Unmanned Aerial Vehicles (UAVs) play a significant role in modern applications ranging from surveillance to enabling communication in disastrous and remote areas, especially in Non-Terrestrail Networks (NTNs). Secure communications between UAVs and ground users are requisite, with secret key exchange being an important candidate for the physical layer security of such security protocols. This paper investigates the environmental factors influencing the efficacy and reliability of secret key exchange in UAVs, utilizing experimental data to draw insights. Through a series of field experiments, we analyze the effects of UAV structure and antenna location on the robustness of key exchange mechanisms. Our results indicate that certain environmental conditions, particularly adverse weather and noise introduced by the UAV body structure and propellers, do not impair the key exchange process. This research contributes to developing more security-resilient UAV communication systems, ensuring the reliability of communications links across diverse environments.

Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.

The circulatory system ensures continuous blood flow to all body tissues. During gestation, progressive changes in the female body and immune system lead to homeodynamic alterations, characterized by increased venous return and cardiac output, enhancing blood flow to meet tissue metabolic demands. This paper presents the Homeodynamic Maternal-Placental Molecular Communication Model (HMP-MC) in the cardiovascular network to accurately represent circulatory components and interactions. The model also analyzes the dynamics of exosomes carrying Zika virus particles throughout pregnancy, integrating new parameters from gestational physiological changes. We quantify the risk of pregnancy over different stages accounting the impact of infection on the placenta viral. This interdisciplinary approach explores cardiovascular interactions during pregnancy analysis over time with the quantification of pregnancy risk.

New challenges in designing scalable and adaptable neural architectures require innovative approaches to semantic representation and processing. The Semantic Entanglement Modeling (SEM) framework introduces a paradigm shift through its integration of modular semantic units, hierarchical attention mechanisms, and tensor-based operations, all configured to enhance coherence and task adaptability across diverse linguistic benchmarks. Its hybrid encoding scheme, combining static and contextual embeddings, demonstrates significant versatility in handling varied input complexities while ensuring resourceefficient operation. Experimental evaluations reveal substantial improvements in contextual alignment, with perplexity and entropy metrics highlighting the framework's robustness in managing high-dimensional data. Noise reduction through gating mechanisms enhances the precision of semantic outputs, particularly in tasks requiring minimal error tolerance. Fewshot learning experiments emphasize the model's adaptability, achieving high accuracy rates with limited task-specific data. Resource utilization analyses underline its scalability, showcasing consistent performance across multiple computational environments. The framework also addresses key inefficiencies in multi-modal integration, ensuring coherent semantic alignment across textual and non-textual inputs. Comparative analyses with existing architectures confirm that SEM not only reduces computational overhead but also extends applicability to specialized and high-complexity tasks. Its contributions are further evidenced in the context of generalization capabilities, where robust performance was observed even under variable input distributions. The SEM framework exemplifies a transformative approach to optimizing neural models for diverse linguistic challenges, delivering significant advancements in performance, efficiency, and scalability.

This paper presents a new Intelligent Anti-Money Laundering Transaction Pattern Recognition System based on Graph Neural Networks (GNNs). The proposed method addresses the limitations of anti-money laundering (AML) by using graphical representation and deep learning techniques. We introduce general methods for creating financial markets that are integrated according to different shapes, including structural and physical. A custom GNN architecture is developed, featuring heterogeneous graph convolution, listening mechanisms, and physical models to capture the exchange patterns. The analysis of the world’s big data shows that the best performance of our method achieved a 35.2% Money Laundering Detection Rate (MLDR) in the top 1% of the flag business, do the best way.

With regard to the Sustainable Development Goals (SDGs), which addresses a range of social, economic and environmental challenges faced by the world today, the present work is targeting a specific indicator of relevance for security. A methodology to support the calculation of the SDG indicator 13.1.1 (Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population ) is presented; while the indicator is currently based on data from national demographic agencies, the proposed method incorporates the use of Earth Observation (EO) data. The workflow has been tested in the Sahel region (Niger), one of the hot spots for climate security concerns.

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.

