Integrating quantum mechanics into cybersecurity frameworks offers transformative opportunities for addressing sophisticated digital threats. The Quantum-Infused Multi-Dimensional Signal Convergence (QIMDSC) framework introduced a pioneering methodology that leverages quantum principles for detecting highly dynamic and obfuscated ransomware behaviors. By encoding behavioral patterns into enriched quantum states and analyzing signal convergence through multidimensional mathematical constructs, the framework achieved significant advances in detection accuracy and computational efficiency. Comprehensive experiments demonstrated its ability to minimize false positives while maintaining high recall rates across diverse ransomware families, including LockBit, Hive, and BlackCat. Comparative analyses demonstrated the framework's superiority over traditional machine learning-based and hybrid approaches, particularly in identifying zero-day ransomware variants. The introduction of probabilistic wavefunction modeling further refined the detection mechanism, enabling the isolation of subtle deviations within high-dimensional feature spaces. Real-time anomaly detection capabilities were validated through extensive simulations, where the framework consistently performed well under varying network and system loads. Its ability to adapt to encryption depth and respond effectively to large-scale ransomware activity emphasizes its robustness in dynamic environments. The scalability of the architecture and its reliance on modular design ensured applicability across critical infrastructure, enterprise networks, and resource-constrained systems. Performance evaluations revealed that the framework excelled under conditions of high file system activity and network variability, demonstrating exceptional resilience to operational noise. The innovative approach bridges theoretical quantum mechanics with practical cybersecurity applications, marking a paradigm shift in threat detection methodologies. Insights derived from this research highlight the potential of quantum-inspired techniques for addressing emerging challenges in modern cybersecurity landscapes.

In recent years, deep learning [1] has revolutionized various fields, such as computer vision, natural language processing [2], and speech recognition, by achieving state-of-the-art performance on numerous complex tasks [3]. These achievements are often attributed to the availability of large-scale, labeled datasets and the capacity of deep neural networks to learn rich feature representations. However, one critical challenge remains: the performance of deep learning models significantly deteriorates when there is a domain shift—a mismatch between the data distributions of the training set (source domain) and the test set (target domain). 

This paper addresses the optimization of the radiation pattern of surface-wave (SW) based metasurface (MTS) antennas. Those antennas are considered as a promising alternative to parabolic reflectors, and phased arrays due to their extremely low profile and their ability to provide high gain, shaped beams and multibeams. However, pattern synthesis with MTS antennas is very challenging because of the single active control point and the need to control surface and leaky-waves through the MTS. An accurate optimization of the radiation pattern, along with the sidelobe level requires a full-wave modelling of the feeding structure, including its coupling with the MTS. MTS synthesis methods existing in the literature usually approximate the feeder model, and neglect its coupling with the MTS. Such approximation may lead to more than 1 dB error in the predicted antenna directivity. This paper presents a technique for optimization of the far-field pattern, built on a Method of Moments (MoM) analysis tool in which the MTS coupling with the feeder, a coax probe, is fully considered. The MTS is modeled as an arbitrarily shaped, spatially modulated electric sheet impedance in a layered medium. At each optimization iteration, the complexity of the underlying analysis is O(N log N) owing to the use of a FFT based acceleration. The effectiveness of the method is demonstrated through the optimization of MTSs radiating a pencil beam and a conical beam with orbital angular momentum (OAM).

Artificial intelligence techniques including machine learning models have shown success in variety of domains. This is more evident with complex models including deep learning. However, such success accompanied by vagueness around how the models work, the internal mechanism and making a decision. Explainable Artificial intelligence (XAI) emerged as a new field of research to uncover the mystery around how the complex models work. The ultimate aim of XAI is to make the complex models more transparent, trustworthy and understandable even by lay-persons with no technical background. XAI could come in different forms including heatmaps applied to images, significant concepts to the model when making a decision, informative features with tabular data, one-feature effect on the outcome, fuzzy logic rules, textual explanation through images captioning, uncertainty quantification and much more. There are several factors affect the XAI forms including the used data and the model. This paper is dedicated to review and group the current XAI methods in the literature based on the outcome form. In addition, the paper discusses the XAI groups, how they work, strengths and weaknesses. Our paper shows that although e XAI methods have been used extensively within the research context, their employability in real life problems especially in sensitive domains might be unreasonable in the current stage.

In this work, we extend the standard quasi-Helmholtz filters to high-order discretizations in the context of the boundary element method. This generalization allows preconditioning integral equations such as the electric field integral equation (EFIE) in the h-refinement regime while preserving the accuracy gain of high-order discretizations. The theoretical framework will be corroborated by numerical results that validate the effectiveness of the proposed strategy when stabilizing the refinement-dependent spectral behavior of the high-order discretized EFIE.

