Soil bioengineering for risk mitigation and environmental restoration in a humid tropical area

The use of soil bio-engineering techniques in developing countries is a relevant issue for disaster mitigation, environmental restoration and poverty reduction. Research on the autochthonal plants suitable for these kinds of interventions and on the economic efficiency of the interventions is essential for the dissemination of such techniques. The present paper is focused on these two issues as related to the realization of various typologies of soil bioengineering works in the humid tropics of Nicaragua.<br> <br> In the area of Río Blanco, located in the Department of Matagalpa, soil bioengineering installations were built in several sites. The particular structures built were: drainages with live fascine mattress, a live palisade, a vegetated live crib wall for riverbank protection, a vegetative covering made of a metallic net and biotextile coupled with a live palisade made of bamboo. In order to evaluate the suitability of the various plants used in these works, monitoring was performed, one on the live palisade alongside an unpaved road and the other on the live crib wall along a riverbank, by collecting data on survival rate and morphological parameters. Concerning economic efficiency, we proceeded to a financial analysis of the works. Once the unit price was obtained, we converted the amount into EPP Dollars (Equal Purchasing Power) in order to compare the Nicaraguan context with the European one.<br> <br> Among the species used we found that <i>Gliricidia sepium</i> (local common name: Madero negro) and <i>Tabebuia rosea</i> (local common name: Roble macuelizo) are adequate for soil bioengineering measures on slopes, while <i>Erythrina fusca</i> (local common name: Helequeme) resulted in successful behaviour only in the crib wall for riverbank protection.<br> <br> In comparing costs in Nicaragua and in Italy, the unit price reduction for Nicaragua ranges from 1.5 times (for the vegetative covering) to almost 4 times (for the fascine mattress), using the EPP dollar exchange rate.<br> <br> Our conclusions with regard to hydrological-risk mitigating actions performed on a basin scale and through naturalistic (live) interventions are that they are not only socially and technically possible, even in hardship areas (by maximizing the contribution of the local labour force and minimizing the use of mechanical equipment), but also economically sustainable

Similitude theory applied to plates in vibroacoustic field: a review up to 2020

Similitude methods are a set of tools which allow the design of scaled-up or scaled-down models of a full-scale structure called a prototype. In this way, the financial and temporal costs of experimental tests, and the problems associated with the set-up of too large (or small) test articles, may be overcome. This article provides a brief review of similitude methods applied to plates in a vibroacoustic field. Particularly, it is dedicated to a thorough analysis of similitude conditions and scaling laws for uncovering commonalities and differences, and physical interpretations, obtained from applying different scaling methods

A Review of Similitude Methods for Structural Engineering

Similitude theory allows, through a set of tools known as similitude methods, to establish the conditions to design a scaled (up or down) model of a full-scale structure, usually defined as prototype. In the last years, to overcome the problems associated with full-scale testing, such as costs and setup, research on similitude methods has grown and their application has expanded in many branches of engineering. The aim of this paper is to provide a review as comprehensive as possible about similitude methods applied to structural engineering; after a brief historical introduction and a more deep analysis of the main methods, the article focuses on the applications classified by test articles

The Importance of Measuring SARS-CoV-2-Specific T-Cell Responses in an Ongoing Pandemic

Neutralizing antibodies are considered a correlate of protection against SARS-CoV-2 infection and severe COVID-19, although they are not the only contributing factor to immunity: T-cell responses are considered important in protecting against severe COVID-19 and contributing to the success of vaccination effort. T-cell responses after vaccination largely mirror those of natural infection in magnitude and functional capacity, but not in breadth, as T-cells induced by vaccination exclusively target the surface spike glycoprotein. T-cell responses offer a long-lived line of defense and, unlike humoral responses, largely retain reactivity against the SARS-CoV-2 variants. Given the increasingly recognized role of T-cell responses in protection against severe COVID-19, the circulation of SARS-CoV-2 variants, and the potential implementation of novel vaccines, it becomes imperative to continuously monitor T-cell responses. In addition to “classical” T-cell assays requiring the isolation of peripheral blood mononuclear cells, simple whole-blood-based interferon-γ release assays have a potential role in routine T-cell response monitoring. These assays could be particularly useful for immunocompromised people and other clinically vulnerable populations, where interactions between cellular and humoral immunity are complex. As we continue to live alongside COVID-19, the importance of considering immunity as a whole, incorporating both humoral and cellular responses, is crucial.</p

Gaussian-Based Machine Learning Algorithm for the Design and Characterization of a Porous Meta-Material for Acoustic Applications

