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. .

Delineation of Protection Zones for the Main Discharge Area of the Gran Sasso Aquifer (Central Italy) through an Integrated Geomorphological and Chronological Approach

The paper proposes a study for the delineation of protection zones in the main discharge area of the Gran Sasso aquifer (Central Italy). At this aim, starting from a detailed geological and hydrogeological reconstruction, the study was divided into the following phases: 1) development of a conceptual model of water flow in the study area;2) creation of a 3D numerical model in order to simulate the groundwater flow in saturated conditions, both at the basin and at fine-scale;3) flow path analysis through deterministic and stochastic approaches;4) assessment of the aquifer vulnerability based on a geomorphological analysis of the catchment area. Conceptual and numerical models were then used to delineate protection zones for wells and springs with chronological criterion and geomorphological-structural criterion (based on the EPIK method). The results show that with a chronological approach protection zones are located along the main flow directions, corresponding to the areas surrounding wells and springs with high hydraulic conductivity values (faults and thrusts) within the satured zone. On the contrary, the geomorphological method has found some important protection zones also quite far from wells and springs, in areas characterized by quick infiltration processes. The protection zones delineated with the stochastic method were finally intersected by the vulnerability map obtained with the EPIK method, to take into account both filtration and infiltration processes. The results show the local vulnerability of the groundwater to the pollution, with protection zones extending 1 to 5 km towards northeast from springs and wells.

Liquid-Solid Partitioning of Precipitation along an Altitude Gradient and Its Statistical Properties: An Italian Case Study

Climate change is a living topic when dealing with modern natural sciences. The increase in the average air temperature, as measured in the last decades, is considered as the most relevant effect of climate change on the Earth system. Since the air temperature has a key role in determining the partitioning between liquid and solid precipitation events at a site, important changes in rainfall dynamics are expected, especially in mountainous areas. Thus, an important issue for modern hydrology is to determine how climate change would affect the liquid-solid partitioning of precipitation and its statistical properties. The main aim here is to determine, via statistical analysis and goodness-of-fit tests, whether the duration of precipitation events under the different forms (namely solid, liquid and mixed) may be characterized by the same probability distribution. Similar issue is tested for the volume of precipitation. For this aim, our study pays attention to hourly data collected along an altitude gradient identified through six automatic weather stations in Trentino region, northeast Italy. To distinguish the different types of events from observed heated pluviometers’ data, a partitioning procedure has been used and validated, through some disdrometer data. Sample data of duration and volume, relatively to solid, liquid and mixed events, are extracted, and univariate and bivariate statistics are calculated. Then, the two-sample Kolmogorov-Smirnov test is used to test if the data distinguished by different types of precipitation can be considered extracted from the same distribution. The results showed that in most cases, durations, as well as volumes of the different types of events, cannot be considered equally distributed. This consideration is particularly clear at high elevations.

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

Assessment of thermal behaviour of thermo-active diaphragm walls based on monitoring data

Thermo-active diaphragm walls have proved their effectiveness in the thermal conditioning of buildings and infrastructures. However, some aspects still need to be investigated in order to tailor methods and tools for an accurate prediction of their energy and structural performance. In this perspective, some issues are addressed that concern the definition of models for the numerical analysis, in particular issues about the modelling of geometry and thermal boundary conditions. Taking advantage of a monitoring programme on a real full-scale structure, this research focuses on the assessment of heat transfer process and thermal response of diaphragm wall and soil mass on the basis of field data. Understanding of the heat transfer process contributes to the definition of the time-dependent thermal boundary conditions at the excavation side. From the analysis of thermal gradients in the wall, the condition at the excavation side is recognised as a major factor that influences the heat transfer process, governing the direction of the heat flux in different seasons of operation of the geothermal system.

On the Effects of Different Interpretations of Stochastic Differential Equations

This paper addresses the problem of the interpretation of the stochastic differential equations (SDE). Even if from a theoretical point of view, there are infinite ways of interpreting them, in practice only Stratonovich’s and Itô’s interpretations and the kinetic form are important. Restricting the attention to the first two, they give rise to two different Fokker-Planck-Kolmogorov equations for the transition probability density function (PDF) of the solution. According to Stratonovich’s interpretation, there is one more term in the drift, which is not present in the physical equation, the so-called spurious drift. This term is not present in Itô’s interpretation so that the transition PDF’s of the two interpretations are different. Several examples are shown in which the two solutions are strongly different. Thus, caution is needed when a physical phenomenon is modelled by a SDE. However, the meaning of the spurious drift remains unclear.

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

Numeric Solution of the Fokker-Planck-Kolmogorov Equation

The solution of an n-dimensional stochastic differential equation driven by Gaussian white noises is a Markov vector. In this way, the transition joint probability density function (JPDF) of this vector is given by a deterministic parabolic partial differential equation, the so-called Fokker-Planck-Kolmogorov (FPK) equation. There exist few exact solutions of this equation so that the analyst must resort to approximate or numerical procedures. The finite element method (FE) is among the latter, and is reviewed in this paper. Suitable computer codes are written for the two fundamental versions of the FE method, the Bubnov-Galerkin and the Petrov-Galerkin method. In order to reduce the computational effort, which is to reduce the number of nodal points, the following refinements to the method are proposed: 1) exponential (Gaussian) weighting functions different from the shape functions are tested;2) quadratic and cubic splines are used to interpolate the nodal values that are known in a limited number of points. In the applications, the transient state is studied for first order systems only, while for second order systems, the steady-state JPDF is determined, and it is compared with exact solutions or with simulative solutions: a very good agreement is found.

