Search Results (36)

In this paper, we investigate massive charged scalar perturbations in four-dimensional charged Lifshitz–AdS black holes with scalar hair within the framework of Einstein–Maxwell–Dilaton (EMD) gravity. Using the improved asymptotic iteration method (AIM), we compute the quasinormal modes (QNMs) and explore their dependence on key parameters, including the Lifshitz dynamical exponent z, the scalar field mass and charge, and the black hole charge, under various spatial curvature settings ($k=0,\pm 1$). Our results reveal rich and sensitive behavior in both the real and imaginary parts of the QNMs. In particular, the decay rates can exhibit monotonic or non-monotonic dependence on the black hole charge, depending on the values of z, $m_s$, and $q_s$. These findings highlight the significant role of field and geometric parameters in governing the dynamical stability of Lifshitz black holes and offer insights into the perturbative properties of non-AdS holographic systems. Full article

The proper design of concrete mixtures is a critical task in concrete technology, where optimal strength, eco-friendliness, and production efficiency are increasingly demanded. While traditional analytical methods, such as the Three Equations Method, offer foundational approaches to mix design, they often fall short in handling the complexity of modern concrete technology. Machine learning-based models have demonstrated notable efficacy in predicting concrete compressive strength, addressing the limitations of conventional methods. This study builds on previous research by investigating not only the impact of computational complexity on the predictive performance of machine learning models but also the influence of different optimization algorithms. The study evaluates the effectiveness of three optimization techniques: the Quasi-Newton Method (QNM), the Adaptive Moment Estimation (ADAM) algorithm, and Stochastic Gradient Descent (SGD). A total of forty-five deep neural network models of varying computational complexity were trained and tested using a comprehensive database of concrete mix designs and their corresponding compressive strength test results. The findings reveal a significant interaction between optimization algorithms and model complexity in enhancing prediction accuracy. Models utilizing the QNM algorithm outperformed those using the ADAM and SGD in terms of error reduction (SSE, MSE, RMSE, NSE, and ME) and increased coefficient of determination (R²). These insights contribute to the development of more accurate and efficient AI-driven methods in concrete mix design, promoting the advancement of concrete technology and the potential for future research in this domain. Full article

We cross-linked unfractionated heparin (H) using epichlorohydrin (E), in the absence or presence of imidazole (I), using various ratios of H, E, and I substances. The objectives and goals were to use the reaction for the preparation of medical materials suitable for blood sample applications. Nuclear magnetic resonance indicated the involvement of an H-end sequence [H-(1→4)-β-D-GlcA-(1→3)-β-D-Gal-(1→3)-β-D-Gal-(1→4)-β-D-Xyl-α-Ser] in the linkage with the 2-hydroxypropyl bridge. The yields of the individual experiments were found to increase in the following ratios: 1H/1E/3I (24%) < 1H/1E/2I (32%) < 1H/3E (42%) < 1H/1E/1I (46%) < 1H/2E (64%) < 1H/1E (77%). According to size-exclusion chromatography with multiple-angle light scattering (SEC-MALS) analysis, the mass at the peak increased in the following order: H (9292 g/mol) < 1H/1E (9294 g/mol) < 1H/2E (9326 g/mol) < 1H/3E (9708 g/mol) < 1H/1E/2I (11,212 g/mol) < 1H/1E/3I (12,301 g/mol) < 1H/1E/1I (13,800 g/mol) and in the reverse order with the increase in amount of epichlorohydrin and imidazole, i.e., 1H/1E > 1H/2E > 1H/3E and 1H/1E/1I > 1H/1E/2I > 1H/1E/3I. X-ray diffraction revealed that all prepared films were amorphous. An evaluation of the surface morphology using atomic force microscopy (AFM) confirmed a relatively low films roughness (~0.9–3.6 nm). The surface reduced elastic modulus, determined by the PeakForce quantitative nanomechanical mapping (PF-QNM) technique, was found to increase by up to ~63% for films cross-linked with E in the absence of I when compared with the results for the H substrate. A negligible change in modulus was, however, observed for films cross-linked in the presence of I, or was even reduced by ~15% (1H/1E/3I) compared to that for the H substrate. This could be explained by the parallel cross-linking of H only with E within its serine end unit and in competition with only one nitrogen of I. According to the highest yield (77%) of 1H/1E, the preferred product is the following: H-(1→4)-β-D-GlcA-(1→3)-β-D-Gal-(1→3)-β-D-Gal-(1→4)-β-D-Xyl-α-Ser-CH₂-CH(OH)-CH₂-OH. For the 1H/1E/1I (46% yield), 1H/1E/2I (32%), and 1H/1E/3I (24%) products, the cross-linked motif was the same, and the difference represented the surplus amount of the imidazolium cation ionically bound to the heparin anionic groups. Full article