Thermophotonic devices are radiative heat engines in which the exchange of electroluminescent radiation between a heated light-emitting diode and a cool photovoltaic cell allows for the conversion of heat into electrical power. Here, we introduce CRESCENT-1D, the solver we have developed to simulate the performance of one-dimensional thermophotonic systems, which is made publicly available on GitHub. It couples photon transport in the far or near field, based on the fluctuational electrodynamics framework, and charge transport in heterostructures, modelled with the drift-diffusion and Poisson equations. We include both thermionic emission and charge carrier tunnelling to precisely model charge transport at heterointerfaces, while the photon chemical potential is computed in a self-consistent manner between the radiative and electrical sections of the solver. Compared to simpler formulations, these models provide accurate results at high voltages, which is essential to achieve high power output. The capabilities of CRESCENT-1D are illustrated with an optimised InGaP/InGaAs thermophotonic heterostructure, whose maximum power reaches 1.6 W.cm −2 for an efficiency of 19.7% considering a 300 K temperature difference between the light-emitting diode and the photovoltaic cell. This solver makes it possible for anyone to design various categories of optoelectronic structures (thermophotonics, light-emitting diode, thermophotovoltaics, thermoradiative, etc.), and represent an important step in the development of near-field radiative heat engines.

The application of artificial intelligence (AI) continues to expand across various industries, especially in enhancing user experience and optimizing business processes. Through deep learning and machine learning algorithms, companies are able to analyze user behavior data and provide personalized recommendations, which effectively improve customer satisfaction and loyalty. This data-driven approach enables businesses to stand out in a highly competitive market. This paper explores the key role of machine learning-based personalized recommendation systems in improving user experience and highlights the importance of behavioral data-driven UI design for business success. Analyzing the practices of industry leaders such as Netflix and Amazon reveals how data-driven decisions can be leveraged to deliver tailored content and product recommendations that enhance user engagement and satisfaction. Research shows that successful recommendation systems not only rely on advanced technology applications, but also need to deeply understand user needs to optimize user interface design and promote effective user interaction. As technology continues to advance, personalized recommendation systems will become more intelligent, and companies should actively explore these innovative ways to increase user engagement and brand loyalty to achieve sustainable business growth.

This paper presents an in-depth learning process for Activities of Daily Living (ADL) and self-care planning in Adult Day Health Centers. The framework integrates multi-modal sensor data fusion with deep learning architectures to provide continuous monitoring and automated evaluation of older people’s legal status. The system uses a hierarchical system combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, enhanced by monitoring systems for physical-spatial learning. Many data streams from wearable devices, environmental sensors, and medical monitoring devices are becoming pre-processed and de-processed. The framework incorporates a dynamic care plan adaptation strategy utilizing reinforcement learning techniques for intervention optimization. Experimental validation conducted across three Adult Day Health Centers with 150 participants over six months demonstrated superior performance compared to traditional assessment methods. The system achieved 92.8% accuracy in ADL recognition tasks, with a 35% reduction in assessment time and a 62% decrease in false alarm rates. Clinical validation through 25 detailed case studies revealed early detection of health deterioration, averaging 3.2 days ahead of conventional methods. The proposed framework significantly enhances the efficiency and accuracy of elderly care delivery while reducing healthcare provider workload by 40%. This research contributes to advancing intelligent healthcare by creating a solution for ADL measurement and self-correction.

In the space sector, the harsh environmental conditions and limited accessibility make robust fault detection systems crucial for ensuring mission success and protecting valuable assets. This work analyzed the use of Real NVP neural networks for on-board fault detection in satellites, along with a self-supervised task based on sensor data permutation. The approach is aimed at improving fault detection in satellite multivariate time series data. We conducted extensive experiments on multiple datasets involving various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Our key contributions include an evaluation of the methods across multiple datasets, including a recently published dataset provided by the European Space Agency (ESA), achieving state-of-the-art results, and an ablation study investigating the impact of introducing faults during both the self-supervision and main loss stages of training. Finally, we performed latency tests on relevant systems-on-chip and artificial intelligence accelerators, ensuring practical deployment feasibility. Results demonstrate significant performance gains across all tested configurations, with self-supervised learning proving especially effective in feature extraction for fault detection on unlabeled samples, highlighting its potential for broader application in space systems.

A DW is needed to efficiently and continuously prepare data for practical real-time data analysis and forecasting with machine learning (ML) algorithms. This paper discusses extraction, transformation, and loading process (ETL) automation and the critical subsystems integration for algorithmic trading ML modelling and stock market forecasting. Additionally, we discuss a new API designed for direct access to the DW. It opens vast opportunities for performance and data processing time to become critical nonfunctional requirements.

