https://acta.imeko.org/index.php/acta-imeko/issue/feedActa IMEKO2024-03-04T07:32:52+00:00Prof. Francesco Lamonaca, Ph.Deditorinchief.actaimeko@hunmeko.orgOpen Journal Systems<p>The online journal of IMEKO - the <a href="https://www.imeko.org">International Measurement Confederation</a>.</p>https://acta.imeko.org/index.php/acta-imeko/article/view/1322Validation of a laboratory method for the traceability of a rainfall weighing gauge2023-06-21T07:45:59+00:00Catarina Ferreira Simõescsimoes@lnec.ptÁlvaro Silva Ribeiroasribeiro@lnec.ptMaria Céu Almeidamcalmeida@lnec.ptDídia Covasdidia.covas@tecnico.ulisboa.ptLuís Diasldias@lnec.ptGustavo Coelhogfceolho@lnec.pt<p>This study aims to develop a laboratory method for the traceability of a rainfall weighing gauge, including an evaluation of the measurement uncertainty. The adopted procedure is similar to the one used for the non-automatic weighing instruments. A static approach is followed to achieve the calibration deviation of the precipitation scale. The method used to evaluate the measurement uncertainty is based on a nonlinear mathematical model. The Monte Carlo method is used to calculate uncertainties and validate estimates following the conventional Guide to the Expression of Uncertainty in Measurement (GUM) approach. Measurement uncertainty contributions of input quantities to the mathematical model used to calculate rainfall also require specific calibration procedures. Results show the accuracy level achievable with rainfall weighing gauges commonly used as a reference for meteorological monitoring networks and data modelling.</p>2024-03-11T00:00:00+00:00Copyright (c) 2024 Catarina Ferreira Simões, Álvaro Silva Ribeiro, Maria Céu Almeida, Dídia Covas, Luís Dias, Gustavo Coelhohttps://acta.imeko.org/index.php/acta-imeko/article/view/1660Photovoltaic cooling techniques’ effect on the average monthly performance2023-09-11T19:11:21+00:00Antonino Rolloantonino.rollo@unical.itVittorio Ferrarovittorio.ferraro@unical.itPiero Bevilacquapiero.bevilacqua@unical.it<p class="Abstract"><span lang="EN-US">Nowadays, mitigating climate-altering emissions resulting from air conditioning and mechanical ventilation of indoor spaces is of utmost importance. Encourage the adoption of renewable energy sources for power generation is a critical approach in this regard. Among the available technologies, photovoltaic technology stands as the most mature option. However, it does have limitations, such as reduced efficiency and performance degradation at elevated temperatures. To enhance the efficiency of photovoltaic systems, various solutions have been proposed over time, with significant research focusing on the exploration of new materials. One of the most promising solutions involves panel cooling through the utilization of external fluids, either in a forced or natural manner. Furthermore, the extracted heat from this cooling process can be effectively reused in other industrial processes, adding to its appeal. Nonetheless, despite its potential, the application of panel cooling technology is relatively recent, and assessing its suitability in specific scenarios at an early stage can be challenging. Currently, there is a lack of clear and straightforward methodologies to evaluate the performance gains achievable through the implementation of panel cooling. The primary objective of this research is to present an innovative methodology that can effectively assess panel cooling efficiency on an average daily-monthly basis. Specifically, we propose corrective parameters that modify the widely used Siegel method, which determines the monthly average daily efficiency of uncooled panels. Throughout the study, it has become evident that the input values derived from the UNI standard do not fully represent the real-world conditions. This finding may indicate the necessity for regulatory updates to accurately account for the practical operational environment.