The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data from ground-based user equipment (UE). This paper addresses the dual challenges of optimizing both UAV deployment and task offloading within such networks to minimize communication latency and efficiently utilize UAV resources, which are limited by battery life and processing capabilities. We propose a bi-level optimization framework that simultaneously tackles the placement of UAVs and the distribution of computational tasks among them. At the higher level, UAV deployment is optimized to ensure minimal distance to the UEs, thereby reducing latency and energy consumption during data transmission. At the lower level, task offloading is optimized to balance the computational load across the UAV network, considering each UAV's capacity and battery constraints. We demonstrate through extensive simulations the significant improvements in system efficiency, latency, and resilience. This approach not only enhances the performance of UAV-assisted MEC networks but also provides scalable solutions adaptable to various operational scenarios.

The emergence of beyond 5G and 6G networks is set to revolutionise telecommunications, addressing the demands of emerging applications through advanced capabilities. At the core of this transformation lies next-generation intelligent service orchestration, which is essential for meeting future Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) such as ultra-low latency, efficient power consumption and resource utilisation. These capabilities enable multi-objective, seamless endto-end service delivery across complex, distributed environments. Cloud-native technologies, underpinned by microservices architectures, will play a pivotal role but also introduce challenges in orchestrating resources efficiently across heterogeneous domains. To address these challenges, this paper proposes a Federated Intelligent multi-objective Service function chain Orchestration (FISO) solution that integrates multi-objective federated profiling to preserve privacy while ensuring efficient end-to-end service delivery. FISO predicts optimal computing and network resources for Service Function Chains (SFCs), enabling the selection of appropriate edge locations, efficient resource allocation, SFC placement, and lifecycle management. Experimental results, demonstrated over a pragmatic testbed, validate FISO's effectiveness in placing requested SFCs efficiently within an administrative domain, predicting optimal CPU, memory, and link capacity resources, and minimising end-to-end latency and energy consumption.

Multi-agent systems (MAS) have gained popularity due to their effectiveness in diverse applications. Among these, decentralized approaches, which rely on inter-agent communication, have emerged as a promising alternative to centralized MAS coordination methods. However, existing communication protocols often learn abstract representations that are difficult for humans to interpret. Also, the limited wireless bandwidth in real-world applications imposes a significant challenge on efficient communication among agents. To address these issues, in this paper, we consider a multi-robot navigation scenario and propose a goal-oriented human-interpretable feature communication protocol, enabling robots to transmit only the most semantically important features. Building on this protocol, we first introduce a Multi-Agent Proximal Policy Optimization (MAPPO)-based Bandwidth-Adaptive Dual-Policy (MAPPO-BDP) framework, which employs a dual-policy architecture to jointly optimize feature selection and navigation under bandwidth constraints. However, as MAPPO-BDP requires retraining for different bandwidth constraints, we further propose a Large Language Model (LLM)-based Bandwidth-Adaptive Dual-Policy (LLM-BDP) framework. By leveraging LLM-based multi-agents, LLM-BDP can adapt to varying bandwidth constraints by simply modifying the input prompt, thereby eliminating the need for retraining. Experimental results show that both MAPPO-BDP and LLM-BDP frameworks successfully complete navigation tasks within a limited number of steps by effectively communicating the most important features under bandwidth constraints. We demonstrate that LLM-BDP achieves better reasoning adaptability by utilizing more advanced LLMs and engineering example prompts. Notably, with human-interpretable feature communication protocol, robots trained with the MAPPO-BDP framework can seamlessly collaborate with robots from the LLM-BDP framework to complete tasks, even without co-training, demonstrating the practical applicability of the proposed communication protocol in new scenarios.

This research investigates the seismic performance of a novel cable bracing system incorporating a central steel plate for retrofitting reinforced concrete (RC) and steel frames. Cable bracing systems, with their high strength-to-weight ratio and flexibility, offer a promising solution but face challenges like low ductility and buckling potential. To enhance the performance of these systems, a central steel plate is introduced to mitigate these issues. The methodology includes reproducing and validating three reference models: steel frames with and without X-bracing, and an RC frame with cable bracing and a central steel plate. These models are subjected to cyclic loading in SAP2000, using non-linear elastoplastic behavior for beams and columns, and nonlinear catenary behavior for the cables. A 2D RC frame is then retrofitted with the cable bracing system and tested under nonlinear time-history analysis to assess its hysteretic behavior and seismic performance. Key findings demonstrate that the addition of cable bracing significantly improves yielding strength, stiffness, and energy dissipation, enhancing overall structural performance under lateral loads. The introduction of the central steel plate further enhances ductility and strength. Sensitivity analyses reveal that plate size, thickness, and cable diameter significantly influence seismic response, with larger and thicker plates, as well as increased cable pre-tensioning, improving performance. This research provides a foundation for future studies, emphasizing the need for experimental validation and exploring the system's performance under diverse seismic conditions and configurations.

