Fundación de Estudios Superiores Comfanorte

La clasificación automática de perturbaciones de calidad de energía suele evaluarse bajo condiciones in-domain, lo que puede sobreestimar el desempeño cuando cambian la distribución de los datos o las condiciones de ruido. Este estudio evaluó la robustez y la capacidad de generalización de modelos de aprendizaje automático y profundo para la clasificación de ocho tipos de perturbaciones eléctricas. Se armonizaron tres dominios de datos —SEED-PQD, PQD-16c y un conjunto sintético— a una representación común de un ciclo, 64 muestras y 3,2 kHz. Se compararon máquinas de vectores de soporte (SVM), Random Forest, XGBoost, 1D-CNN y CNN-LSTM mediante Macro-F1, exactitud balanceada, MCC, generalización cross-domain y degradación frente a ruido blanco gaussiano aditivo entre 30 y 0 dB. Los modelos profundos alcanzaron Macro-F1 in-domain superiores a 0,98 en SEED y en el dominio sintético cuando dispusieron de suficientes datos, aunque mostraron elevada sensibilidad al tamaño del conjunto de entrenamiento y al ruido. SVM registró el mayor Macro-F1 cross-domain promedio (0,6407) y los valores más altos de AUC de robustez (0,4328) y AUC de retención (0,5039). La prueba de Friedman confirmó diferencias significativas entre los modelos bajo ruido (p<0,001; W=0,5265), mientras que en generalización cross-domain no se detectaron diferencias globales significativas (p=0,1257). Los resultados muestran que un elevado desempeño in-domain no garantiza generalización ni robustez, y evidencian la necesidad de evaluar conjuntamente precisión, transferencia entre dominios y tolerancia al ruido antes de seleccionar modelos para aplicaciones reales de calidad de energía.

La presente investigación ha tenido como objetivo principal identificar los factores que afectan directamente la productividad debido a la mala gestión de los procesos dentro del departamento de compras MRP dentro de una empresa de manufactura de giro medico ubicada en la frontera Mexico-Estados Unidos. Con un enfoque Cualitativo-cuantitativo se analizan historiales y reportes de SAP donde se identifican fallas sistémicas que impactan negativamente a los KPI’s críticos dentro de la organización como tiempos de entrega provocando paros temporales en las operaciones productivas. Entre los principales hallazgos se evidencia una acumulación critica de facturas sin procesar por más de 90 días, así como órdenes de compra con atraso de mas de seis meses, falta de sincronía en la información entre departamentos los cual ha provocado acciones de alto costo para la misma. El análisis revela un problema no solo técnico si no de gestión. La investigación concluye con la identificación de los factores intervinientes en la problemática lo cual indica una urgente intervención para mejorar los procesos de capacitación de comunicación, auditoria y liderazgo.

The fundamental purpose of this project is to develop a model to analyze and understand the reasons behind academic dropout rates at the ITM School of Engineering. To achieve this, dashboards will be implemented to simplify decision-making at the institutional level. The process is structured in several phases, beginning with an exploratory analysis where statistically significant variables are identified. The dashboard is then constructed, where the results are presented in a precise and understandable manner using graphs, tables, and visualizations. The results of the exploratory analysis reveal variables such as age, stratum, level, seniority, PP, and origin, which are correlated with academic status. These findings are fundamental to understanding behavioral patterns and factors influencing student dropout rates. The next step involves implementing a machine learning model, where six key variables are used for its design and training. Seniority, age, and level are identified as crucial variables in predicting academic status. Model performance is anticipated to improve with the addition of more data, and exploration of new variables to enhance predictions is suggested. In conclusion, this project offers a comprehensive approach that combines statistical analysis and machine learning models to understand and prevent academic dropout.

Personal financial planning is important and crucial for maintaining control over income and expenses, creating an emergency fund, achieving financial goals, optimizing resources, reducing debt, encouraging investments, and improving financial literacy. This provides stability, security, and facilitates the achievement of short- and long-term financial goals. For this article, sources such as Google Scholar and Scielo were reviewed using a documentary design. This research discusses how to carry out successful personal financial planning, covering topics such as investing, which helps maintain the value of money against inflation and generate additional income, a tool for perseverance and increasing wealth. Savings, which allows for financial security and achieving goals. Financial management, which provides control over income and expenses and aids in strategic decisions. Debt management allows us to minimize costs and maintain a good credit score. All of these aspects are important for maintaining sound personal finances, free from errors that could be detrimental to one's income, and for maintaining a healthy financial life and preparing for emergencies. Results: The research also addresses financial security, which is achieved with a stable income, spending less than you earn, avoiding debt, and setting financial goals. Controlling your finances, supporting decisions, and measuring financial performance guides you toward the efficient use of resources, protecting your assets, and ensuring the economic viability to achieve your financial goals and live a more peaceful and financially secure life.

The lack of financial education and its importance for today's youth, as well as the consequences of over-indebtedness and the lack of financial education programs, are identified through bibliographic and documented research by several authors in Google Scholar, the journal Convicciones, Scielo, and economics journals. It is identified that few young people have the opportunity to pursue financial education. The worrying lack of financial culture leads to making inappropriate economic decisions and uncontrolled debt. However, we describe the importance of teaching young people to differentiate between needs and desires, controlling their emotions to avoid impulsive financial decisions. We believe that financial education should be integrated as an educational method in schools, teaching basic concepts such as income, expenses, savings, and investment, as well as promoting skills for making smart decisions throughout their financial lives. Schools and families play a very important role in educating students and children with extracurricular programs that provide our youth with knowledge and real-life skills in financial education. Good, comprehensive financial education addresses not only technical aspects but also emotional ones. This involves preparing young people to make informed and responsible financial decisions in the future. It also involves implementing a savings and investment model to foster a financial strategy, resulting in young people developing both professional and personal skills. Financial education is key to fostering this culture.