Modern multi-agent systems (MAS) often rely on static task allocation methods, which are insufficient for dynamic environments requiring continuous task reassignment and optimization [10]. This paper introduces a theoretical framework that integrates fine-tuned large language models (LLMs) into MAS to enable semantic task evaluation and dynamic task distribution [15]. By analyzing language-based task states, the system identifies hidden dependencies and adapts task allocation in real time [12]. While the proposed system is in the conceptual phase, it offers a promising solution for automating complex workflows, reducing human intervention, and improving scalability in dynamic environments. Future work will focus on prototyping and validating the framework in real-world scenarios [1].

We focus on stochastic geometry analysis of a low Earth orbit (LEO) narrowband terrestrial-satellite uplink with satellite base stations (SBSs) in a uniform constellation equipped with narrow Gaussian beams. The served and interfering omnidirectional user equipments (UEs) are distributed on the Earth's surface according to a homogeneous Poisson point process (HPPP) with Nakagami faded signals. This study presents a detailed but comprehensive mathematical analysis of several key metrics: the signal-to-interference ratio (SIR), the SIR meta distribution (MD), the signal-to-interference-plusnoise ratio (SINR), and the average throughput. Many results are presented in simple analytical and closed forms containing more insight than the expressions proposed in prior works. The results indicate an optimal UE density depending on the altitude, elevation angle, and the width of the antenna gain, maximizing the average throughput. However, this optimal density leads to a significant variance in the user experience regarding link quality (i.e., the users are not treated fairly).

The trend towards sensor miniaturization has heightened interest in optimizing sensor array configurations across various scientific and industrial applications that imply multichannel measurements. In this article, we present an extended version of our previous approach, adapting it to the realworld conditions in magnetoencephalography (MEG). At each step the method selects the sensor location that maximizes the ROI-related signal-to-noise ratio (SNR), followed by the projection recursively applied to the gain matrix to orthogonalize the subsequent stereotypic steps with respect to the source subspace served by the already selected sensors. This algorithm offers high computational efficiency and flexibility with respect to physical constraints. We dub our approach as Optimal Layout Design via Recursively Applied Leadfield Elimination (OLD RALFE). Our analysis of the optimized MEG sensor layouts obtained with OLD RALFE shows that the proposed algorithm performs similarly to several other significantly more computationally demanding sensor layout optimization approaches in terms of both SNR and the Cramér-Rao lower bound (CRLB) on the spatial resolution of the inverse problem solution. OLD RALFE demonstrated in application to MEG, is broadly applicable to many other multichannel measurement challenges.

Quantum Computing is revolutionary paradigm that harnesses the principles of quantum mechanics such as superposition and entanglement to process the information in fundamentally new ways. It has shown potential across various fields, including Cryptography, Materials Science, Machine Learning, Optimization and many others. Among its many applications, Combinatorial Optimization problems stand out as a critical area where Quantum Computing can address problems that are often intractable for classical methods. This paper surveys current quantum approaches including Quantum Annealing, Variational Algorithms, Quantum Approximation Optimization Algorithm (QAOA) and asses their effectiveness in solving Classical Intractable Combinatorial Optimization (CICO) problems such as Max Cut Problem, Traveling Salesman Problem, Graph Coloring and Knapsack Problems. The paper explores the QAOA, approach to solve the CICO problems. Practical applications of quantum optimization approaches are highlighted in the fields such as finance, material science, communication networks. Additionally, the case studies demonstrate the algorithm's efficacy in real world scenarios. However, the paper also discusses the challenges that remain including issues related to noise, scalability and complexity of the algorithms. Finally, the survey discusses the future directions for research, emphasizing the potential of hybrid-quantum classical frameworks and the development of new quantum algorithms, aiming to enhance the practical applications in complex optimization scenarios. This survey provides valuable insights for researchers and practitioners interested in leveraging quantum advantages to solve combinatorial optimization problems effectively.

