Environmental problems such as air pollution monitoring and prevention, flood detection and prevention, land use, forest management, river water quality, wastewater treatment supervision, management of biodiversity etc. are more complex than typical realworld problems usually AI faces to.This added complexity rises from several aspects, such as the randomness shown by most of environmental processes involved, the 2D/3D nature of involved problems, the temporal aspects, the spatial aspects, the inexactness of the information, the multigranularity of the information etc.In fact, environmental problems belong to the most difficult problems with a lot of inexactness and uncertainty, and possibly conflicting objectives to be solved according to several classifications such as the one by Funtowicz Ravetz (Funtowicz Ravetz, 1999), which states that there are 3 kinds of problems.Also, they are non-structured problems in the classification proposed by H. Simon (Simon, 1966).All this complexity means that to effectively solve those problems a lot of knowledge is needed.This knowledge can be theoretical knowledge expressed in mechanistic models, such as the Gravidity Newton's Theory, or it can be empirical knowledge that can be expressed by means of empirical models, originated by some data and observations (data-driven knowledge) or by the expertise gathered by people when coping with such problems (model-driven knowledge, particularly expert-based knowledge).The KDD 2024 Special Day for AI for environment brings together researchers and practitioners to present their perspective on this very timely topic on how AI can be used for good, and improving the environment where we all live in.

Gender gap in computer science related fields is one of the most critical among all technological areas. This paper describes the methodology used to create gender commissions and providing them of a territorial structure around informatics engineering and artificial intelligence, two close areas where the presence of women is critical both at professional level, as well as training level. Territorial structure provides an additional potential very useful for the impact of the activities of the commission. Here the creation of two gender structures in Catalonia is presented.

Classical pre-post intervention studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines. For this reason, the trajectory map is proposed as a visual tool where dietary patterns of individuals can be followed during the intervention and can also be related with nutritional prescriptions. The trajectory analysis is also proposed allowing both analysis: 1) adherence to the intervention and 2) intervention effects. The analysis is made by projecting the differences of the target parameters over the resulting trajectories between states of different time-stamps which might be considered either individually or by groups. The proposal has been applied over a real nutritional study showing that some individuals adhere better than others and some individuals of the control group modify their habits during the intervention. In addition, the intervention effects are different depending on the type of individuals, even some subgroups have opposite response to the same intervention.

BACKGROUND: Analysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose, mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To allow performing phenotype analyses of inherited disorder related genes we implemented new original modules within GFINDer http://www.bioinformatics.polimi.it/GFINDer/, a Web system we previously developed that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining. RESULTS: New GFINDer modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications. To achieve this we exploited, normalized and structured the information present in textual form in the Clinical Synopsis sections of the Online Mendelian Inheritance in Man (OMIM) databank. Such valuable information delineates numerous signs and symptoms accompanying many genetic diseases and it is divided into phenotype location categories, either by organ system or type of finding. CONCLUSION: Supporting phenotype analyses of inherited diseases and biomolecular functional evaluations, GFINDer facilitates a genomic approach to the understanding of fundamental biological processes and complex cellular mechanisms underlying patho-physiological phenotypes.