Guidelines & Submission

Track Description:

This track invites original research contributions on Artificial Intelligence methods for diagnostic tasks performed in complex systems. Diagnostics is defined as the automated detection, identification, classification, localization, and prediction of anomalous conditions, faults, pathological states, or security threats from heterogeneous data sources.
The track emphasizes practical and implementable approaches that integrate machine learning and deep learning techniques with signal processing, sensing technologies, and embedded computing.
Contributions addressing multimodal data (images, signals, sensor streams, logs, and behavioral data) and operating in real-time or near-real-time environments are particularly encouraged.
Topics include data-driven, hybrid, and physics-based models designed to operate under conditions of uncertainty, limited supervision, noisy measurements, and domain shifts.
Applications of interest include, but are not limited to, industrial predictive maintenance, healthcare and biomedical analytics, sports performance assessment and athlete monitoring, assistive technologies, monitoring of autonomous and unmanned systems, intelligent infrastructure, and cybersecurity diagnostics.
Contributions focusing on edge computing, hardware-based artificial intelligence, reliability, explainability, and operational validation are welcome.
The goal of the program is to promote reproducible methodologies and engineering solutions that can support decision-making, improve safety, and enable proactive management strategies in engineering and science.

Track Topics:

• Fault Detection, Anomaly Detection, and Condition Monitoring
• Predictive Maintenance and Prognostics
• Medical and Biomedical Diagnostic Analytics
• Sports Performance Diagnostics and Athlete Monitoring
• Sensor-Based and Multimodal Data Diagnostics
• Edge Artificial Intelligence and Real-Time Diagnostic Systems
• Visual Perception and Biometric Recognition for Diagnostics
• Monitoring of Autonomous and Cyber-Physical Systems
• Explainable and Reliable Diagnostic Artificial Intelligence
• Dataset Creation, Validation, Benchmarking, and Deployment of AI Diagnostic Solutions

Track Description:

Digital Manufacturing encompasses the methods, tools, and technologies that enable the digital integration of design, manufacturing, inspection, and lifecycle activities within modern industrial systems. While a broad range of digital technologies contributes to this vision, CAD models increasingly play a pivotal role as structured, authoritative sources of product and process information across the digital thread. This track focuses on digital manufacturing approaches in which CAD models act as key enablers—either as central information carriers or as integrated components within larger data-driven and intelligent manufacturing ecosystems. Contributions are encouraged that explore how CAD models, when enriched with semantic, manufacturing, or quality-related information, support Model-Based Definition (MBD), interoperability, automation, and decision-making, while remaining connected to other digital manufacturing technologies. Relevant topics include the interaction between CAD models and CAx tools, digital twins, cyber-physical systems, Artificial Intelligence, and data analytics, as well as the role of standards and lightweight representations in ensuring scalable and flexible information exchange. The track welcomes both methodological contributions and industrial case studies that demonstrate how CAD-enabled digital manufacturing supports intelligent, adaptive, and reconfigurable production systems in real-world contexts.

Track Topics:

• Digital manufacturing architectures and digital thread concepts
• CAD models as enablers within digital manufacturing ecosystems
• Model-Based Definition (MBD) and Model-Based Enterprise (MBE)
• Semantic enrichment and lifecycle use of CAD models
• Integration of CAD data with AI, analytics, and decision-support systems
• Digital twins and cyber-physical systems for manufacturing
• Interoperable data formats and standards for digital manufacturing
• Plug-and-Produce and reconfigurable manufacturing systems
• Digital manufacturing for inspection, assembly, and process planning
• Industrial applications and case studies in CAD-enabled digital manufacturing

Track Description:

Recent breakthroughs in deep learning have significantly accelerated progress in computer vision, enabling models to achieve expert-level performance across various domains, including cybersecurity, healthcare, autonomous systems, and environmental monitoring. This track focuses on advancements related to the accuracy, efficiency, and explainability of visual recognition systems. With the growing deployment of computer vision models in high-stakes applications such as cybersecurity and medical imaging, transparency and reliability have become essential. As a result, the field is moving toward architectures that combine powerful feature extraction with robust explainability frameworks to ensure trustworthy decision-making.
The track welcomes contributions exploring novel neural architectures, such as Vision Transformers, hybrid CNN-Transformer models, and multimodal systems, as well as innovative training strategies, self-supervised learning paradigms, and domain adaptation methods. Emphasis is placed on explainable AI (XAI) techniques, including visualization tools, attribution methods, and model auditing strategies that help reveal the internal reasoning of modern vision systems. Additionally, the track encourages work addressing fairness, robustness, and real-world applicability, particularly in sensitive fields like medical diagnostics. By highlighting these advances, the track aims to foster interdisciplinary collaboration and support the development of computer vision systems that are not only high-performing but also interpretable, reliable, and ethically deployable.

