Effective resource orchestration for network slicing is critical for optimizing the performance of diverse applications running on next generation communication networks. This paper presents a novel approach that leverages advancements in multiagent reinforcement learning (MARL) to adaptively learn the resource requirements of various applications in network slices and orchestrate resources in real-time. Our proposed MARL-based orchestration scheme aims to balance the varying requirements of individual network slices, ensuring optimal performance amid dynamic application deployments with limited network information. Simulation results and comparative analyses validate the efficiency and efficacy of our methodology, demonstrating its superiority over traditional methods in terms of system performance and resource utilization. Simulation results indicate that our strategy significantly enhances system utility and efficiency, particularly with limited resources.

Grid-forming (GFM) inverters, like synchronous generators (SGs), form voltage at their terminals and are considered crucial for enabling high renewable energy integration. Most GFM controllers mimic the power-synchronization characteristics of the SG rotor's swing dynamics to synchronize with the system. However, GFM inverters differ significantly from SGs in both design and control. This paper systematically explores these differences. The analysis reveals that the coupling between steady-state and transient performance in traditional GFM controllers, both influenced by the active power-frequency droop gain, along with the absence of features like large stator inductance, damper windings, and power system stabilizers (PSS), may result in SGs having superior disturbance rejection in the small-signal domain. Building on these insights, a novel GFM control technique with improved disturbance rejection performance is proposed. A comparative stability analysis is performed to evaluate the robustness of the proposed technique against conventional GFM controls under various grid conditions. The effectiveness of the proposed control method is further validated through MATLAB/Simulink simulations and real-time hardware-in-the-loop testing in diverse power system scenarios.

The proliferation of the Internet of Things (IoT) and the rapid advancement of Neural Networks (NNs) jointly facilitate the Internet of Artificially Intelligent Things. However, the shift towards cloud-based solutions raises significant privacy concerns, as sensitive data may be misused during NN inference tasks (predictions). To address these concerns, Homomorphic Encryption (HE) was introduced, allowing computations to be performed directly on encrypted data. Integrating NNs with HE presents challenges when introducing non-linearity into the model due to the constraints of current HE schemes that support only linear/polynomial functions. This often necessitates finding approximations for traditional activation functions (AFs) in NNs, leading to several issues: (i) Fixing an AF from the outset limits the model’s flexibility and may not yield the optimal fit for the data; (ii) Approximating a fixed AF requires significant effort to explore various approximation techniques and often leads to degradation in classification performance in exchange for reduced computational complexity; (iii) Avoiding certain approximations, if possible, to maintain accuracy increases the computational burden on the client side in the form of extra operations/communications. To overcome these challenges, we introduce CryptoKANs—Kolmogorov-Arnold Networks over Encrypted Data—a novel approach that enables NNs to operate over encrypted data by leveraging learnable AFs and symbolization to enhance privacy-preserving machine learning (PPML) for inference tasks. CryptoKANs allow the model to learn HE-suitable AFs as part of the training process. Experimental results demonstrate that CryptoKANs outperform traditional PPML models, achieving superior accuracy and, in some cases, even surpassing the performance of original models that operate on plaintext data. These findings underscore the potential of CryptoKANs to provide efficient, interpretable, accurate, and scalable private inference, marking a significant advancement in the field of PPML.

This paper extends the Generalized Steinmetz Equation (GSE) to account for the influence of mechanical stress on ferrite core losses. Experimental measurements are used to quantify the effects of compressive and tensile stresses on the relative permeability and the core losses of different ferrite materials. Mechanical stress is found to significantly affect the core losses, depending on the relative orientation of the magnetic flux density and the applied mechanical stress. Specifically, the losses monotonically increase when the flux and compressive stress vectors are parallel, while perpendicular vectors lead to a more complex response depending on the level of mechanical stress. The measured loss characteristics are translated in an extension of the GSE (X-GSE), which is validated for different ferrite materials, and provides a useful tool for a first rough estimation of the core losses under mechanical stresses. Finally, FEM simulations demonstrate how thermally induced mechanical stresses in a ferrite core can redistribute the magnetic flux and therefore impact the resulting core losses, which underlines the importance of considering the stress dependence of core losses in ferrite, especially in the development of magnetic components to be encapsulated in incompressible materials.