Text-to-image (T2I) generation has gained increasing attention as it provides flexible and intuitive synthetic data for downstream geoscience applications. However, obtaining large-scale, high-quality image-text datasets in remote sensing (RS) is challenging because of high annotation costs and specialized domain knowledge. In this paper, we propose a dual loop data cleaning (DLDC) method, which leverages contrastive multimodal quality evaluations to generate high-quality RS image-text training data automatically. By constructing an external generation loop (EGL) based on a multimodal basic model and an internal evaluation loop (IEL) based on contrastive learning metrics, DLDC can automatically perform layout description and image-text matching evaluation on remote sensing images. Our approach effectively filters out noisy samples and curates a refined dataset without manual intervention. Experimental results show that our dual loop evaluation can accurately determine the optimal data cleaning ratio for different scenes, improving image generation quality. Compared with state-of-the-art (SOTA) pre-trained models, our fine-tuned models reduce FID values by over 35%, increase CLIP scores by more than 25%, and improve RemoteCLIP scores by over 10.5%. Experimental results demonstrate that our automatically generated image-text data is of a similar quality to manually annotated data, opening new pathways for rapid, cost-effective, and reliable RS data generation.

Vascular and cardiac interventions often rely on Xray fluoroscopy for real-time imaging guidance, which involves the risk of ionizing radiation exposure. MRI presents a safer alternative, offering soft tissue contrast and lack of ionizing radiation. However, the use of MRI for procedural guidance has been limited by several technical challenges. This study aims to enhance MRI-guided interventions by optimizing an interactive MRI sequence, utilizing a CNN for the detection of a passive endovascular guidewire, and incorporating MR imagebased haptic feedback into the MR-safe CathBot platform. MRI sequences were optimized for contrast-to-noise ratio while preserving temporal resolution. The CNN-based detection identified passive markers on the guidewire in near real-time. The CathBot platform was finally integrated with the CNN detection system to provide real-time haptic feedback via the main controller. This integrated approach introduces a new concept for increasing control and precision during MRI-guided interventions, allowing for improved clinical outcomes.

Functional near-infrared spectroscopy (fNIRS) is well-suited for hyperscanning in naturalistic situations, offering significant potential for assessing social brain function in everyday life. Previous studies have reported inter-brain synchrony during social interactions and sought to explore its mechanisms by correlating behavioral events with brain signals. However, commonly used regression analyses, such as General Linear Models (GLM), rely on target events hypothesized as explanatory variables. This reliance introduces a dependency on the researcher's assumptions, which can compromise replicability in social neuroscience. While such dependency may be less problematic in strictly controlled experimental paradigms focused on specific hypotheses, it poses significant challenges in naturalistic experiments like social interactions, where numerous events and signals may serve as potential explanatory variables. To address this limitation, we introduced a new approach: signed-normalized mutual information (sNMI). This method enables a two-way analysis to evaluate relationships between event sequences and brain synchrony. Through simulations and real-data applications, we evaluated the performance of the proposed method. The results showed that sNMI analysis performed comparably to regression analysis in detecting both inter-brain synchrony and within-brain synchrony (i.e., brain connectivity). Moreover, applying sNMI to a tapping dataset revealed the expected patterns of synchrony between the lateral sides of the motor area. These findings validate the effectiveness of sNMI as a robust two-way analytical tool for studying brain synchrony.

In today’s fast-paced and data-driven world, organizations are under increasing pressure to optimize profitability and make informed, strategic decisions in real-time. Advanced analytics solutions are no longer optional but essential for organizations striving to stay competitive and responsive to market demands. SAP Analytics Cloud (SAC) for Profitability Analysis (COPA) is emerging as a game-changer, particularly for industries such as manufacturing, medical, and retail, where large volumes of complex data and rapidly changing conditions make it difficult to gain clear, actionable insights without the right tools.SAC for COPA enables organizations to integrate and analyze data from multiple sources in a unified platform, facilitating a deeper understanding of profitability drivers across different business units, product lines, and regions. With the ability to model cost and revenue allocations dynamically, SAC empowers decision-makers to perform advanced forecasting, scenario planning, and root-cause analysis, all within a single, user-friendly interface.The flexibility of SAC supports both strategic and operational decision-making by providing customizable dashboards and reports, while its predictive capabilities drive proactive business responses. By leveraging real-time insights and powerful data visualization tools, SAC for COPA helps businesses optimize their cost structures, improve financial performance, and drive growth. This solution is especially valuable in industries where the margin for error is small, and quick, data-backed decisions are critical for staying ahead in a highly competitive and ever-evolving market landscape.

