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Integration of Photovoltaic (PV) generation into oil and gas power networks has transformed fault behaviour by creating highly complex sensitivity challenges for conventional Overcurrent Relay (OCR) coordination and trip...

Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework ...

Talent training is a critical issue of social development. Particularly, talent training in research-oriented universities plays a key role in human resources management. However, achieving effective talent development w...

Understanding how semantic information flow reorganizes market topology remains an open challenge in financial network analysis. Prior work typically emphasizes price correlations or sentiment polarity, but rarely quanti...

Traffic congestion degrades urban mobility and economic productivity, yet existing sensor-based monitoring systems detect only where congestion occurs without distinguishing its underlying infrastructure causes. This pap...

Accurate velocity estimation plays a key role in ensuring precise motion control and safe navigation of ground robots. Ground-view event cameras provide a reliable sensing modality for this task. They exploit the static ...

This research describes a deep learning-based artificial neural network (ANN) approach for the detection, classification, and localization of series faults (open conductor faults) in power distribution networks. The ANN ...

In a decentralized organization such as a university, recruitment is critical for the development of departments. With the limited resources for recruitment, how to allocate the recruitment quotas to different department...

Real-time routing on large-scale road networks remains a critical challenge for intelligent transportation systems, particularly under high query loads and increasing graph sizes. While classical shortest path algorithms...

Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient alternative to conventional artificial neural networks (ANNs). However, deep SNNs struggle to process complex EEG signals because their spike...

Recently, in the image super-resolution (SR) domain, Transformers have outperformed Convolution Neural Networks (CNNs) with reduced computational complexity and parameters by modeling long-range dependencies input-depend...

This paper investigates the distributed state estimation problem for a class of continuous-time nonlinear systems over sensor networks. The nonlinearities considered are assumed to satisfy the Incremental Quadratic Const...

Graph-based text classification has become an effective paradigm for modeling corpus-level structure, particularly under limited supervision. However, most existing approaches rely on document–word representations...

ARM-based systems are widely deployed in edge and IoT settings, where penetration-testing tools must operate under strict memory, power, and thermal constraints, yet prior work offers limited deployment-oriented guidance...

The remarkable success of large language models (LLMs) in code generation has transformed software development, with systems like Codex, CodeGen, and StarCoder demonstrating strong capabilities in synthesizing functional...

After an earthquake, numerous buildings are destroyed, and the frequency of structural collapses increases significantly in regions lacking comprehensive geophysical surveys. Furthermore, the number of deaths increases d...

High accuracy positioning has become a key capability for fifth generation (5G) mobile networks and their future generations; however, the achievable performance is still severely limited by network time synchronization,...

Sixth-generation (6G) mobile networks are expected to be AI-native by design, with intelligence embedded directly into the Radio Access Network (RAN) control loop through architectures such as O-RAN, near-real-time RAN I...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

User populations in modern recommendation systems consist of diverse communities with distinct and often non-overlapping preferences. However, most existing models, such as traditional Variational Autoencoders (VAEs), op...

This study compares seven quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quant...

Dairy farms increasingly deploy sensor networks that generate high-frequency behavioral and spatial data, yet translating these multimodal streams into actionable welfare insights remains a key challenge. This study pres...

This paper presents the design and experimental validation of a two-dimensional (2-D) circularly cylindrical phased array antenna with integrated wide-angle beam-switching capability for multibeam radiation in the Ku-ban...

Modern edge–cloud deployments integrate devices, edge servers, private networks, and cloud platforms into heterogeneous, multi-layer systems. Each layer exposes distinct metrics, measurement granularities, and tim...