Track Topics:

• Vision Transformers (ViT), hybrid architectures, and advanced attention mechanisms
• Explainability and interpretability methods (Grad-CAM, Attention, Rollout, CLS-based attribution, etc.)
• Deep learning for medical imaging, including ocular and non-ocular disease diagnosis
• Self-supervised, weakly supervised, and few-shot learning in vision tasks
• Domain adaptation, generalization, and transfer learning across imaging modalities
• Robustness, fairness, and bias mitigation in computer vision systems
• Multimodal and cross-modal learning (image–text, image–signal, etc.)
• Large-scale visual representation learning and foundation models
• Real-time and efficient inference methods for edge and resource-limited environments
• Benchmarking, evaluation protocols, and visual model auditing tools

Track Description:

Management of Cybersecurity and Trust is essential in any IT based architecture, application and system. Cybersecurity has to be engineered at design phase and has to be treated as a continuous and holistic process. This track welcomes submissions presenting original results on applied cybersecurity techniques, protocols and applications, especially if presented with their application and impact to real systems, software and architectures. Submissions addressing but not limited to the following topics are welcome.

Track Topics:

• Authorization and authentication mechanisms
• Trust management protocols and applications
• Innovative and Post-Quantum ready cryptography
• Trustworthy and Ethical assessment techniques also with their application to AI
• Secure Software Development (DevSecOps) and Vulnerability Identification
• Secure Communication Protocols
• Resilient System Design
• Anomaly Detection and Intrusion Detection
• Malware Dissection and Analysis

Track Description:

This track invites original research contributions on cognitive robotics, focusing on how autonomous robots can perceive, learn, reason, and interact as intelligent agents in complex environments. Cognitive robotics is defined as the design and implementation of models, architectures, and algorithms that enable robots to acquire, represent, and use knowledge for decision-making, adaptive behaviour, social interaction, and collaborative problem-solving, supporting learning processes analogous to human cognition.
The track emphasises, but is not limited to, approaches that integrate learning, perception, reasoning, and action, including developmental and lifelong learning, cognitive and metacognitive architectures, multimodal grounding of language and perception, reinforcement learning, learning from demonstration, and social cognition. Contributions addressing embodied cognition, cooperative behaviour through language, cognitive vision, and novel paradigms or hybrid systems are particularly encouraged. The goal of the track is to promote reproducible, cognitively grounded methodologies and engineering solutions that advance both the scientific understanding of robotic cognition and its practical deployment in real-world systems. Contributions focusing on explainability, trust, ethical and socially aware behaviour, and operational validation are welcome.

Track Topics:

• Learning, memory, attention, and control in robots
• Multimodal grounding of language and perception
• Embodied, developmental, and lifelong learning
• Cognitive and metacognitive architectures for autonomous robots
• Reinforcement learning for cognitive control and reasoning
• Learning from demonstration
• Cognitive vision and applications in robotics
• Cooperative behaviour through language
• Social cognition, trust, and Theory of Mind
• New paradigms and systems for robotics

Track Description:

This session examines research at the crossroads of dynamical-systems theory, control engineering, computational reasoning, and sensory processing, with control framed as the unifying principle connecting models of biological and engineered systems across scales. We welcome work that advances theoretical foundations, such as stability, observability, and model-based control, as well as practical innovations in learning-enabled and adaptive controllers, neuro-inspired architectures for perception and decision-making, and sensorimotor integration. Contributions that develop methods for quantifying uncertainty, resilience, and collective behavior are especially encouraged.
We seek submissions that combine rigorous analytical models, data-driven inference, simulation, and experimental validation, and that highlight control principles transferable across scales. Presenters are encouraged to include reproducible evaluation frameworks and realistic deployment scenarios. Relevant topics (but not limited to) include physiological cybernetics and in-host modeling, population dynamics, adaptive control, navigation and guidance systems, and applications of artificial intelligence to biological systems. This program is aimed at researchers and practitioners pursuing rigorous, cross-disciplinary approaches to complex adaptive systems. Panel discussions will surface open challenges, efforts toward standardization, and opportunities for collaboration among theoretical researchers, experimentalists, and industry practitioners worldwide.

