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.

Contextual Cascade Representations (CCR) have been introduced as a transformative framework designed to optimize the scalability and efficiency of large-scale language model architectures. The methodology employs hierarchical parameter activation, dynamically adjusting computational pathways to align with the contextual demands of specific tasks. Experimental findings demonstrated substantial reductions in energy consumption, inference latency, and parameter count, highlighting the framework's potential to achieve computational efficiency without sacrificing accuracy. Through a combination of sequentially weighted pruning mechanisms and dynamic thresholding, the approach maintained linguistic coherence across diverse tasks while ensuring adaptability to domain-specific challenges. Ablation studies demonstrated the synergistic contributions of individual components, including cross-layer coherence mechanisms, which preserved semantic alignment during the pruning process. Results from domain-specific evaluations revealed consistent improvements in accuracy and resource utilization, particularly within medical, legal, and financial datasets, where specialized linguistic structures posed significant challenges. A comprehensive analysis of error distributions further indicated that CCR excelled in mitigating inaccuracies in syntactically and semantically complex tasks. The proposed methodology leveraged context-sensitive parameter selection, enabling robust performance even in high-sparsity regimes. Quantitative metrics, including perplexity, BLEU scores, and classification accuracy, affirmed the advantages of CCR over conventional pruning and compression techniques. Comparisons with baseline architectures highlighted its ability to balance model size and performance more effectively. Additionally, insights from energy consumption analysis emphasized its contribution to environmentally sustainable computational practices. Contextual Cascade Representations exemplify a significant advancement in addressing the challenges of efficiency, adaptability, and performance retention in contemporary language model research.

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.

Integrated Gradients is a well-known technique for explaining deep learning models. It calculates feature importance scores by employing a gradient based approach computing gradients of the model output with respect to input features and accumulating them along a linear path. While this works well for continuous features spaces, it may not be the most optimal way to deal with discrete spaces like word embeddings. For interpreting LLMs (Large Language Models), there exists a need for a non-linear path where intermediate points, whose gradients are to be computed, lie close to actual words in the embedding space. In this paper, we propose a method called Uniform Discretized Integrated Gradients (UDIG) based on a new interpolation strategy where we choose a favorable nonlinear path for computing attribution scores suitable for predictive language models. We evaluate our method on two types of NLP tasks-Sentiment Classification and Question Answering and show that our method yields better results than existing approaches.

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.

The UpOn platform leverages cutting-edge modular smart contract architecture to redefine Real World Asset (RWA) management by creating a legally compliant, decentralized ecosystem for virtual office investment, launching, and rental. Built with a focus on transparency, security, and compliance, UpOn allows users to seamlessly participate in real estate investment through Non-Fungible Tokens (NFTs) that represent virtual office spaces. By integrating law firms for legal oversight, UpOn ensures that all transactions align with global legal standards, providing users and investors with trust and security. The platform comprises four main components-Invest, Launch, Rent and Legal-each of which serves a unique function within the ecosystem. The Invest section enables investors to fund new office projects through NFT minting, while Launch allows owners of established offices to tokenize their assets. In the Rent section, users can securely lease office NFTs with contract-backed agreements tailored to local regulations, and the dashboard provides realtime data retrieval from smart contracts, ensuring a transparent user experience. Finally, the Legal component, backed by law firms, safeguards user rights and enforces compliance in all jurisdictions. With its modular design and elimination of centralized databases, UpOn showcases the full potential of Web3 by simplifying the traditional complexities of RWA projects, lowering costs, and offering unprecedented accessibility to global markets.

Reflection is widely recognized as a cornerstone of student development, fostering critical thinking, self-regulation, and deep conceptual understanding. Traditionally, reflective skills are cultivated through structured feedback, mentorship, and guided self-assessment. However, these approaches often face challenges such as limited scalability, difficulties in delivering individualized feedback, and a shortage of instructors proficient in facilitating meaningful reflection. This study pioneers the exploration of generative AI, specifically large language models (LLMs), as an innovative solution to these limitations. By leveraging the capacity of LLMs to provide personalized, context-sensitive feedback at scale, this research examines their potential to serve as effective facilitators of reflective exercises, maintaining the depth of engagement and promoting critical thinking. Through an in-depth analysis of prompt engineering strategies and the efficacy of LLMs in simulated multi-turn dialogues between tutors and students, this study demonstrates that, with pedagogically aligned prompts, LLMs can function as accessible and adaptive tools for automating reflective guidance and objectively assessing the performance of both tutors and students. This work also contributes to the evolving understanding of AI's role in reflective pedagogy and highlights new possibilities for AI-driven intelligent tutoring systems.