In this article, we consider the correction of metric matrices in quasi-Newton methods (QNM) from the perspective of machine learning theory. Based on training information for estimating the matrix of the second derivatives of a function, we formulate a quality functional and minimize it by using gradient machine learning algorithms. We demonstrate that this approach leads us to the well-known ways of updating metric matrices used in QNM. The learning algorithm for finding metric matrices performs minimization along a system of directions, the orthogonality of which determines the convergence rate of the learning process. The degree of learning vectors’ orthogonality can be increased both by choosing a QNM and by using additional orthogonalization methods. It has been shown theoretically that the orthogonality degree of learning vectors in the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is higher than in the Davidon–Fletcher–Powell (DFP) method, which determines the advantage of the BFGS method. In our paper, we discuss some orthogonalization techniques. One of them is to include iterations with orthogonalization or an exact one-dimensional descent. As a result, it is theoretically possible to detect the cumulative effect of reducing the optimization space on quadratic functions. Another way to increase the orthogonality degree of learning vectors at the initial stages of the QNM is a special choice of initial metric matrices. Our computational experiments on problems with a high degree of conditionality have confirmed the stated theoretical assumptions. Full article

Grafted cucumber plants were grown in a new hydroponic system (“Kappa Land”, Mitsubishi Chemical Aqua Solutions, Co., Ltd., Tokyo, Japan). Two different nutrient management methods were applied to the plants as treatments: Electrical Conductivity-based Management (ECM) and Quantitative Nutrient Management (QNM). During the growth period, we examined plant growth characteristics and productivity, fruit growth characteristics and quality, and nutrient use characteristics. The results revealed that the QNM technique significantly reduced the nutrient supply rate per plant for Ca²⁺, SO₄²⁻, and N by 28.5%, 25.5%, and 23.3%, respectively. Similarly, the absorption rates per plant of SO₄²⁻, K⁺, and PO₄³⁻ were reduced by 17.8%, 11.9%, and 10.9%, respectively. However, N, Ca²⁺, and Mg²⁺ absorption rates slightly increased in the QNM treatment. The nutrient wastes generated per kilogram of produced fruits were also reduced by 66.4%, 60.7%, and 30.2% for N, Ca²⁺, and SO₄²⁻, respectively. Although the QNM technique reduced the plant’s leaf area, it significantly increased its total length by 9.4%. The total and marketable yields were not significantly different between the ECM (9.0 and 8.0 kg plant⁻¹) and QNM (9.1 and 8.2 kg plant⁻¹) treatments. However, the QNM treatment produced the highest total dry matter of 617 g plant⁻¹, surpassing the ECM treatment by 6.9%. On the other hand, differences in nutrient management methods did not significantly affect fruit quality, including total soluble solids, water content, skin color, size, and shape. These results suggest that with the QNM method, it is possible to produce quality cucumbers with high nutrient use efficiency while protecting the environment from nutrient wastes. Full article

Cells of two molecular genetic types of breast cancer—hormone-dependent breast cancer (ZR-75 cell line) and triple-negative breast cancer (BT-20 cell line)—were studied using atomic force microscopy and an optical nanomotion detection method. Using the Peak Force QNM and Force Volume AFM modes, we revealed the unique patterns of the dependence of Young’s modulus on the indentation depth for two cancer cell lines that correlate with the features of the spatial organization of the actin cytoskeleton. Within a 200–300 nm layer just under the cell membrane, BT-20 cells are stiffer than ZR-75 cells, whereas in deeper cell regions, Young’s modulus of ZR-75 cells exceeds that of BT-20 cells. Two cancer cell lines also displayed a difference in cell nanomotion dynamics upon exposure to cytochalasin D, a potent actin polymerization inhibitor. The drug strongly modified the nanomotion pattern of BT-20 cells, whereas it had almost no effect on the ZR-75 cells. We are confident that nanomotion monitoring and measurement of the stiffness of cancer cells at various indentation depths deserve further studies to obtain effective predictive parameters for use in clinical practice. Full article

