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Directional propagation of interface modes in topological acoustic metamaterials via spin-momentum locking
Symbolic and domain-generalized machine learning for interpretable solubility modeling in supercritical CO₂
Abstract Accurate prediction of drug solubility in supercritical CO₂ remains challenging due to the limited generalizability of compound-specific correlations and the black-box nature of most machine learning models. This study proposes a domain-aware symbolic regression framework that discovers closed-form analytical expressions for ln( y ), enabling interpretable and transferable solubility modeling across chemically diverse pharmaceutical compounds. A leave-one-drug-out (LODO) validation strategy is employed to rigorously assess extrapolation performance on unseen drug domains across a curated dataset of 196 experimental data points spanning 9 antihypertensive compounds. Under this evaluation, the domain-adversarial neural network (DANN) component of the proposed framework achieved RMSE = 0.33 ± 0.08, MAE = 0.24 ± 0.06, and R² = 0.89 ± 0.05, outperforming standard machine learning baselines including Random Forest, XGBoost, and Multi-Layer Perceptron in cross-domain generalization. The symbolic regression component additionally recovered compact closed-form analytical expressions that accurately represent the full experimental dataset (in-sample expression fit: R² = 0.962, RMSE = 0.031, MAE = 0.024) while remaining physically interpretable and analytically tractable. The combined framework demonstrates that domain-aware learning and symbolic discovery can jointly address the dual challenge of predictive robustness under domain shift and model interpretability in supercritical pharmaceutical solubility modeling.
Enhancing the tensile performance of GFRE composites: a comparative study of micro- and nano-copper fillers
Impact of electrode drying time on capacitive performance of honey-derived graphene nanosheets for supercapacitors
Abstract Graphene nanosheets have a significant impact in the energy storage field, particularly in the realm of supercapacitors and capacitive deionization, due to their exceptional properties. This research aims to develop a facile method for preparing graphene nanosheets from biomass and examine the influence of the electrode drying time on the electrochemical properties of the prepared electrodes. Honey, as a carbon source, offers advantages over typical biomass because of its uniform composition, high carbon content, and ability for controlled low-temperature carbonization, which improves porosity and wettability. This chemical process was followed by KOH chemical activation with N 2 gas injection. The physical and chemical properties of graphene nanosheets were examined with X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), Transmission Electron Microscope (TEM), low-temperature nitrogen adsorption-desorption for isothermal characterization, zeta potential, and particle size measurements. The honey-based graphene nanosheets have a specific surface area of 1427 m² g⁻¹ and a pore volume of 0.654 cm³g⁻¹. Consequently, the electrochemical performance of the prepared electrodes was assessed via galvanostatic charge-discharge (GCD), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV). However, the optimum drying time (24 h) had a significant effect on the specific capacitance of electrodes (maximum of 240 Fg⁻¹), which was achieved at a current density of 0.3 Ag⁻¹ when tested in a 0.5 M Na 2 SO 4 aqueous electrolyte. These results present promising rate capability and competitive performance for supercapacitor applications compared with other graphene-based supercapacitor electrodes. Moreover, this sustainable and cost-effective method may enhance the performance of supercapacitor electrodes.
Strata behavior and advanced control technologies in longwall top coal caving of extra-thick coal seams under hard roofs
Unraveling the therapeutic mechanisms of placenta hominis for polycystic ovary syndrome through network pharmacology, molecular docking, and experimental validation
Advancing sustainable agriculture through multi-parameter fuzzy soft set–based plant disease classification
Mitigation of arsenic toxicity in natural waters using a bacterial biomineralizing system
Sexual health education improves psychological health, social well-being, and sexual self-efficacy among married older women: a randomized controlled trial
Design and Optimization of AlGaN/GaN Graded Barrier MOS-HEMT Biosensor for Breast Cancer Biomolecule detection
Abstract In this paper, we have designed and simulated an AlGaN/GaN graded barrier metal oxide semiconductor high electron mobility transistor (MOS-HEMT) heterojunction biosensor for detection of Breast Cancer biomolecules. A graded AlGaN barrier layer is utilized to enhance polarization-induced charges, thus increasing the density of 2DEG and improving carrier mobility in the channel. The proposed device uses Triple Metal Gate (TMG) with different work functions (M1, M2, M3) for controlling the electric field profile along the channel and improves the electrostatic control of the proposed biosensor. AlGaN graded barrier MOS-HEMT heterojunction biosensor was evaluated using Silvaco TCAD. The performance of proposed device analyzed using healthy Breast Cancerous biomolecules (K = 4.5(MCF-10)) and non-healthy Breast Cancerous biomolecules (K = 22(Hs578T), 27.5(MCF-7), 32(T47D)). The device characteristics are evaluated through transfer characteristics (I d –V gs ), output characteristics (I d –V ds ), electron (e - ) mobility and surface potential. Moreover, the effect of different positions for cavity fillings on I on /I off ratio sensitivity and V th sensitivity is examined under different conditions. The sensitivity of the device is 0.147 × 10 2 , subthreshold swing (SS) value is 60 mV/dec, I on /I off ratio sensitivity is 3.67 × 10 5 at K = 32. The results reveal that the sensitivity of the biosensor is based on the number of biomolecules rather than their position. The proposed biosensor has a relatively higher sensitivity compared to existing biosensors, making it a promising device for detecting Breast Cancer biomolecules.
