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].

Human digital twin (HDT) is envisioned as a system interconnecting physical twins (PTs) in the real world with virtual twins (VTs) in the digital world, enabling advanced humancentric applications. Unlike optimizing the quality of experience (QoE) of users in single-modal signal transmission for conventional services, users' QoE in multi-modal signal transmission required by HDT is difficult to guarantee. To tackle this, we study an optimization of QoE in multi-modal transmission, focusing on joint visual and haptic signal feedback transmissions from VT to its PT, for providing immersive interactions in HDT. To evaluate a synthesized performance of visual and haptic experiences, we design a comprehensive QoE model, taking into account video quality, continuous video quality switching rate and average haptic feedback error. Then, to maximize QoE with a guarantee on synchronization between visual and haptic signal transmissions, we dynamically optimize bandwidth allocation, bitrate and rendering mode of the video, and haptic signal's compression threshold. To this end, we propose a deep reinforcement learning based algorithm, called VisHap. Furthermore, we build an HDT multi-modal interaction platform for collecting an authentic dataset, and by using it, we conduct experiments, showing that VisHap is not only feasible but also outperforms the counterparts.

Indoor localization within buildings is paramount due to its diverse applications, including the Internet of Things (IoT), healthcare, and personnel monitoring. To improve the performance of indoor localization, this study proposes an algorithm combining Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) used within a hybrid model that uses both cellular technology, i.e, base station (BS) and Wi-Fi access point (AP). Within a realistic environment, we evaluate the performance of our approach across four scenarios: (1) (2 APs + 2 BSs), (2) (1 AP + 2 BSs), (3) (2 APs), and (4) (2 BSs). Additionally, we consider both stationary and seated user positions. Our results demonstrate significant improvements over existing methods. Specifically, in the 2 APs+2 BSs scenario, we achieve an average Euclidean distance error of 1.04 meters, with a maximum error of 2.51 meters, and the Root Mean Square Error (RMSE) of 1.15 meters. We also obtain the corresponding Cumulative Distribution Function (CDF) for the error which indicates that 90% of the time, the error is less than 1.69 meters. Our hybrid model achieves over 99% accuracy in classifying different building floors and over 88% accuracy in identifying user states (e.g., standing or sitting). We validate our approach using smartphone-based indoor localization in a twostory residential building with robust construction materials and double-layered walls.

Recent innovations in genetic technologies and epigenetic analysis have reshaped personalized medicine (Wu & Li, 2022). Additionally, employing encryption and data management mechanisms—such as hash/token frameworks integrated with blockchain-based approaches—offers enhanced data security, transparency, and efficient governance in the digital age (Chen et al., 2022). This paper introduces a novel perspective on combining DNA data and epigenetic profiles with hash/token technology , underscored by blockchain solutions to manage large-scale storage, authentication, and transactions of biological data. The proposed ecosystem is implemented via the Internet of Personal Health Things (IoPHT), augmented by AI-driven analytics, quantum-safe cryptography, and stringent regulatory compliance (HIPAA, MHRA, ISO, etc.). By elucidating the theoretical basis of DNA tokenization, presenting a methodological roadmap for real-world validations, and highlighting how this synergy could transform preventive healthcare and personalized treatment, the paper aspires to lay the groundwork for a fully regulated marketplace in genomic–epigenetic data exchange. Addressing pressing issues of data integrity, consent, and multidisciplinary collaboration, our framework paves the way for a new personalized healthcare industry , empowering both patients and key stakeholders (Brown et al., 2023).

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.

The advent of 6G non-terrestrial networks (NTNs) offers unprecedented opportunities for continuous Earth observation (EO), enabling enhanced global monitoring capabilities. However, NTNs face significant challenges, including high latency, signal disruptions, and bandwidth constraints, which hinder their ability to efficiently transmit real-time data during monitoring and connectivity provision. We propose an integrated framework that leverages multi-source semantic communication (SC) and intelligent denoising to address these limitations. By transmitting essential meaning rather than raw data, the SC framework reduces bandwidth requirements and enhances resilience to noise, optimizing network performance in NTNs. The proposed multisource satellite fusion and denoising framework within the 6G NTN framework enables the integration of diverse satellite data sources and improves data quality in adverse monitoring and communication conditions. We evaluate the proposed framework through important simulations demonstrating significant improvements in bandwidth efficiency, semantic accuracy, and robustness against channel impairments. The paper also highlights future research directions, including further integrating advanced machine learning (ML) techniques and adaptive denoising algorithms to enhance 6G NTN-based EO and satellite connectivity capabilities.

