Applied System Innovation

FIFO buffers are widely employed in networking devices to store packets prior to transmission. Their impact on aggregate traffic has been extensively studied and is well documented in the literature. In contrast, significantly less attention has been allocated to the impact of FIFO buffers on individual flows contributing to the aggregate traffic. In this paper, the throughput of each flow traversing a FIFO buffer supplied with complex traffic composed of numerous flows, potentially exhibiting heterogeneous statistical properties, is investigated. A full queuing model of a FIFO buffer fed by many flows with different characteristics is considered first. This model is very precise with respect to each flow, but cannot be solved in practice. Then, a simplification of the full model based on a limiting theorem is proposed. For the simplified model, exact formulae for throughput and loss ratio of each participating flow are derived. In numerical examples, the throughput of flows of diverse types in scenarios with various buffer sizes, buffer loads, and transmission time distributions is calculated. It is also examined, how these factors influence per-flow throughput. Finally, it is demonstrated that in typical scenarios, the results of the simplified model differ by only a few percent from those obtained through simulations of the full, precise model.

Public procurement systems are prone to risks such as collusion, contractual concentration, and irregular subcontracting, which undermine transparency and accountability. Traditional fiscal oversight approaches remain largely retrospective, limiting their ability to anticipate irregularities and prevent potential losses. Addressing the gap between theoretical machine learning models and real-world institutional deployment, this study introduces an applied system innovation that integrates two complementary approaches at a national scale: a Contractual Network Model (Mallas Contractuales) and a Predictive Risk Model for Contractors. The first component uses graph-based analytics, employing an Entity–Link–Property schema to represent relationships among entities, contractors, and contracts, thereby enabling the detection of structural patterns associated with collusive or anomalous behavior. The second component implements supervised machine learning models, trained on more than 16 million contracts and 2.6 million contractors from sources such as SECOP, RUES, DIAN, and national sanction registries. Models, including Random Forests and Gradient Boosted Trees, were optimized via cross-validated hyperparameter search and evaluated on a separate hold-out set using ROC AUC and Gini metrics, achieving strong discriminatory performance under the available retrospective validation setting while maintaining operational interpretability. Both approaches were deployed in a modular architecture that integrated Databricks, i2 Analyst’s Notebook, and Power BI dashboards, providing interactive visualizations and risk scores at multiple levels. Together, these systems demonstrate how the convergence of graph analytics and predictive modeling enables proactive fiscal auditing, strengthens institutional capacity, and offers a replicable framework for public sector accountability.

Automated thermal inspection supports scalable photovoltaic asset management by reducing the subjectivity and limited temporal coverage of manual surveys. This study benchmarks a lightweight machine vision framework for low-resolution infrared inspection of photovoltaic modules using native $24\times 40$ pixel thermal images. Morphological and textural descriptors, namely HOG, LBP, and GLCM, were evaluated with optimized SVM, Random Forest, and XGBoost classifiers under a unified experimental protocol. The HOG + SVM_Opt configuration achieved the best performance, with a Macro F1-score of $0.80\pm 0.02$ and an average accuracy of $0.80\pm 0.02$. The same pipeline maintained an end-to-end CPU latency of $12.45\pm 0.85$ ms per image, including preprocessing, descriptor extraction, and prediction. The results indicate that gradient-based structural descriptors provide the most favorable balance between predictive performance and computational cost among the evaluated configurations. The proposed pipeline is therefore presented as an interpretable reference for first-stage thermal screening in low-cost photovoltaic inspection workflows.