Effective detection of sophisticated cyber threats requires innovative frameworks capable of analyzing the complex and evolving behaviors of malicious software. The Temporal Behavior Chain Analysis (TBCA) framework introduces a novel methodology for identifying ransomware activity through the examination of temporally dependent system interactions. By constructing directed acyclic graphs to represent behavioral sequences, the framework captures subtle anomalies that traditional detection methods fail to identify. Machine learning techniques, including Hidden Markov Models and neural networks, enhance the framework's ability to discern malicious patterns from benign behaviors with high accuracy and efficiency. Evaluations demonstrated superior detection rates across multiple ransomware families, including LockBit, BlackCat, and Hive, even when advanced obfuscation techniques were employed. The modular design of TBCA ensures seamless integration into existing infrastructures while maintaining scalability for deployment in diverse operational environments. Resource efficiency was validated through extensive scalability testing, which confirmed the framework's capability to process high volumes of data with minimal computational overhead. Comparative analyses revealed significant improvements over traditional detection approaches, particularly in handling emerging and previously unseen ransomware variants. Temporal modeling of sequential dependencies enables early detection, reducing potential system damage and facilitating real-time mitigation strategies. The integration of adaptive decision-making mechanisms minimizes false positives, enhancing reliability in high-stakes environments. Cross-platform evaluations further highlighted the robustness of the framework, demonstrating its effectiveness on Windows, Linux, and macOS systems. The TBCA framework establishes a comprehensive foundation for advancing proactive cybersecurity defenses against increasingly complex and dynamic threats.

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.

In this brief we prove that both forms of Middlebrook's formula for the transfer impedance T of a two-port can be seen as particular instances of the more general expression T = T* (p_nq_3 + q_np_3)/(p_d q_3 + q_d p_3) , which involves a generic reference immittance, not necessarily an open-circuit or a short-circuit. The p-and q-parameters arising above owe to the use of a homogeneous description of the extra element and of the double null and the single injection immittances. The formula holds under minimal regularity assumptions.

Machine learning (ML) has proven to be a key enabler of various industrial cyber-physical systems-empowered functionalities, such as predictive maintenance (PdM) in Industry 4.0. Among many ML methods, the scope of this paper is self-organizing map (SOM). SOM has many attractive properties for industry applications (e.g., unsupervised learning, noise robustness). Further, it can yield various tasks (e.g., clustering, dimensionality reduction, health indicator construction, visualization, etc.), rendering it versatile for data-driven PdM, including anomaly detection, diagnosis, and prognosis tasks. This papers presents a brief review of the main SOM applications in the PdM field, as well as future research opportunities.

In this work, we present a novel thermomolecular communications gateway allowing communication through the human skin into the human body connecting the internet of things (IoT) with the internet of bio-nano things (IoBNT). We developed an experimental setup as a proof of concept and provide a detailed description of the testbed, its assembly and fabrication. Mathematical models for all components are derived and verified experimentally. Finally we analyze the communications performance of the system and elaborate on future works.

Traumatic brain injury (TBI) is a major public health concern that often leads to mortality or long-term disability. Accurate prediction of outcomes, such as mortality and Functional Status Scale (FSS) scores, improves treatment strategies and guides clinical decisions. This study applies supervised machine learning (ML) techniques to predict mortality and FSS score in a real-world dataset of 300 pediatric TBI patients compiled by the University of Colorado School of Medicine. The dataset includes detailed clinical information, such as demographics, injury mechanisms, and hospitalization outcomes. Eighteen ML models predict mortality, while thirteen models forecast FSS scores. Performance metrics, including accuracy, ROC AUC, F1-score, and mean squared error, evaluate the models. Logistic regression and Extra Trees show high precision in mortality prediction, while linear regression achieves the best FSS score prediction with an R² score of 0.62. Feature selection reduces 103 clinical variables to the most relevant ones, improving model efficiency and interpretability. The study highlights how ML models help clinicians identify high-risk cases and support personalized interventions. This research demonstrates the potential of ML-driven predictive analytics to integrate into clinical workflows, providing healthcare professionals with data-driven tools to improve TBI care.

In the modern business landscape, the ability to couple and derive value from Big Data has become essential for organizational success. This paper explores the strategic importance of Big Data Value Realization, focusing on methodologies, challenges, and case studies that illustrate effective practices. By aligning data initiatives with business goals and leveraging advanced analytics, organizations can transform vast datasets into actionable insights. This article aims to provide a comprehensive guide to understanding and implementing Big Data strategies that drive innovation and enhance decision-making.