Improving the resolution and accuracy of the mechanical properties of organic-rich shale is very important. The results can reveal the mechanical properties of shale from micro scale and serve as a guide for the design of hydraulic fracture optimization parameters. This study introduced an advanced technique to obtain the mechanical properties of shale with high resolution (58.6 nm/pixel) by combining SEM, EDS, and Atomic Force Microscopy (AFM). To locate the target area in SEM and AFM accurately, a positioning technique that uses special distributions of pyrite was established. AFM PeakForce QNM mode was selected due to its advantages at capturing topography and mechanical properties in material. Results illustrated the ability of AFM to obtain the mechanical properties (modulus) of individual mineral components in shale, the detailed topography of crack, and mechanical properties of minerals in a specific area. In particular, the mechanical properties of minerals around crack explained the layered distribution of minerals around the fractures, and the cracks developed in the clay mineral layer was detected. This article demonstrates the great potential application of AFM in shale. Full article

Based on the five-dimensional Einstein–Maxwell theory, Bah et al. constructed a singularity-free topology star/black hole [Phys. Rev. Lett. 126, 151101 (2021)]. After performing the Kaluza–Klein reduction, i.e., integrating the extra space dimension, it can obtain an effective four-dimensional spherically static charged black hole with scalar hair. In this paper, we study the quasinormal modes (QNMs) of the scalar, electromagnetic, and gravitational fields in the background of this effective four-dimensional charged black hole. The radial parts of the perturbed fields all satisfy a Schrödinger-like equation. Using the asymptotic iteration method, we obtain the QNM frequencies semianalytically. For low-overtone QNMs, the results obtained using both the asymptotic iteration method and the Wentzel–Kramers–Brillouin approximation method agree well. In the null coordinates, the evolution of a Gaussian package is also studied. The QNM frequencies obtained by fitting the evolution data also agree well with the results obtained using the asymptotic iteration method. Full article

Silica hollow spheres with a diameter of 100–300 nm and a shell thickness of $8\pm 2$ nm were synthesized using a self-templating amphiphilic polymeric precursor, i.e., poly(ethylene glycol)-substituted hyperbranched polyethoxysiloxane. Their elastic properties were addressed with a high-frequency AFM indentation method based on the PeakForce QNM (quantitative nanomechanical mapping) mode enabling simultaneous visualization of the surface morphology and high-resolution mapping of the mechanical properties. The factors affecting the accuracy of the mechanical measurements such as a local slope of the particle surface, deformation of the silica hollow particles by a solid substrate, shell thickness variation, and applied force range were analysed. The Young’s modulus of the shell material was evaluated as $E=26\pm 7$ GPa independent of the applied force in the elastic regime of deformations. Beyond the elastic regime, the buckling instability was observed revealing a non-linear force–deformation response with a hysteresis between the loading and unloading force–distance curves and irreversible deformation of the shell at high applied forces. Thus, it was demonstrated that PeakForce QNM mode can be used for quantitative measurements of the elastic properties of submicon-sized silica hollow particles with nano-size shell thickness, as well as for estimation of the buckling behaviour beyond the elastic regime of shell deformations. Full article

This paper describes an effective strategy based on Lerch polynomial method for solving mixed integral equations (MIE) in position and time with a strongly symmetric singular kernel in the space $L_2(-1,1)\times C[0,T],(T<1).$ The Quadratic numerical method (QNM) was applied to obtain a system of Fredholm integral equations (SFIE), then the Lerch polynomials method (LPM) was applied to transform SFIE into a system of linear algebraic equations (SLAE). The existence and uniqueness of the integral equation’s solution are discussed using Banach’s fixed point theory. Also, the convergence and stability of the solution and the stability of the error are discussed. Several examples are given to illustrate the applicability of the presented method. The Maple program obtains all the results. A numerical simulation is carried out to determine the efficacy of the methodology, and the results are given in symmetrical forms. From the numerical results, it is noted that there is a symmetry utterly identical to the kernel used when replacing each x with y. Full article