Track Topics:

• Physiological cybernetics and in-host modeling
• Population dynamics and multi-agent biological systems
• Navigation, guidance, and autonomous control systems
• Applications of artificial intelligence to biological and bio-inspired systems
• Dynamical-systems modeling of biological and engineered processes
• Stability, observability, and model-based control methods
• Learning-enabled and adaptive control strategies
• Neuro-inspired architectures for perception and decision-making
• Sensorimotor integration and closed-loop behavior
• Methods for quantifying uncertainty, resilience, and collective dynamics

Track Description:

Agriculture is undergoing a rapid digital transformation toward more intelligent, efficient, and sustainable production systems. Emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, robotics, and data analytics are reshaping how crops, resources, and agro-ecosystems are monitored and managed.
This special track invites original research contributions, case studies, and applied works addressing the design, development, and deployment of digital and smart technologies in agricultural and agri-food systems. The track aims to showcase innovative solutions that improve productivity, resource-use efficiency, environmental sustainability, and decision-making processes across agricultural contexts. Topics of interest include, but are not limited to, intelligent sensing systems, predictive modeling, precision agriculture, robotics and automation, remote sensing integration, climate-smart farming, and digital tools for supply chain traceability. Contributions bridging technological innovation, real-world implementation, and sustainability challenges are particularly encouraged. This track seeks to foster interdisciplinary dialogue among researchers, engineers, data scientists, and practitioners working on digital technologies for the future of agriculture.

Track Topics:

• IoT-based sensing and monitoring systems for agriculture
• AI and machine learning for crop modeling, disease detection, and yield prediction
• Precision agriculture and site-specific management technologies
• Agricultural robotics and autonomous systems
• Remote sensing, UAVs, and data fusion approaches
• Decision Support Systems (DSS) and digital platforms for farm management
• Smart irrigation, water management, and resource optimization
• Climate-smart and sustainability-driven digital solutions
• Digital technologies for agri-food supply chains and traceability
• Big data analytics and predictive agriculture
• Sensor networks and environmental monitoring
• Digital twins and simulation models for agro-ecosystems

Track Description:

The AECO industry is undergoing a steady digital transformation, increasingly integrating Artificial Intelligence (AI) to foster a proactive and resilient digital ecosystem. This special track invites original research, case studies, and applied works that explore this evolution. We aim to showcase innovative solutions focused on productivity enhancement and risk mitigation. Key topics of interest include, but are not limited to, Generative, Agentic, and Physical AI across design and construction stages, as well as their implementation in the operations of buildings, infrastructure, and smart grids. This track seeks to foster interdisciplinary dialogue among researchers, engineers, data scientists, and practitioners shaping the future of AI in the built environment.

Track Topics:

• Built Environment & Generative AI
• Built Environment & Agentic AI
• Construction Site Management & Robotics
• Construction Management and IoT-Based Solutions
• AI-Supported Tendering, Bidding & Awarding Management
• AI-Supported Project Management
• AI-Supported Health & Safety Management
• AI-Supported Digital Twinning
• AI Safety in the AECO Industry
• World Modelling & AECO Industry
• AI-Supported Automated Code Compliance