The increasing complexity of modern cyber threats requires the development of more advanced and dynamic detection systems, capable of adapting to new and evolving attack vectors. Dynamic Behavior Trace Profiling (DBTP) introduces a novel method for ransomware detection, utilizing real-time behavior analysis to identify malicious activities without relying on static signatures. Through continuous monitoring of systemlevel events, such as file I/O operations, network anomalies, and process manipulation, DBTP constructs detailed behavioral profiles that distinguish between benign and malicious activities. The system's modular architecture enables it to efficiently handle large volumes of data while maintaining low detection latency, making it suitable for real-time deployment in diverse operational environments. Experimental results highlight DBTP's high detection accuracy, particularly in identifying file-based ransomware activities, while also demonstrating its adaptability to previously unseen ransomware variants. False positive and false negative rates remain low across different test scenarios, indicating the system's reliability. Moreover, DBTP's capacity to function under high system load conditions with minimal resource overhead positions it as an effective solution for modern cybersecurity infrastructures. Overall, DBTP provides a scalable, automated, and adaptive tool for detecting and mitigating ransomware threats, significantly enhancing the resilience of systems against emerging and sophisticated ransomware attacks.

6G networks aim to facilitate the global sustainability drive. However, sustainability of 6G networks itself will pose major research questions due to the increasing number of devices, systems, and services that can exacerbate the consumption of key resources. In this work, we focus our investigation to 6G security, since sustainability is mostly overlooked when it comes to security. Emerging security technologies which are highly dependent on developing concepts in the realm of security such as AI and blockchain require huge amounts of resources. This article aims to stir further research on sustainability of 6G security through highlighting the main research challenges and future research directions in this domain.

Using advanced data mining techniques, specifically association rule mining (ARM) and clustering, this study presents a novel approach to maritime CO2 emissions analysis, revealing hidden patterns and relationships that traditional statistical models cannot capture. Various ship types can be analyzed for operational and technical efficiency to provide actionable insights into reducing emissions. In addition, robust cybersecurity measures are integrated to ensure the integrity and reliability of the data, allowing compliant and secure decision-making. The findings indicate that oil tankers and LNG carriers, which emit significant amounts of pollution, are prime candidates for retrofitting and implementing cleaner technologies in the near future.

A critical component for advancing driving automation is trajectory prediction, which enables vehicles to navigate complex driving scenarios and make informed decisions in unfamiliar environments. However, training models exclusively on trajectory supervision presents significant challenges for trajectory prediction in autonomous systems. We propose a Velocity-Angle Trajectory Network (VATNet), an integrated approach that enhances trajectory prediction tasks with an additional velocity prediction and angle prediction task in a multi-task learning framework. This assists the model in cross-validating and correcting errors in trajectory prediction, leveraging temporal relationships and directional context to achieve more reliable outcomes. To address the challenges posed by the periodic nature of angles, we use a classification approach. This method discretizes angles into bins and focuses on probability distributions rather than precise values, thereby reducing numerical instability associated with regression. To mitigate the issue of overfitting to any single task, we introduce an uncertainty-aware weighted loss that dynamically assigns higher priority to tasks identified as challenging or uncertain. The VATNet model has been evaluated on Argoverse 2 dataset and outperforms state-of-the-art models by a margin of 3.76%-33.83%, 5.68%-42.97%, 11.84%-90.78% and 0.37%-29.73% on the minADE6, minFDE6, MR6 and b-FDE6 respectively.

