1,721,083 research outputs found
Correlation between tree-ring series as a dendroprovenancing evaluation tool
Full text linkThe use of correlation between tree-ring series as a proximity indicator in dendroprovenance analyses is often questioned. High correlations may occur between series at a great distance, but conversely, low correlations may occur between series that are close to each other. This discrepancy has prompted the exploration of alternative dendroprovenancing methods, but many of them have proven to be unreliable or impractical. In this study, approximately 12,000 geolocalised tree-ring series from the three main Alpine conifers—spruce, larch, and fir—were analysed to investigate the extent to which correlation analysis can be used as a dendroprovenance tool. The results clearly indicate a significant increase of correlation at low distance and validate the proposed correlation approach. The large dataset also made it possible to develop a simplified quantile regression model that could be used to estimate distance in kilometres based on correlation values between the tree-ring series. Spruce exhibited the most promising results, which is attributed in part to the extensive dataset available, while there were challenges with fir in accurately determining distances between sites. Finally, the study also evaluated the impact of altitude on distance estimation and showed how this environmental factor influences variations in dendroprovenance analyse
Adaptive Automation: status of research and future challenge
Full text linkAutomation modifies workplaces, tools, and production activities, leading to new modalities of human-machine interaction. Traditionally, the allocation of functions in automated systems is static over time, i.e., functions are assigned to humans or machines. Adaptive Automation (AA) makes functions allocation dynamic, resulting from system conditions, performance, and human attributes to face emerging or unpredictable contingencies, and to cope with traditional automation challenges and limits. Tracing the evolutionary stages of the topic, the paper provides an extensive literature review. First, the review details the current definition of AA, the starting motivations for AA, and the temporal evolution of the topic considering the pioneers’ theories. Then, the paper presents the design elements involved in AA systems, i.e., the Level Of Automation (LOA), the Human-Machine Interfaces (HMIs), and the different approaches than can guide the adaptive shift. Finally, the practical applications of AA in manufacturing are reported. In such a way, the research offers the state of the art of the topic, providing the main distinguishing features between static and AA, also outlining the open challenges and the future developments in manufacturing
Percolation and clustering in supercritical aqueous fluids
No full textNeutron diffraction data for pure supercritical water and supercritical mixtures of water and
CO2 are analyzed by using the empirical potential structure refinement Monte Carlo simulation,
and molecular configurations compatible with the experimental data are recorded. The analysis
of the distribution functions of water cluster size in these fluids allows the identification of a
percolation line, which separates gas-like states from liquid-like ones, in pure supercritical
water. Solvation of CO2 in supercritical water inhibits cluster percolation, although the radial
distribution functions show liquid-like behavior: this is likely to be due to the excess volume
being localized around the CO2 molecules
Natural Language Processing applications in manufacturing: a systematic literature review
No full textAmong the manufacturing sector several applications of Natural Language Processing (NLP) are emerging. NLP is a branch of Artificial Intelligence (AI) aimed at understanding, interpreting, and manipulating human language through computer-based data processing. This application is quite powerful and prospective in manufacturing context, considering the ever-increasing amount of data available within the organizations, often unstructured, non-standardized, and free text. Therefore, human analysis to extract information and useful knowledge results in a long and tedious task with limited added value. The automation of these activities moves workers to more meaningful and value-added activities; it improves efficiency in searching for and extracting information, with benefits for decision-making processes. The paper presents a systematic literature review concerning NLP applications in manufacturing, conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement methodology. Basing on the documents retrieved, a comparative analysis of the literature is presented. The analysis is carried out following two different rationales: an objective analysis, which highlights and compares the different purposes with which NLP is applied in the manufacturing field, such as knowledge base, ontology, predictive maintenance, human machine interaction and decision support system. The second analysis investigates NLP applications by exploring different production process phases involved in manufacturing activities. The research identified mature NLP applications, transversally implemented in several production process phases, with specific objectives. The paper provides a comprehensive and in-depth overview on the topic. Finally, possible future directions of development of NLP in manufacturing were defined. © 2022, AIDI - Italian Association of Industrial Operations Professors. All rights reserved
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