Active Truss Metamaterials: Modelling and Tuning of Band Gaps

Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergence of so-called band gaps. So far, the optimization of the metamaterial properties and therefore of the band gaps has been typically performed on passive PCs and AMMs. Hence, the band gap properties cannot be tuned anymore after the production process of the metamaterials;this problem can be overcome thanks to the use of active materials. In this work, material and geometric nonlinearities are exploited to actively tune the frequency ranges of the band gaps of an architected AMM characterized by a three-dimensional periodicity. Specifically, a hyperelastic piezoelectric composite, that can be obtained by embedding piezo nanoparticles in a soft polymeric matrix, is considered to assess the effects of the nonlinearities on the behavior of sculptured microstructures, taking advantage of instability-induced pattern transformation and piezoelectricity to actively tune the band gaps. .

Active Truss Metamaterials: Modelling and Tuning of Band Gaps

Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergence of so-called band gaps. So far, the optimization of the metamaterial properties and therefore of the band gaps has been typically performed on passive PCs and AMMs. Hence, the band gap properties cannot be tuned anymore after the production process of the metamaterials;this problem can be overcome thanks to the use of active materials. In this work, material and geometric nonlinearities are exploited to actively tune the frequency ranges of the band gaps of an architected AMM characterized by a three-dimensional periodicity. Specifically, a hyperelastic piezoelectric composite, that can be obtained by embedding piezo nanoparticles in a soft polymeric matrix, is considered to assess the effects of the nonlinearities on the behavior of sculptured microstructures, taking advantage of instability-induced pattern transformation and piezoelectricity to actively tune the band gaps. .

Hepatocellular carcinoma and microbiota:Implications for clinical management and treatment

Gut microbiota plays an essential role in host homeostasis.It is involved in several physiological processes such as nutrients digestion and absorption,maintenance of intestinal epithelial barrier integrity and immune system self-tolerance.Especially the gut microbiota is assumed to play a crucial role in many gastrointestinal,pancreatic and liver disorders.Its role in hepatic carcinogenesis is also gaining increasing interest,especially regarding the development of therapeutic strategies.Different studies are highlighting a link between some bacterial strains and liver disease,including hepatocellular carcinoma(HCC).Indeed,HCC represents an interesting field of research in this perspective,due to the gut-liver axis,to the implication of microbiota in the immune system and to the increasing number of immunotherapy agents investigated in this tumour.Thus,the assessment of the role of microbiota in influencing clinical outcome for patients treated with these drugs is becoming of increasing importance.Our review aims to give an overview on the relationship between microbiota and HCC development/progression and treatment.We focus on potential implications on the available treatment strategies and those under study in the various stages of disease.We highlight the pathogenic mechanisms and investigate the underlying molecular pathways involved.Moreover,we investigate the potential prognostic and/or predictive role of microbiota for target therapies,immune checkpoint inhibitors and loco-regional treatment.Finally,given the limitation of current treatments,we analyze the gut microbiota-mediated therapies and its potential options for HCC treatment focusing on fecal microbiota transplantation.

How to improve metastatic pancreatic ductal adenocarcinoma patients’selection:Between clinical trials and the real-world

As underlined in the minireview by Blomstrand et al,given the poor prognosis and the paucity of data on a therapeutic sequence in pancreatic ductal adenocarcinoma(PDAC),additional randomized controlled trials and real-world evidence studies addressing current and novel regimens are needed.The real-world outcomes of first-line chemotherapy regimens such as FOLFIRINOX and gemcitabine/nab-paclitaxel are thoroughly reviewed and seem to be largely generalizable in a real-world context.Regarding second-line chemotherapy,the key question about the optimal sequence of regimens remains uncertain.Precisely in this setting,it is therefore useful to encourage the implementation of clinical studies that may contribute to the scarcity of data available up to now.We report our experience with a small group of patients treated with second-line liposomal irinotecan(nal-IRI)plus 5-fluorouracil and leucovorin.To improve the treatment of patients affected by PDAC,it is useful to identify subgroups of patients who may benefit from target treatments(e.g.,BRCA mutant)and it is also important to focus on any prognostic factors that may affect the survival and treatment of these patients.

A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors

Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length- and time-scales. Smart Micro Electro-Mechanical Systems (MEMS) are also characterized by different physical phenomena affecting their properties at different scales. Data-driven formulations can then be helpful to deal with the complexity of the multi-physics governing their response to the external stimuli, and optimize their performances. As an example, Lorentz force micro-magnetometers working principle rests on the interaction of a magnetic field with a current flowing inside a semiconducting, micro-structured medium. If an alternating current with a properly set frequency is let to flow longitudinally in a slender beam, the system is driven into resonance and the sensitivity to the magnetic field may result largely enhanced. In our former activity, a reduced-order physical model of the movable structure of a single-axis Lorentz force MEMS magnetometer was developed, to feed a multi-objective topology optimization procedure. That model-based approach did not account for stochastic effects, which lead to the scattering in the experimental data at the micrometric length-scale. The formulation is here improved to allow for stochastic effects through a two-scale deep learning model designed as follows: at the material scale, a neural network is adopted to learn the scattering in the mechanical properties of polysilicon induced by its polycrystalline morphology;at the device scale, a further neural network is adopted to learn the most important geometric features of the movable parts that affect the overall performance of the magnetometer. Some preliminary results are discussed, and an extension to allow for size effects is finally foreseen.