The scope of this work is to consolidate research dealing with the vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design and characterization of global vibroacoustic treatments based on foam cores with embedded periodic patterns, which allow passive control of acoustic paths in layered concepts. Firstly, a numerical test campaign is carried out by considering some perfectly rigid inclusions in a 3D-modeled porous structure; this causes the excitation of additional acoustic modes due to the periodic nature of the meta-core itself. Then, through the use of the Delany–Bazley–Miki equivalent fluid model, some design guidelines are provided in order to predict several possible sets of characteristic parameters (that is unit cell dimension and foam airflow resistivity) that, constrained by the imposition of the total thickness of the acoustic package, may satisfy the target functions (namely, the frequency at which the first Transmission Loss (TL) peak appears, together with its amplitude). Furthermore, when the Johnson–Champoux–Allard model is considered, a characterization task is performed, since the meta-material description is used in order to determine its response in terms of resonance frequency and the TL increase at such a frequency. Results are obtained through the implementation of machine learning algorithms, which may constitute a good basis in order to perform preliminary design considerations that could be interesting for further generalizations

A Linear Transformation for the Reconstruction of the Responses of Systems in Similitude

Recent years have seen an increasing interest towards similitude methods. In fact, the possibility of testing a scaled model, instead of a full-scale prototype, leads to many advantages: financial and time savings, easier experimental setups, etc. However, similitudes have drawbacks, too, mainly due to non-scalable effects and partial similitude, which prevent from an accurate reconstruction of the prototype response. For these reasons, an alternative method which can bypass these limitations is needed. A new method, called VOODOO (Versatile Offset Operator for the Discrete Observation of Objects), is herein proposed: it is based on the definition of a transformation matrix which links the outputs of a given linear systems to those belonging to another system, which may be a scaled model. The responses are acquired on a discrete number of points for both the systems. This work aims at investigating the method’s strengths and limitations of the method. The results show that, although VOODOO exhibits some lack of accuracy in off-design conditions due to the loss of spatial correlation, it is able to overcome some major restrictions that affect all similitude methods

AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle

This scientific publication presents the application of artificial intelligence (AI) techniques as a virtual sensor for tailpipe emissions of CO, NOx, and HC in a high-performance vehicle. The study aims to address critical challenges faced in real industrial applications, including signal alignment and signal dynamics management. A comprehensive pre-processing pipeline is proposed to tackle these issues, and a light gradient-boosting machine (LightGBM) model is employed to estimate emissions during real driving cycles. The research compares two modeling approaches: one involving a unique “direct model” and another using a “two-stage model” which leverages distinct models for the engine and the aftertreatment. The findings suggest that the direct model strikes the best balance between simplicity and accuracy. Furthermore, the study investigates two sensor setups: a standard configuration and an optimized one, which incorporates an additional lambda probe in the exhaust line after the main catalyst. The results indicate a significant enhancement in performance for NOx and CO estimations with the introduction of the third lambda probe, while HC results remain relatively unchanged. Additionally, the AI model is tested on two different electronic control unit (ECU) software calibrations, yielding excellent results in both cases. This suggests that machine learning models are robust to control software variation and can be used to optimize software calibrations in a virtual environment, reducing the reliance on extensive experimental testing. Moreover, the AI model’s performance demonstrates compatibility with real-time implementation. In conclusion, this work establishes the viability and efficiency of AI techniques in accurately estimating tailpipe emissions from an engine in an industrial context. The study showcases the potential for AI to contribute to emission estimation and optimization processes, offering a promising pathway for an innovative industrial practice

Transmission Loss Analyses on Different Angular Distributions of Periodic Inclusions in a Porous Layer

The scope of this paper is to investigate the sound transmission loss of an acoustic package of glass wool with embedded periodic inclusions, considering the possibility to improve a standard configuration and inserting the innovative package in a practical configuration used in the aeronautic field for noise suppression. Periodic inclusions are introduced to enhance the sound transmission loss performance of the acoustic package in the mid-high range of frequencies. The main interest of the present work, with respect to the state of the art, is represented by the arrangement of the inclusions one respect to the others, then creating an inclusion pattern that improves the performance of the periodicity peak. To reach this goal, a numerical model of the package is studied, and the effect of the patterns of periodic inclusions is simulated. The pattern behavior is evaluated for eight configurations, which are different from each other for the cubic dimensions and the inclusion radii. Furthermore, an optimized configuration for aeronautical applications is designed starting from the studied acoustic package; then, the results in terms of mass and performance are discussed. Results are presented in terms of tables and graphs, which may constitute a good basis to perform preliminary design consideration that could be interesting for further generalizations