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As network systems advance and become more sophisticated, the associated security challenges grow more complex. The zero trust model emerges as a new paradigm to address this, significantly emphasizing robust and continuous identity verification. Multi-factor authentication (MFA) methods have become crucial for enhancing authentication security within this framework. Additionally, the integration of blockchain technology is increasingly recognized for its potential to strengthen identity trustworthiness further, complementing the zero trust approach by providing a more secure and transparent identity verification process. However, privacy concerns remain, especially in public blockchain environments where personal data is vulnerable to inadvertent exposure. Also, using centralized servers for authentication, even in systems integrated with blockchain, presents the risk of creating single points of failure. This paper introduces a privacy-preserving MFA system that harnesses the decentralized capabilities of blockchain technology to enable a Distributed Authentication Mechanism (DAM) as a network of authenticators for enhancing the reliability of the authentication process. This system utilizes blockchain-based Zero-Knowledge Proofs (ZKP) as a privacy mechanism to prove the knowledge of a One-Time Password (OTP). This approach not only ensures the authenticity of the proof authenticity but also confirms the identity of the prover. In the final stage of the MFA process, non-transferable, non-fungible tokens (NFTs) are employed as authentication tokens for identity verification. Our experimental results and comparative security analyses suggest a relevant contribution to secure, private, and dependable MFA framework research.

In this research, we present an advanced predictive framework designed to assess the turbulence induced by low-level wind shear near the runways at Hong Kong International Airport (HKIA), utilizing data from Pilot Reports (PIREP). This framework integrates the TabNet architecture with SHapley Additive exPlanations (SHAP), thereby enhancing both predictive accuracy and interpretability. Given the imbalance in the PIREP data, we implement various data augmentation techniques and employ Bayesian optimization to fine-tune the hyperparameters of the TabNet model. Our analysis revealed that the TabNet model, when applied to data balanced with the Support Vector Machine - Synthetic Minority Over-sampling Technique (SVM-SMOTE), demonstrated superior performance, evidenced by a Geometric Mean (G-Mean) of 0.74, a Matthews Correlation Coefficient (MCC) of 0.37, a Balanced Accuracy (BA) of 0.74, and an Area Under the Receiver Operating Characteristic (AU-ROC) of 0.739. SHAP analysis further enhanced the interpretability of the TabNet model by identifying key contributing factors, including the magnitude of wind shear and altitude, which significantly influence the likelihood of significant turbulence occurrence. Specifically, SHAP insights demonstrated the critical impact of wind shears between 15 and 25 knots and the pronounced effects of complex terrain and sea breezes on significant turbulence occurrence, predominantly at altitudes below 1200 feet. These findings not only demonstrate the efficacy of the TabNet-SHAP model in enhancing aviation safety through improved turbulence prediction but also provide actionable insights for operational and safety protocols at airports prone to low-level wind shear.

An improved ripple correlation control (iRCC) algorithm is proposed to enable conventional ripple correlation control (cRCC) to track maximum power under uniform and partial shading condition (PSC). The proposed iRCC has two parts; the first part detects the global maximum power point (GMPP) under PSC, the second part executes the ripple correlation control to operate the photovoltaic (PV) array at GMPP. The proposed iRCC stays with the second part under uniform irradiation condition and moves to the GMPP only when it detects a dynamic change in weather condition. The proposed iRCC is validated through simulation in Matlab and by hardware prototype. The performance improvement is verified in contrast with the conventional RCC method. The results showcase that the proposed iRCC algorithm accurately tracks the GMPP compared to the conventional RCC method. Consequently, the proposed iRCC method, under partial shading condition 1 (PSC-1), produce 131 % more output power compared with the cRCC method. For PSC-2, the shading resulted in power peaks such that the proposed iRCC power yield is almost equal to the cRCC method. This suggests that the proposed iRCC method can outperform the cRCC method depending on the shading pattern.

Millions of lives are lost in road accidents annually, underscoring the severity of traffic incidents. Despite numerous studies on crash severity forecasting with static ensemble machine learning, the potential of Dynamic Ensemble Selection (DES) for combining classifiers and improving performance remains relatively unexplored in this field. In contrast to previous research focused on static ensemble machine learning models, our study introduces an interpretable Bayesian Optimized Dynamic Ensemble Selection strategy, reinforced with instance hardness and region of competence considerations, for crash risk assessment. Utilizing six algorithms – Random Forest, Extra Tree, Cat Boost, Light GBM, KT Boost, and XGBoost – optimized through cross-validation, we employed bagging as a hybrid ensemble selection criterion to ensure ensemble diversity for DES models. Addressing class imbalance with cost-sensitive learning, we evaluated six DES algorithms, ‘KNORA-E, KNORA-U, DES-KNN, DES-RRC, DES-KL, and META-DES’ using a pool of input classifiers, based on key metrics such as Balanced Accuracy Score, Sensitivity, G-mean, and Matthews Correlation Coefficient. Our results revealed that the KNORA-E ensemble with Cat Boost achieved the highest performance, with a Balanced Accuracy (BCA) of 69.3%, Matthews Correlation Coefficient (MCC) of 0.36, and a G-mean score of 69.10%. DES-RRC demonstrated notable performance across all base classifiers, particularly in addressing imbalanced classes, and DES-RRC with LightGBM emerged as the second-best performer. SHAP analysis for the KNORAE-Cat Boost model identified key factors in crash severity as, road user gender, airbag deployment, vehicle age and road surface conditions. This approach could be a valuable tool for informed decision-making and targeted interventions.