HVDC technology has long been recognized for its efficiency in high-capacity, long-distance power transmission. Unlike conventional AC systems, HVDC allows for the direct transmission of electricity without the losses typically associated with long-distance AC transmission. The ability to control power flows across large networks with minimal loss, while also facilitating the integration of renewable energy, makes HVDC an ideal solution for modern power grids.As global energy demands continue to grow and as more renewable energy sources are integrated, there is a pressing need to enhance the control mechanisms of HVDC systems. The traditional methods for controlling HVDC systems, which often rely on manually configured parameters and linear models, are no longer sufficient to handle the complexities of contemporary grids. This is where Artificial Intelligence (AI) comes into play. AI can improve the flexibility, speed, and responsiveness of HVDC control systems, optimizing their performance in real-time and ensuring that they meet the evolving demands of modern power grids.

This paper analyzes the performance of Alamouti coded short-packet non-orthogonal multiple access (NOMA) systems with hybrid multicast and unicast transmission over Nakagami-m fading, where only the statistical channel state information is available at the transmitter, and the multicast and unicast signals are intended for all users and a particular user, respectively. Due to some practical limitations, channel estimation errors (CEE) caused by a linear minimum mean-square error estimator and residual hardware impairments (RHI) caused by imperfect hardware implementation are taken into account. We first derive the approximate closed-form expressions for the average block error rate (BLER) and the corresponding asymptotic expressions at all users. Using such expressions, we analyze the diversity performance including conventional diversity order and finite-SNR diversity order. After this, we quantify the relationship among the common blocklenth, power allocation, and pilot sequence length under users' reliability constraints. Finally, numerical and simulation results verify our theoretical analysis and show that i) CEE and RHI greatly affects the transmission blocklength, ii) RHI leads to the error floor phenomenon at high SNRs and the finite-SNR diversity order is the effective performance metric in the presence of RHI, iii) there exist the optimal power allocation coefficients and pilot sequence length to minimize the transmission blocklength in the considered hybrid multicast-unicast system, and iv) the NOMA scheme is superior to the OMA counterpart by achieving low-latency transmission.

Observations made using antenna arrays are often limited by the noise coming from the antenna system itself and from the environment. In demanding applications such as radioastronomy, the thermal noise emitted by the multilayered substrate below the antenna array may become significant and needs to be properly characterized. Both the intensity of the noise at each port and the correlation of the noise at pairs of ports need to be quantified. In this paper, we provide a spectral formulation to calculate the noise correlation matrix of antenna arrays. The method is based on the propagative and evanescent plane wave spectrum emitted by each pair of active antenna ports into the lossy layered medium, and can thus be implemented as a post-processing step combined with any full-wave numerical solver. This formulation allows us to account for layered media with different physical temperatures at each layer. Numerical examples are provided using Square Kilometer Array (SKA) stations lying on a finite ground plane, itself lying on a layered semi-infinite soil. The behaviours of noise power and noise correlation coefficients are studied for different positions of the antennas on the ground plane. This is done considering a multilayered soil with different moisture levels. A very good agreement with the commercial software FEKO is observed regarding the evaluation of the noise correlation coefficients. Moreover, the efficiency of the algorithm in terms of computational time is shown by comparison with FEKO. The direction-dependent system noise temperature picked up by a full SKA station in presence of the soil is also given.

We plan seek triplets corresponding y3-plet within n-DJC groups n D. A n D has three constituents, its DJC, removable conflict matrix L and rotation matrix L. In this paper, we will discuss the calculation of removable conflict matrix L which is given by crossed union of JEDS in different DJC of n D. Moreover, the configurations with crossed conflicts and unreasonable conflict can be determined to be 3-uncolorable. And the calculation of L of 4 D is given synchronously.

This paper investigates the computation of the radar cross section (RCS) of equilateral triangular targets using Monte Carlo numerical integration. The analysis employs the Physical Theory of Diffraction (PTD) to model the scattering behavior and numerically evaluates the RCS for a canonical planar geometry. A Python-based implementation is provided for an equilateral triangle with side lengths of 2 meters, illuminated by a 10 GHz radar wave. The simulation yields an RCS value of approximately 0.0292 m², showcasing the effectiveness of the Monte Carlo approach for RCS estimation in simple geometries. This work contributes to the understanding of electromagnetic scattering and stealth design methodologies.

Modern urbanization necessitates efficient parking management solutions, given increasing vehicle densities and limited space availability. Smart parking systems, which utilize advanced object detection algorithms, offer potential solutions. This paper explores various methods, including vision transformers, YOLO models, and traditional sensor-based approaches, discussing their efficacy in practical implementations.

Current probes have long been used for Electromagnetic Compatibility (EMC) pre-compliance and diagnostic purposes. Dipole and transmission line-based models are often used to relate Common-Mode (CM) current levels to potential Radiated Emission (RE) levels. This relationship enables RE estimates to be performed without the use of accepted certification methodologies – reducing project costs and timelines. These models are based on a set of assumptions which cannot fully match real-world scenarios. This article expands the discussion by examining the relationship between the CM current and RE of a representative metallic structure using both measurements and a Finite Element Method (FEM) code. The relationship is shown to be frequency dependent, resulting in both over and under prediction of the RE levels. A practical recommendation is made on how to exploit the equivalence of CM current-based estimations of system RE.

