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Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with <t>Grafana</t> and Alertmanager for <t>visualisation</t> and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate <t>data</t> and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.
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Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with <t>Grafana</t> and Alertmanager for <t>visualisation</t> and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate <t>data</t> and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.
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RT-qPCR validation of <t>RNA-sequencing.</t> Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.
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Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

Journal: Sensors (Basel, Switzerland)

Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

doi: 10.3390/s25196248

Figure Lengend Snippet: Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

Techniques: Derivative Assay, Control

Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

Journal: Sensors (Basel, Switzerland)

Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

doi: 10.3390/s25196248

Figure Lengend Snippet: Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

Techniques: Isolation

RT-qPCR validation of RNA-sequencing. Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.

Journal: bioRxiv

Article Title: Adult organotypic brain slice cultures recapitulate extracellular matrix remodelling in haemorrhagic stroke

doi: 10.1101/2025.07.15.665021

Figure Lengend Snippet: RT-qPCR validation of RNA-sequencing. Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.

Article Snippet: All visualisation of RNA- sequencing data was performed using GraphPad Prism 10.3.

Techniques: Quantitative RT-PCR, Biomarker Discovery, RNA Sequencing, Expressing