AbstractProtein structures are dynamic, and contacts observed in a single structure may not remain stable across different conformations. However, most structure-based analyses rely on individual structures and do not explicitly account for this variation. Here, we quantify contact-area persistence in experimentally de...
Kliment Olechnovič, Sergei Grudinin· Zenodo (CERN European Organi...· 0 citations
Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) has exceptionally high mortality, largely because of late diagnosis, while established blood biomarkers such as CA19-9 have limited sensitivity for early detection. We evaluated whether modelling serum proteomic measurements as supervised pairwise statistic...
Alexey Zaikin, A. Gentry-Maharaj, S. Apostolidou et al.· Diagnostics· 0 citations
Modern artificial intelligence is structurally constrained by the von Neumann bottleneck, which enforces an energy-intensive physical separation between arithmetic logic circuits and memory storage banks. This paper formalizes the architectural paradigm of Hardware Neurotechnology, an invention authored by Dr. Nitnem S...
Sodhi Dr. Nitnem Singh· Indian Journal of Computer S...· 0 citations
Accurate assessment of urban patrol cases plays an important role in maintaining urban safety and improving the efficiency of urban governance. The hazard level of urban patrol cases can be reflected by the urgency level and prioritizing high-urgency cases can help reduce potential risks to public safety. However, it r...
Yirui Jiang, Hao Wu, Tiejun Yang et al.· PLoS ONE· 0 citations
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Early detection of Alzheimer’s disease (AD) is essential for timely clinical intervention, yet remains challenging due to subtle neural changes in its initial stages. This study proposes a non-invasive EEG-based framework that integrates multiscale signal processing, dynamic brain connectivity analysis, nonlinear fea...
G. Vinuja, S. Arunachalam, N. Ashok· Discover Applied Sciences· 0 citations
Addressing the limitations of existing employability prediction frameworks in cross-domain data integration and heterogeneous knowledge modelling, this study proposes a graduate employability knowledge graph construction framework grounded in multi-source heterogeneous data fusion and graph neural networks. A bidirecti...
The collapse of underground mine workings remains a severe
safety problem in artisanal and small-scale mining, where
engineered ground support, geotechnical instrumentation, and
systematic rock-mass characterization are often unavailable.
Motivated by the 12 August 2026 Kassa mine-pit collapse in Barkin
Ladi, Plateau S...
Unknown authors· International Journal of Adv...· 0 citations
Traffic speed prediction is vital for intelligent transportation systems. To achieve accurate forecasting, spatio-temporal graph neural networks have been widely adopted; however, despite recent advances, many existing methods still aggregate spatial features synchronously and rely on static or weakly adaptive dependen...
Xiang Wang, Zirong Wang, Di Wu et al.· Discover Applied Sciences· 0 citations
Leakage diagnosis in water distribution networks is constrained by sparse instrumentation, uncertain models, and leak concurrency. We present a physics-informed spatio-temporal graph neural network evaluated end-to-end on the open Battle of the Leakage Detection and Isolation Methods (BattLeDIM) and L-Town benchmark (7...
Mohammed EL Hachoumi, Abdeslam Taleb, Mohammed Karim Benhachmi et al.· Preprints.org· 0 citations
Background The complex interaction of socio-economic, educational, and demographic factors makes it challenging to accurately predict youth self-leadership confidence.Objective This study aims to develop an effective deep learning framework for predicting self-leadership confidence among young individuals by identifyin...
Jong-Teak Seo, Suk-Ju Yun, Hee-Lee et al.· Journal of Business Analytic...· 0 citations
Full pipeline, leakcheck auditor, and per-snapshot results for the IEEE TKDE submission on leakage-safe evaluation of dynamic graph neural networks. Pre-registration: https://osf.io/2edhx/
The Cognitive Federated Graph Learning (CFGL) framework is introduced to jointly optimise three interdependent resources in cognitive radio networks: (i) frequency channel allocation for secondary-user transmissions, (ii) routing paths for multi-hop packet traversal, and (iii) transmit-power budgets as determined by pe...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
