicating spontaneous interactions. Linoleic acid ethyl ester showed the strongest binding affinity with a docking score of –10.85 kcal/mol, followed by (9E)-9-octadecenoic acid (–10.42 kcal/mol) and 9-octadecenoic acid methyl ester (–10.20 kcal/mol). These compounds interacted with key residues in the PPARG ligand-binding domain, including Cys285, Tyr473, and His323, through hydrophobic interactions and hydrogen bonding, indicating a stable ligand–receptor complex. Conclusion: The findings of this study demonstrate that specific phytochemicals from Amorphophallus muelleri possess strong binding affinity and favorable interaction profiles with PPARG, supporting their potential relevance in the molecular mechanism underlying PCOS therapy.

The natural aortic heart valve exhibits an exceptional balance of durability and efficiency, enabling over two billion cycles during a human lifespan. Designing a prosthetic valve that replicates these attributes presents significant challenges. The development of polymeric heart valves offers a promising alternative to existing biologic and mechanical options, aiming to improve durability and hemodynamic performance. This study focuses on the optimized design of the Foldax TRIA polymeric heart valve, leveraging computational modeling to minimize strain energy and enhance structural integrity. A fully three-dimensional computational model of the TRIA valve was developed using LS-Dyna to simulate its behavior across a full cardiac cycle and optimize for fully open and fully closed configurations. The model incorporated an explicit finite element formulation without symmetry constraints, ensuring accurate representation of valve dynamics. The leaflets, composed of LifePolymer™ (a proprietary silicone urethane-urea), and the frame, made from Solvay Zeniva® PEEK, were analyzed for strain energy distribution. A perturbation analysis was conducted by varying leaflet width to assess its impact on strain distribution, durability, and kinematic efficiency. Additionally, hydrodynamic performance was evaluated using a pulse duplicator system. The computational analysis identified an optimal leaflet width that minimized strain energy and provided uniform stress distribution, reducing the potential for long-term material fatigue. Leaflets that deviated from this optimal width exhibited excessive strain at critical points, leading to potential durability concerns. Hydrodynamic testing demonstrated that the TRIA valve exhibited a low pressure gradient and an efficient equivalent orifice area (EOA) compared to a leading bioprosthetic control valve. Long-term durability testing indicated stable valve performance over 600 million cycles, equivalent to nearly 20years of use. The optimized design of the Foldax TRIA polymeric heart valve successfully minimizes strain energy while maximizing hydrodynamic efficiency. Computational and experimental results suggest that this novel polymeric valve provides a viable, long-lasting alternative to traditional heart valve prostheses. Future studies should focus on in vivo validation to further establish clinical efficacy and longevity.

We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness, and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed method is assessed in a series of numerical examples, with particular focus in the lifted Heston model.

Active inference, grounded in the free energy principle, provides a powerful lens for understanding how agents balance exploration and goal-directed behavior in uncertain environments. Here, we propose a new planning framework that integrates Monte Carlo tree search (MCTS) with active inference objectives to systematically reduce epistemic uncertainty while pursuing extrinsic rewards. Our key insight is that MCTS, already renowned for its search efficiency, can be naturally extended to incorporate free energy minimization by blending expected rewards with information gain. Concretely, the cross-entropy method (CEM) is used to optimize action proposals at the root node, while tree expansions leverage reward modeling alongside intrinsic exploration bonuses. This synergy allows our planner to maintain coherent estimates of value and uncertainty throughout planning, without sacrificing computational tractability. Empirically, we benchmark our planner on a diverse set of continuous control tasks, where it demonstrates performance gains over both stand-alone CEM and MCTS with random rollouts.

This research proposes a novel hybrid metaheuristic optimization framework that combines the Aquila Optimization algorithm with the Sine-Cosine Optimizer to find equilibrium points of reacting components under specified operational reaction conditions. The method aims to address the exploitative limitations of the standard Aquila algorithm by incorporating oscillatory sine-cosine movements into the hybrid optimizer, which is one of the significant drawbacks of the base Aquila algorithm that should be addressed. The effectiveness of the hybrid approach is thoroughly tested on a suite of 100 multidimensional unimodal and multimodal benchmark cases, with results compared to those from well-known literature optimizers. Additionally, twenty-eight 30-dimensional benchmark functions from the 2013 Congress on Evolutionary Computation competition are used to evaluate the prediction performance. Three multidimensional constrained engineering design problems are also solved, and their results are compared with those from other literature optimizers. The findings show that the hybrid algorithm produces the best estimates and ranks first among competing algorithms based on average ranking results. To further verify its robustness and accuracy, three more complex chemical equilibrium problems are solved using the Gibbs Free Energy minimization method. The predictions are benchmarked against recent metaheuristic algorithms for each case, demonstrating that the proposed hybrid effectively overcomes the challenges of highly nonlinear and non-convex free energy surfaces, achieving higher solution consistency while finding minimum objective function values across different chemical equilibrium scenarios.