CHARGE (Charged Hyperspherical Autoencoder with Repulsive Gradient Encoding) is a novel autoencoder designed with a unique latent space that is hyperspherical and of n dimensions. The model leverages a specialized loss function based on Coulomb's Law or the Inverse Square Law, promoting a more effective and efficient latent space. This approach enables CHARGE to perform better on generative AI tasks compared to traditional Variational Autoencoders (VAEs), as it is deterministic rather than probabilistic. The results show that CHARGE provides superior performance in generating realistic data and encoding meaningful latent representations, making it a promising candidate for various generative tasks in AI.

This work introduces a novel concept for the realization of biological artificial neural networks (ANNs), called shared volume computing (SVC). The central units in this concept are biological cells, that function as individual neurons of the ANN. These cells reside inside a shared volume, where they sense signaling molecules released by cells of the previous layer or a biological process. They respond by the production of a chemical that they release into the shared volume, thereby creating the input into the next layer. We introduce the SVC, present mathematical models of it and show, how these models can be used to design a biological ANN for a given task. Specifically, we discuss the design process on a first example: breast cancer detection from biomarkers. We show in simulation, that the proposed system is capable of solving this task with an accuracy of up to 75%, making it especially useful for rapid testing.

Modern cybersecurity demands innovative approaches capable of addressing the rapidly evolving nature of sophisticated threats targeting critical systems. Temporal Entropy Fingerprinting introduces a novel framework for detecting malicious activities by leveraging the dynamic properties of entropy patterns over time. Through the systematic analysis of entropy fluctuations, the approach identifies behaviors associated with encryption, file manipulation, and anomalous system activity, key characteristics of ransomware attacks. The framework achieves high detection rates by integrating probabilistic models, dynamic thresholding, and advanced segmentation techniques to quantify anomalies across varying operational contexts. Experiments conducted with datasets encompassing contemporary ransomware variants, such as LockBit and BlackMatter, demonstrated consistent performance across diverse scenarios, ensuring both accuracy and scalability. The entropy-based architecture supports real-time anomaly detection while maintaining computational efficiency, addressing the limitations observed in traditional static and dynamic analysis methods. By enabling a granular understanding of entropy distributions during critical ransomware operations, the framework provides actionable insights that enhance situational awareness. Furthermore, modular design principles facilitate seamless integration with existing cybersecurity infrastructures, ensuring adaptability to resource-intensive environments. The results indicate significant improvements in detection precision and recall, particularly in identifying previously unseen ransomware families. Comparative analysis against baseline methods highlighted the framework's capability to maintain low false positive rates even under highvolume data processing conditions. With entropy metrics serving as a foundation, the proposed approach offers a resilient and effective solution for mitigating modern ransomware threats.

Dementia is a progressive condition that significantly impairs quality of life, with limited effective treatments available for managing its late stages. Music therapy has emerged as a promising intervention, potentially improving well-being in dementia patients through physiological and emotional engagement. This study explores the physiological responses of late-stage dementia patients undergoing a music therapy intervention, utilizing manifold learning to analyze data recorded over three sessions: a control session, after 1 week of intervention, and after 6 weeks. We focus on characterizing changes in heart rate (HR), electrodermal activity (EDA), movement (ACC), and skin temperature (ST) to identify patterns associated with potential improvements in well-being. Manifold learning was applied to these physiological metrics to discern nonlinear relationships that may highlight the intervention's effects. Statistical analyses further examine these differences, providing quantitative support for improvements in physiological responses, indicating enhanced well-being in response to prolonged music therapy interventions.

This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the apparent absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper aims to provide some of the foundational pieces for more standardized security, safety, and transparency in the development and operation of AI models and the larger open ecosystems and communities forming around them.

This paper analyses the limit cycle oscillation (LCO) phenomena that characterize some of the most commonly adopted digital algorithms for maximum power point tracking (MPPT) in photovoltaic (PV) generators. The paper identifies necessary conditions for the LCO existence and clarifies the relationship between its amplitude, and frequency, and some relevant system parameters, e.g. DC-DC converter control strategy, adopted MPPT algorithm and iteration frequency, A/D converter resolution. The analysis allows to devise an optimized design procedure, where the algorithm iteration frequency can be maximized without causing limit cycle amplification. The correctness of the results is verified performing computer simulations as well as experimental tests on a 1 kW peak photovoltaic (PV) generator.