</span></p>2024-03-04T00:00:00+00:00Copyright (c) 2024 Vittorio Ferraro, Piero Bevilacqua, Antonino Rollohttps://acta.imeko.org/index.php/acta-imeko/article/view/1687Performance evaluation of a spark ignition engine using gasoline and essential oil fuel blend2023-11-10T16:27:46+00:00Marthen Paloboranmarthen.paloboran@unm.ac.idThesya Atarezcha Pangruruktesyatareskaaa@gmail.comMustari Lamadamustarilamada@gmail.comZulhajjizulhajji@unm.ac.idHaruna H. Latangharuna@unm.ac.idSlamet Widodoslamet.widodo@unm.ac.id<p>Clove oil is an essential oil that has recently been used not only as a health or aromatherapy ingredient but is also widely used as an additive in fuel, especially for compression ignition engines. Essential oils are extracted through <em>distillation </em>from various parts of the clove tree, such as flowers, tree bark, leaves, and even fruit. This study aims to evaluate the combustion performance of a spark-ignition engine fueled by gasoline and essential oil at a concentration of 5-20 % as a blend. This type of research has not been conducted by many researchers, making it difficult to find scientific references related to this type of research. Experiments carried out on a research engine with engine speed variations of 1400–1800 rpm and a constant load of 3 kg. The results show that increasing the essential oil content increases the fuel energy and indicative power, thereby increasing the thermal efficiency. However, the brake power will decrease because most of it is lost owing to heat transfer and friction; therefore, the mechanical efficiency decreases if the percentage of essential oil in gasoline increases. Meanwhile, increased essential oils will reduce CO<sub>2</sub> emissions, but HC and CO emissions will increase, especially at high engine speeds.</p>2024-03-04T00:00:00+00:00Copyright (c) 2024 Paloboran Marthen, Thesya Atarezcha Pangruruk, Mustari Lamada, Zulhajji, Haruna H. Latang, Slamet Widodohttps://acta.imeko.org/index.php/acta-imeko/article/view/1679A comprehensive review of image super-resolution metrics: classical and AI-based approaches2024-01-13T21:36:29+00:00Mukhriddin Arabboevmukhriddin.9207@gmail.comShohruh Begmatovbek.shohruh@gmail.comMokhirjon Rikhsivoevmrikhsivoev@gmail.comKhabibullo Nosirovn.khabibullo1990@gmail.comSaidakmal Saydiakbarovsaidakmalflash@gmail.com<p>Image super-resolution is a process that aims to enhance the quality and resolution of images using various techniques and algorithms. The process aims to reconstruct a high-resolution image from a given low-resolution input. To determine the effectiveness of these algorithms, it's crucial to evaluate those using specific metrics. In this paper, we take a closer look at the most commonly used image super-resolution metrics, including classical approaches like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). We also discuss advanced metrics like Learned Perceptual Image Patch Similarity (LPIPS), Fréchet Inception Distance (FID), Inception Score (IS), and Multi-Scale Structural Similarity Index (MS-SSIM). Furthermore, we provide an overview of classical and AI-based super-resolution techniques and methods. Finally, we discuss potential challenges and future research directions in the field and present our experimental results by applying image super-resolution metrics. In the result and discussion section, we have practiced some given metrics and proposed our image super-resolution results.</p>2024-03-12T00:00:00+00:00Copyright (c) 2024 Mukhriddin Arabboev, Shohruh Begmatov, Mokhirjon Rikhsivoev, Khabibullo Nosirov, Saidakmal Saydiakbarovhttps://acta.imeko.org/index.php/acta-imeko/article/view/1704A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory2024-01-12T14:10:45+00:00Behnam Karim Sarmadibkarimsarmadi@gmail.comAhmad Salehi Dobakhsharisalehi_ahmad@guilan.ac.ir<p>This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.</p>2024-03-14T00:00:00+00:00Copyright (c) 2024 Behnam Karim Sarmadi, Ahmad Salehi Dobakhsharihttps://acta.imeko.org/index.php/acta-imeko/article/view/1742An investigation into vibration analysis for detecting faults in vehicle steering outer tie-rod2024-02-13T13:50:28+00:00Yousif Alarajiyoal-araji@stu.okan.edu.trSina Alpsina.alp@okan.edu.tr<p class="Abstract"><span lang="EN-US">This study presents a novel fault detection method in car gear steering systems, employing MSC Adams and MATLAB simulations to analyze angular acceleration from the outer tie rod. The approach closely mimics real accelerometer data to differentiate between normal and faulty conditions, including wear and obstacle navigation. Emphasis is on noise robustness, utilizing advanced noise injection and denoising techniques. The efficacy of wavelet scattering, discrete wavelet transform (DWT) methods, and classifiers like Support Vector Machines (SVM) and Neural Networks (NN) is extensively evaluated. Among fifteen fault detection methods, the combination of wavelet scattering with Long Short-Term Memory (LSTM) Neural Networks, optimized with Adam tuning, is notably stable across four scenarios. The research highlights the importance of precise feature selection, employing techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Recursive Feature Elimination (RFE). This research significantly advances the reliability of autonomous driving systems and provides essential insights into fault detection in gear steering systems.