Project management remains as one of the most popular post graduate management courses for international students at UK Universities. Students perceive or are advised that the skill set is highly transferable to a wide range of management opportunities and can be built upon almost any undergraduate discipline. However, many practitioners claim that the interconnectedness of the real life of projects can only ever be fully understood through on-the-job real life work experience. Hence the student dilemma, how to gain experience without experience? Project management simulation exercises attempt to bridge this gap. This research considers the effectiveness of a well proven simulation at a UK university comparing the applied knowledge of 17 different skill sets before and after the exercise through the lens of the student perspective. Furthermore, in depth semi-structured interviews with experienced tutors aim to decipher the mechanisms of learning so transferable principles can be prioritised for future exercise design. The study carried out during the 2020/21 academic year demonstrates that from a student perspective such educational approaches are highly effective with all the skill set analysed (a broad set of hard and soft skills) showing notable deltas before and after. In addition, students engage deeply with this method and find it fun and memorable which is important for future knowledge retrieval. The knowledge and experience of the tutors highlighted that game design was only 1 of 3 important elements crucial for learning to take place. Game management and the skill and motivation of the tutor have equal bearing on success. This research offers a conceptual framework to synthesis the findings and recommends that educationalists and employers collaborate and invest in future game development together rather than seek off the shelf solutions.

This research bridges the gap between claimed and actual ethical behavior by analyzing subtle "hidden honest signals" in language using AI and Natural Language Processing (NLP). It categorizes individuals into four "tribes": "doves" (ethical, collaborative), "wolves" (loyal, diligent), "sharks" (competitive, aggressive), and "parasites" (self-serving, exploitative). Using data from migrant workers in Taiwan, the study examines how ethical behavior correlates with productivity in healthcare and manufacturing sectors. Results show that ethical behavior influences work outcomes, though not always in expected ways. The findings suggest that developing ethical training programs could improve productivity, particularly for migrant workers in these settings.

Numerical methods are essential for simulating transient physical phenomena, providing crucial support to engineers and researchers in fields such as defense, space, and the natural sciences. By modeling these phenomena with partial differential equations (PDEs), deeper insights can be gained. Among the various PDE-solving methods, the Crank-Nicolson technique stands out for its efficiency in handling time-dependent PDEs, offering precise and stable solutions across diverse applications. In this work, we present a 3D GPU-accelerated code optimized for parallel execution in three-dimensional space-time domains. The PDE solver is applied to quantum wave function information processing, specifically in the context of the circular double-slit experiment. Additionally, we utilize the solver to simulate a dual Mach-Zehnder interferometer to model the Fenna-Matthews-Olson (FMO) complex, developing a natureinspired algorithm based on its quantum dynamics. This simulation demonstrates near-perfect intelligence in a photosynthetic system without any neural network. This work may help in the design of solar cells, potentially increasing their efficiency to a greater extent.

Structural Health Monitoring (SHM) is vital to guarantee the early identification of any deterioration in bridge structures and to prevent unexpected collapses. As structures degrade, one of the most efficient techniques in SHM is vibrationbased damage diagnosis, which evaluates damage through analysing the dynamic features of structures. Identification of bridge deterioration may be done by measuring the vibration response caused by cars crossing a bridge. This paper proposes a unique Vehicle-Bridge Interaction (VBI)-based damage detection method to assess the bridge's condition.First, the method's foundations of theory are studied. A half-car dynamic model represents the vehicle and its suspension system, while the Finite Element Method is used to describe the bridge superstructure. The equations of motion for the bridge and the vehicle are obtained, respectively, using the mode superposition technique, D'Alembert's principle, and the finite element method. After that, the Newmark-beta approach is used to solve the linked dynamic issue. The roughness of the bridge road surface is taken into consideration during the examination.The proposed method for determining bridge damage is based on an analysis of the pitching rotation of a passing vehicle. The three main theoretical elements of this response are the bridge natural frequency, the vehicle frequency, and a pseudo-frequency component related to the vehicle speed. The Empirical Mode Decomposition (EMD) approach dissects the signal into its constituent parts, or intrinsic mode functions. A recommended damage detection method makes use of the intrinsic mode functions (IMFs) corresponding to the vehicle speed component of the response obtained from the passing vehicle. Through the study, it is observed that the proposed technique is capable of effectively identifying various damage cases considered in this research.

Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service with non-terrestrial networks. With the large-scale deployment of both non-terrestrial and terrestrial networks, limited spectrum resources will not be allocated enough. Consequently , dynamic spectrum sharing is crucial for their coexistence in the same spectrum, where accurate spectrum sensing is essential. However, spectrum sensing in space is more challenging than in terrestrial networks due to variable channel conditions, making single-satellite sensing unstable. Therefore, we first attempt to design a collaborative sensing scheme utilizing diverse data from multiple satellites. However, it is non-trivial to achieve this collaboration due to heterogeneous channel quality, considerable raw sampling data, and packet loss. To address the above challenges, we first establish connections between the satellites by modeling their sensing data as a graph and devising a graph neural network-based algorithm to achieve effective spectrum sensing. Meanwhile, we establish a joint sub-Nyquist sampling and autoencoder data compression framework to reduce the amount of transmitted sensing data. Finally, we propose a contrastive learning-based mechanism compensates for missing packets. Extensive experiments demonstrate that our proposed strategy can achieve efficient spectrum sensing performance and outperform the conventional deep learning algorithm in spectrum sensing accuracy.

