Acta IMEKO <p>The online journal of IMEKO - the <a href="">International Measurement Confederation</a>.</p> IMEKO en-US Acta IMEKO 0237-028X <p>Authors who publish with this journal agree to the following terms:</p> <ul> <li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="" target="_new">The Effect of Open Access</a>).</li> </ul> Deep learning methodologies based on metaheuristics for predictive engine maintenance <p>Recently, there has been an increase in concerns about the accessibility, security, and reliability of aviation engines. To prevent engine failures which can be quite serious, it is important to take effective measures. The objective is to create a deep learning simulation that can accurately predict an aircraft engine's viability and remaining usefulness using meta-heuristic techniques to improve its performance. These techniques discover the optimal hyper parameters and architecture for the deep learning model. This will help minimize downtime and maintenance costs for the aircraft fleet by handling complex data such as sensor readings and past maintenance records while also adapting to changing conditions over time. Since training deep learning models can be computationally intensive, meta-heuristic methods increase their robustness. The aim is to enhance performance by increasing the accuracy rate and reducing mean squared losses of multiple deep learning methods used for predicting aircraft engine maintenance by hybridizing them with metaheuristic algorithms.</p> Pradeep Kumar D Sowmya B J Anita Kanavalli Supreeth S Shruthi G Copyright (c) 2024 Pradeep Kumar D, Sowmya B J, Anita Kanavalli, Supreeth S, Shruthi G 2024-05-21 2024-05-21 13 2 1 15 10.21014/actaimeko.v13i2.1667 EEG measurements-based study for evaluating acoustic human perception: A pilot study <p class="Abstract"><span lang="EN-US">Sound quality analysis and sound design are well-known human-centered strategies to evaluate the subjective perception of noise and to design machines and environments with pleasant and comfortable acoustic signatures. The subjective acoustic perception is conventionally measured by means of sound quality metrics determined through a correlation process with jury test results. The exploitation of electroencephalogram (EEG) measurements during the jury test for the registration of the brain activity in response to the acoustic stimuli presented to the jurors can allow us to estimate the jurors’ perception directly from their physiological response. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different acoustic stimuli. Forty-three participants were recruited, and the EEG signals were recorded using the wearable sensor. The analysis of power spectral densities (PSDs) was performed to investigate features correlated to acoustic sensation induced by audio stimuli. Statistically significant differences were found between the three audio stimuli. The results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to acoustic perception evaluation.</span></p> Silvia Angela Mansi Marco Arnesano Milena Martarelli Reza Jamali Jamali Gianmarco Battista Paolo Chiariotti Paolo Castellini Copyright (c) 2024 Silvia Angela Mansi, Marco Arnesano, Milena Martarelli, Reza Jamali Jamali, Gianmarco Battista, Paolo Chiariotti, Paolo Castellini 2024-05-16 2024-05-16 13 2 1 9 10.21014/actaimeko.v13i2.1698 Modelling photovoltaic modules with enhanced accuracy using particle swarm clustered optimization <p>Accurately simulating and operating photovoltaic (PV) modules is vital for thoroughly analyzing their performance under different conditions. The main focus of this paper is to address the inherent nonlinearity in solar PV systems. To achieve this, the particle swarm clustered optimization (PSCO) is applied to extract parameters of solar modules, allowing for a more comprehensive understanding of their behavior. PSCO aims to enhance the accuracy and effectiveness of PV module analysis. For that, PSCO utilizes clusters within the particle population, enabling localized communication and information sharing. By doing so, it effectively facilitates efficient exploration and exploitation of diverse regions, fostering a comprehensive understanding of the behavior of PV modules under different conditions. Through this approach, PSCO maximizes the accuracy and effectiveness of parameter extraction, contributing to advancements in PV system analysis and performance evaluation. The effectiveness of PSCO is demonstrated in extracting parameters for the three-diode model (TDM) of the STP6-120/36 and Photowatt-PWP201 PV modules. PSCO surpasses state-of-the-art algorithms with significantly low root mean square error (RMSE) values of 0.0145 A and 0.0019 A, showcasing its superior accuracy. Additionally, PSCO achieves the lowest power errors of 0.16054 W and 0.01484 W for the respective modules, emphasizing its excellent performance.