The Federated Unified Secure Exchange (FUSE) algorithm represents a breakthrough in computer science and cybersecurity, building upon three foundational elements: Homomorphic Encryption, Zero-Knowledge Proofs, and Federated Learning. By deeply integrating these three pillars, FUSE not only guarantees data integrity and privacy in decentralized environments but also maintains high performance and remarkable scalability. This paper details the architecture, mathematical principles, and real-world applications of FUSE across Internet of Things (IoT), healthcare, finance, and supply chains. In addition, it compares FUSE with several “classic” algorithms that have dramatically changed the world—such as QuickSort, RSA, Dijkstra, PageRank, FFT, Backpropagation, SHA, Monte Carlo, Gradient Descent, and Blockchain—highlighting how FUSE addresses existing limitations in data protection and distributed computation.

This paper presents the first outdoor field trials of Deep joint source-channel coding (DeepJSCC) for image transmission over an industrial 5G system, aiming to address the growing demand for efficient and reliable communication in low-SNR environments. DeepJSCC, a deep learning-based joint source-channel coding method, integrates source and channel coding within a unified framework, enabling robust image transmission while mitigating the cliff effect commonly observed in traditional methods. We modify a commercially available 5G base station (gNB) and user equipment (UE) to process DeepJSCC signals, and conduct experiments in both indoor and outdoor environments, including line-of-sight (LoS) and nonline-of-sight (NLoS) conditions. The results demonstrate that DeepJSCC outperforms conventional JPEG2000 and LDPCbased schemes in maintaining image quality in terms of peak signal-to-noise power ratio (PSNR) under extreme transmission conditions. Notably, DeepJSCC achieves superior PSNR stability, particularly in low-SNR regions, and demonstrates the ability to transmit images without corruption, even when the methods employing JPEG, LDPC, and normal QAM modulation fail. Additionally, the system exhibits a lower peak-to-average power ratio (PAPR) compared to typical modulation formats such as QPSK and 16QAM.This preprint is currently under review at IEEE-OJCOMS (IEEE Open Journal of the Communications Society).

This paper proposes a deep joint source-channel coding (DJSCC) to minimize the age of information (AoI) for image transmission. A new content-based AoI metric called age of misclassified information (AoMI) is introduced to estimate the freshness of the information in an image classification system. AoMI is a critical metric in timely information delivery, measuring the age of the most recently received and correctly classified image at the receiver. The proposed system leverages a deep neural network at the transmitter to map image pixels directly to channel input symbols, eliminating the need for separate source and channel coding. At the receiver, the channel output is processed to perform image classification. To analyze the AoMI performance of the system, a stochastic hybrid systems (SHS) approach is employed. Closed-form expressions for the average AoMI (AAoMI) are derived, providing insights into the impact of system parameters on the AoMI. Simulation results demonstrate the effectiveness of the proposed DJSCC-based system in achieving lower AoMI compared to traditional separate source and channel coding schemes. The findings highlight the potential of deep learning techniques to maintain the freshness of the information in wireless communication systems. This work paves the way for the design of wireless communication systems that prioritize the freshness of delivered information-this is crucial in applications such as real-time monitoring, surveillance, and control systems.

Spam SMS often appears similar to legitimate (ham) messages but contains potential threats or malicious links designed to hack personal information such as bank account details, passport details, etc. Many mobile users have been affected because they unknowingly access spam messages and provide information through the link given, and they have lost a huge amount of money. Many industries are working on this to prevent such spam messages from entering the user's inbox using advanced machine or deep learning techniques. Any Artificial Intelligence technique requires enough data to train the model to perform better during real-time testing. Recently, there have been enough benchmark spam SMS datasets available for English; however, there are fewer for Indian languages. This paper provides an overview of an SMSDHL dataset comprising Dravidian languages-Tamil, Telugu, Kannada, and Malayalam with Hindi and English. The dataset can be utilized for various applications in Information Extraction, Information Retrieval, and Natural Language Processing (NLP).