Federated Learning (FL) is a decentralized ML approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at a central server. This methodology eliminates the need for data sharing between IoT devices while fostering collaborative model enhancement. Nonetheless, concerns arise due to the lack of transparency surrounding the shared local models and the aggregation techniques employed. This lack of transparency can potentially lead to model poisoning attacks and hinder collaborators from using alternative aggregation methods that better align with their specific use cases. To address this issue, this paper proposes a blockchainbased approach with FL to ensure transparent and immutable records of model updates, thereby bolstering security and trust for intrusion detection in IoT devices. In contrast to traditional synchronization or periodic update-based approaches, this paper proposes a novel time-independent aggregation method in FL blockchain, allowing for flexibility. Additionally, the proposed blockchain allows various users to utilize their own aggregation methods rather than a fixed one, based on their needs, resources, and availability. We also develop a user interface for the proposed blockchain system that assists in visualizing different aspects of the method, such as model aggregation. The proposed system is tested using traditional metrics like AI model performance as well as extensive user testing.

Contextual reasoning in language models has historically faced challenges in maintaining coherence over extended sequences and adapting to evolving contextual requirements. Context-Dependent Memory Synthesis (CDMS) offers a novel mechanism that integrates dynamic memory pathways into transformer-based architectures, enabling improved retention, retrieval, and synthesis of contextual information. The framework incorporates multi-layered memory modules and adaptive attention mechanisms, allowing language models to dynamically encode and retrieve contextually relevant information across diverse linguistic tasks. Experimental evaluations demonstrated significant reductions in perplexity and enhanced memory utilization, highlighting the efficiency of CDMS in handling longterm dependencies. Robustness against noisy inputs was achieved through dynamic memory updates, ensuring coherence even in challenging scenarios with imperfect data. Scalability assessments revealed consistent performance across varying model sizes, with notable improvements in latency and throughput metrics. The generalization capabilities of the architecture were validated through tasks spanning scientific, legal, and conversational domains, achieving high accuracy and retention rates without extensive fine-tuning. Computational benchmarks confirmed the feasibility of integrating CDMS into existing transformer frameworks, offering a balance between resource efficiency and taskspecific adaptability. Quantitative results also demonstrated low variability in latency across diverse tasks, affirming the system's stability and responsiveness. The findings demonstrate the transformative potential of CDMS in addressing key limitations in traditional language model architectures while providing a robust foundation for future innovations in neural memory systems.

Vehicular Knowledge Networking (VKN) is a paradigm where vehicles exchange knowledge instead of data. Named Data Networking (NDN) is a resilient architecture for sharing data between vehicles without information about the hosting vehicle. NDN might not be adapted for sharing knowledge, as the peculiar complexity of knowledge requires knowledge-driven NDN functions as well as a specific simulation environment enabling joint knowledge perception, inference and reasoning. In this paper, we present an open-source cosimulation framework connecting NDN, traffic, dynamic control and perception simulators for knowledge perception & inference, and with an AI-as-a-Service (AIaaS) platform for knowledge reasoning. This paper notably describes new interfaces and functions between the NDN daemon and the AIaaS micro-services for handling interest naming, caching and forwarding between knowledge producers and consumers. We demonstrate the benefit of the proposed framework and knowledge-specific extensions through an AI-driven vehicular intersection management.

This paper focuses on Ambimorphic prototype research in the field of information engineering. Firstly, it elaborates the research background, purpose and significance, and analyzes the concept of Ambimorphic prototype and related theoretical foundations. It then introduces in detail the application of public key-private key simulation in information security and communication systems, as well as the design and implementation of telephone semantic tools, including requirements analysis, architecture design and key technologies. Through experiments, performance tests and result analyses are conducted on both, including individual tests and comprehensive application tests. Finally, it summarizes the research results, limitations, and looks forward to future research directions, providing new ideas and methods for the development of information engineering.

Reconstructing the human connectome at the neuron level presents a formidable challenge, requiring approximately 400 trillion hours of manual work by trained neuroscientists. However, with advancements in machine learning and deep learning, it has become feasible to automate this process. This paper explores various automated methods for reconstructing the 3D geometry of neural tissue through electron microscopy (EM) image segmentation. We focus on enhancing existing segmentation pipelines by improving both their efficiency and accuracy. Our contributions include the development of a modular segmentation pipeline, DeepSeg, written in Python, allowing for flexible experimentation at different stages of the computational pipeline. Furthermore, we investigate multiple neural network architectures to improve segmentation accuracy and robustness, mitigating error propagation across stages. By leveraging learned alignment strategies and evaluating the performance of different approaches, this work offers a promising path towards the ultimate goal of generating neuron-level connectivity graphs, which can significantly advance our understanding of neural structures and brain function.

Fleets of autonomous vehicles offer an innovative solution to enhance logistic operations in manufacturing systems by increasing efficiency and safety. However, navigating in unstructured environments with moving obstacles presents significant challenges. This paper addresses this problem by proposing a novel control architecture that combines grid based mapping with receding horizon control techniques. Specifically, unicycle models are used to describe the dynamics of the robots, and sequences of feasible and safe way-points are computed by leveraging the real-time grid map of the entire operating scenario. A distributed set-theoretic model predictive control scheme leverages this information to ensure safe navigation. The effectiveness of the proposed approach is demonstrated through experimental results in a real-world context, where a team of three autonomous robots assists human workers along a steel production line manufacturing angle pipes, block-stages, and caps.