Parametric Investigation on the Effectiveness of FRM-Retrofitting in Masonry Buttressed Arches

Buttressed arches represent some of the most vulnerable elements in historical masonry buildings under seismic actions. Given their structural, architectural and often artistic importance, it is paramount to investigate effective retrofitting measures which satisfy reversibility and compatibility requirements for historical heritage. Among these, Fibre-Reinforced Mortar (FRM), characterised by enhanced ductility, has recently emerged in the scientific literature. In this paper, a numerical investigation aimed at exploring the effectiveness of this seismic retrofitting technique, when applied at intrados or extrados of various typologies of buttressed arches, is presented. An automatic tool for limit analysis is described and validated against a nonlinear Discrete Macro-Element modelling approach. This precedes an extensive parametric analysis, which has highlighted the effect of various geometrical features of the system on both collapse mechanism and maximum acceleration in the unreinforced configuration, and the high increase in seismic capacity provided by the retrofitting. This is particularly remarkable in case of local mechanism, i.e., wholly within the arch, in which case the FRM may be responsible of an overall shifting to a semi-global failure type.

Perirenal Adipose Tissue Inflammation: Novel Insights Linking Metabolic Dysfunction to Renal Diseases

A healthy adipose tissue (AT) is indispensable to human wellbeing. Among other roles, it contributes to energy homeostasis and provides insulation for internal organs. Adipocytes were previously thought to be a passive store of excess calories, however this view evolved to include an endocrine role. Adipose tissue was shown to synthesize and secrete adipokines that are pertinent to glucose and lipid homeostasis, as well as inflammation. Importantly, the obesity-induced adipose tissue expansion stimulates a plethora of signals capable of triggering an inflammatory response. These inflammatory manifestations of obese AT have been linked to insulin resistance, metabolic syndrome, and type 2 diabetes, and proposed to evoke obesity-induced comorbidities including cardiovascular diseases (CVDs). A growing body of evidence suggests that metabolic disorders, characterized by AT inflammation and accumulation around organs may eventually induce organ dysfunction through a direct local mechanism. Interestingly, perirenal adipose tissue (PRAT), surrounding the kidney, influences renal function and metabolism. In this regard, PRAT emerged as an independent risk factor for chronic kidney disease (CKD) and is even correlated with CVD. Here, we review the available evidence on the impact of PRAT alteration in different metabolic states on the renal and cardiovascular function. We present a broad overview of novel insights linking cardiovascular derangements and CKD with a focus on metabolic disorders affecting PRAT. We also argue that the confluence among these pathways may open several perspectives for future pharmacological therapies against CKD and CVD possibly by modulating PRAT immunometabolism.

Ontology of a Wavefunction from the Perspective of an Invariant Proper Time

All the arguments of a wavefunction are defined at the same instant implying a notion of simultaneity. In a somewhat related matter, certain phenomena in quantum mechanics seem to have non-local causal relations. Both concepts are in contradiction with special relativity. We propose to define the wavefunction with respect to the invariant proper time of special relativity instead of standard time. Moreover, we shall adopt the original idea of Schrodinger suggesting that the wavefunction represents an ontological cloud-like object that we shall call ‘individual fabric’ that has a finite density amplitude vanishing at infinity. Consequently, measurement can be assimilated to a confining potential that triggers an inherent non-local mechanism within the individual fabric. It is formalised by multiplying the wavefunction with a localising gaussian as in the GRW theory but in a deterministic manner.

Cross-Modality Person Re-Identification Based on Heterogeneous Center Loss and Non-Local Features

Cross-modality person re-identification is the study of images of people matching under different modalities (RGB modality, IR modality). Given one RGB image of a pedestrian collected under visible light in the daytime, cross-modality person re-identification aims to determine whether the same pedestrian appears in infrared images (IR images) collected by infrared cameras at night, and vice versa. Cross-modality person re-identification can solve the task of pedestrian recognition in low light or at night. This paper aims to improve the degree of similarity for the same pedestrian in two modalities by improving the feature expression ability of the network and designing appropriate loss functions. To implement our approach, we introduce a deep neural network structure combining heterogeneous center loss (HC loss) and a non-local mechanism. On the one hand, this can heighten the performance of feature representation of the feature learning module, and, on the other hand, it can improve the similarity of cross-modality within the class. Experimental data show that the network achieves excellent performance on SYSU-MM01 datasets.

Out-of-plane local mechanism analysis with finite element method

The stability check of masonry structures is a debated problem in Italy that poses serious problems for its extensive use. Indeed, the danger of out of plane collapse of masonry walls, which is one of the more challenging to evaluate, is traditionally addressed not using finite element models (FEM). The power of FEM is not properly used and some simplified method are preferred. In this paper the use of the thrust surface is suggested. This concept allows to to evaluate the eccentricity of the membrane stresses using the FEM method. For this purpose a sophisticated, layered, finite element with a no-tension material is used. To model a no-tension material we used the smeared crack method as it is not mesh-dependent and it is well known since the early ’80 in an ASCE Report [1]. The described element has been implemented by the author in the program Nòlian by Softing.

Mesotherapy: From Historical Notes to Scientific Evidence and Future Prospects

Intradermal therapy, known as mesotherapy, is a technique used to inject a drug into the surface layer of the skin. In particular, it involves the use of a short needle to deposit the drug in the dermis. The intradermal microdeposit modulates the drug’s kinetics, slowing absorption and prolonging the local mechanism of action. It is successfully applied in the treatment of some forms of localized pain syndromes and other local clinical conditions. It could be suggested when a systemic drug-sparing effect is useful, when other therapies have failed (or cannot be used), and when it can synergize with other pharmacological or nonpharmacological therapies. Despite the lack of randomized clinical trials in some fields of application, a general consensus is also reached in nonpharmacological mechanism of action, the technique execution modalities, the scientific rationale to apply it in some indications, and the usefulness of the informed consent. The Italian Mesotherapy Society proposes this position paper to apply intradermal therapy based on scientific evidence and no longer on personal bias.

Pyramid Constrained Self-Attention Network for Fast Video Salient Object Detection

Spatiotemporal information is essential for video salient object detection (VSOD) due to the highly attractive object motion for human's attention. Previous VSOD methods usually use Long Short-Term Memory (LSTM) or 3D ConvNet (C3D), which can only encode motion information through step-by-step propagation in the temporal domain. Recently, the non-local mechanism is proposed to capture long-range dependencies directly. However, it is not straightforward to apply the non-local mechanism into VSOD, because i) it fails to capture motion cues and tends to learn motion-independent global contexts; ii) its computation and memory costs are prohibitive for video dense prediction tasks such as VSOD. To address the above problems, we design a Constrained Self-Attention (CSA) operation to capture motion cues, based on the prior that objects always move in a continuous trajectory. We group a set of CSA operations in Pyramid structures (PCSA) to capture objects at various scales and speeds. Extensive experimental results demonstrate that our method outperforms previous state-of-the-art methods in both accuracy and speed (110 FPS on a single Titan Xp) on five challenge datasets. Our code is available at https://github.com/guyuchao/PyramidCSA.