Search Results (51)

Today, blockchain technologies are a separate, rapidly developing area. With rapid development, they open up a number of scientific problems. One of these problems is the problem of reliability, which is primarily associated with the use of cryptographic primitives. The threat of the emergence of quantum computers is now widely discussed, in connection with which the direction of post-quantum cryptography is actively developing. Nevertheless, the most popular blockchain platforms (such as Bitcoin and Ethereum) use asymmetric cryptography based on elliptic curves. Here, cryptographic primitives for blockchain systems are divided into four groups according to their functionality: keyless, single-key, dual-key, and hybrid. The main attention in the work is paid to the most significant cryptographic primitives for blockchain systems: keyless and single-key. This manuscript discusses possible scenarios in which, during practical implementation, the mathematical foundations embedded in the algorithms for generating a digital signature and encrypting data using algorithms based on elliptic curves are violated. In this case, vulnerabilities arise that can lead to the compromise of a private key or a substitution of a digital signature. We consider cases of vulnerabilities in a blockchain system due to incorrect use of a cryptographic primitive, describe the problem, formulate the problem statement, and assess its complexity for each case. For each case, strict calculations of the maximum computational costs are given when the conditions of the case under consideration are met. Among other things, we present a new version of the encryption algorithm for data stored in blockchain systems or transmitted between blockchain systems using elliptic curves. This algorithm is not the main blockchain algorithm and is not included in the core of modern blockchain systems. This algorithm allows the use of the same keys that system users have in order to store sensitive user data in an open blockchain database in encrypted form. At the same time, possible vulnerabilities that may arise from incorrect implementation of this algorithm are considered. The scenarios formulated in the article can be used to test the reliability of both newly created blockchain platforms and to study long-existing ones. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards. Full article

The optimal selection of conductors (OCS) in radial distribution networks is a critical aspect of system planning, directly impacting both investment costs and energy losses. This paper proposed a mixed-integer convex (MI-Convex) optimization framework to solve the OCS problem under balanced operating conditions, integrating the costs of conductor investment and energy losses into a single convex objective. This formulation leveraged second-order conic constraints and was solved using a combination of branch-and-bound and interior-point methods. Numerical validations on standard 27-, 33-, and 85-bus test systems confirmed the effectiveness of the proposal. In the 27-bus grid, the MI-Convex approach achieved a total cost of $550,680.25, outperforming or matching the best results reported by state-of-the-art metaheuristic algorithms, including the vortex search algorithm (VSA), Newton’s metaheuristic algorithm (NMA), the generalized normal distribution optimizer (GNDO), and the tabu search algorithm (TSA). The MI-Convex method demonstrated consistent and repeatable results, in contrast to the variability observed in heuristic techniques. Further analyses considering three-period and daily load profiles led to cost reductions of up to 27.6%, and incorporating distributed renewable generation into the 85-bus system achieved a total cost of $705,197.06—approximately 22.97% lower than under peak-load planning. Moreover, the methodology proved computationally efficient, requiring only 1.84 s for the 27-bus and 12.27 s for the peak scenario of the 85-bus. These results demonstrate the superiority of the MI-Convex approach in achieving globally optimal, reproducible, and computationally tractable solutions for cost-effective conductor selection. Full article

Thyroid disease is a health concern related to the thyroid gland, which is vital for controlling the metabolism of the human body. Predominantly affecting women in their fourth or fifth decades of life, thyroid disease can result in physical and mental issues. This research focuses on improving the diagnostic process by creating a classification model that utilises various machine learning models and a deeplearning model to categorise three types of thyroid disease conditions. This research developed an automated system capable of classifying three thyroid conditions using five machine learning models and a deep learning model. Resampling techniques, such as SMOTE oversampling and Random undersampling, are utilised to correct the issue of class imbalance in the dataset. Finally, a web-based application is developed utilising the most effective model, GBC, which facilitates easy classification of thyroid diseases. The experimental analysis showed that the Gradient Boosting Classifier (GBC), using oversampling techniques, achieved the highest level of performance in classifying thyroid diseases, obtaining an accuracy and F1-Score of 99.76%. This study demonstrated that TSH was the most indicative biomarker for thyroid disease classification. The experimental results proved that the Gradient Boosting Classifier (GBC) utilising the oversampling technique achieved a superior performance compared to other classifier models, with an accuracy and F1-Score of 99.76%. This research presented insights that can assist healthcare practitioners in promptly diagnosing thyroid diseases. Full article

