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Recent Entries
E124715050426
Named Data Networking (NDN) is considered a promising paradigm that enables content-centric communication and in-network caching. However, challenges of mobility, scalability, and security limit its effectiveness and impact in dynamic IoT environments. Existing mobility management approaches and strategies, including anchor-based and anchor-free schemes, are unable to jointly optimise latency, security, and multihoming efficiency, even under highly dynamic conditions. This research work proposes a Mobile Producer Handoff in the Named-data Emulated Mobility framework known as MP-HNEM. This is a blockchain-based adaptive routing strategy that integrates predictive handoff, cross-layer optimisation, and a lightweight Proof-of-Authority consensus mechanism to provide and enhance mobility support in multihomed NDN-based wireless sensor networks. The framework also addresses a crucial gap in secure and latency-aware producer mobility. A hybrid simulation and emulation method is employed using ndnSIM v2.9 and a Mini-NDN to evaluate MP-HNEM performance under varying mobility patterns, trust thresholds, and network densities. We analyzed latency, throughput, packet delivery ratio, energy consumption, and trust validation delay as key metrics. MP-HNEM results show a 42.7% reduction in latency, a 73% increase in throughput, and a 39% reduction in energy consumption compared to baseline schemes. The packet delivery ratio increases by 31.5%, indicating improved reliability across all handoff events. Security analysis shows detection accuracy over 90% and block validation success rates over 98% under mobility conditions. Using ANOVA, we conducted Statistical validation and achieved p < 0.05, confirming the impact and significance of these improvements. The major contributions of this research work are: (i) a blockchain-integrated ARS developed to secure multihoming mobility, (ii) a reinforcement learning-based predictive handoff mechanism for smart support, and (iii) a hybrid validation framework that combines both simulation and emulation procedures. The results indicate that MP-HNEM is a scalable, energy-efficient, and secure mobility solution for NDN-based IoT systems, suitable for applications such as smart healthcare and industrial IoT. Future work intends to focus on real-world deployment and heterogeneous IoT integration.
H127515080726
Thalassemia is a hereditary condition that affects haemoglobin formation, leading to ineffective erythropoiesis, anaemia, and iron overload, resulting in serious complications and reduced quality of life. While modern medical care, such as blood transfusions and iron chelation, has considerably extended patients’ lifespan, prolonged therapy has been accompanied by various side effects, financial burden, and low compliance. In addition, due to the complex pathogenesis of thalassemia, which includes oxidative stress, inflammation, disrupted iron homeostasis, and cellular damage, there is a need to develop new methods that can address all these processes simultaneously. This fact has led researchers to show great interest in plant-based remedies containing naturally active biological substances. In this study, we explored the therapeutic efficacy of the plant Phyllanthus niruri by investigating its association with target proteins and biological pathways related to iron homeostasis in thalassemic patients, employing a comprehensive bioinformatics approach. Active compounds found in Phyllanthus niruri were recognized and filtered. Then, target identification, along with disease-gene associations, was performed for common diseases and compounds. For biological insights, targets associated with thalassemia and phytoconstituents were analysed employing network pharmacology, protein-protein interaction networks, Gene Ontology enrichment, and KEGG pathway analyses. In vitro molecular docking experiments to predict interactions between phytoconstituents and their targets were performed, and ADMET analysis was conducted to assess pharmacokinetics and drug likeness. Among the identified hub genes were those associated with iron metabolism, oxidative stress, inflammation, and erythrocyte formation. The pathways enriched in the functional enrichment analysis included those associated with iron metabolism, cytokine signalling, apoptosis, and cellular responses to stress. The molecular docking study revealed favourable interactions between the phytoconstituents, particularly corilagin, geraniin, and quercetin, and the target proteins. ADMET predictions indicated favourable pharmacokinetics for some compounds. All in all, the results demonstrate the multitarget potential of Phyllanthus niruri as an important source of bioactive substances that can address complications of thalassemia, especially in cases of iron overload. The study lays an important foundation for the subsequent experimental verification of the results.
F127415070626
Investigation and prediction of defects in software is one of the important solutions to ensure software quality and reliability. Machine learning algorithms are used across a wide array of fields to solve real-world problems by building large, complex models. Many researchers have made significant contributions by developing predictive models for software defects using statistical and machine-learning approaches. But only a few frameworks have discussed the issue of building a universal software defect prediction model. Most existing models have been trained on limited datasets, which results in good performance on the training data but poor performance on unseen data. These limitations have motivated researchers to explore and develop more generalised and universal models for software defect prediction. Moreover, the growing complexity of contemporary software systems. Such limitations have encouraged researchers to investigate and build more generalised and universal models for software defect prediction. In addition, the increasing complexity of modern software systems and the rapid growth of software repositories have driven a demand for intelligent prediction techniques capable of handling heterogeneous data. Research is being conducted to investigate advanced machine learning and deep learning methods, including ensemble learning and transfer learning, to enhance prediction accuracy and adaptability across different software projects. These approaches aim to reduce development and maintenance costs and increase the overall reliability and performance of software products.
E127115060526
Industrial process control has relied on proprietary DCS for over five decades. The model worked. But working and working well are different things, and in 2026, the gap between what proprietary DCS delivers and what operators in manufacturing, oil and gas, chemical processing, and water treatment need has grown wide enough to drive real change. Vendor lock-in, hardware obsolescence that stretches across decades, and maintenance contracts that give a single supplier complete leverage over upgrade decisions are no longer tolerable when the alternative, open and software-defined control, has been proven at an industrial scale. The Open Process Automation Standard (O-PAS), the IEC 61499 function block model, and OPC UA connectivity together provide the technical foundation for software-defined control systems (SDCS) that break these dependencies. Documented lifecycle cost savings reach approximately 52% over twenty-five years when compared to equivalent proprietary DCS platforms [4]. This paper reviews the standards, examines real deployments, and confronts the barriers that still slow adoption, particularly the near-complete absence of IT-domain competencies in OT workforces. An original Software Defined Automation Risk Mapping Model (SD-ARMM) is proposed, providing practitioners with a five-dimensional, risk driven tool to determine which migration strategy fits their specific organisational and operational reality.