Understanding brain dynamics during motor tasks is a significant challenge in neuroscience, often limited to studying pairwise interactions. This study provides a comprehensive hierarchical characterization of node-specific, pairwise and higher-order interactions within the human brain's motor network during handgrip task execution. Methods: The cortical activity was reconstructed from the scalp EEG signals of ten healthy subjects performing a motor task, identifying five brain regions within the contralateral and ipsilateral motor networks. Using the spectral entropy rate as the basis for the decomposition of dynamic information in the alpha and beta frequency bands, we assessed the predictability of the individual rhythms within each brain region, the information shared between the activity of pairs of regions, and the higher-order interactions among groups of signals from more than two regions. Results: An overall decrease in hierarchical interactions at various orders within the motor network is observed during motor task execution, primarily in the alpha frequency band, due to the well-known sensorimotor mu rhythm. Conclusions and Significance: This work emphasizes the importance of examining brain interactions at multiple levels within the frequency domain using a unified framework which fully captures the complex dynamics of the motor network highlighting the critical role of its hierarchical organization.

The "No Free Lunch" (NFL) theorem in machine learning asserts that there is no universal algorithm or model that excels across all possible problem instances. In the context of machine learning applications for stock market prediction, this theorem implies that no single predictive model can consistently outperform others under all market conditions. The NFL theorem, introduced by David Wolpert in 1996, emphasizes the importance of considering the specific characteristics of the problem domain. In this paper, we extend the NFL Theorem as "No Free Money(NFM) Theorem for Machine learning applications in stock market predictions. The data gathered from Machine Learning competition for stock market prediction are utilized for providing experimental evidence for NFM theorem. For stock market prediction, the NFM theorem underscores the need for tailored approaches, acknowledging that the effectiveness of machine learning models is contingent on factors such as market dynamics, economic conditions, and the quality of historical data. It cautions against assuming a one-size-fits-all solution and highlights the challenge of developing models that generalize well to diverse and evolving market scenarios. The theorem prompts practitioners to approach stock market prediction with a realistic understanding of the limitations inherent in algorithmic approaches, encouraging careful consideration of the data, features, and context relevant to each specific application.

A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.

This study aims to categorize the following dogs' primary emotional states: anger, fear, and happiness. The research team utilized Transfer Learning (TL) and advanced 2D/3D Convolutional Neural Networks (CNNs) to identify these emotions from two small datasets-static images and video clips. The image dataset covers various canine emotional expressions across various contexts, forming the basis for the training of Dog Emotion Recognition-Transfer Learning Neural Network (DER-TLNN) algorithm. DER-TLNN has an accuracy rate of 70% for emotion classification based on static images (400 images). In addition, DER-TLNN is the basis for the Video Dog Emotion Recognition Network (VDERNet) algorithm, which is designed to categorize video clips depicting dogs' emotional states. Trained on video data sourced online (39 videos), VDERNet achieves an accuracy of 96%, highlighting AI's potential in understanding canine emotions in dynamic contexts with non-invasive techniques. These findings demonstrate the efficacy of TL and 3D CNNs in animal behavior analysis and contribute to a better understanding of canine emotions. The research provides promising applications in improving human-dog interactions and enhancing canine welfare, showcasing the immense potential of AI in animal behavior studies. The achievement of notable results with small datasets, along with data augmentation and regularization techniques, underscores our innovative approach and the potential impact of AI in animal behavior studies.

This study introduces a novel transformer architecture that integrates Gaussian Attention, adapter modules, and Rotary Position Embeddings (RoPE) to enhance efficiency in natural language processing tasks. Preliminary experiments with a low-quality dataset demonstrated the model's ability to generate responses, though the output lacked coherence and relevance. These findings highlight the necessity for further training on high-quality datasets, which is currently hindered by limited computational resources. Consequently, this research seeks collaborative opportunities and funding to facilitate comprehensive pretraining and fine-tuning of the model, aiming to develop efficient and effective language models accessible to researchers with constrained capabilities.

