Food production faces significant challenges, mainly due to the increase in the Earth’s population, combined with climate change. This will create extreme pressure on food industries, which will have to respond to the demand while protecting the environment and ensuring high food quality. It is, therefore, imperative to adopt innovative technologies, such as Artificial Intelligence, in order to aid in this cause. To do this, we first need to understand the adoption process that enables the deployment of those technologies. Therefore, this research attempts to identify the factors that encourage and discourage the adoption of Artificial Intelligence technologies by professionals working in the fields of agriculture, livestock farming and aquaculture, by examining the available literature on the subject. This is a systematic literature review that follows the PRISMA 2020 guidelines. The research was conducted on 38 articles selected from a pool of 225 relevant articles, and led to the identification of 20 factors that encourage and 21 factors that discourage the adoption of Artificial Intelligence. The factors that appeared most were of economic nature regarding discouragement (31.5%) and product-related regarding encouragement (28.1%). This research does not aim to quantify the importance of each factor—since more original research becoming available is needed for that—but mainly to construct a list of factors, using spreadsheets, which could then be used to guide further future research towards understanding the adoption mechanism.

Solving global optimization problems is a significant challenge, particularly in high-dimensional spaces. This paper proposes a selective multistart optimization framework that employs a modified Latin Hypercube Sampling (LHS) technique to maintain a constant search space coverage rate, alongside Interval Arithmetic (IA) to prioritize sampling points. The proposed methodology addresses key limitations of conventional multistart methods, such as the exponential decline in space coverage with increasing dimensionality. It prioritizes sampling points by leveraging the hypercubes generated through LHS and their corresponding interval enclosures, guiding the optimization process toward regions more likely to contain the global minimum. Unlike conventional multistart methods, which assume uniform sampling without quantifying spatial coverage, the proposed approach constructs interval enclosures around each sample point, enabling explicit estimation and control of the explored search space. Numerical experiments on well-known benchmark functions demonstrate improvements in space coverage efficiency and enhanced local/global minimum identification. The proposed framework offers a promising approach for large-scale optimization problems frequently encountered in machine learning, artificial intelligence, and data-intensive domains.

Higher education serves a pivotal role in enhancing citizens’ quality of life and, therefore, must uphold high standards of quality. This study evaluated the quality of services provided by departmental administrative offices using the SERVQUAL instrument, which measures quality through the difference between perceived and expected service performance. Our primary aim was to investigate the marginal behavior of this assessment by capturing the underestimated and overestimated perceptions and expectations. To achieve this, we introduced a modified version of SERVQUAL, replacing traditional Likert scales with continuous scales. This enabled a detailed mapping of the area between underestimation and overestimation, enhancing the instrument’s ability to yield more comprehensive insights. The study focused on the secretariat of the Department of Tourism Management, at the University of Patras, Greece, with second- and third-year students as assessors. Data analysis utilized the endpoints of these continuums. The results revealed that perceived service performance consistently and significantly fell below expectations, with reliability identified as the most important dimension. Furthermore, perception was found to be relatively objective, whereas expectation exhibited greater subjectivity. The findings demonstrate that this approach not only enhances service quality assessment but also provides a new perspective for evaluating tourism services, as a novel research tool.

This study aims to explore aquaculture professionals’ perspectives on, attitudes towards and understanding of Mediterranean farm fish health management, regarding Artificial Intelligence (A.I.), and to shed light on the factors that affect its adoption. A survey was distributed during a major fish health management conference, representing more than 70% of Greek domestic production. A total of 73 questionnaires were collected, for which descriptive statistics and statistical analysis followed. Gender and age were shown to affect interest in A.I. and in viewing A.I. as a partner rather than a competitor. Age was additionally shown to affect trust in A.I. estimates and anticipation that A.I. will contribute to professional development. Education level shows no significant effect. Knowledge of A.I. is positively correlated with A.I. usage (r = 0.43, p < 0.05), as is interest in learning about A.I. (r = 0.64). A.I. usage is in turn positively correlated with eagerness to see its contribution (r = 0.72). Despite the fact that 64.4% characterized their knowledge as little or non-existent, 67.1% expressed interest in learning more, while 43.8% believe that A.I. will revolutionize aquaculture and 74% do not fear they will be replaced by A.I. in the future. The findings highlight the importance of targeted educational initiatives to bridge the knowledge gap and encourage trust in A.I. technologies.