The exoskeleton: A solution for seismic retrofitting of existing buildings

An exoskeleton is an external steel self-supporting system rigidly linked to an existing building that need to be safeguarded against seismic actions in order to comply with the current technical standards. Its application can guarantee an innovative seismic adjustment that combines structural and safety goals with sustainable properties. The present study deals with the performances of the developed coupled system under seismic actions when a suitable exoskeleton structure is applied to a real construction. It is designed with an in-plane rigid behaviour at each floor and a non-dissipative rigid link connects the primary building to the external structure. Early descriptions of the inner and the external constructions forerun the dynamic analysis, which allows to understand seismic response of the system especially in terms of frequencies and periods of vibration, floor displacements, stiffness and shear forces. Ensuing outcomes highlight the capability the exoskeleton has in taking base and floor shear forces as well as in reducing displacements and deformations of the primary building, so that it is protected from a potential earthquake collapse

The exoskeleton technology as a solution to seismic adjustment of existing buildings

The high seismic vulnerability of the Italian territory and its ancient building heritage require attention regarding planning inter-ventions on existing buildings. In fact, they show both structural and technological design deficiencies mainly due to the period of construction, the lack of design to withstand horizontal forces and the type of material used, which is mainly masonry and reinforced concrete. Therefore, it is extremely difficult to intervene with solutions of seismic improvement or adjustment also concerning the economic point of view. This work suggests an advanced approach to address the problem employing the innovative concept of an external self-supporting steel system: The exoskeleton technology. It has been applied to guarantee the seismic adjustment of an existing structure that aims at reaching higher safety targets as well as new aesthetic and sustainable features. Due to the overcoming of lifespan limit of 50 years, the explored residential construction no longer complies with the current technical standards; the issue has been solved connecting the two structures by a non-dissipative rigid link to create a coupled system whose floors show an in-plane rigid behaviour while maintaining separated their response to seismic actions. Initial explanations of the internal and the outer constructions advance the dynamic analysis, which allows to highlight the main seismic properties of the whole model such as frequencies and periods of vibration, floor displacements and shear forces. Following outcomes do not just focus on how the exoskeleton can take base and floor shear forces but also the way it manages to strongly reduce displacements and deformations of the primary building, so that it can bear earthquake actions preventing not only collapse but also reducing non-structural elements damage

Leptin Increases: Physiological Roles in the Control of Sympathetic Nerve Activity, Energy Balance, and the Hypothalamic-Pituitary-Thyroid Axis

: It is well established that decreases in plasma leptin levels, as with fasting, signal starvation and elicit appropriate physiological responses, such as increasing the drive to eat and decreasing energy expenditure. These responses are mediated largely by suppression of the actions of leptin in the hypothalamus, most notably on arcuate nucleus (ArcN) orexigenic neuropeptide Y neurons and anorexic pro-opiomelanocortin neurons. However, the question addressed in this review is whether the effects of increased leptin levels are also significant on the long-term control of energy balance, despite conventional wisdom to the contrary. We focus on leptin's actions (in both lean and obese individuals) to decrease food intake, increase sympathetic nerve activity, and support the hypothalamic-pituitary-thyroid axis, with particular attention to sex differences. We also elaborate on obesity-induced inflammation and its role in the altered actions of leptin during obesity

Multidomain switching in the ferroelectric nanodots

Controlling the polarization switching in the ferroelectric nanocrystals, nanowires and nanodots has an inherent specificity related to the emergence of depolarization field that is associated with the spontaneous polarization. This field splits the finite-size ferroelectric sample into polarization domains. Here, based on 3D numerical simulations, we study the formation of 180$^{\circ }$ polarization domains in a nanoplatelet, made of uniaxial ferroelectric material, and show that in addition to the polarized monodomain state, the multidomain structures, notably of stripe and cylindrical shapes, can arise and compete during the switching process. The multibit switching protocol between these configurations may be realized by temperature and field variations

Convex plumbings in closed hyperbolic 4-manifolds

We show that every plumbing of disc bundles over surfaces whose genera satisfy a simple inequality may be embedded as a convex submanifold in some closed hyperbolic four-manifold. In particular its interior has a geometrically finite hyperbolic structure that covers a closed hyperbolic four-manifold

Finding functional motifs in protein sequences with deep learning and natural language models

Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machine learning and encoding schemas are available for predicting different structural/functional motifs. Particularly interesting is the adoption of protein language models to encode proteins in addition to evolution information and physicochemical parameters. A thorough analysis of recent predictors developed for annotating transmembrane regions, sorting signals, lipidation and phosphorylation sites allows to investigate the state-of-the-art focusing on the relevance of protein language models for the different tasks. This highlights that more experimental data are necessary to exploit available powerful machine learning methods

Machine learning solutions for predicting protein–protein interactions

Proteins are social molecules. Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein-aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein-binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never-seen-before examples. ML tools address the problem of protein–protein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML-computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind datasets. Results indicate that the state-of-the-art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI-functional interaction