The Distance-based Neighborhood k-Search (DNkS) algorithm, introduced in this article, offers a novel approach to enhancing meter-transformer connectivity modeling in low-voltage grids. By employing a local k-neighborhood search strategy, DNkS effectively subdivides the grid into manageable sections, ensuring robust connectivity assessments. Utilizing metrics such as Adjusted Mutual Information and Accuracy, DNkS demonstrated superior performance in trials, achieving up to 100% accuracy in certain cases, significantly outperforming existing state-of-the-art methods such as deep convolutional time-series clustering and spectral embedding-based meter-transformer mapping. Although DNkS is effective, its performance critically depends on the accuracy of meter and transformer coordinates. In comparative analyses across various network configurations, DNkS consistently outperformed other methods, affirming its utility and effectiveness. The versatile nature of the algorithm would allow its integration into existing systems in various ways, for example, through an API or a web interface. Implementing DNkS promises substantial improvements in the reliability and accuracy of utility network models, directly contributing to enhanced grid management practices.

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system’s performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system’s behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system’s robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).

The rapid growth of Large Language Models (LLMs) has revolutionized business operations across sectors such as finance, healthcare, customer service, and more. These models are no longer experimental but are becoming core components in streamlining workflows and decision-making. However, the integration of LLMs brings new challenges: ensuring the accuracy of outputs, monitoring model performance, and maintaining strict compliance with industry regulations[1]. This white paper provides an in-depth review of leading LLM tracing and evaluation tools, examining their strengths, limitations, and relevance for real-world use cases. We aim to guide organizations in choosing the right tools to enhance model performance, ensure compliance with data security protocols, and generate actionable insights.

This article presents a system of robot identifiers that taxonomically classify and identify robotic products based on their fundamental attributes as autonomous systems. Robotic products have grown increasingly complex, diverse, autonomous, and pervasively ubiquitous in recent years, rapidly outgrowing traditional definitions and categorization standards. New generations of robotic products are no longer limited to traditional mechatronic devices, expanding in various forms such as cyber-physical systems, software platforms, nano and organismic systems, soft and flexible robots, and developmental robotics. Without effective tools to identify and compare their fundamental differences, not only is efficient development stifled due to duplicated and entrenched efforts, but social adoption and safety are also impacted. This article addresses these challenges and provides key contributions in three areas: analysis, synthesis, and application. First, it examines in depth the current industry standards and academic literature, identifying major issues in building an effective taxonomy. Second, it establishes fundamental principles to classify broad classes of machines and develops a coding system for consistent naming, identification, classification, and organization. Third, it provides use cases to demonstrate the utility of the proposed system. The result is a unified taxonomic framework that identifies robots and autonomous systems from the perspectives of anthropogenic, ecological, and phylogenetic factors. This biologically grounded, interdisciplinary approach offers a robust and inclusive framework that encompasses a broad spectrum of technologies, including software platforms and nano robots, thereby future-proofing the taxonomy in anticipation of the imminent emergence of intelligent robots with new capabilities.

We examine the impact of samarium doping on the Curie temperature ( Tc ) of magnetite through density functional theory (DFT) calculations. Upon calculating the total energies of the different spin orientations among the cations in Fe3O 4:Sm, we realized that the Sm atom prefers to substitute an Fe from the octahedral site with a spin opposing that of the atom it replaces. Our results show that Sm doping weakens the ferrimagnetic J coupling between the octahedral and tetrahedral Fe atoms. As a result, the normalized magnetization profile across a broad temperature range shows that the samarium doped compound ( Fe3O 4:Sm ) has a Tc of approximately 319 K, which aligns well with the target range for self-regulated magnetic nanoparticle hyperthermia (MNH) applications. Furthermore, we demonstrated that Sm doping in magnetite with high electron concentrations of 1022 cm −3 and 1023 cm −3 almost nearly preserves the Hall coefficient (RH), implying that Fe3O 4:Sm can be synthesized without significantly altering magnetite's ability for tumor tissue identification based on the Hall effect.

The cross-fertilization of the fast-developing AI technology and spatial indexing has given rise to spatial learned indexes. However, these indexes rely on historical data distributions to build models, which limits their ability to anticipate data that has not yet arrived. To address this, we propose a novel Spatio-Temporal Update Method (STUM) that enhances conventional spatial learned indexes by introducing a Spatial Delta Area (SDA) for updates without altering their hierarchical structure. STUM learns spatio-temporal auto-correlation from historical data and integrates predicted future distributions. We apply STUM to the Spatial Learned Block Range INdex (SLBRIN), resulting in the development of the Spatio-Temporal Updatable learned Block Range INdex (STUBRIN), which adopts Revmap to integrate spatio-temporal sequence predictions with the spatial block range. STUBRIN optimizes the retraining process by learning the temporal continuity from spatial distribution and fusing it into the error threshold control mechanism and historical delta learning mechanism. Our results show that STUBRIN achieves 1.9-2.4×, 1.8-13.3×, 3.4-6.7× better build, query and update performance compared to state-ofthe-art methods. Additionally, STUBRIN offers superior query and update stability. For concurrent learned indexes, we have also designed parallel scheduling for STUBRIN, which improves build, query and update performance by 6.2-6.8×, 0.3-4.2×, 2.6-5.5×, without increasing the index size.