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This paper contains new results about the well-posedness and the asymptotic dynamics of solutions for a general abstract coupled system that arises in connection with thermoelastic Balakrishnan–Taylor beam models with fractional operators. [ABSTRACT FROM AUTHOR]

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN – a multi-view, weakly-supervised deep clustering method – which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes. [ABSTRACT FROM AUTHOR]

Introduction: Femur head necrosis (FHN) is a challenging clinical disease with unclear underlying mechanism, which pathologically is associated with disordered metabolism. However, the disordered metabolism in cancellous bone of FHN was never analyzed by gas chromatography-mass spectrometry (GC-MS). Objectives: To elucidate altered metabolism pathways in FHN and identify putative biomarkers for the detection of FHN. Methods: We recruited 26 patients with femur head necrosis and 22 patients with femur neck fracture in this study. Cancellous bone tissues from the femoral heads were collected after the surgery and were analyzed by GC-MS based untargeted metabolomics approach. The resulting data were analyzed via uni- and multivariate statistical approaches. The changed metabolites were used for the pathway analysis and potential biomarker identification. Results: Thirty-seven metabolites distinctly changed in FHN group were identified. Among them, 32 metabolites were upregulated and 5 were downregulated in FHN. The pathway analysis showed that linoleic acid metabolism were the most relevant to FHN pathology. On the basis of metabolites network, L-lysine, L-glutamine and L-serine were deemed as the junctions of the whole metabolites. Finally, 9,12-octadecadienoic acid, inosine, L-proline and octadecanoic acid were considered as the potential biomarkers of FHN. Conclusion: This study provides a new insight into the pathogenesis of FHN and confirms linoleic acid metabolism as the core. [ABSTRACT FROM AUTHOR]

The paper is concerned with the exponential attractors for the viscoelastic wave model in Ω ⊂ R 3 : u tt - h t (0) Δ u - ∫ 0 ∞ ∂ s h t (s) Δ u (t - s) d s + f (u) = g , with time-dependent memory kernel h t (·) which is used to model aging phenomena of the material. Conti et al. (Am J Math 140(2):349–389, 2018a; Am J Math 140(6):1687–1729, 2018b) recently provided the correct mathematical setting for the model and a well-posedness result within the novel theory of dynamical systems acting on time-dependent spaces, recently established by Conti et al. (J Differ Equ 255:1254–1277, 2013), and proved the existence and the regularity of the time-dependent global attractor. In this work, we further study the existence of the time-dependent exponential attractors as well as their regularity. We establish an abstract existence criterion via quasi-stability method introduced originally by Chueshov and Lasiecka (J Dyn Differ Equ 16:469–512, 2004), and on the basis of the theory and technique developed in Conti et al. (2018a, b) we further provide a new method to overcome the difficulty of the lack of further regularity to show the existence of the time-dependent exponential attractor. And these techniques can be used to tackle other hyperbolic models. [ABSTRACT FROM AUTHOR]

The paper investigates the well-posedness and the complete regularity of the weak solutions, and the existence of strong global attractor for the strongly damped wave equation with critical nonlinearities on R 3 : u tt - Δ u - Δ u t + h (x , u t) + g (x , u) = f (x) . We show that when both nonlinearities h (x , u t) and g(x, u) are of at most critical growth, (1) the model is well-posed and its weak solution is of higher complete regularity as t > 0 , which ensures that the weak solution is exactly the strong one; (2) the related dynamical system (S (t) , H) possesses a strong (H , H 2) -global attractor of optimal topological property, which is also the standard global attractor of optimal regularity of S(t) in H . The method developed here allows breaking through the long-standing restriction for this model on R 3 : the partial regularity of the weak solutions and almost linearity of h (x , u t) , and helps obtaining the optimal complete regularity of the weak solutions and the existence of strong global attractor. [ABSTRACT FROM AUTHOR]

Previous earthquake damage investigations in Japan have shown that prestressed high-strength concrete (PHC) piles experience shear failure in the event of an earthquake. To investigate the shear behavior of PHC piles, tests were conducted by varying the shear span-effective depth ratio and deformed bars. By verifying the correctness of an ABAQUS model, a finite element model of a pile was established and the effects of the shear span-effective depth ratio, axial load, concrete strength, and deformed bars on the shear capacity were studied. The results indicate that reducing the shear span-effective depth ratio and increasing the prestressing bar ratio and concrete strength can improve the shear capacity of PHC piles. Compared to a common PHC pile, a prestressed high-strength concrete pile reinforced with deformed bars (PRC piles) has higher shear capacity. As the shear span-effective depth ratio increases from 1.0 to 2.0 in steps of 0.25, the shear capacity decreases by 31.73%, 18.45%, 18.73%, and 16.09% in order. When the diameter of the prestressing bar increases from 7.1 to 9.0, 10.7, and 12.6 mm, the shear capacity increases by 19.25%, 9.32%, and 7.35% in order. When the diameter of the deformed bar increases from 12 to 18 mm in steps of 2 mm, the shear capacity increases by 7.2%, 7.4%, and 7.5% in order. As the axial compression ratio increases from 0 to 0.45 in steps of 0.15, the shear capacity of the PHC pile increases by 27.01%, 17.75%, and 12.27% in order, whereas the shear capacity of the PRC pile increases by 17.93%, 13.43%, and 9.77% in order. As the concrete strength increases from 60 to 80 and 100 MPa, its shear capacity increases by 6.30% and 5.87%, respectively. [ABSTRACT FROM AUTHOR]