Integrated Sensing and Communication (ISAC) technology is pivotal in advancing multifunctional radio applications for nextgeneration wireless communications. One of the primary challenges in ISAC systems lies in understanding and interpreting wireless channels for both sensing and communication purposes. A clear relationship between these channels would allow ISAC systems to reduce channel estimation complexity. This paper introduces a comprehensive framework for reconstructing communication channels using monostatic sensing channels within an ISAC system. We first consider the frequency spacing of an ISAC system to geometrically illustrate the synergies and differences between the sensing and communication regions. Subsequently, we construct a communication propagation channel based on overlap determined by this geometric relationship and a simplified monostaticto-bistatic equivalence theorem. Lastly, we combine multipaths located in the same delay bin to construct channel state information (CSI), taking bandwidth limitation into account. Simulations demonstrate that the proposed framework efficiently generates communication channels across various configurations, with numerical results highlighting the impacts of environmental and system constraints on key channel characteristics.

This manuscript summarizes work on the Capsule Vision Challenge 2024 by MISAHUB. To address the multi-class disease classification task, which is challenging due to the complexity and imbalance in the Capsule Vision challenge dataset, this paper proposes CASCRNet (Capsule endoscopy-Aspp-SCR-Network), a parameter-efficient and novel model that uses Shared Channel Residual (SCR) blocks and Atrous Spatial Pyramid Pooling (ASPP) blocks. Further, the performance of the proposed model is compared with other well-known approaches. The experimental results yield that proposed model provides better disease classification results. The proposed model was successful in classifying diseases with an F1 Score of 78.5% and a Mean AUC of 98.3%, which is promising given its compact architecture.

The limitations of fifth generation (5G) networks in simultaneously addressing the growing demands of diverse applications, including ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), and high-throughput services, drive the exploration of 6G technologies. Given the multifaceted nature of 6G use cases and the proliferation of edge functions, such as computing nodes, charging nodes, and sensing targets, selecting an optimal multiple access (MA) technique becomes paramount. This article reviews the literature to identify key milestones and insights from the recent progression of MA technology, exploring robust research directions and optimization aspects of developing an intelligent, unified MA framework for 6G. The article examines promising access schemes such as rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) and discusses the pivotal role of the latest paradigms, such as artificial intelligence (AI) and Open radio access network (RAN), in optimizing the MA framework.

Wireless sensor networks (WSNs) were originally envisioned to consist of a massive number of diminutive, lowcost, and long-life wireless sensors. However, this vision has faced significant obstacles, particularly in transferring sensing information to data centers. This article proposes a novel protocol, termed relaying-as-a-service (RaaS), to leverage public smart devices to relay sensing information to data centers. By offloading major communications and networking operations to these more powerful smart devices and well-developed fifth generation (5G) or sixth generation (6G) networks, the size and cost of sensors can be significantly reduced, and the need for extensive network infrastructure can be eliminated. This reduction in complexity can greatly streamline the development and implementation of WSNs and Internet of Things (IoT) solutions. Furthermore, using low-power sensors enables large-scale frequency-reuse, which can help alleviate the load on the already congested spectrum.

Quantum computing is poised to revolutionize computational performance and capabilities, offering unprecedented efficiency in solving complex problems that surpasses classical approaches. This potential is particularly evident in fields such as optimization and AI. This chapter delves into critical priority areas in quantum computing, including the development and application of quantum software tools. We place a strong emphasis on reimagining the use of quantum computing for modeling and simulation, sensing, and secure communication. Furthermore, we explore current trends like quantum communications for 6G networks, quantum cloud and serverless computing, scalable qubit arrays, and the pursuit of robust and reliable quantum systems. Lastly, we address emerging research areas and the open challenges ahead, encompassing advancements in foundational theory, education, ethical and societal considerations, and pathways to commercialization.