The P versus NP problem is a cornerstone of theoretical computer science, asking whether problems that are easy to check are also easy to solve. "Easy" here means solvable in polynomial time, where the computation time grows proportionally to the input size. While this problem's origins can be traced to John Nash's 1955 letter, its formalization is credited to Stephen Cook and Leonid Levin. Despite decades of research, a definitive answer remains elusive. Central to this question is the concept of NP-completeness. If even one NP-complete problem could be solved efficiently, it would imply that all problems in NP could be solved efficiently, proving P equals NP. This research proposes that a notoriously difficult NP-complete problem can be solved efficiently, thereby potentially establishing the equivalence of P and NP.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a decisive role in the design, optimization, and evaluation of communication systems between UAV and ground terminal. This paper proposes a three-dimensional (3D) model for U2G communication channels, enhanced with ultra-wideband (UWB) features and frequency non-stationarity. This model integrates large-scale and small-scale fading components, introducing bandwidth-dependent path numbers and the UAV posture matrix for realistic scenario representation. It encompasses specific UWB U2G channel phenomena such as the channel hardening, UAV 3D movements, and posture variation effect. The channel parameters, including spatial largescale parameters (LSPs), bandwidth-correlated path numbers, delay-posture-correlated path power, and frequency-correlated path phase, are generated to capture channel non-stationary characteristics across time and frequency domains. Employing ray-tracing (RT) for the path number and optimization methods for the path delay, the proposed model ensures reliable parameter evolution. The proposed model is assessed through key statistical properties, including space-time-frequency correlation functions, power delay profile, root-mean-square delay spread, Doppler power spectrum density, and the energy variance. It is demonstrated that both posture and bandwidth variations have crucial effects on channel characteristics. The validity and practicability of this research is demonstrated by comparing the simulated outcomes with the measurement data.

Emerging threats in cybersecurity require innovative approaches to counter increasingly complex attacks targeting critical systems. A novel detection framework has been proposed, leveraging the concept of Proactive Cryptographic Residue Mapping to address challenges posed by obfuscated and polymorphic ransomware variants. Through systematic analysis of bytecode sequences and cryptographic residues, the methodology integrates entropy-driven insights with advanced feature extraction to distinguish malicious encryption behaviors. Automated classification pipelines enhance accuracy while minimizing computational overhead, ensuring scalability across diverse deployment environments. Experimental results demonstrate robust detection performance, achieving high accuracy and low falsepositive rates across multiple ransomware families. The use of sequential analysis further mitigates the impact of variantspecific obfuscation, enabling reliable identification of malicious patterns. Processing efficiency evaluations highlight the framework's suitability for resource-constrained applications, offering practical implications for real-time threat mitigation. Comparative analyses against traditional methods underline significant improvements in both accuracy and robustness. Scalability assessments indicate consistent performance across varying dataset sizes and computational configurations. Entropy distribution studies provide deeper understanding of cryptographic behaviors, reinforcing the methodological foundations. Error rates under simulated noise levels reveal opportunities for further refinement, especially in challenging environments. The proposed framework presents a significant contribution to automated cybersecurity solutions, balancing analytical sophistication with operational feasibility.

Contemporary academic research covers a wide range of topics, illustrating the complex and dynamic challenges and opportunities faced by societies worldwide. This paper examines the influence of literature in forming cultural identities and the significant effects of technology on conventional frameworks. Present academic works provide indepth perspectives on the integration of humanistic considerations and technological progress, highlighting their joint importance in addressing modern societal problems.

The discrete complex image method (DCIM) is applied to compute the complete set of electric, magnetic, and potential Green functions (or integral equation kernels) for planar multilayers with uniaxial anisotropy. It is assumed that the layered medium is of infinite lateral extent and the optic axis is normal to the stratification. Sample results are presented and are shown to be in good agreement with reference data obtained by a rigorous integration method.

