Smart hospitals deploy Internet of Medical Things (IoMT) sensors and MQTT brokers to stream clinical telemetry over resource-constrained edge gateways. Centralized network intrusion detection systems (NIDS) expose sensitive traffic traces and create single-point failures; federated learning (FL) avoids raw-data centralization but remains vulnerable to client-side model poisoning and server-side inspection of client updates. This study presents Trust-Weighted Federated Aggregation (TW-Fed), a reputation-aware defense framework for federated learning that integrates 1) cosine similarity-based trust scoring using low-dimensional update sketches and 2) secure aggregation of trust- and data size-weighted model updates through pairwise masking with dropout recovery. TW-Fed has been evaluated on the CICIoMT2024 benchmark (Wi-Fi and MQTT subsets) with a four-class NIDS task (Benign, MQTT-Connect-Flood, TCP-DDoS, ARP-Spoofing) and 20 edge clients emulating IoMT gateways. In the clean setting, TW-Fed achieves 99.21% accuracy and 98.74% macro-F1, exceeding FedAvg by 0.82 macro-F1 points. Under label flipping with 30% malicious clients, TW-Fed sustains 97.06% accuracy versus 82.14% (FedAvg), 85.33% (FedProx), and 92.07% (Krum). Under backdoor injection, TW-Fed reduces attack success rate from 91.5% (FedAvg) to 6.8%. On Raspberry Pi 4 clients, TW-Fed adds 0.09 s local overhead per round while reducing server aggregation time by 34% relative to Krum. Secure aggregation increases uplink volume by 9.7% while preventing per-client update disclosure.
Suspended particle pollution (PM10) represents a serious public health problem in Metropolitan Lima, since its high levels are related to an increase in respiratory and heart diseases. This work aims to anticipate PM10 concentrations in seven districts of Lima using a multiple linear regression (OLS) model with the Ridge and Lasso regularization techniques. PM2.5 and NO2 were selected as predictors because they were the only pollutant variables available in the SENAMHI monitoring dataset; hourly records collected between 2015 and 2024 were used, yielding 169,308 observations after removing missing values and outliers. The data were separated into 80% for training and 20% for validation, applying cross-validation to determine the most appropriate regularization parameter. The final model reached a coefficient of determination (R2) of 0.23 and a mean squared error (MSE) of 521 in the test set. The findings indicate that PM2.5 is the main factor that predicts PM10, while NO2 exerts a secondary impact. Although the predictive capacity of the models is limited, the use of both Ridge and Lasso helped stabilize the coefficients and minimize overfitting. Given this limited explanatory power, the model should be regarded as an initial, interpretable baseline for PM10 monitoring in Metropolitan Lima rather than an operational forecasting tool. It is suggested to include meteorological variables and investigate non-linear models in future studies to enhance the accuracy and usefulness of the model in air quality management.
Production AI systems typically apply governance—compliance checking, auditing, and explanation—as an external control layer that observes outputs after they are produced. In this paper, embedded governance is proposed: a governance gate that forms part of the system architecture itself, positioned between inference and release, so that no output leaves the system without a measurable governance assessment. For each candidate output, a governance score is computed from three factors that are checked mechanically rather than judged by a model: intent conformance, formulation traceability, and verification. On the basis of this score, each output is released, remediated, or blocked. Governance efficiency η, the ratio of intercepted error cost to governance cost, is further defined, by which the claim that governance pays for itself becomes falsifiable rather than asserted. Weights and thresholds are calibrated by maximizing η on a validation split and reported on held-out data. Evaluation uses the full credit-card fraud detection dataset of 284,807 transactions under a temporal split, so that the evaluation period follows the training period and distribution drift arises naturally. The calibrated gate intercepts 99% (95% CI 94.6–99.8) of severely shifted requests at a mean overhead of 3.74 ms, and η reaches 4.52 when releasing unreliable outputs carries cost, while remaining below break-even under accuracy-only cost models. A severity sweep locates the detection boundary and shows interception falling to 2% under mild shift. An ablation identifies distributional checks as the load-bearing components and reveals that η can be inflated by a gate that governs less, so efficiency must be read alongside coverage. Under identical workload, external monitoring and post-hoc assessment detect the same errors but prevent none, whereas inline placement reduces released error cost by 82.
In the aviation industry, flight delays represent a major challenge due to their economic impact, operational disruptions, and adverse effects on passenger satisfaction and transportation efficiency. This study proposes an integrated feature engineering framework for flight delay prediction that combines three groups of engineered features, namely Departure Disruption Features (DDFs), Airline-Route Stability Features (ARSFs), and Schedule Intensity Features (SIFs), with the original flight delay dataset. These feature groups are designed to capture complementary operational, historical, and scheduling characteristics that are not directly represented in the original data. Five machine learning classifiers were evaluated across five experimental scenarios. These included the original feature set, the original feature set combined separately with each engineered feature group (DDFs, ARSFs, and SIFs), and the original feature set combined with all three engineered feature groups. This design enabled assessment of the individual and complementary contributions of the proposed features to flight delay prediction. The results indicated that the engineered features improved prediction performance across the evaluated configurations. In particular, combining all three engineered feature groups with the original feature set outperformed the baseline feature set and each individually combined feature-set configuration, supporting the complementary predictive value of the proposed engineered features. The complete feature set with LightGBM and SMOTE-Tomek achieved the best performance, with an accuracy of 0.9820 and an AUC of 0.9884, compared with 0.9487 and 0.9663, respectively, for the baseline feature set.
Based on Sommerville’s robust software reliability theory and eight design principles based on best practices (DPG), this study conducted a comparative assessment of two Dexcom software platforms: the Dexcom Clarity web portal and the Dexcom ONE+ iPhone application. The first phase of the study employed a “black-box” assessment framework, combining off-the-shelf security analysis tools with application-layer behavioral testing workflows developed specifically for this project. The initial two phases focused on the web portal, analyzing communication encryption and HTTP security header configurations. Subsequent phases involved a more in-depth implementation assessment, focusing on actual interactive features, data types, and automated responses triggered by glucose sensor alerts within the application. However, this approach also revealed that certain system elements required access to the source code for verification. The resulting workflow enables the assessment of all externally visible security attributes while identifying system components that necessitate source code access for validation. Of the eight assessment criteria, four were found to have issues, either in whole or in part. Key issues identified include: session cookies missing the SameSite attribute (G1); acceptance of clinically abnormal carbohydrate values without rejection or warning (G2); inclusion of the unsafe-inline directive in the Content Security Policy (CSP), increasing the risk of Cross-Site Scripting (XSS) attacks (G4); and the absence of a dedicated alert mechanism for Wi-Fi connectivity loss during critical medical functions (G7). Due to the requirement for source code access, this “black-box” approach could not evaluate the remaining four criteria (G3, G5, G6, and G8). Finally, this paper presents a re-producible evaluation procedure and offers applicable, standards-compliant mitigation recommendations for each identified issue.
