Charith Perera

The construction industry faces significant challenges regarding material waste and sustainable practices, necessitating innovative solutions that integrate automation, traceability, and decentralised decision-making to enable efficient material reuse. This paper presents a blockchain-enabled digital marketplace for sustainable construction material reuse, ensuring transparency and traceability using InterPlanetary File System (IPFS). The proposed framework enhances trust and accountability in material exchange, addressing key challenges in industrial automation and circular supply chains. A framework has been developed to demonstrate the operational processes of the marketplace, illustrating its practical application and effectiveness. Our contributions show how the marketplace can facilitate the efficient and trustworthy exchange of reusable materials, representing a substantial step towards more sustainable construction practices. The evaluation is conducted on a prototype implementation under controlled conditions to analyse system behaviour and scalability trends, providing initial validation of the proposed approach.

Artificial intelligence techniques have made notable progress in supporting learning processes, with increasing adoption across educational contexts. However, despite the increasing work on AI-assisted techniques, explicit and implicit learning mechanisms in AI educational assistants have not been systematically categorised. The study of how these techniques aid in and are implemented for learning remains underexplored. Therefore, a more systematic categorisation of how these techniques support learning through user interaction is needed. This paper presents a systematic review of 38 studies published between 2000 and 2024, spanning domains including programming education, cognitive skills, language learning, and the AI field. This review was conducted and reported in accordance with the PRISMA 2020 guidelines. In this review, we propose a taxonomy of explicit and implicit learning features. We analyse implementation aspects (e.g., knowledge representation, algorithms, and interaction modalities) and synthesise how prior work evaluates learning support capabilities. The findings show that (i) 79% of reviewed studies support explicit and 21% supported implicit learning through interaction; (ii) written interaction dominates (45%), followed by visualisation (34%), while voice-based interaction remains underrepresented (9%); (iii) some implementations lack details (e.g., knowledge bases and validation methods); and (iv) evaluation practices remain uneven, with most studies relying on experiment evaluation, highlighting the need for robust evaluation practices.

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge.

Human-Building Interaction (HBI) is an emerging field that enhances building design, construction, and operation by facilitating interactions between occupants and buildings. HBI supports managers and occupants in achieving energy efficiency, sustainability, and improved livability, driving the evolution of smart buildings. The Internet of Things (IoT) integrates diverse building systems into networks of connected devices, generating data that informs adaptive responses to occupant needs and promotes sustainable operations. This survey reviews the role of IoT sensors in HBI, emphasizing their potential to improve communication between buildings and occupants to achieve sustainability objectives. It examines how sensors can be used to generate actionable insights, helping stakeholders meet sustainability goals. To provide a focused analysis, this review is constrained to the two most prominent sustainability objectives identified in global standards: energy efficiency and health and well-being. We identify key factors, sensor types, and benefits shaping sustainable environments. Furthermore, we describe HBI advancements supporting sustainability, alongside challenges regarding IoT integration, occupant engagement, and system constraints. Finally, this review situates IoT sensors within HBI, linking human-building engagement and sustainability goals to provide a comprehensive understanding of their role in shaping smart, adaptive, and sustainable buildings.

While large language models (LLMs) have captured global attention for their linguistic abilities, our work harnesses their power to overcome traditional barriers in querying Linked Data (LD) and Resource Description Framework (RDF) triplestores. This paper presents an innovative framework that integrates LLMs into conversational user interfaces (UIs), enabling the dynamic generation of precise SPARQL queries without the necessity for constant retraining. Most conversational UI models struggle with adaptability as they require frequent retraining whenever datasets are updated or expanded. This limitation impedes their effectiveness as general-purpose extraction tools. To address this challenge, our approach ¹ seamlessly incorporates LLMs into the conversational UI process, fostering a more sophisticated understanding and interpretation of user queries and enhancing overall responsiveness. By leveraging the advanced natural language processing capabilities of LLMs, our method improves RDF entity extraction in web systems that utilise conventional chatbots. Furthermore, it extends the functionality of these chatbots, allowing them to respond directly to queries based on the RDF schema while providing an assistive interface that deepens understanding of the dataset and its underlying domain. This facilitates the extraction of more meaningful information. By adopting this approach, interactions become more refined and context-sensitive, a crucial advancement for managing the intricate query structures commonly found in RDF datasets and Linked Open Data (LOD) endpoints. We have evaluated our approach in practical settings by assessing the tool’s ability to address complex queries and to answer general ecological queries, with the outputs evaluated by human experts. The results demonstrate a notable improvement in system expressiveness and response accuracy, showcasing the potential of LLMs to transform information retrieval. The findings not only confirm their adaptability in enhancing existing systems but also open up exciting possibilities for their deployment in specialised web information domains, paving the way for future research in this evolving field.

Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively.

Sustainable forest management (SFM) is essential for preserving biodiversity, maintaining ecosystem services, and mitigating climate change. This systematic review synthesizes global trends and innovations in SFM practices, analyzing peer-reviewed literature from 2015 to 2025 to identify effective strategies and emerging technologies. The review examines a diverse range of approaches, including forest health index, forest health sensing techniques, emphasizing remote sensing, ground-based monitoring, and the application of machine learning (ML) and artificial intelligence (AI). Moreover, the review highlights SFM practices, including ecosystem-based approaches, community and indigenous involvement, carbon sequestration strategies, and local and global policy frameworks. By integrating technological advancements with policy-driven initiatives, this study provides a comprehensive understanding of current trends and innovations in forest management, offering valuable insights for researchers, policymakers, and practitioners.