The principle of maximum entropy is a well-established technique for choosing a distribution that matches available information while minimizing bias. It finds broad use across scientific disciplines and in machine learning. However, the principle as defined by Jaynes (1957) is susceptible to noise and error in observations. This forces real-world practitioners to use relaxed versions of the principle in an ad hoc way, negatively impacting interpretation. To address this situation, we present a new principle we call uncertain maximum entropy that generalizes the classic principle and provides interpretable solutions irrespective of the observational methods in use. We introduce a convex approximation and expectationmaximization based algorithm for finding solutions to our new principle. Finally, we contrast this new technique with two simpler generally applicable solutions theoretically and experimentally show our technique provides superior accuracy.

Escalating threats from increasingly sophisticated ransomware attacks pose significant risks to data integrity, financial stability, and operational continuity for organizations across sectors. Addressing these threats requires a novel detection framework that overcomes the limitations of traditional signature-based and machine learning-based approaches, particularly their vulnerability to adaptive ransomware tactics and resource constraints. The proposed Dynamic Threat Imprint Analysis (DTIA) method leverages real-time behavioral analysis combined with adaptive learning mechanisms, enabling it to detect both known and previously unseen ransomware variants with high accuracy. Experimental evaluation demonstrates that DTIA achieves a 98.7% detection rate for known ransomware while effectively adapting to new variants, maintaining a 95% detection rate for previously unseen threats. Additionally, DTIA operates with minimal impact on system performance, showcasing scalable and efficient detection capabilities suited for deployment in environments with diverse computational resources. These findings demonstrate the potential of DTIA as a sophisticated and adaptable solution for the evolving challenge of ransomware detection, enhancing cybersecurity resilience through advanced behavioral analysis and dynamic learning capabilities.

The presence of foreign bodies in packaged food is a serious concern for both final consumers (allergies, injuries, choking) and food manufacturers (reputation and economic losses). In particular, low-density plastics, glass and wood splinters are hard to detect even by the most advanced X-ray imagers. One solution is Machine-Learning-based Microwave Sensing (MLMWS): a non-invasive, contactless, and real-time method which uses a machine-learning (ML) classifier to analyze the scattered microwaves from the irradiated target object. In this paper, we want to extend our previous work about contaminant detection in cocoa-hazelnut spread jars by proposing an enhanced ML flow to increase the accuracy of the ML classifier. For the first time in this case study, we use a multi-class classifier, we train it with scattering parameters measured at multiple microwave frequencies, with a new pre-processing scaler, data augmentation, quantization-aware training and a pruning schedule. The results show a contaminant detection multi-class accuracy of 94.167% with a latency of 26 µs when targeting an AMD/Xilinx Kria K26 FPGA. Finally, we released our datasets publicly to OpenML.1

Federated Learning (FL) is a decentralized ML approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at a central server. This methodology eliminates the need for data sharing between IoT devices while fostering collaborative model enhancement. Nonetheless, concerns arise due to the lack of transparency surrounding the shared local models and the aggregation techniques employed. This lack of transparency can potentially lead to model poisoning attacks and hinder collaborators from using alternative aggregation methods that better align with their specific use cases. To address this issue, this paper proposes a blockchainbased approach with FL to ensure transparent and immutable records of model updates, thereby bolstering security and trust for intrusion detection in IoT devices. In contrast to traditional synchronization or periodic update-based approaches, this paper proposes a novel time-independent aggregation method in FL blockchain, allowing for flexibility. Additionally, the proposed blockchain allows various users to utilize their own aggregation methods rather than a fixed one, based on their needs, resources, and availability. We also develop a user interface for the proposed blockchain system that assists in visualizing different aspects of the method, such as model aggregation. The proposed system is tested using traditional metrics like AI model performance as well as extensive user testing.

Quantitative capillary-free zone (CFZ) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of retinal diseases. However, its clinical deployment is hindered by time-consuming manual segmentation procedures for periarterial and perivenous CFZs, which often focus solely on large vessels. In this study, we introduce deep learning for the automated segmentation of periarterial and perivenous CFZs in OCTA. Both convolutional neural networks (CNNs) and vision transformers (ViTs) were evaluated for automated CFZ segmentation. Nine quantitative features were derived to characterize CFZ changes associated with diabetic retinopathy (DR). Quantitative analysis revealed significant changes in CFZ ratios, counts, and mean sizes that can reliably differentiate control subjects, diabetic patients without DR (NoDR), and those with mild DR, underscoring their potential as sensitive biomarkers for early disease detection, progression monitoring, and treatment assessment.

