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The parallel Naples airshed model (PNAM) is a parallel software package for the numerical simulation of photosmog episodes in urban scale domains. It solves the atmospheric diffusion equations, which model the air pollution dynamics in a Eulerian approach, using a symmetric time‐splitting, where the advection is separated from the (coupled) diffusion and chemistry. Presents some results of a numerical simulation of a severe photochemical smog episode, which occurred in the Naples area. A preliminary comparison with measured data is reported, as a first step toward the validation of PNAM. Some parallel performance results, obtained on an IBM SP machine, are also shown.

The purpose of this paper is to introduce a methodology aimed to detect debonding induced by low impacts energies in typical aeronautical structures. The methodology is based on high frequency sensors/actuators system simulation and the application of elliptical triangulation (ET) and probability ellipse (PE) methods as damage detector. Numerical and experimental results on small-scale stiffened panels made of carbon fiber-reinforced plastic material are discussed.

The damage detection methodology is based on high frequency sensors/actuators piezoceramics system enabling the ET and the PE methods. The approach is based on ultrasonic guided waves propagation measurement and simulation within the structure and perturbations induced by debonding or impact damage that affect the signal characteristics.

The work is focused on debonding detection via test and simulations and calculation of damage indexes (DIs). The ET and PE methodologies have demonstrated the link between the DIs and debonding enabling the identification of position and growth of the damage.

The debonding between two structural elements caused in manufacturing or in-service is very difficult to detect, especially when the components are in low accessibility areas. This criticality, together with the uncertainty of long-term adhesive performance and the inability to continuously assess the debonding condition, induces the aircrafts’ manufacturers to pursuit ultraconservative design approach, with in turn an increment in final weight of these parts. The aim of this research’s activity is to demonstrate the effectiveness of the proposed methodology and the robustness of the structural health monitoring system to detect debonding in a typical aeronautical structural joint.

This study investigated how green innovation (GINO), using practices like green marketing orientation (GMO), manufacturing (GMfP), investment (GINV) and HRM, improves environmental performance (EnP) in Ethiopian leather, textile and garment businesses.

The study used a cross-sectional, quantitative design. It surveyed 201 participants, selected via stratified random sampling. Researchers examined the hypothesis using PLS-SEM.

Our findings showed that green practices, especially GMP, GMO, GHRM, GINV and GINO, greatly boost firms’ environmental performance. GMP, GMO and GINV had a strong synergy on green innovation. GHRM had no significant effect. Except for GHRM, results showed that green innovation helps most green practices. It links them to better environmental performance.

For managers and policymakers working in the leather, textile and apparel industries in Ethiopia, this report provides insightful information. Strategic planning that supports and prioritizes the adoption of green practices is shown to be essential. Such activities are expected to improve corporate competitiveness and green innovation, ultimately resulting in ecologically sustainable performance.

Unique findings illuminate emerging nations’ leather, textile and apparel industries. This pioneering study reveals critical links that were previously unexplored. Its relevance extends across developing economies, offering fresh insights into these interconnected sectors.