Free and open source software for geospatial applications(FOSS4G)to support Future Earth

The development,integration,and distribution of the information and spatial data infrastructure(i.e.Digital Earth;DE)necessary to support the vision and goals of Future Earth(FE)will occur in a distributed fashion,in very diverse technological,institutional,socio-cultural,and economic contexts around the world.This complex context and ambitious goals require bringing to bear not only the best minds,but also the best science and technologies available.Free and Open Source Software for Geospatial Applications(FOSS4G)offers mature,capable and reliable software to contribute to the creation of this infrastructure.In this paper we point to a selected set of some of the most mature and reliable FOSS4G solutions that can be used to develop the functionality required as part of DE and FE.We provide examples of large-scale,sophisticated,mission-critical applications of each software to illustrate their power and capabilities in systems where they perform roles or functionality similar to the ones they could perform as part of DE and FE.We provide information and resources to assist the readers in carrying out their own assessments to select the best FOSS4G solutions for their particular contexts and system development needs.

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.

Extracellular polymeric substances as paper coating biomaterials derived from anaerobic granular sludge

Recovering extracellular polymeric substances(EPS)from waste granular sludge offers a cost-effective and sustainable approach for transforming wastewater resources into industrially valuable products.Yet,the application potential of these EPS in real-world scenarios,particularly in paper manufacturing,remains underexplored.Here we show the feasibility of EPS-based biomaterials,derived from anaerobic granular sludges,as novel coating agents in paper production.We systematically characterised the rheological properties of various EPS-based suspensions.When applied as surface sizing agents,these EPS-based biomaterials formed a distinct,ultra-thin layer on paper,as evidenced by scanning electron microscopy.A comprehensive evaluation of water and oil penetration,along with barrier properties,revealed that EPS-enhanced coatings markedly diminished water absorption while significantly bolstering oil and grease resistance.Optimal performance was observed in EPS variants with elevated protein and hydrophobic contents,correlating with their superior rheological characteristics.The enhanced water-barrier and grease resistance of EPS-coated paper can be attributed to its non-porous,fine surface structure and the functional groups in EPS,particularly the high protein content and hydrophobic humic-like substances.This research marks the first demonstration of utilizing EPS from anaerobic granular sludge as paper-coating biomaterials,bridging a critical knowledge gap in the sustainable use of biopolymers in industrial applications.

Assessing OSM building completeness for almost 13,000 cities globally

OpenStreetMap(OSM)is an essential source for acquiring building data,although such data may suffer from quality issues.Many studies have focused on assessing OSM building data quality but few have been carried out on a global scale.This study aims to assess OSM building completeness(a quality measure)for 12,975 cities across the globe.This was achieved by employing population grid data as a proxy for reference building data.Not only the completeness of each city but also that of the grids within that city was assessed.The assessment results were evaluated based on calculating the overall accuracy and the r-square value between estimated and reference OSM building completeness values.Results showed that for 75%of cities,the completeness is lower than 20%;no more than 9%of cities have an estimated completeness higher than 80%.The overall accuracies of most countries were higher than 80%.The estimated completeness was also highly correlated with the reference completeness,which verifies the effectiveness of our approach.These results may be useful for acquiring and updating building data in OSM.A global and open dataset related to OSM building completeness has been made available for public use.

Air-coupled PMUTs array with residual stresses:experimental tests in the linear and non-linear dynamic regime

The mechanical characterization of a 4×4 air-coupled array of Piezoelectric Micromachined Ultrasonic Transducers(PMUTs)is pre-sented.The experimental campaign consists of three set of experi-mental tests,namely:topography measurements,small signal dynamic measurements,and vibrometry in the non-linear dynamic regime.The behavior of three different kinds of PMUT are reported.They differ according to the thermo-electrical treatment that has been applied to the piezoelectric material.The presence of the fabrication induced residual stresses is investigated and the treat-ment effect is evaluated in terms of the initial deflected configura-tion.The results reported in this paper represent an experimental mechanical investigation useful for the design of PMUT structures with advanced functionalities in the linear and non-linear regime.

Cornea modelling

Background:Biomechanics introduces numerous technologies to support clinical practice in ophthalmology,with the goal of improving surgical outcomes and to develop new advanced technologies with minimum impact on clinical training.Unfortunately,a few misconceptions on the way that computational methods should be applied to living tissues contributes to a lack of confidence towards computer-based approaches.Methods:Corneal biomechanics relies on sound theories of mechanics,including concepts of equilibrium,geometrical measurements,and complex material behaviors.The peculiarities of biological tissues require the consideration of multi-physics,typical of the eye environment,and to adopt customized geometrical models constructed on the basis of advanced optical imaging and in-vivo testing.Results:Patient-specific models are able to predict the outcomes of refractive surgery and to exploit the results of in-vivo test to characterize the material properties of the corneal tissue.Conclusions:Corneal biomechanics can become an important support to clinical practice,provided that methods are based on the actual multi-physics and use customized geometrical and mechanical models.