PeakForce quantitative nanomechanical AFM mode (PF-QNM) is a popular AFM technique designed to measure multiple mechanical features (e.g., adhesion, apparent modulus, etc.) simultaneously at the exact same spatial coordinates with a robust scanning frequency. This paper proposes compressing the initial high-dimensional dataset obtained from the PeakForce AFM mode into a subset of much lower dimensionality by a sequence of proper orthogonal decomposition (POD) reduction and subsequent machine learning on the low-dimensionality data. A substantial reduction in user dependency and subjectivity of the extracted results is obtained. The underlying parameters, or “state variables”, governing the mechanical response can be easily extracted from the latter using various machine learning techniques. Two samples are investigated to illustrate the proposed procedure (i) a polystyrene film with low-density polyethylene nano-pods and (ii) a PDMS film with carbon–iron particles. The heterogeneity of material, as well as the sharp variation in topography, make the segmentation challenging. Nonetheless, the underlying parameters describing the mechanical response naturally offer a compact representation allowing for a more straightforward interpretation of the high-dimensional force–indentation data in terms of the nature (and proportion) of phases, interfaces, or topography. Finally, those techniques come with a low processing time cost and do not require a prior mechanical model. Full article

The ability of bumblebee gravity models to explain dark energy, which is the phenomenon responsible for the universe’s observed accelerated expansion, is one of their most significant applications. An effect that causes faster expansion can be linked to how much the Lorentz symmetry of our universe is violated. Moreover, since we do not know what generates dark energy, the bumblebee gravity theory seems highly plausible. By utilizing the physical changes happening around a rotating bumblebee black hole (RBBH), we aim to obtain more specific details about the bumblebee black hole’s spacetime and our universe. However, as researched in the literature, slow-spinning RBBH (SRBBH) spacetime, which has a higher accuracy, will be considered instead of general RBBH. To this end, we first employ the Rindler–Ishak method (RIM), which enables us to study how light is bent in the vicinity of a gravitational lens. We evaluate the deflection angle of null geodesics in the equatorial plane of the SRBBH spacetime. Then, we use astrophysical data to see the effect of the Lorentz symmetry breaking (LSB) parameter on the bending angle of light for numerous astrophysical stars and black holes. We also acquire the analytical greybody factors (GFs) and quasinormal modes (QNMs) of the SRBBH. Finally, we visualize and discuss the results obtained in the conclusion section. Full article

This work is dedicated to the investigation of the superradiant stability of a rotating black hole derived from the nonlinear Maxwell theory of gravity, $f\left(R\right)$. The evaluation of stability and instability in this study will be based on the absence and presence of the magnetic field, respectively, when the magnetic field constant is $c_4=0$ and $c_4\ne 0$. For the black hole under discussion, analyses of the greybody factors (GFs) and quasi-normal modes (QNMs) are also carried out. To this end, we first consider the Klein–Gordon equation for the scalar waves propagating in the black hole’s geometry. The resulting radial equation is then reduced to a one-dimensional Schrödinger-like wave equation with effective potential energy. The effects of the nonlinear Maxwell $f\left(R\right)$ gravity theory parameters (q, c, and $c_4$) on the effective potential, GFs, and QNMs are examined. The results demonstrate that, although the parameters q, c, and $c_4$ all influence the effective potential, they do not affect the GFs and QNMs. All results are presented and summarized using appropriate graphics and tables. Full article

Overcoming the short lifespan of current dental adhesives remains a significant clinical need. Adhesives rely on formation of the hybrid layer to adhere to dentin and penetrate within collagen fibrils. However, the ability of adhesives to achieve complete enclosure of demineralized collagen fibrils is recognized as currently unattainable. We developed a peptide-based approach enabling collagen intrafibrillar mineralization and tested our hypothesis on a type-I collagen-based platform. Peptide design incorporated collagen-binding and remineralization-mediating properties using the domain structure conservation approach. The structural changes from representative members of different peptide clusters were generated for each functional domain. Common signatures associated with secondary structure features and the related changes in the functional domain were investigated by attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and circular dichroism (CD) spectroscopy, respectively. Assembly and remineralization properties of the peptides on the collagen platforms were studied using atomic force microscopy (AFM). Mechanical properties of the collagen fibrils remineralized by the peptide assemblies was studied using PeakForce-Quantitative Nanomechanics (PF-QNM)-AFM. The engineered peptide was demonstrated to offer a promising route for collagen intrafibrillar remineralization. This approach offers a collagen platform to develop multifunctional strategies that combine different bioactive peptides, polymerizable peptide monomers, and adhesive formulations as steps towards improving the long-term prospects of composite resins. Full article

The correspondence between the shadow radius and the real part of the quasinormal modes (QNMs) of a Kerr–Sen black hole is studied. By using the equation of the shadow radius of Kerr–Sen black hole and the angular separation constant of the QNMs, the expression of QNMs related to shadow radius is established in the eikonal limit. We found that, our formula can reduce to the previous result of Kerr black hole when Kerr-Sen parameter b sets to zero. Full article