This study examines how well SSL/TLS and blockchain work to secure online transactions, especially purchase orders. Reviewing literature from 2013 to 2024 shows each method's key themes, benefits, and weaknesses. SSL/TLS is known for its strong encryption and solid framework but often has issues like centralization and threats from different attacks, leading to transaction delays and bottlenecks. On the other hand, blockchain technology uses decentralized protocols and features such as zero-knowledge proofs, promising better scalability and security, allowing for smooth and safe transactions without traditional middlemen. This comparison clarifies how effective each method is. It highlights the rise of blockchain as a viable option to the limits of SSL/TLS, deserving more study in e-commerce security.

Many internet services are personalized to the interests of users. The personalization is usually based on the user profiles extracted from the interactions of users with internet applications. The profile, however, may naturally reveal private information about the user. Hence, the users urge the applications to protect their privacy, although they are interested in getting the personalization services as far as possible. To formulate such requirements, we propose differential privacy with semantic neighboring, which relies on a novel notion of neighboring datasets. Two datasets are basically defined to be the neighbors of one another if one is obtained by eliminating the records from the other proportional to the extent to which the records are semantically related to what the user deems private. We also develop a Laplace mechanism for enforcing the privacy policies specified in this way. The main challenge in devising this mechanism is deciding on an effective definition of the sensitivity of a given query on log datasets so that the mechanism can arrive at higher levels of utility. The results of our experimental study demonstrate that the proposed mechanism can achieve acceptable privacy parameters while retaining the usefulness of the underlying personalization service.

Supervised machine learning classifiers often encounter challenges related to performance, accuracy, and overfitting. This paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning classifier inspired by the human liver's detoxification function. The ALC is characterized by its simplicity, speed, hyperparameters-free, ability to reduce overfitting, and effectiveness in addressing multi-classification problems through straightforward mathematical operations. To optimize the ALC's parameters, an improved FOX optimization algorithm (IFOX) is employed as the training method. The proposed ALC was evaluated on five benchmark machine learning datasets: Iris Flower, Breast Cancer Wisconsin, Wine, Voice Gender, and MNIST. The results demonstrated competitive performance, with the ALC achieving 100% accuracy on the Iris dataset, surpassing logistic regression, multilayer perceptron, and support vector machine. Similarly, on the Breast Cancer dataset, it achieved 99.12% accuracy, outperforming XGBoost and logistic regression. Across all datasets, the ALC consistently exhibited lower overfitting gaps and loss compared to conventional classifiers. These findings highlight the potential of leveraging biological process simulations to develop efficient machine learning models and open new avenues for innovation in the field.

As big data applications become increasingly complex, their architectures-comprising various middleware and processing components-demand continuous refinement to maintain optimal performance. This paper presents OSTIA (On-the-fly Static Topology Inference Analysis), a tool designed to support the continuous architecting of dataintensive applications (DIAs) by addressing three key areas: detecting common anti-patterns, performing algorithmic manipulations, and facilitating formal verification of architectural models. OSTIA operates by reverse-engineering the topologies of big data applications, such as those built on Apache Storm and Apache Hadoop, to ensure compliance with framework-specific constraints and identify potential design flaws. Through case studies involving both industrial and open-source applications, I demonstrate OSTIA's effectiveness in improving the reliability and performance of streaming and batch-processing systems. This paper also highlights OSTIA's recent enhancements, including formal verification capabilities and additional heuristics, and discusses its impact on the iterative process of continuous architecting.

Assessing urban environments using intelligent tools helps decision-makers improve citizen quality of life. Our research introduces a Geospatial Augmented Reality (AR) System for Aesthetic and Environmental Assessment of the University. This framework integrates user presence from geospatial analysis, greenery evaluated through image processing, user sentiments derived from biosensor data using deep learning, and contextual information. These factors are synthesized via Multi-Criteria Decision Making (MCDM) to generate scores. Subsequently, the system utilizes voice recognition to identify user-selected areas, overlaying scores, and relevant contextual data through AR technology. This approach empowers decision-makers to enhance urban spaces effectively.

