This study examines job satisfaction among mobile digital platform workers in Chile, addressing the gap in understanding the interplay between autonomy, social support, and technology in the platform economy. It explores how these factors shape job satisfaction through the following research question: How do autonomy, social support, and technological factors influence job satisfaction in the platform economy? Using a quantitative methodology, 398 platform workers from three Chilean regions participated in a survey, with data analyzed via logistic regression models to evaluate the impact of sociodemographic and psychological variables. The findings highlight that autonomy and social support significantly enhance job satisfaction, while technological usability and transformation are pivotal for positive work experiences. The study concludes that fostering worker autonomy, robust support systems, and user-friendly technologies is critical for improving job satisfaction in the gig economy. These insights contribute to the academic literature and inform strategies for policymakers and platform operators, emphasizing the need for tailored approaches to enhance worker well-being and organizational outcomes.

Canopy temperature (CT) is widely used to assess crop water and heat status, but it is often recorded at a single hour, implicitly treating CT as a stable trait. Here, we show that canopy cooling is a dynamic phenotype whose expression depends on time of day, phenological stage, and environment. First, we monitored 184 spring wheat (Triticum aestivum L.) genotypes in two Mediterranean environments (fully irrigated vs. rainfed, contrasting atmospheric demand) using UAV-based thermal imaging. CT was measured six times per day (10:30–17:30 h) at four reproductive stages (anthesis, milk-grain, milk-dough, and dough), enabling quantification of diurnal plasticity, seasonal shifts, and environmental effects on canopy cooling. Second, repeated-measures mixed models confirmed that Location, Stage, and Time of day, and all interactions, were highly significant (p < 0.001). Variance-component analyses showed a strong genetic signal within each Stage × Environment combination, with 87.6–97.7% of total variance attributable to genotypic effects pooled across hours. Third, the optimal phenotyping window was context dependent: under rainfed conditions, genotypic discrimination consistently peaked around mid-afternoon (~15:00 h), whereas under irrigation, the optimal window shifted with stage (13:30–15:00 h). Genotype rankings were also markedly less stable across hours under rainfed conditions, indicating substantial within-day re-ranking as atmospheric demand increased. Finally, thermal exposure analyses showed that exceeding a physiologically relevant threshold (CT > 32 °C) depended strongly on time of day and stage; maximum CT captured short heat events missed by daily means. Clustering and alluvial analyses revealed frequent reclassification across stages, with only a small subset remaining consistently cooler, particularly under stress. Random regression of CT on vapor pressure deficit (VPD) indicated that CT–VPD sensitivity was largely environment-dependent and showed weak cross-environment correspondence (Spearman ρ = −0.166). Overall, single-time-point CT phenotyping provides an incomplete view of thermal status, underscoring the need for multi-temporal protocols and context-specific measurement windows for breeding and physiological interpretation under drought and heat.

The quality of life index is an indicator published yearly since 2010 by the Institute on Urban and Territorial Studies and the Chilean Chamber of Construction, involving 99 municipalities and communes from the national territory. This research provides an approach to understanding how various dimensions and variables interact with quality of life in Chilean communes considering multiple factors and perspectives through information from public sources and social indicators. For the research, variables were analyzed considering demographic, sociodemographic, economics and urban indicators, where the model developed allows for an understanding of how the variables are related. In addition, it was discovered that education, own incomes, municipal spending and green areas directly relate to quality of life, while overcrowding and municipal funds negatively affect rates of communal welfare. Moreover, the variables chosen as explanatory variables allow for the development of an efficiency model. For this purpose, Cobb–Douglas and trans-logarithmic forms were tested, and it was found that Cobb–Douglas fits better to the data set and structures of the variables. The results of the efficiency model show that education, municipal funds and own incomes significantly affect efficiency, with a mean value of approximately 47%, minimum values close to 30% and maximum values of approximately 60%. Finally, a cluster analysis was developed through k-means, k-medoids and hierarchical clustering algorithms, where, in all cases, the results were similar, suggesting four groups with differences and variations in analyzed variables, especially in overcrowding, education, quality of life and wellness.

The assessment of urban quality of life (UQoL) is a multidimensional challenge involving socioeconomic, environmental, and infrastructure-related variables that, due to changes in the behavior and structures of social issues, requires robust and effective analytical techniques. This study introduces a novel machine learning-based approach incorporating bilevel programming methods to optimize predictive models for UQoL. A key contribution is the development of a Support Vector Machine (SVM) model for group-specific regression, enhanced by a bilevel programming reformulation that optimizes the hyperparameters of the model over groups of data. The bilevel models consider linear and quadratic loss functions. The proposed approach is benchmarked against Linear Regression, Regression Tree, Random Forest, and Gradient Boosting models. The evaluation is conducted using a cross-validation procedure computing the Mean Absolute Error, the Mean Squared Error, and the Mean Absolute Percentage Error as performance metrics. The results indicate that the bilevel quadratic loss SVM model outperforms other models, achieving the lowest prediction error. Furthermore, explainable AI techniques are applied with the usage of Shapley values to analyze feature importance, revealing that own incomes, teacher-to-population ratios, and birth rates are key predictors of UQoL. These findings provide valuable insights for policymakers, supporting data-driven decision-making processes for urban development and resource allocation. This study highlights the potential of AI-driven methodologies in advancing UQoL assessments and urban planning strategies as support systems.

This article analyzes the unequal distribution of perceived work flexibility in Chile, drawing on data from the 2023 National Time Use Survey (ENUT). Using multilevel multinomial logistic regression models, this study explores how individual and contextual variables shape the likelihood of perceiving jobs as fully flexible, partially flexible, or non-flexible. The findings reveal that flexibility is a stratified experience: women are more likely to access partial—but not full—flexibility; workers with higher educational attainment report lower perceptions of flexibility, and longer working hours are associated with diminished flexibility. Among the contextual factors, occupation stands out as the strongest predictor, explaining a substantial share of variance across the models. Employers are more likely to report full flexibility, while dependent workers tend to experience only limited forms of autonomy. These results challenge the notion of flexibility as a universally empowering feature of modern labor markets, underscoring the need for policies and organizational practices that address the structural inequalities embedded in flexible work arrangements.

Whether due to natural causes or human carelessness, forest fires have the power to cause devastating damage, alter the habitat of animals and endemic species, generate insecurity in the population, and even affect human settlements with significant economic losses. These natural and social disasters are very difficult to control, and despite the multidisciplinary human effort, it has not been possible to create efficient mechanisms to mitigate the effects, and they have become the nightmare of every summer season. This study focuses on forecast models for fire measurements using time-series data from the Chilean Ministry of Agriculture. Specifically, this study proposes a comprehensive methodology of deterministic and stochastic time series to forecast the fire measures required by the programs of the National Forestry Corporation (CONAF). The models used in this research are among those commonly applied for time-series data. For the number of fires series, an Autoregressive Integrated Moving Average (ARIMA) model is selected, while for the affected surface series, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is selected, in both cases due to the lowest error metrics among the models fitted. The results provide evidence on the forecast for the number of national fires and affected national surface measured by a series of hectares (ha). For the deterministic method, the best model to predict the number of fires and affected surface is double exponential smoothing with damped parameter; for the stochastic approach, the best model for forecasting the number of fires is an ARIMA (2,1,2); and for affected surface, a SARIMA$(1,1,0){(2,0,1)}_4$, forecasting results are determined both with stochastic models due to showing a better performance in terms of error metrics.