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\"Since it opened in 1854, McSorley's Old Ale House has been a New York institution ... In addition to the bar's rich history, McSorley's is home to a ... story about two men: Rafe Bartholomew, the writer who grew up in the landmark pub, and his father, Geoffrey 'Bart' Bartholomew, a career bartender who has been working the taps for forty-five years ... For Rafe, the bar means home. It's the place where he and his father have worked side by side, serving light and dark ale, always in pairs ... Where they've celebrated victories, like the publication of his father's first book of poetry, and coped with misfortune, like the death of Rafe's mother. Where Rafe learned to be part of something bigger than himself\"--Provided by publisher.

Dynamic simulation of fictional contact between vehicle tires and road surfaces necessitates fine meshes both in spatial and temporal scales, leading to high computational costs. Herein, the method of nonsmooth model order reduction is proposed to predict efficiently the transient tire–road contact dynamics with friction. The Arbitrary Lagrangian–Eulerian description based on the absolute nodal coordinate formulation for modeling the tire motion and the nonsmooth approach based on the cone complementarity problem for computing the frictional contact are merged into the reduction process. These approaches ensure the prediction efficiency and accuracy of frictional contact dynamics of tires during transient events. Then, the linearization procedures based on the Craig-Bampton modal reduction are performed successively within the separated time intervals to reduce the nonlinear dynamics equations of the nonsmooth multibody system to the low-dimensional modal spaces. And the map between the modal coordinates from a prior time interval to a subsequent time interval is fulfilled via the velocity transformation at their shared time node. Finally, five numerical examples and an experiment are presented to verify the accuracy and efficacy of the nonsmooth ALE reduced-order models of cable or tire systems. The nonlinear dynamics with large rotations, large deformations and nonsmoothness can be analyzed accurately with the reduced-order models. And the proposed method achieves a 50% reduction of computation time for the simulation of a single tire and a 30% reduction of computation time for that of an entire car with four wheels while maintaining the same computational accuracy.

•A multimodal semantic control network was delineated with formal meta-analyses.•Semantic control recruits inferior and medial frontal and posterior temporal cortex.•A large extent of posterior temporal cortex was implicated and no parietal regions.•Semantic control is left-lateralised but regions show differential lateralisation.•The semantic control regions were situated in the context of the wider semantic network. Semantic control, the ability to selectively access and manipulate meaningful information on the basis of context demands, is a critical component of semantic cognition. The precise neural correlates of semantic control are disputed, with particular debate surrounding parietal involvement, the spatial extent of the posterior temporal contribution and network lateralisation. Here semantic control is revisited, utilising improved analysis techniques and a decade of additional data to refine our understanding of the network. A meta-analysis of 925 peaks over 126 contrasts illuminated a left-focused network consisting of inferior frontal gyrus, posterior middle temporal gyrus, posterior inferior temporal gyrus and dorsomedial prefrontal cortex. This extended the temporal region implicated, and found no parietal involvement. Although left-lateralised overall, relative lateralisation varied across the implicated regions. Supporting analyses confirmed the multimodal nature of the semantic control network and situated it within the wider set of regions implicated in semantic cognition.

Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders. •We quantitatively summarize the literature on emotion regulation (ER) using ALE.•Using MACM and quantitative functional inference we develop a neural model of ER.•DLPFC is related to higher order “cold” regulatory processes.•VLPFC evaluates salience and indicates need to regulate.•STG, angular gyrus and SMA are associated to execution of regulation.

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain‐general network perspective, suggesting the existence of a “prediction network,” these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain‐general predictive network that encompasses all well‐established cognitive domains exists remains unanswered. The present meta‐analysis aims to address this gap by testing the hypothesis that PP relies on a large‐scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta‐analytic approach was employed, along with Meta‐Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena‐independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross‐domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large‐scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross‐domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta‐Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain‐general, large‐scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context‐ and stimulus‐dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world. Predictive processing units operate within a cognitive domain‐general network encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. The role played at a cross‐domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta‐Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network.

The constrained layer damping structure is used to resist the impact damage from blast load and furthermore the two-scale topology optimization of the damping layer can deduce the additional mass of micro damping material while satisfying the required objective for restraining impact response. However, in their optimization model, the non-uniform pressure of blast load is usually simplified into a concentrated or uniform load equivalently, for the thin-walled structure, it is necessary to regard the load as a non-uniform distribution. In this paper, in order to reveal the rule between blast load and optimization layouts, a concurrent topology optimization approach is proposed, where the non-uniform distribution of blast load is considered. Firstly, Arbitrary Lagrange–Euler method is used to obtain the blast load as an input condition of dynamic response. Then, the optimization process is applied by combining using the Precise Integration Method used to solve the dynamic response, an improved Adjoint Variable Method for sensitivity study, and Optimization Criterion method employed to update the design variables. In order to make sure both the micro-layouts and their distribution are optimized, the micro sensitivity depends on the vectors of element displacements affected by the different macro load forms. The results of numerical examples show that the non-uniform load has a notable influence on the shape and size of micro-topology layouts as well as the position of macro-reserved material.