</span></p>2024-03-18T00:00:00+00:00Copyright (c) 2024 Yousif Alaraji, Sina Alphttps://acta.imeko.org/index.php/acta-imeko/article/view/1619Heat stress measuring methods in dairy cows2023-10-22T20:47:00+00:00Alessandra Aloiaa.aloia4@studenti.uniba.itAristide Maggiolinoaristide.maggiolino@uniba.itLucrezia Fortelucrezia.forte@uniba.itPasquale De Palopasquale.depalo@uniba.it<p>The most widely used predictor to assess the incidence of thermal stress in livestock is THI, the temperature humidity index. However, it is an indicator that disregards the individual animal and the specific farm conditions. This review aims to list and summarize other thermal stress predictor factors, by using non-invasive and cost-effective strategies, in particular with the aid of Precision Livestock Farming technologies. When it comes to dairy animals the metabolic load is already increased by milk production, so the effect of heat stress can exacerbate the overall welfare of the cow. Therefore, the animals enact copying mechanisms that may result in physiological, behavioral and productive alterations. Those animal-based parameters can be used as early predictors of heat stress, allowing the farmer to collect real time data and address the condition operating management strategies in order to prevent further detrimental effect on the performance and consequent economic losses.</p>2024-03-14T00:00:00+00:00Copyright (c) 2024 Alessandra Aloia, Aristide Maggiolino, Lucrezia Forte, Pasquale De Palohttps://acta.imeko.org/index.php/acta-imeko/article/view/1682Assessing accelerometer thresholds for cow behaviour detection in free stall barns: a statistical analysissis2023-10-22T12:17:12+00:00Simona Maria Carmela Portosimona.porto@unict.itMarco Bonfantimarco.bonfanti@unict.itDominga Mancusouni378271@studium.unict.itGiovanni Casconegiovanni.cascone@unict.it<p>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.</p> <p>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.</p>2024-03-14T00:00:00+00:00Copyright (c) 2024 Simona Maria Carmela Porto, Marco Bonfanti, Dominga Mancuso, Giovanni Casconehttps://acta.imeko.org/index.php/acta-imeko/article/view/1721Prediction of sheep bulk milk coagulation properties from mid-infrared spectral data2024-01-22T17:53:05+00:00Carlo Bosellicarlo.boselli@izslt.itAlberto Guerraalberto.guerra@unipd.itAngela Costaangela.costa2@unibo.itMassimo De Marchimassimo.demarchi@unipd.it<p>The technological features of milk are essential for cheese manufacturing. This is particularly true for Italy, where most of the milk produced by sheep is intended for cheese production. The possibility to evaluate technological characteristics and coagulation aptitude of milk in advance, before any treatment, is crucial for decision-making at industry level. In the present study, we tested the ability of mid-infrared spectroscopy for prediction of coagulation traits (rennet coagulation time and curd firmness) by using more than 4000 bulk milk samples of 344 sheep herds. The models developed with a partial least square regression showed that spectral data points can be successfully used to predict the two traits. The coefficient of determination in external validation was 0.42 for rennet coagulation time and 0.28 for curd firmness, indicating that sheep milk delivered to dairies can undergo a preliminary screening only to assess the expected coagulation time. This finding will allow manufacturers to evaluate the milk received from farmers. Further investigation will be need to improve the prediction of rennet coagulation time that can be coupled with composition traits to define premiums or penalties in the payment system.</p>2024-03-04T00:00:00+00:00Copyright (c) 2024 Carlo Boselli, Alberto Guerra, Angela Costa, Massimo De Marchihttps://acta.imeko.org/index.php/acta-imeko/article/view/1725Short review of current limits and challenges of application of machine learning algorithms in the dairy sector2024-01-22T17:54:52+00:00Lucia Trapaneselucia.trapanese2@unina.itMiel Hostensm.m.hostens@uu.nlAngela Salzanoangela.salzano@unina.itNicola Pasquinonpasquin@unina.it<p class="Abstract"><span lang="EN-US">In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning <s>-</s> a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.</span></p>2024-03-15T00:00:00+00:00Copyright (c) 2024 Lucia Trapanese, Miel Hostens , Angela Salzano , Nicola Pasquino