Combined impact of recent sexual activity and vaginal product use on genital immune activation in South African women: implications for HIV risk
Design, control, and computational validation of a mechanically decoupled 3-DOF ankle rehabilitation device
Abstract This paper presents the design methodology, kinematic analysis, finite element structural validation, and closed-loop computational simulation of a novel 3-degree-of-freedom (3-DOF) ankle-foot rehabilitation exoskeleton. The proposed exoskeleton addresses the simultaneous rehabilitation of dorsiflexion/plantarflexion (DF/PF), abduction/adduction (AB/AD), and inversion/eversion (INV/EV) in a seated configuration suited to patients with weight-bearing restrictions. Three NEMA 23 stepper motors independently actuate each axis through custom two-stage spur gear transmissions, achieving positive torque margins of + 6%, + 35%, and + 25% and sub-degree angular resolution. Full kinematic decoupling between all three axes is established through two independent architectural strategies. A finite element analysis (FEA) conducted on all four critical Al 6061-T6 structural components confirms safety factors between 2.82 and 4.87 under worst-case loading. A closed-loop PID control framework with adaptive range-of-motion (ROM) regulation, simulated in MATLAB/SIMULINK, demonstrates tracking performance characterised by maximum overshoots of 0.02°–0.12°, settling times of 3.7–7.8 s, and steady-state errors below ± 0.04° across all nine simulation scenarios. The results confirm the mechanical feasibility, structural integrity, and control effectiveness of the proposed exoskeleton as a viable platform for multi-DOF seated ankle rehabilitation.
Frailty mediates the association between sleep duration and physical, psychological, and cognitive multimorbidity in Chinese middle-aged and older adults
A markerless virtual coach for home-based exercise: movement guidance and training intensity feedback
Impact of imbibition on the regulation of antioxidant metabolism in naturally versus artificially aged chickpea seeds
Monocytes strongly induce (myo)fibroblast contraction in a 3D skin model to understand inflammation-fibrosis crosstalk
Abstract Systemic sclerosis (SSc) is an autoimmune disease characterized by excessive fibrosis and tissue stiffness, in which monocytes and macrophages are increasingly recognized as key contributors to pro-fibrotic myofibroblast formation, although the underlying mechanisms remain incompletely understood. Here, we used a three-dimensional (3D) skin model to study how CD14 + monocytes, M1 and M2-like macrophages induce fibroblasts activation and contraction in collagen type I hydrogels. We identified that the co-culture of dermal (myo)fibroblasts with monocytes displayed strong spontaneous hydrogel contraction, coupled with an upregulation of myofibroblast activation-associated markers, such as alpha-smooth muscle actin (α-SMA). Using transcription factor-responsive reporter constructs and small-molecule inhibitors, we demonstrated that monocytes-(myo)fibroblasts communication was mediated by JAK/STAT3 and TGF-β/Smad2/3 signaling pathways. Flow cytometry analyses revealed that monocytes, after interacting with (myo)fibroblasts, differentiated into a mixed M1/M2 polarization phenotype, characterized by CD163, CD206, CD86, and HLA-DR expression. Both M1 and M2-like macrophages promoted significant (myo)fibroblast contraction, which could be mimicked by supernatant transfer. TGF-β neutralization but not IL-6 blocking abolished this effect. This study demonstrates that monocytes/macrophages can strongly induce (myo)fibroblasts activation/contraction. Together, our work contributes to elucidating pathways and mechanisms associated with skin fibrosis in SSc and paves the way for developing new platforms for targeted therapy testing.