Power electronics embed intelligence into energy systems. Modern artificial intelligence systems place massive demand on electrical power, and in return, introduce new opportunities in improving energy systems design and implementation. Power electronics systems demand extreme performance and sophisticated functions. Innovations are needed from new and better semiconductors, novel passive components and devices, and more complex system modeling and control. Modern machine learning tools can learn highly complex physical characteristics and control strategies of future power electronics systems, given sufficient training data and computational power. Generative artificial intelligence are rapidly developing. Structured, cognitive understanding of abstracted human intelligence is in demand. Power "The strive for more power is infinite, and the strive for more intelligence is also infinite, leading to infinite entanglement between artificial intelligence and power electronics systems." "We can do it, if we choose to do it."

There are already a good number of electric trucks available, and many more are anticipated to join the market in the coming years as electrification continues to pick up steam. A few states and cities are leading the way in the electrification of trucks by starting to switch from fossil fuel-driven vehicles for their fleets. Vocational vehicles are the most varied segment due to their wide range of uses. There are still many variations in terms of applications, powertrain, battery capacity, and other technologies utilized. This paper summarizes recent trends in the field of electrification of vocational trucks to provide a comprehensive understanding of present and future developments in this area.

Polyspectral models are a class of nonlinear behavioral models that were applied to power amplifiers over 20 years ago but have not seen subsequent development. Polyspectral models have a multiple-branch topology that is a special case of the Volterra series. They provide closed-form, optimum solutions to their constituting filters. Here we bring the model construction measurements up to date using modern test equipment. The two-branch version presented here is simple to implement and provides good accuracy and computational efficiency for system simulations. We present results for a GaN amplifier subject to orthogonal frequency-division multiplexing for signals of bandwidth up to 200 MHz.

Given three coils whose surface normals are co-planar, with any two coils having a 120° displacement from each other, we can find the (set of) excitation inputs to the coils that should produce a rotating magnetic field (or vector) in any direction. The aim of this paper is to find such excitation inputs for a set of three coils having coplanar surface normal vectors (or simply coplanar coils) with arbitrary spatial orientation. In doing this, the idea of "Sequence Components" of a three-phase system is generalized to any three-phase system with arbitrary spatial orientation. In addition, we define a "Sequence Matrix" and discuss some of its properties.

With the increasing popularity of Integrated Sensing and Communication (ISAC) systems in recent years, numerous studies have been conducted on the use of a joint waveform for both radar and communication functions in these systems. When defining the joint waveform, the most important criterion to consider is that it meets the system requirements for range and Doppler resolution in radar functionality. On the other hand, parameters such as symbol error rate (SER) and bit rate are also crucial for the communication function. The ambiguity function is used by radar designers to analyze the effects of radar waveforms on radar performance (range and Doppler resolution). Therefore, in this study, the ambiguity function is mathematically derived and analyzed for biphasecoded waveforms for ISAC systems. Theoretical expressions have been compared with simulation results. Additionally, to improve the radar and communication performance of the system, optimal binary phase codes were generated using a genetic algorithm, and the performance of the Barker-coded waveform was compared with that of the generated binary codes for both the radar and communication functions.

Developing channel models typically requires aggregating channel measurements and the corresponding extracted propagation parameters from different research institutions to form a sufficiently large data basis. However, uncertainties arising from limitations of the sounding hardware and algorithms may greatly impact the comparability between sounding results. Especially, (sub-) THz channel sounding is challenged by a potentially low SNR. At the same time, high Doppler shifts may occur due to the high carrier frequencies, limiting the time spans for coherent or incoherent data processing. Hence, the channel dynamics additionally restrict the processing gain for the SNR. In this paper, we address these challenges metrologically from several perspectives. First, we discuss methods of baseband waveform precoding to adapt it to the sounder hardware or the employed high-resolution estimation algorithms. This allows using available transmit power in an optimal sense. Second, the assessment of the sounder performance requires a traceable reference allowing tracing back measurements (or estimated propagation parameters) to a physical ground truth. Therefore, we propose and discuss an over-the-air artifact allowing a joint verification of delay and Doppler parameters in a multipath scenario. The evaluations of exemplary sub-THz measurements with a multicarrier-based sounder highlight the strong interplay between sounder hardware and estimation algorithms, especially when coping with the mutual interference of parameters from multiple propagation paths. Hence, a metrological assessment always requires considering the full processing pipeline from the unprocessed measurements up to the extracted propagation parameters.