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, like SAT, could be solved efficiently, it would imply that all NP problems could be solved efficiently, proving P=NP. This research proposes a groundbreaking claim: SAT, traditionally considered NP-complete, can be solved in polynomial time, establishing the equivalence of P and NP.

Optimal control approaches, such as model predictive (MPC) and reinforcement learning (RL) control, are promising tools in decentralized grid applications, but face various challenges, e.g., in the presence of model inaccuracy or with concern to safety-related limitations. While contemporary research articles mainly cover simulation-based investigations, this contribution presents the application and head-to-head comparison of both control algorithms on commercial inverter hardware, with the topology being a three-phase, three-level inverter with an LC-filter and a common neutral point. Herein, the safety-shielded RL controller is trained from scratch utilizing an edge learning toolchain while simultaneously interacting with the hardware. Both control approaches benefit from system identification, allowing to decouple achievable controller and safeguard performance from potentially unreliable or incomplete a priori information. During transients, both methods exhibit almost identically fast settling characteristics when operated on the same safety constraints. In steady state, MPC achieves a normalized mean absolute error (w.r.t. the reference voltage amplitude) of 0.33 %, with the RL controller even reaching 0.18 %. Lastly, it is empirically demonstrated that the RL controller manages to compensate inverter non-linearities, which contrary reduce the MPC's tracking performance noticeably in the partial load range if left unchecked.

This article explores hardware backdoors, highlighting examples like Rakshasa and rosenbridge. Discusses prevention and detection methodologies, vulnerability analysis and research tools. It also addresses future research directions and recommendations for industry and academia, emphasizing the importance of a robust approach to hardware security.

This is the first of its kind systematic review paper that explores transformative architecture of Smart Grid 3.0 by integrating cutting-edge technologies such as artificial intelligence (AI), digital twins, blockchain in the metaverse environment. It presents the novel architectural frameworks, that is the proposed transformations of nanogrid, microgrid, and VPP topologies to their Grid 3.0 counterparts. This illustrates their enhanced operational capabilities in the context of emerging technologies. For the first time, this study systematically analyzes the application of advanced algorithms and technologies across all hierarchical subsystems-nanogrid 3.0, microgrid 3.0, VPP 3.0, and Smart Grid 3.0. These digital technologies have the transformative capabilities. The digital twins can perform real-time monitoring, simulation, and predictive analysis; blockchain ensures secure, decentralized energy transactions; and the metaverse creates immersive, interactive environments for system management. This review also explores the role of AI in power grid which is to optimize energy scheduling, fault detection, and energy management (DSM, DR, Energy trading). This paper adds to the literature by systematically addressing subsystems of Smart Grid 3.0, including energy generation, transmission, distribution, communication, and storage. Challenges such as interoperability, scalability, data integrity, and cybersecurity are discussed, and solutions are proposed which highlights the need of interdisciplinary approach. These include cyber-attack detection and mitigation mechanisms, advanced simulation tools, and robust policy frameworks. This review provides insights as to how the technological advancements align with the dynamic needs of electricity sector. By integrating these technologies across hierarchical energy systems, this study establishes a foundation for future research in transforming conventional smart grid infrastructure into a resilient, efficient, and interconnected cyber-physical energy network called as Smart Grid 3.0 as the peak of this evolution so far.

