Motivated by certain models arising in the field of statistical process control and acceptance sampling, we study the waiting-time distribution for the first occurrence of a pattern belonging to a specific family of patterns in a bivariate sequence of trinomial trials. Such a setup can be applied in cases where the final quality of the output of an industrial process is determined by two correlated variables that are used to classify the two measured characteristics as “quite close to target quality,” “of satisfactory quality,” and “far from target quality.” A typical case where this happens is whenever registering the exact value of the characteristic of interest is expensive or difficult (e.g., when the observations come in the form of grouped data, due to the use of m-step gauges). The exact distribution of the waiting time is derived by using a Markov chain-embedding technique, which is then used in order to determine the most appropriate design for the quality-control problems discussed.

The present work presents a statistical framework for analysing and evaluating the environmental performance of 21 wines (protected designation of origin or protected geographical indication) produced in Greece, through their complete lifecycle. For this purpose, the life cycle assessment methodology was used. It is well known that lifecycle thinking is a scientific approach that can support businesses in decision making towards sustainable consumption and production. However, such techniques provide a large amount of multi-dimensional data that are difficult to comprehend and interpret. Therefore, the application of an appropriate statistical framework to aid this assessment, which should be as unambiguous and reliable as possible, is needed. This statistical framework should be based on the lifecycle inventory results, on an appropriate multivariate technique such as principal component analysis, and on probability distributions, thereby providing an objective framework to assist the evaluation of the environmental performance of the products. Applying the proposed framework to 21 Greek wines, we found that the proposed framework could be used for categorizing the examined wines according to their environmental impact severity, as well as the impact types associated with them.

Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.