Abstract
We study the optimal consumption, investment and life-insurance purchase and selection strategies for a wage-earner with an uncertain lifetime with access to a financial market comprised of one risk-free security and one risky-asset whose prices evolve according to linear diffusions modulated by a continuous-time stochastic process determined by an additional diffusive nonlinear stochastic differential equation. The process modulating the linear diffusions may be regarded as an indicator describing the state of the economy in a given instant of time. Additionally, we allow the Brownian motions driving each of these equations to be correlated. The life-insurance market under consideration herein consists of a fixed number of providers offering pairwise distinct contracts. We use dynamic programming techniques to characterize the solutions to the problem described above for a general family of utility functions, studying the case of discounted constant relative risk aversion utilities with more detail.

Abstract
We model the financial markets as a game and make predictions using Markov chain estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/her risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then, we estimate the market's mixed probabilities by defining Markov chains and utilizing its transition matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. From this, we obtained a model for the financial markets that has a good performance in terms of accuracy and profitability.

Abstract
A model of Edgeworthian economies is studied, in which participants are randomly chosen at each period to exchange two goods to increase their utilities, as described by the Cobb-Douglas utility function. Participants can trade deviating from their bilateral equilibrium, provided that the market and the trade follow appropriate symmetry conditions. The article aims to study the convergence to equilibrium in a situation where individuals or small groups of participants trade in a market, and prices are determined by interactions between the participants rather than by demand and supply alone. A dynamic matching and bargaining game is considered, with statistical duality imposed on the market game, ensuring that each participant has a counterpart with opposite preferences. This guaranties that there is sufficient incentive for trade. It is shown that, in each period, the expected logarithm of the trading price in the Edgeworthian economy equals the expected Walrasian price. This demonstrates that, under symmetry conditions, the trading price in the Edgeworthian economy is related to the Walrasian price, indicating convergence of the trading price in the Edgeworthian economy towards the Walrasian price. The study suggests that, under the right conditions, the decentralized trading model leads to price convergence similar to what would be expected in a more classical Walrasian economy, where prices balance demand and supply.

Abstract

Abstract
The textile industry, a vital sector in global production, relies heavily on dyeing processes to meet stringent quality and consistency standards. This study addresses the challenge of identifying and mitigating non-conformities in dyeing patterns, such as stains, fading and coloration issues, through advanced data analysis and machine learning techniques. The authors applied Random Forest and Gradient Boosted Trees algorithms to a dataset provided by a Portuguese textile company, identifying key factors influencing dyeing non-conformities. Our models highlight critical features impacting non-conformities, offering predictive capabilities that allow for preemptive adjustments to the dyeing process. The results demonstrate significant potential for reducing non-conformities, improving efficiency and enhancing overall product quality.

Abstract
Background: The role of mindful eating (ME) and intuitive eating (IE) in improving eating behavior, diet quality, and health is an area of increasing interest. Objective: The objective of this review was to identify the instruments used to assess ME and IE among higher education students and outcomes related to these dimensions. Methods: This review was carried out according to the PRISMA statement, through systematic searches in PubMed, Web of Science, PsycInfo, and Scopus. The inclusion criteria selected for higher education students, levels of ME and/or IE reported, and observational and clinical studies. The exclusion criteria selected against reviews, qualitative studies, and case studies. Quality was assessed using the Academy of Nutrition and Dietetics Quality Criteria Checklist. Results: A total of 516 initial records were identified, from which 75 were included. Cross-sectional studies were the most common research design (86.7%). Most studies were conducted with samples that were predominantly female (90.7%), White (76.0%), aged 18 to 22 years (88.4%), with BMI < 25 kg/m(2) (83.0%), and in the United States (61.3%). The Intuitive Eating Scale (IES), the Mindful Eating Questionnaire (MEQ), and their different versions were the most used instruments. The outcomes most studies included were eating behavior and disorders (77.3%), anthropometric assessments (47.8%), mental health (42.0%), and body image (40.6%). Regarding the quality of studies, 34.7% of studies were assigned a positive, 1.3% a negative, and 64.0% a neutral rate. Conclusions: IES and MEQ were the most used instruments. RCT and cohort studies are scarce, and future research with a higher level of quality is needed, especially on the topics of food consumption, diet quality, and biochemical markers.