Eye-gaze writing technology holds significant promise but faces several limitations. Existing eye-gaze-based systems often suffer from slow performance, particularly under challenging conditions such as low-light environments, user fatigue, or excessive head movement and blinking. These factors negatively impact the accuracy and reliability of eye-tracking technology, limiting the user’s ability to control the cursor or make selections. To address these challenges and enhance accessibility, we created a comprehensive dataset by integrating multiple publicly available datasets, including the Eyes Dataset, Dataset-Pupil, Pupil Detection Computer Vision Project, Pupils Computer Vision Project, and MPIIGaze dataset. This combined dataset provides diverse training data for eye images under various conditions, including open and closed eyes and diverse lighting environments. Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. For object detection, we implemented YOLOv8, SSD, and Faster R-CNN. For image segmentation, we employed DeepLab and U-Net. Finally, for self-supervised learning, we utilized the SimCLR algorithm. Our results indicate that the Haar classifier achieves the highest accuracy (0.85) with a model size of 97.358 KB, while YOLOv8 demonstrates competitive accuracy (0.83) alongside an exceptional processing speed and the smallest model size (6.083 KB), making it particularly suitable for cost-effective real-time eye-gaze applications. Full article

Estimating the amplitude of a sinewave from a set of data points is a common procedure in various applications. This is typically achieved using a least squares method that provides closed-form estimators. The sampling process itself is often affected by different non-ideal phenomena like additive noise, phase noise, or sampling jitter, for example. Here, the precision of the estimation of a sinewave amplitude when the samples are affected by phase noise or sampling jitter is studied in the case of coherent sampling. The mathematical expression derived is useful in obtaining the confidence intervals for the estimated sinusoidal amplitude. It is also valuable at the time of choosing the proper number of samples to acquire from a signal in order to reach a certain desired level of sinewave amplitude estimation precision. The analytical expression presented is validated using both numerically generated data and experimental data. Various non-ideal factors, such as a fixed, uncontrollable amount of jitter in the setup, additive noise, analog-to-digital converter non-linearity, and limited signal bandwidth, are observed and discussed. Additionally, this work presents an exhaustive overview of the technical aspects involved in the experimental validation, including the implementation of the Monte Carlo type procedure, instrument interface, programming language, and the general development of automated measurement systems, which may be useful to other engineers. Full article

(1) Background: Survival analysis models in clinical research must effectively handle censored data, where complete survival times are unknown for some subjects. While established methodologies exist for validating standard machine learning models, current benchmarking approaches rarely assess model robustness under varying censoring conditions. This limitation creates uncertainty about model reliability in real-world applications where censoring patterns may differ from training data. We address this gap by introducing a systematic benchmarking methodology focused on censoring sensitivity. (2) Methods: We developed a benchmarking framework that assesses survival models through controlled modification of censoring conditions. Five models were evaluated: Cox proportional hazards, survival tree, random survival forest, gradient-boosted survival analysis, and mixture density networks. The framework systematically reduced observation periods and increased censoring rates while measuring performance through multiple metrics following Bayesian hyperparameter optimization. (3) Results: Model performance showed greater sensitivity to increased censoring rates than to reduced observation periods. Non-linear models, especially mixture density networks, exhibited higher vulnerability to data quality degradation. Statistical comparisons became increasingly challenging with higher censoring rates due to widened confidence intervals. (4) Conclusions: Our methodology provides a new standard for evaluating survival analysis models, revealing the critical impact of censoring on model performance. These findings offer practical guidance for model selection and development in clinical applications, emphasizing the importance of robust censoring handling strategies. Full article

Many efforts have proved that deep learning models are effective for disease prediction using electronic health records (EHRs). However, these models are not yet precise enough to predict diseases. Additionally, ethical concerns and the use of clustering and classification algorithms on small datasets limit their effectiveness. The complexity of data processing further complicates the interpretation of patient representation learning models, even though data augmentation strategies may help. Incomplete patient data also hinder model accuracy. This study aims to develop and evaluate a deep learning model that addresses these challenges. Our proposed approach is to design a disease prediction model based on deep Q-learning (DQL), which replaces the traditional Q-learning reinforcement learning algorithm with a neural network deep learning model, and the mapping capabilities of the Q-network are utilized. We conclude that the proposed model achieves the best accuracy (98%) compared with other models. Full article

