This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation of this optimization problem is proposed, where the processed flows of material and their timing are explicitly considered. Its exact solution is discussed, and, considering its computational complexity, a comprehensive heuristic, balancing energy performance and due time constraint satisfaction, is developed to provide acceptable solutions in polynomial time to the minimum-energy flexible job-shop scheduling problem, even when considering its dynamic environment. The proposed approach is illustrated through a small-scale example.

Computer trading is currently relatively limited to quantitative trading in paper markets, such as, futures, swaps, and options, ignoring the large volume of physical commodities trading. Numerical algorithms and mathematical methods are frequently applied in the trading of non-physical commodities and stock markets. However, the physical trade of oil does not have a computer program that represents an unquestionable development that includes more complex and subjective factors that traders take into consideration when entering a transaction. Using fuzzy logic, this work proposes a new approach to base a method that can rank oils and the variables, commonly involved in the trading. It uses fuzzy logic to blend objective and subjective factors and brings in two key hierarchical steps to the process. It is shown that this model can also be useful for apprising a decision-making process, improving internal communication, and for the management of physical commodities trading. Here the options of trading are assessed accordingly to its numerical and linguistic judgments, considering even abrupt outcomes. The wide-ranging options, successive filters, and factors evaluation that mirror the technical and expert trading decision making process make it possible for the future application of artificial intelligence (AI) in physical commodities trading.

This study analyzes changes in precipitation over the extended Brazilian region (10° N–40° S; 75° W–25° W) based on data from meteorological stations, reanalyses (ECMWF ERA5, NASA MERRA-2), the NOAA PREC reconstruction, and an ensemble of 32 CMIP6 models. According to weather station data and the ERA5 reanalysis for the period 1996–2025, a slight decrease in mean precipitation (P) is observed in the whole region (ΔP_{Weather station} = −44 mm/year, ΔP_ERA5 = −174 mm/year). A comparison of the periods 1996–2025 and 1940–1969 using CMIP6 ensemble also indicates a reduction in regional average precipitation (ΔP_Ave = −18 mm/year). The reanalyses show a decline in precipitation beginning approximately in the 1980s, accompanied by significant interannual and interdecadal variability associated with natural climate variability. Future projections under high radiative forcing scenarios (SSP3-7.0 and SSP5-8.5) indicate a continued decline in average precipitation in the study region throughout the 21st century. Low forcing scenarios (SSP1-2.6 and SSP2-4.5) suggest only weak changes in average precipitation over the period 2026–2099. However, the precipitation changes are spatially heterogeneous in the study region. According to the ERA5 reanalysis (1940–2025), precipitation has increased over the northern and southern parts of the Brazilian territory, while a decrease was observed in its central portion. All CMIP6 SSP ensemble projections considered (2026–2099) indicate a decrease in precipitation in the north and an increase in the south of the Brazilian region. Moreover, with increasing radiative forcing, the reduction in precipitation in the Amazon River basin and adjacent territories will intensify.

The analysis of agroecological behaviors, based on indicators using a mathematical system to support decision-making, is something very unusual in academia. Therefore, we propose the use of indicators that take into account the qualitative and quantitative nature. The hypothesis assumes that information expressed through natural language, proposed in fuzzy logic, can lead to a better adaptation of data processing from informants. The comparative perspective addresses the environment and human beings, with their customs and productions in Nova Friburgo (Brazil) and Mendoza (Argentina), and uses fuzzy methodology, in order to facilitate modeling and make the problem solution more viable and realistic for several complex problems, involving many variables. As a result, we have a model that can analyze and identify which factors (peasantry, sustainability, social organization and rural development agency) and respective indicators make the greatest contribution to the adoption of agroecological practices. Encouraging decision makers and policymakers to strategically reflect on scenarios, in the context of the agroecological transition, is fundamental and can be facilitated by implementing an innovative monitoring and evaluation approach.