The paper investigates the well-posedness and the regularity of the solutions, the existence and the continuity of the strong attractors for the structurally damped Kirchhoff wave models with subcritical-critical nonlinearities: u tt - (1 + ϵ ‖ ∇ u ‖ 2 ) Δ u + (- Δ) α u t + h (u t) + g (u) = f (x) , where ϵ ∈ [ 0 , 1 ] is a perturbed extensibility parameter, α ∈ [ 1 / 2 , 1) is a dissipative index. We show that when the nonlinearity g(u) is of either critical growth as α ∈ (1 / 2 , 1) or subcritical growth as α = 1 / 2 , while h (u t) is of critical growth depending on α , the model is well-posed and its weak solution is exactly the strong one; the related solution semigroup S ϵ (t) has a strong (X, Y)-global attractor and a strong (X, Y)-exponential attractor, which are also the standard global and exponential attractor of optimal regularity of S ϵ (t) in X, respectively, where X is the energy space and Y is the strong solution space; these global attractors are upper semicontinuous and these exponential attractors are Hölder continuous with respect to perturbed parameter ϵ in the sense of Y-topology, respectively. [ABSTRACT FROM AUTHOR]

In the abrasive belt grinding process, actual material removal is an important parameter that affects its accuracy. At present, for obtaining the actual material removal, offline measurements are required to establish the mathematical prediction model. To improve the accuracy and efficiency of abrasive belt machine grinding, this paper proposes a novel method for monitoring material removal using multiple sensors and a two-dimensional (2D) convolutional neural network (2D-CNN) learning algorithm. In this method, features of multiple types (color, texture, and shape) are extracted from vision signals, and that of multiple domains (time, frequency, and time–frequency domain) are extracted from sound and tactile signals. These features are constructed into a 2D feature matrix as the input model, and the 2D-CNN prediction model is established between the multisensor features and the material removal rate of the abrasive belt grinding process. An experimental dataset is used to train and verify the established model. The results show that the proposed method can identify that sensor signals are sensitive to the material removal rate. After optimizing and tuning the model parameters, the coefficient of determination of the prediction results is as high as 94.5% and the root mean square error is 0.017. Therefore, the proposed method can be employed for the prediction of material removal rate for different belt specifications and different grinding parameters. Compared to traditional machine learning methods, this method can yield better training results without feature selection and optimization. [ABSTRACT FROM AUTHOR]

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment. Alzheimer's disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information. [ABSTRACT FROM AUTHOR]

The paper investigates the robustness of pullback attractors and pullback exponential attractors of the non-autonomous Kirchhoff wave models with strong nonlinear damping: u tt - (1 + ϵ ‖ ∇ u ‖ 2 ) Δ u - σ (‖ ∇ u ‖ 2) Δ u t + f (u) = g (x , t) , where ϵ ∈ [ 0 , 1 ] is an extensibility parameter. It shows that when the growth exponent p of the nonlinearity f(u) is up to the supercritical range: 1 ≤ p < p ∗ ∗ ≡ N + 4 (N - 4) + , (i) the related process has a pullback attractor A ϵ in natural energy space H = (H 0 1 ∩ L p + 1) × L 2 for each ϵ , and it is upper semicontinuous on the perturbation ϵ ; (ii) the related process has a partially strong pullback exponential attractor M ϵ for each ϵ , and it is Hölder continuous on ϵ ∈ [ 0 , 1 ] . These results deepen and extend those in recent literatures (Chueshov in J Diff Equ 252:1229–1262, 2012; Ding et al. in Appl Math Lett 76:40–45, 2018; Wang and Zhong in Discrete Contin Dyn Syst 7:3189–3209, 2013). The main novelty of this paper is that it provides a new method to investigate the upper semicontinuity of pullback attractors and the stability of pullback exponential attractors in supercritical nonlinearity case. [ABSTRACT FROM AUTHOR]