Skin Conductance Response (SCR) is a physiological index associated with arousing emotions. Previous studies have not explored the relationship between SCR signals and emotions in situations where multiple emotions dynamically fluctuate. Moreover, methods suitable for analyzing such conditions have not yet been established. In this study, we recorded the temporal changes in multiple emotions as subjectively reported by participants using the Temporal Dominance of Emotions (TDE) method. We then matched these subjective reports with the evolving SCR signals through regression analysis. This approach reveals which emotions contribute to increased SCR signals in prolonged, emotionally charged scenarios, such as watching videos or playing video games. To validate our method, we recorded SCR signals while participants played a video game. Participants then performed the TDE task to recall their emotions while viewing recorded footage. This study involved 20 participants. Our analysis showed that emotions such as excitement, tension, and frustration significantly covaried with the physiological signals. These arousing emotions are known to evoke SCR, supporting the validity of our method. This approach introduces a novel experimental methodology for comparing subjective reports and high-responsive physiology signals in settings where multiple emotions dynamically change. Full article

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers. Full article

Cloud computing has revolutionized the digital era by providing a more efficient, scalable, and cost-effective infrastructure. Secure systems that encrypt and protect data before it is transmitted over a network and stored in the cloud benefit the entire transmission process. Transmission data can be encrypted and protected with a secure dynamic substitution box (S-box). In this paper, we propose the QuantumGS-box, which is a dynamic S-box for high-speed cloud-based storage encryption generated by bit shuffling with a genetic algorithm and a quantum random number generator (QRNG). The proposed work generates the S-box optimized values in a dynamic way, and an experimental evaluation of the proposed S-box method has been conducted using several cryptographic criteria, including bit independence criteria, speed, non-linearity, differential and linear approximation probabilities, strict avalanche criteria and balanced output. The results demonstrate that the QuantumGS-box can enhance robustness, is resilient to differential and provide improved linear cryptoanalysis compared to other research works while assuring non-linearity. The characteristics of the proposed S-box are compared with other state of the art S-boxes to validate its performance. These characteristics indicate that the QuantumGS-box is a promising candidate for cloud-based storage encryption applications. Full article

Hepatitis C virus (HCV) infection affects over 71 million people worldwide, leading to severe liver diseases, including cirrhosis and hepatocellular carcinoma. The virus’s high mutation rate complicates current antiviral therapies by promoting drug resistance, emphasizing the need for novel therapeutics. Traditional high-throughput screening (HTS) methods are costly, time-consuming, and prone to false positives, underscoring the necessity for more efficient alternatives. Machine learning (ML), particularly quantitative structure–activity relationship (QSAR) modeling, offers a promising solution by predicting compounds’ biological activity based on chemical structures. However, the “black-box” nature of many ML models raises concerns about interpretability, which is critical for understanding drug action mechanisms. To address this, we propose an explainable multi-model stacked classifier (MMSC) for predicting hepatitis C drug candidates. Our approach combines random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), and k-nearest neighbors (KNN) using a logistic regression meta-learner. Trained and tested on a dataset of 495 compounds targeting HCV NS3 protease, the model achieved 94.95% accuracy, 97.40% precision, and a 96.77% F1-score. Using SHAP values, we provided interpretability by identifying key molecular descriptors influencing the model’s predictions. This explainable MMSC approach improves hepatitis C drug discovery, bridging the gap between predictive performance and interpretability while offering actionable insights for researchers. Full article

Operational risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures, or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technical failures and failures in the execution and administration of processes. However, the low frequency with which a loss event occurs creates a technological challenge for insurers in estimating the operational value at risk (OpVar) for the protection derived from an organization’s business activities. Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). For the estimation of OpVar, the DFCS model incorporates a novel activation function based on the generalized log-logistic function to model random variables of frequency and severity that define a loss event (linguistic random variables), as well as a credibility surface to integrate the magnitude and heterogeneity of losses in a single structure as a result of the integration of databases. The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. These features make the DFCS model a reference for estimating the OpVar to protect the risk arising from an organization’s business operations by integrating internal and external loss event databases. Full article

Rural roads, often characterized by winding paths and nearby settlements, feature frequent curvature changes, junctions, and closely spaced private accesses that lead to significant speed variations. These variations are typically represented by average speed or v₈₅ profiles. This paper examines complete speed distributions along rural two-lane roads using Floating Car Data (FCD). The Wasserstein distance, a non-parametric similarity measure, is employed to compare speed distributions recorded by a radar Control Unit (CU) and a selected FCD sample. Initially, FCD speeds were validated against CU speeds. Subsequently, differences in speed distributions between the CU location and specific sections identified by sharp curves, intersections, or accesses have been assessed. The Wasserstein Distance is proposed as the most effective synthetic indicator of speed distribution variability along roadways, attributed to its metric properties. This measure offers a more concise and immediate assessment compared to an extensive array of statistical metrics, such as mean, median, mode, variance, percentiles, v₈₅, interquartile range, kurtosis, and symmetry, as well as qualitative assessments derived from box plot trends. Full article