This paper presents an efficient beam pattern modeling method for reflector antennas where the performance of a specific feed antenna, as a function of frequency and geometric variations of the antenna, is known on a different (but similar) reflector antenna. Instead of modeling patterns for the desired reflector directly, the difference patterns between the desired and previously modeled reflectors are employed. Scaling and normalization measures are applied to the patterns to simplify their modeling as Characteristic Basis Function Patterns (CBFPs), which can be accurately interpolated over the desired design parameter space. The CBFP coefficients drive an adaptive sampling algorithm that aims to accurately obtain the antenna's performance across the entire parameter space with as few electromagnetic simulations as possible. Two examples are presented using basic reflector optics and a simple corrugated horn and demonstrate the advantage of using a reference design compared to directly modeling the reflector patterns, showing that fewer simulations are required to obtain a certain level of accuracy.

This paper explores enhancements in Adaptive Cruise Control (ACC) using Frequency Modulated Continuous Wave (FMCW) radar to optimize performance in varied environmental clutter conditions, including rain, fog, and insect interference. By implementing Constant False Alarm Rate (CFAR) algorithms, the study reduces clutter impact, enhancing radar accuracy for both range and velocity measurements of multiple targets. Results demonstrate that CFAR integration mitigates errors in velocity and distance estimation, particularly under adverse conditions, supporting ACC reliability over a range of up to 200 meters. This study underscores the effectiveness of advanced clutter rejection in improving safety and functionality in ACC systems for real-world applications.

As ransomware attacks grow in complexity, traditional detection methods face significant challenges in identifying the advanced tactics, techniques, and procedures that define contemporary ransomware behaviors. Developing detection methodologies that capture the intricate and dynamic interactions of ransomware across encrypted networks presents an opportunity for innovation in cybersecurity. Hypergraph-Driven Anomalous Behavior Profiling (HDABP) offers a novel and scalable solution through representing complex network relationships in hypergraph structures, allowing for more precise detection of multi-faceted ransomware behaviors that evade conventional graph-based and signature-driven techniques. HDABP extends beyond simple pairwise relations, modeling high-order dependencies and unique interaction patterns within ransomwareinfected networks, thus revealing subtle anomalies associated with ransomware propagation and lateral movement. A comprehensive evaluation demonstrates HDABP's effectiveness through its high detection accuracy, resource efficiency, and adaptability in encrypted environments, where traditional detection methods are limited. Tested across multiple ransomware families and network conditions, HDABP achieves consistent low-latency performance and low false-positive rates, providing reliable detection capabilities crucial for real-time cybersecurity applications. HDABP's design also enables deployment within sectors that prioritize data privacy, such as finance and healthcare, while maintaining efficacy in recognizing evolving ransomware tactics without direct payload inspection. Through bridging theoretical hypergraph analysis with practical cybersecurity applications, HDABP represents a substantial advancement in ransomware detection, enhancing resilience against increasingly sophisticated ransomware threats.

This article provides a research viewpoint on the training procedure of a Bidirectional Encoder Representations from Transformers (BERT) model starting from the beginning, employing the Tensor Processing Unit (TPU), with a particular emphasis on the Spanish language. This article focuses on the techniques and strategies, such as the whole word masking (WWM) technique, that are essential for achieving successful BERT training in multiple languages, including English. The goal is to provide efficient development of natural language processing applications for understanding the Spanish language. The BEThiz model exhibits exceptional proficiency in comprehending contextual cues and producing accurate responses for masked inputs, surpassing other models within the same category and language. This characteristic sets our model apart and solidifies its role as a particularly potent tool in the field of natural language processing.