To address the critical global challenge of microplastic pollution and the limitations of alignment-dependent genomic tools in analyzing fragmented environmental data, this study introduces an alignment-free computational framework for identifying microbial bioremediation potential. We established a standardized, multi-domain dataset encompassing Bacteria, Protists, Archaea, and Fungi, integrating metadata on polymer interactions to fill existing data gaps. Utilizing tetranucleotide frequency patterns (k=4), we developed a novel analysis method to isolate predictive genomic signatures, identifying C-rich motifs such as ‘CCCC’ as primary indicators of degradation capability.A statistical scoring model was subsequently implemented to rank candidate taxa, effectively prioritizing high-value organisms from complex metagenomes derived from plastic-associated microbial assemblages. Our approach demonstrates significantly reduced computational time and more sensitive than the older methods, identifying potential degraders that alignment techniques often miss. It connects the theoretical side of detecting functional genes with real-world environmental checks, creating a flexible tool to speed up finding new microbes for tackling plastic waste and restoring ecosystems.
U. Luke, Emem Okon Abang, Etimbuk Daniel Akpan et al.· E3S Web of Conferences· 0 citations
The integration of Internet of Things (IoT) systems with blockchain-based security mechanisms offers improved trust, auditability, and decentralization, but introduces significant challenges related to interoperability, transaction latency, and energy consumption at the network edge. Most existing blockchain-assisted intrusion detection systems (IDS) are limited to single-ledger deployments and rely on computationally intensive machine-learning inference, which restricts their applicability in resource-constrained IoT environments. Presented in the work is a cross-chain and energy-efficient intrusion detection framework that combines lightweight machine-learning–based anomaly detection with smart-contract–driven verification and interoperable blockchain communication. Intrusion detection models are optimized using pruning and INT8 quantization to reduce inference overhead on IoT gateways, while verified intrusion alerts are securely propagated across heterogeneous blockchain platforms through cross-chain messaging protocols. Smart contracts automate alert validation and response actions, enabling coordinated defense across multiple ledgers. Experimental evaluation using CICIDS2017 and BoT-IoT datasets, physical IoT gateway hardware, and a multi-chain blockchain testbed demonstrates that the proposed framework achieves a detection accuracy of 97.4%, reduces inference energy consumption by up to 56%, and improves alert propagation latency by 42% compared to single-chain baselines. These results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments.
S. Bassey, Emmanuel Udoh, B. Stephen et al.· E3S Web of Conferences· 0 citations