Optimization techniques are pivotal in neural network training, shaping both predictive performance and convergence efficiency. This study introduces Foxtsage, a novel hybrid optimisation approach that integrates the Hybrid FOX-TSA with Stochastic Gradient Descent for training Multi-Layer Perceptron models. The proposed Foxtsage method is benchmarked against the widely adopted Adam optimizer across multiple standard datasets, focusing on key performance metrics such as training loss, accuracy, precision, recall, F1-score, and computational time. Experimental results demonstrate that Foxtsage achieves a 42.03% reduction in loss mean (Foxtsage: 9.508, Adam: 16.402) and a 42.19% improvement in loss standard deviation (Foxtsage: 20.86, Adam: 36.085), reflecting enhanced consistency and robustness. Modest improvements in accuracy mean (0.78%), precision mean (0.91%), recall mean (1.02%), and F1-score mean (0.89%) further underscore its predictive performance. However, these gains are accompanied by an increased computational cost, with a 330.87% rise in time mean (Foxtsage: 39.541 seconds, Adam: 9.177 seconds). By effectively combining the global search capabilities of FOX-TSA with the stability and adaptability of SGD, Foxtsage presents itself as a robust and viable alternative for neural network optimization tasks.

With the increasing global demand for ultra-highspeed, low-latency communication, the development of 6G networks is inevitable. However, achieving seamless global coverage faces challenges due to complex geographical conditions and dynamic demand, making traditional terrestrial networks insufficient. Low Earth Orbit (LEO) satellites, with their low latency and high spectral efficiency, play a critical role in 6G networks. This paper focuses on LEO satellite constellation orbit design and coverage optimization, proposing a dynamic optimization strategy and AI-based framework using deep learning and reinforcement learning algorithms. First, a regional priority model is built using deep learning to identify high-demand areas based on global population distribution and terrestrial network characteristics. Then, deep reinforcement learning is applied to optimize orbit parameters, adjusting satellite deployment density to enhance coverage performance and resource utilization in priority areas.Simulation results show that this strategy significantly improves global coverage and resource efficiency, especially in high-population-density and remote areas, providing higher-quality communication services. This research offers innovative design ideas for global 6G coverage and provides a foundation for dynamic optimization and deployment strategies in future communication networks.

Data encryption is an essential element to a functioning network of networks, otherwise known as the internet. It is important for users to maintain security, whether it be their online assets or secure communication amongst other parties via a network. Data encryption algorithms are put in place to ensure these values, where AES and DES are the two most well known variations. The core purpose of this paper will be to determine why the AES algorithm has proven itself over the DES algorithm as a community preferred standard, mainly in reference to the field of CyberSecurity. This paper will look at the key differences between the DES and AES algorithms. This will include the cost efficiency differences in terms of computational power between the two algorithms, as well as the reason why the AES algorithm is more easily adaptable in today's society. This comparison will be made by dating back to the origins of both algorithms, as well as noting on why AES implementations are typically more accepted in CyberSecurity, as well as other real world sectors such as finance, healthcare and government. Key points will be made on why the DES algorithm has been proven to be susceptible to encryption attacks over time, and why the AES algorithm is able to overcome these faults by looking at their statistical differences in terms of block/key sizes, and the speed at which the algorithms take to encrypt a message. By utilizing real world examples that detail breach attempts on DES algorithms, a clear notion can be established on the weaknesses of the algorithm and how these cons were improved upon through the AES algorithm. This paper will also include the future of both algorithms by comparing them to newer encryption models and techniques, touching on how the currently accepted AES model can continue to be adapted to the newly expected era of technology and cryptography.

This paper studies the feasibility of direct flip-chip bonding of silicon carbide (SiC) chips onto alumina substrate for high-temperature applications up to 600℃. SiC dummy chips were prepared with sputtered thin-film Ti/TaSi2/Pt metal conductive pads, while alumina substrates were made with screen-printed thick-film gold conductive pads with a film thickness of about 12 µm. Thermocompression flip-chip bonding was implemented with gold stud bumps made using a wire bonder. Two strategies were used in the flip-chip bonding and compared: bump-on-substrate (BoS), where gold stud bumps were made on the conductive pads of the alumina substrate, and bump-on-chip (BoC), where gold stud bumps were made on the thin-film conductive pads of the SiC chip. The SiC flip-chip packages showed a trend of decreasing daisy chain electric resistance after thermal aging and stabilized. Die shear tests and daisy chain electrical resistance measurements were carried out for the BoS and BoC packages before and after up to 16 days of thermal aging in lab air at 600℃. Die shear tests showed that the bonding strength of the packages increased from about 15 gram force per bump (gf/bump) before thermal aging to about 40 gf/bump after thermal aging because of the effects of thermal annealing.