Microaggressions constitute a persistent yet often underestimated source of psychological strain in organizational environments. Their subtle and ambiguous nature complicate detection and regulation, while their cumulative effects contribute to emotional exhaustion, disengagement, and organizational silence. This study develops an agent-based modeling (ABM) framework to simulate the emergence and diffusion of microaggressions, emotional distress, and intervention outcomes within organizational social networks. Agents interact on a small-world topology and follow probabilistic behavioral rules grounded in empirical research on aggressors, bystanders, and targets. Three scenarios are evaluated: abase-line with no intervention, a bystander-training program, and a combined policy package integrating bystander training, perceived organizational support, and anonymous reporting. Results show that the combined intervention yields nonlinear improvements, including an approximate 55% reduction in microaggression incidents, a 41% increase in internal reporting, and a 35% faster recovery from emotional distress. The framework demonstrates the value of ABM as a decision-support tool for designing evidence-based organizational policies.
This research contributes to the literature by empirically demonstrating the effectiveness of the random forest method and its variants, incorporating technical and on-chain metrics, as well as neural networks, for Bitcoin price prediction. Our research focuses on extracting meaningful information from large amounts of bitcoin prices, such as price and volume, from January 2015 to September 2, 2023. The Root Mean Square Error (RMSE) and the confusion matrix are used to evaluate the predictive level of the models. The model with the highest accuracy and consistency is the Random Forest model, with on-chain hash rate metrics of $769.61 USD and confusion matrix accuracy of 86% versus neural networks with $2761.69 USD and 17%, respectively. This research contributes to the literature by empirically demonstrating the use of the random forest method with on-chain data is better than the other methods analyzed.
This paper focuses on the rising interest in the consideration of Environmental, Social, and Governance (ESG) criteria in investment decisions in the investment fund industry. It examines the existing discrepancy between financial and ESG ratings, highlighting the absence of a tool that effectively combines these two criteria into a single score. To address this gap, the use of TOPSIS, a multi-criteria decision model, is proposed to rank a variety of investment funds based on both financial and ESG criteria. This novel approach provides investors with a more comprehensive and flexible assessment, enabling them to make investment decisions from a sustainable investment perspective, considering both financial and non-financial factors.
Cryptocurrencies have emerged as a disruptive investment, offering opportunities for investors. This study assesses the financial viability of investing in Bitcoin by analyzing potential price fluctuations over a five-year horizon. The research employs Monte Carlo simulations based on four stochastic models: Geometric Brownian Motion, Jump Diffusion A core component of the analysis involves a back test using historical data from 2020 to 2025. Each model is applied to analyze real price movements to determine which one most accurately captures the behavior and volatility of Bitcoin, incorporating key events such as Bitcoin halving. The results indicate that the Feedforward Neural Network exhibited superior predictive power compared to the traditional stochastic methodologies. As a result, this study contributes to assessing which projection methodology provides a more accurate simulation of Bitcoin prices relative to traditional forecasting models used in traditional financial markets.
The growth of large photovoltaic projects increases competition with agricultural production for land use, especially in areas with a Mediterranean climate. Agrovoltaics combines crop and energy production, with the consequent increase in performance per surface unit and its contribution to the electrification of isolated communities. From the agronomic perspective, the shading produced by these installations can favor water savings, although it can also lead to a loss in crop production. In the present work, shade netting equipped with photovoltaic panels were used, which is deployed when solar radiation is high. A Muchamiel tomato crop grown in a greenhouse under different production strategies was assessed: grafting, water deficit, and shading. The different treatments used were ranked from a multicriteria perspective, using the CRiteria Importance Through Inter-Criteria Correlation (CRITIC). This is a multicriteria method that aims to rank the different alternatives starting with the values from the criteria themselves combined with the Ordered Weighted Averaging (OWA), which allows introducing the attitudinal character of the decision-maker. The Induced OWA (IOWA) was used, using a penalty function as the inducible variable, and was compared with others to observe the effect of a change in the induced variable. The use of IOWA was also proposed when obtaining the correlations between the different criteria. The results indicate that Muchamiel tomato is highly adapted to Mediterranean conditions. The combination of shading and deficit irriga- tion is a strongly recommended strategy for this type of crop.