This paper presents new advances in the arbitrary Lagrangian–Eulerian modal method (ALEM) recently developed for the systematic simulation of the dynamics of general reeving systems. These advances are related to a more convenient model of the sheaves dynamics and the use of axial deformation modes to account for non-constant axial forces within the finite elements. Regarding the sheaves dynamics, the original formulation uses kinematic constraints to account for the torque transmission at the sheaves by neglecting the rotary inertia. One of the advances described in this paper is the use of the rotation angles of the sheaves as generalized coordinates together with the rope-to-sheave no-slip assumption as linear constraint equations. This modeling option guarantees the exact torque balance at the sheave without including any nonlinear kinematic constraint. Numerical results show the influence in the system dynamics of the sheave rotary inertia. Regarding the axial forces within the finite elements, the original formulation uses a combination of absolute position coordinates and transverse local modal coordinates to account for the rope absolute position and deformation shape. The axial force, which only depends on the absolute position coordinates, is constant along the element because linear shape functions are assumed to describe the axial displacements. For reeving systems with very long rope spans, as the elevators of high buildings, the constant axial force is inaccurate because the weight of the ropes becomes important and the axial force varies approximately linearly within the rope free span. To account for space-varying axial forces, this paper also introduces modal coordinates in the axial direction. Numerical results show that a set of three modal coordinates in the axial direction is enough to simulate linearly varying axial forces.

Friction Stir Welding (FSW) is a novel kind of welding for joining metals that are impossible or difficult to weld by conventional methods. Three-dimensional nature of FSW makes the experimental investigation more complex. Moreover, experimental observations are often costly and time consuming, and usually there is an inaccuracy in measuring the data during experimental tests. Thus, Finite Element Methods (FEMs) has been employed to overcome the complexity, to increase the accuracy and also to reduce costs. It should be noted that, due to the presence of large deformations of the material during FSW, strong distortions of mesh might be happened in the numerical simulation. Therefore, one of the most significant considerations during the process simulation is the selection of the best numerical approach. It must be mentioned that; the numerical approach selection determines the relationship between the finite grid (mesh) and deforming continuum of computing zones. Also, numerical approach determines the ability of the model to overcome large distortions of mesh and provides an accurate resolution of boundaries and interfaces. There are different descriptions for the algorithms of continuum mechanics include Lagrangian and Eulerian. Moreover, by combining the above-mentioned methods, an Arbitrary Lagrangian–Eulerian (ALE) approach is proposed. In this paper, a comparison between different numerical approaches for thermal analysis of FSW at both local and global scales is reviewed and the applications of each method in the FSW process is discussed in detail. Observations showed that, Lagrangian method is usually used for modelling thermal behavior in the whole structure area, while Eulerian approach is seldom employed for modelling of the thermal behavior, and it is usually employed for modelling the material flow. Additionally, for modelling of the heat affected zone, ALE approach is found to be as an appropriate approach. Finally, several significant challenges and subjects remain to be addressed about FSW thermal analysis and opportunities for the future work are proposed.

Since the publication of the first neuroscience study investigating emotion with music about two decades ago, the number of functional neuroimaging studies published on this topic has increased each year. This research interest is in part due to the ubiquity of music across cultures, and to music's power to evoke a diverse range of intensely felt emotions. To support a better understanding of the brain correlates of music-evoked emotions this article reports a coordinate-based meta-analysis of neuroimaging studies (n = 47 studies with n = 944 subjects). The studies employed a range of diverse experimental approaches (e.g., using music to evoke joy, sadness, fear, tension, frissons, surprise, unpleasantness, or feelings of beauty). The results of an activation likelihood estimation (ALE) indicate large clusters in a range of structures, including amygdala, anterior hippocampus, auditory cortex, and numerous structures of the reward network (ventral and dorsal striatum, anterior cingulate cortex, orbitofrontal cortex, secondary somatosensory cortex). The results underline the rewarding nature of music, the role of the auditory cortex as an emotional hub, and the role of the hippocampus in attachment-related emotions and social bonding.

To achieve high-throughput and high-efficiency separation based on optically induced dielectrophoresis (ODEP), an ODEP-based transient numerical model containing microparticles is developed under alternating current (AC) electric field coupling with an open flow field. In this model, the MST method is employed to calculate the time-averaged AC DEP force and the fluid viscous resistance acting on the particle, the Arbitrary Lagrangian–Eulerian (ALE) method is used to numerically solve the strong coupling electric-fluid–solid mechanics, and the efficient and continuous separation of microparticles is achieved. The results show that the trajectories of particles with different conductivity are clearly differentiated due to two different DEP actions, which enables separation of particles, and its separation performance can be optimized by adjusting the key parameters, including bright area width, applied alternating current (AC) electric voltage and inlet flow velocity. This study explains the continuous separation mechanism of particles under the combined action of AC electric field and flow field, and provides theoretical support for the design of high-efficiency ODEP microparticles separation device.