The rapid expansion of Internet of Things (IoT) ecosystems has revolutionized industries by enabling real-time data exchange and interconnected operations. However, this growth presents significant security challenges, including scalability, resource constraints, and the need for real-time adaptability to evolving cyber threats. To address these issues, this study proposes an innovative, AI-driven framework that integrates lightweight intrusion detection systems (IDS), blockchain-based authentication, and edge computing. This qualitative research synthesizes peer-reviewed literature to identify existing gaps and design a multi-layered security solution tailored for resource-constrained IoT environments. The proposed framework enhances scalability by leveraging decentralized blockchain systems and edge computing for distributed data processing. Lightweight AI algorithms are employed to optimize resource efficiency and ensure real-time adaptability. Analytical comparisons with traditional security models demonstrate the framework's superiority in mitigating IoT vulnerabilities while maintaining computational feasibility. This study contributes to the theoretical and practical understanding of IoT security, offering a scalable, resource-efficient, and adaptive solution for emerging threats.

In this paper, Avalanche is introduced as a robust peer-to-peer payment system that leverages the Snowball consensus protocol to facilitate secure and efficient cryptocurrency transactions. Building on the foundations laid by the Snowflake and Slush protocols, Avalanche incorporates a dynamic directed acyclic graph (DAG) to enhance transaction efficiency and security. By maintaining a single sink at the genesis vertex, the DAG intertwines transaction fates, making past decisions resistant to reversal by malicious actors. My consensus mechanism is designed to accommodate a varying presence of adversarial nodes while ensuring that virtuous transactions achieve safety and liveness guarantees. Through probabilistic decision mechanisms, Avalanche efficiently handles conflicting transactions through Snowball instances, ensuring that only valid transactions are confirmed. My findings highlight the effectiveness of the Avalanche protocol in real-world applications, presenting a significant advancement in decentralized payment systems.

Voltage source behind an impedance is a term widely used in grid-codes requirements for grid forming (GFM) power electronic converters. However, the exact meaning of this requirement is hard to quantify and verify, and its necessity is not adequately justified. This paper identifies key characteristics that GFM controls should possess and evaluates how GFM controls, with and without a current controller, meet these characteristics. The study utilizes the dq-frame admittance and the frequency-domain power flow Jacobian to analyze an ideal voltage source behind an RL impedance, a synchronous generator, and various GFM and grid-following controls implemented in modular multilevel converters (MMCs). The research identifies several essential voltage source characteristics, which can be mathematically calculated and used to define the frequency ranges where the requirements are met. The paper demonstrates that the GFM structures meet the essential voltage source characteristics in a limited frequency range. The GFM with the current controller is particularly affected due to the nonpassivities of the current controller and virtual impedance. The requirements calculated from the essential voltage source characteristics can serve as a screening method to assess the effectiveness of converter controls, and the insights gained can be utilized to enhance the design thereof.

Digital leisure activities—online games, short videos, and extracurricular reading—are popular among elementary school students. These activities can extend learning beyond the classroom but may also compete for time and energy, potentially impacting academic performance, especially for students with less self-control. This study investigates the relationship between these activities and exam scores, using advanced analytical methods. The findings reveal a significant correlation between learning outcomes and the frequency, duration, and fatigue associated with these leisure activities. Specifically, the frequency of extracurricular reading negatively correlates with science performance (effect coefficient -0.242), while the duration positively correlates (effect coefficient 0.440). An intriguing ”hourglass effect” was observed, where exam scores are heavily influenced by the time and energy students with low self-control spend on recreational activities. These results provide valuable insights into balancing digital leisure with academic responsibilities, offering practical guidance for educators in planning school content and developing educational strategies. By understanding the impact of digital leisure on learning, this research supports the development of more effective learning technologies to enhance academic outcomes in elementary education.

由于太赫兹无线通信拥有广阔的未开发频谱，它正在成为未来 6G 网络的关键技术。使用模拟相控天线阵列对于缓解太赫兹信号的显著衰减特别有效，从而确保高质量的通信。通过采用精心设计的移相器，这些阵列可以实现波束成形，将信号增益集中在空间中以补偿信号损失。然而，在超宽带情况下，相控阵系统的性能会受到影响，因为移相器通常是为中心频率设计的，但应用于整个频带。当子载波频率与中心频率不同时，这将导致标准波束成形方法中出现明显的波束偏差和散焦。在本文中，我们提出了模拟波束成形方法来增强视距太赫兹超宽带相控发射机阵列系统的性能。利用空间啁啾特性，我们开发了移相器设计方案，以在整个频带上实现更均匀的等效信道增益，以适应各种应用场景。我们通过模拟结果证明了所提出方法的有效性和优势，与最先进的模拟波束形成方案相比，结果显示出显着的改进，包括提高吞吐量和降低误码率。