Toll-Interacting Protein (TOLLIP) serves as key adaptor molecule in innate immune signaling, modulating toll-like receptors (TLRs) and interleukin-1 (IL-1) pathway. Despite its central role, the functional impact of non-synonymous single nucleotide polymorphism (nsSNPs) on TOLLIP remains unclear. Using an integrated computational approach, we screened 150 TOLLIP nsSNPs through consensus predictive tools including PROVEAN, PANTHER, SNPs & GO and SIFT. This approach identified four high confidence deleterious variants (R28Q, T40M, P59L, and R200C) with strong potential to compromise TOLLIP protein stability and function. Structural analysis and energy minimization suggested subtle confirmation changes and destabilizing effect, while TM-align displayed preservation of overall folding (TM-score >0.99, RMSD <0.54 Å). Evolutionary conservation, phylogenetic analysis, and protein-protein interaction (PPI) analysis underscored the functional and confirmation importance of these residues. Notably, molecular docking and dynamic simulations revealed that T40M and R200C variants significantly enhance binding affinity for the Afimetoran. Additionally, molecular dynamics (MD) simulations highlighted the altered flexibility, solvent accessibility and modified hydrogen bonds in mutant proteins structure, suggesting potential mechanisms for functional disruption. Collectively, these findings elucidate the structural and functional consequences of nsSNPs on TOLLIP protein stability, and provide a rational base for targeted therapeutic strategies in immune related diseases.

Parameter estimation is a fundamental component of statistical modeling, shaping the reliability of inferences across disciplines such as finance, engineering, and risk management. Accurate and stable estimation is particularly critical when modeling tail risks and extreme events. The Shifted Weibull Distribution (SWD) provides flexibility in reliability and financial applications; however, traditional estimation methods often struggle to balance accuracy, robustness, and computational efficiency. This study proposes a Continuous Hopfield Neural Network (CHNN)-based framework for estimating SWD parameters and compares its performance with the Newton–Raphson (NR-SWD) and Artificial Neural Network (ANN-SWD) estimators. The CHNN-SWD model is formulated through Lyapunov energy minimization to ensure stable convergence. Its performance was evaluated through simulations with varying sample sizes, using estimation accuracy, model adequacy, and computational efficiency as evaluation criteria. Empirical validation was conducted using Malaysian Shariah-compliant property investment returns (2008–2022), with risk assessment performed through Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), and Kupiec backtesting. Results indicate that CHNN-SWD outperformed both NR-SWD and ANN-SWD in terms of robustness, accuracy, and stability, particularly in modeling extreme risk behavior. Overall, CHNN-SWD demonstrated superior performance across all validation criteria, establishing it as a reliable and computationally efficient estimator suitable for tail-dependent risk modeling applications.

The pyrolysis process of high-density polyethylene (HDPE) was investigated using molecular dynamics simulation with the reactive force field ReaxFF. The aim was to determine the qualitative and quantitative composition of the gaseous pyrolysis products and to identify the optimal temperature regimes that maximize the yield of valuable products while minimizing environmental impact. The simulations were carried out using the Materials Studio and LAMMPS software packages. An atomistic model of HDPE with added oxygen molecules was created, followed by geometry optimization, energy minimization, and equilibration in the NVT ensemble at 300 K. Pyrolysis was simulated at temperatures ranging from 600 °C to 2000 °C. The results suggest that 1000 °C is the optimal temperature for the thermal decomposition of HDPE. These findings confirm the effectiveness of ReaxFF in analyzing the chemical processes of pyrolysis and can be used in the development of polymer waste recycling technologies.

This study addresses the challenges of efficiency and cost in traditional sulfur hexafluoride (SF6) degradation methods and the throughput limitations of common plasma technologies, with the aim of promoting sustainable treatment of potent greenhouse gases. A method of premixing SF6 with plasma media before entering the plasma discharge region was employed to systematically investigate the effects of three atmospheres—nitrogen, air, and hydrogen—on the degradation efficiency, product distribution, and energy efficiency of SF6. An experimental setup was constructed, and Gibbs free energy minimization simulations were conducted to analyze the degradation performance under different conditions. The results show that the premixed gas injection method achieves a degradation removal efficiency of over 99.84% when the SF6 flow rate is lower than 4 slm, which is significantly better than the staged mixing method. When the discharge current increases from 40 A to 100 A, the degradation effect of SF6 improves significantly, but the improvement becomes marginal when the current is further increased to 120 A. Compared with nitrogen, air and hydrogen atmospheres can effectively enhance the degradation removal rate, with the air atmosphere achieving the highest energy yield of 271 g/kWh. This research reveals the regulatory mechanism of medium components on SF6 degradation, providing a theoretical basis for the sustainable, full-process treatment of industrial-scale reactors and contributing to the mitigation of greenhouse gas emissions.

Abstract One of the most important structural and functional elements of lymph nodes (LNs) is the fibroblasts reticular network (RN). Placed in vivo in the LN space, lymphocytes can move directionally, in fact, just along the RN, which acts as a central immune highway. However, despite the multiple experimental studies, mechanisms regulating the lymphocytes motion are not fully understood. In this paper, we propose a modelling study of the basic mechanisms of the lymphocyte migration along the reticulum linear part at the subcellular level. Model simulations were performed in order to test several possibilities of the stochastic T cells motion along the RN driven by chemotaxis. The main goal of the work is to answer the question, what mechanisms are required to provide persistent and non-detached T cells gliding along whole length of the fibronectin fiber, maintaining the T cell integrity, using free energy minimization technique – Cellular Potts Modeling. As a result, a wide range of possible hypotheses and various CPM Hamiltonians were tested. The spatial chemokine gradient is not a universal solution to the problem. The linear chemokine gradient (haptotaxis) of the concentration distributed along the fiber does not solve the problem. Additionally, the production of chemokines by FRC fibers and their diffusion from the fiber into the lymph are not sufficient for a satisfactory solution as well. According to the proposed model, biologically relevant description of immune cells gliding along the RN can be achieved via a combination of haptotaxis and a spatially distributed gradient without a component normal to the fiber. The spatially distributed chemokine gradient becomes a successful solution in combination with the active type of cell motion and fibronectin fibers defined as spatial corridors, which in fact is in line with various experimental evidence.