This study evaluates the influence of quality certificates on sustainable food production in Poland, considering economic, social, and environmental dimensions. Analyzing 25 different certificates, the research explores their criteria, procedures, and costs across various food product categories, including meat, fish, and plant-based products. The study provides a detailed review of certification processes, from initiation to audits and inspections. It identifies both commonalities and differences among certificates, each addressing unique aspects such as environmental impact, worker rights, and product origins. Despite the diversity in standards and procedures, the study underscores the need for standardized international criteria to improve transparency and meet consumer expectations, highlighting the significant role of quality certificates in advancing sustainable food production.

The scarcity of the insulators that are required for refrigeration has made it necessary to use locally available materials that can achieve the desired refrigeration. This work presents the performance evaluation of a refrigerator utilizing a locally available material, which is wood particles that have been converted to particle board, as one of its insulators. A vapor compression refrigeration system was designed and fabricated to chill and preserve agricultural products, which are eggs, yogurt, and tomatoes. The various temperatures at which the agricultural products became chilled were compared with their theoretical preservation temperatures obtainable in literature, thereby evaluating the performance of the refrigerator. The temperature of 11 ℃, which was recorded for the egg in the present experiment, is lower than the theoretical preservation temperatures of 18 ℃ to 21 ℃ for an egg. The temperature of 7 ℃, which was recorded for the yogurt, is approximately equal to its theoretical preservation temperature of 5 ℃. The temperature of 8 ℃, which was recorded for the tomato, is lower than the theoretical preservation temperatures of 7 ℃ to 10 ℃ of tomato. This work has revealed that wood particles have the potential to achieve refrigeration, as well as chill and preserve agricultural products.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.