</p> Mouncef El Marghichi Abdelilah Hilali Azeddine Loulijat Abdelhak Essounaini Abdelkhalek Chellakhi Copyright (c) 2024 Mouncef El Marghichi, Abdelilah Hilali, Azeddine Loulijat, Abdelhak Essounaini, Abdelkhalek Chellakhi 2024-05-16 2024-05-16 13 2 1 10 10.21014/actaimeko.v13i2.1699 A new proposal for the Architectural Stratigraphic Analysis and the resulting diagram <p>The stratigraphic analysis is a non-destructive method based on archaeology that illustrates the relationships and sequences of the stratigraphic layers of excavations by listing all their constituting elements, to be later represented in a stratigraphic diagram. Although the constant improvements and applications of the stratigraphic analysis in several scientific fields since 1973, this study proposes specific modifications to the current method as well as an adaptation of the diagram scheme to each case study of conservation. The main goal of this study was the elaboration of a detailed and comprehensive diagram that encompasses the entire monument, rather than one for each individual section of the monument. The first step was the identification of the main stratigraphic units and their classification based on their primary function: structural or decorative. The second step concerned a simplification of the current relationships of the architectural units into three groups, according to their roles within the entire system to obtain a simpler stratigraphic sequence. The final step was the new incorporation of pathology-related information and the addition of the missing elements as a reconstruction process. These adjustments allowed the diagram to arrange all data gathered from heritage analysis and will permit historians, architects, archaeologists, and others to engage in a global reading of the built. The stratigraphic diagram will serve as a tool to visually represent the analysis and synthesis in a coded manner, which will be comprehensible to both the researchers and the scientific community.</p> Roberto Villalobos Copyright (c) 2024 Roberto Villalobos 2024-05-16 2024-05-16 13 2 1 8 10.21014/actaimeko.v13i2.1729 Weed control in secondary archaeological sites by means of precision agriculture techniques <p>The development of intervention approaches that lessen biodeterioration and enable the realization of cultural heritage is crucial for the improvement of secondary archaeological sites. A challenge faced by tiny archeological sites is the emergence of spontaneous vegetation, particularly ruderal plants. Here, we describe the development of a weeding system that applies precision agriculture techniques. Drones will be used to identify vegetation that is considered noxious and to apply herbicides where and when they are really needed. Additionally, the efficacy of the treatments can be tracked by using a multispectral sensor.</p> Fabio Leccese Mariagrazia Leccisi Giuseppe Schirripa Spagnolo Copyright (c) 2024 Fabio Leccese, Mariagrazia Leccisi, Giuseppe Schirripa Spagnolo 2024-05-21 2024-05-21 13 2 1 9 10.21014/actaimeko.v13i2.1753 The Via Severiana and the Tabula Peutingeriana: valuation of landmarks precision and town expansions <p class="Abstract"><span lang="EN-US">This work deals with the examination of the route of the Via Severiana, traced in the imperial period, from a historical and technical point of view. Then, results were compared with the Tabula, which we can consider one of the first synthetic representations of general viability. We have explored the development of the various sectors of the via Severiana, considering the various utility and presence on the territory, using the Tabula Peutingeriana as a benchmark. Subsequently, we have examined the precision of the positioning of the various landmarks along the via Severiana and the movement of the inhabited centers as a function of density. Modern technologies, including Geographic Information Systems and mathematical models, allow us to help the archaeologists to overlay ancient maps like the Tabula Peutingeriana onto contemporary maps, aiding in the identification of locations and understanding ancient landscapes. </span></p> Enrico Petritoli Fabio Leccese Copyright (c) 2024 Enrico Petritoli, Fabio Leccese 2024-05-23 2024-05-23 13 2 1 8 10.21014/actaimeko.v13i2.1754 Rapid detection of microplastics in feed using near-infrared spectroscopy <p>The presence of microplastics in the forage and feedstuffs of domestic animals represents an imminent threat to the entire food chain that may reach humans since the particles could be transferred into the intestinal barriers and contaminate blood and animal products. Until now, there is no simple, rapid, sustainable, and reliable method to detect microplastics in animal feed. The objective of this study was to investigate the ability of near-infrared spectroscopy (NIRS) to detect microplastics in ruminant feeds. Two types of instruments were tested using four feeds (corn silage, mixed hay, rye grass silage, soybean meal) and a total mixed ration. Two types of crumbled contaminants, low-density polyethylene and polystyrene, were accurately mixed at ratios of 0, 1, 3, and 5 mg g<sup>-1</sup>. The pool of the five matrices examined by the benchmark instrument (714-3333 nm) yielded an accuracy of approximately 0.8 mg g<sup>-1</sup> and a detection limit of about 1 mg g<sup>-1</sup>, however, the errors could be halved in separate calibrations. A short wavelength range (714-1070 nm) or a smart NIRS instrument proved an acceptable discrimination of the concentrations. Following these preliminary results, any validation on other samples with different and powerful NIRS tools is encouraged.</p> Giorgio Masoero Salvatore Barbera Hatsumi Kaihara Sabah Mabrouki Sara Glorio Patrucco Khalil Abid Sonia Tassone Copyright (c) 2024 Giorgio Masoero, Salvatore Barbera, Hatsumi Kaihara, Sabah Mabrouki, Sara Glorio Patrucco, Khalil Abid, Sonia Tassone 2024-05-16 2024-05-16 13 2 1 6 10.21014/actaimeko.v13i2.1663