Nowadays, laser hardening is a consolidated process in many industrial sectors. One of the most interesting aspects to be considered when treating the surface-hardening process in steel materials by means of laser devices is undoubtedly the evaluation of the heat treatment quality and surface finish. In the present study, an innovative method based on fractal geometry was proposed to evaluate the quality of surface-steel-laser-hardened treatment. A suitable genetic programming study of SEM images (1280 × 950 pixels) was developed in order to predict the effect of the main laser process parameters on the microstructural geometry, assuming the microstructure of laser-hardened steel to be of a structurally complex geometrical nature. Specimens hardened by anthropomorphic laser robots were studied to determine an accurate measure of the process parameters investigated (surface temperature, laser beam velocity, laser beam impact angle). In the range of variation studied for these parameters, the genetic programming model obtained was in line with the complexity index calculated following the fractal theory. In particular, a percentage error less than 1% was calculated. Finally, a preliminary study of the surface roughness was carried out, resulting in its strong correlation with complex surface microstructures. Three-dimensional voxel maps that reproduce the surface roughness were developed by automating a routine in Python virtual environment.

In the present work, a spar-buoy scaled model was designed and built through a “Lab-on-Sea” unit, equipped with an energy harvesting system. Such a system is based on deformable bands, which are loyal to the unit, to convert wave motion energy into electricity by means of piezo patch transducers. In a preliminary stage, the scaled model, suitable for tests in a controlled ripples-type wave motion channel, was tested in order to verify the “fixed-point” assumption in pitch and roll motions and, consequently, to optimize energy harvesting. A special type of structure was designed, numerically simulated, and experimentally verified. The proposed solution represents an advantageous compromise between the lightness of the used materials and the amount of recoverable energy. The energy, which was obtained from the piezo patch transducers during the simulations in the laboratory, was found to be enough to self-sustain the feasible on-board sensors and the remote data transmission system.

The analysis of motor activity has been revealed to be essential for monitoring dairy cows’ behavior, with the main aim of identifying the onset of oestrus in time. Pedometers used for oestrus detection have a current average working life on the market of about 5 years. At the end of that period, devices are disposed of, posing a relevant question regarding environmental sustainability. The present work proposed a method to achieve an eco-design of pedometers compliant with the guidelines of the Green Deal. Specifically, a new thermo-plastic organic compound made of polyamide 66 reinforced with organic hemp fibers (trade name SDS Nylon) was adopted. The feasibility, benefits, and performance of this material were assessed with a major emphasis on strength, lightweight, and surface finish. The material in addition to ensuring adequate chemical and mechanical resistance is biocompatible and recyclable. It assures better animal welfare and reduces both environmental impacts and management costs for farmers. Other innovations introduced in this study consisted of the adoption of a double casing. An external case was conceived with a protective function of the measurement system and fixed to a cow’s foreleg by an easy anchor system. An internal case was specifically designed to house the electronic components and to be moved from one cow to another after the pregnancy diagnosis. The solutions proposed in this research will contribute to guaranteeing pedometers a longer lifetime and better recyclability than existing commercial ones, consequently limiting the environmental load derived from their disposal.

Monitoring daily cow behavioural activities of cows in livestock farms is strategic for improving the herd management. For this reason, IoT techniques and smart sensors are become the most common technological support in barns. The aim of this paper is to validate the use of predefined accelerometer thresholds in timely detecting of cow behavioural activities through the statistical analysis of the data acquired from accelerometers housed in collars. Applying ANOVA and TUKEY tests to the median of the accelerations measured with 4 Hz sampling, the behavioural activities analysed in this study, i.e., feeding, lying, rumination, were found to be discriminable along one or more axes. This could allow the implementation of threshold-based algorithms in the firmware of devices housed in the cow collars.

Livestock farming represents one of the primary sources of ammonia (NH₃) and greenhouse gas (GHG) emissions, including methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂), having a significant environmental impact. Reducing emissions and recovering gas systems from these livestock buildings necessitate measuring gas concentrations to mitigate environmental impacts using an accurate, high-cost portable device. This study aims to evaluate the concentration of NH₃ and GHGs in a semi-open dairy farm located in southern Sicily, a region with a hot climate. The measurement campaign was carried out during the spring of 2025. The concentrations of NH₃, CH₄, CO_2, and N₂O were measured in different barn areas (i.e., manger, feeding alley, and service alley) using a portable gas detector (GASMET GT5000) based on Fourier Transform Infrared (FTIR) technology. Statistical analysis revealed that NH₃ concentrations were highest in the feeding alley, while CH₄ concentrations peaked at the manger. N₂O levels stayed low because there was no straw. Future research should investigate gas concentrations across different seasons (e.g., winter, summer) to analyze gas patterns under different climatic conditions. Additionally, the use of an accurate portable device enables further investigations into other barn typologies within the Mediterranean area to assess how farm construction and management practices influence gas production.