Recent advancements in scaling large language models and complex neural architectures have yielded unprecedented AI capabilities, yet these systems largely remain opaque. This track focuses on the emerging field of mechanistic interpretability, which seeks to reverse-engineer neural networks to deeply understand their internal states, algorithms, and representations. As models are increasingly deployed in high-stakes domains, decoding their internal mechanisms, such as implicit planning, internal world models, and reasoning processes, has become essential to ensure trustworthy decision-making. The track welcomes research contributions exploring methodological approaches to circuit discovery, representation geometry, causal analysis, model editing, and unsupervised feature extraction, including dictionary learning and sparse autoencoders. Of particular interest is the geometric structure of neural representations, encompassing polysemanticity and monosemanticity, the superposition hypothesis, and the linear or curved structure of embedding spaces, which have become central research threads in the field. The track also explicitly welcomes interpretability-guided model intervention work, where mechanistic understanding is leveraged not only to explain model behaviour but to actively modify it, including activation steering, circuit-level editing, targeted unlearning, and alignment-oriented interventions for safety and ethics. Attention is placed on the practical application of these tools for auditing safety-relevant behaviours, such as biases, hidden objectives, deception, or jailbreak mechanisms, as well as debugging logical errors and enabling inference-time monitoring. Interesting topics may also include the interpretability of reasoning models and the faithfulness of chain-of-thought processes. The track additionally welcomes emerging work on the interpretability of agentic and tool-augmented systems, investigating how mechanisms for planning, tool selection, memory management, and multi-step decision-making are internally formed and represented in models operating in interactive or multi-turn environments. The track strongly encourages rigorous empirical studies that critically assess the reliability and validity of interpretability methods themselves, including works that investigate whether existing tools are causally faithful, expose failure modes, or demonstrate so-called interpretability illusions where apparent explanations do not reflect true model computation, as well as well-documented negative results, replications, and the development of open-source interpretability tools and datasets. By highlighting this research, the track aims to promote reproducible, science-driven methodologies that transform opaque AI systems into transparent, reliable, and ethically deployable technologies.

Track Topics:

• Circuit discovery and validation (e.g., transformer circuits, pathway decomposition)
• Causal interventions and counterfactual analysis (activation/weight patching, ablations, causal scrubbing)
• Attribution methods and mechanistic explanations (attribution graphs, path-based analyses)
• Unsupervised feature discovery and dictionary learning (sparse autoencoders, monosemantic features, feature dashboards)
• Interpreting reasoning and chain-of-thought faithfulness (when “thoughts” reflect computation vs rationalisation)
• Safety-relevant auditing (hidden goals, deception/alignment faking, jailbreak mechanisms, bias/hallucinations)
• Monitoring and inference-time interventions (probes, anomaly detection in activations, steerability and guardrails grounded in internals)
• Benchmarks and evaluations for interpretability (objective tasks that require internal understanding; sanity checks and baselines)
• Scaling and generalisation of interpretability methods (from small models to frontier; what breaks and what transfers)
• Developmental interpretability (how circuits/features form during training; phase changes, grokking-like dynamics)
• Multimodal and non-transformer mechanistic interpretability (VLMs, diffusion/state-space models, GNNs)
• Automation of interpretability workflows (LLM-assisted hypothesis generation, automated circuit finding, interpretability agents)
• Open-source tools, datasets, and model releases enabling reproducible mech interp research (software, benchmarks, model organisms)
• Rigorous replications, negative results, and methodological critiques (failure modes, measurement validity, reproducibility)

The global energy landscape is undergoing a fundamental paradigm shift from historically centralized structures toward decentralized, community-driven systems. Smart grids and Energy Communities (ECs) have emerged as pivotal instruments to promote local generation and shared consumption, essential for meeting decarbonization targets. However, managing the technical complexities of distributed renewable resources, such as the intermittency of solar and wind, requires advanced technological frameworks to ensure efficiency and grid stability.

This track explores the synergistic application of Artificial Intelligence (AI) and information management to optimize these local energy ecosystems. AI methodologies, including machine learning and reinforcement learning, are revolutionizing resource forecasting, enabling predictive maintenance for distributed assets, and optimizing Battery Energy Storage Systems (BESS). Furthermore, the integration of technologies like Blockchain and IoT supports sophisticated Peer-to-Peer (P2P) trading platforms and demand-side management, effectively empowering prosumers. We invite contributions addressing technical design, mathematical modeling, and innovative business frameworks. By bridging cutting-edge AI with collective energy actions, this track aims to define the roadmap for a resilient and sustainable energy future.

Track Topics:

• AI-based resource assessment, demand-side management and energy forecasting
• Optimization algorithms for Peer-to-Peer (P2P) energy sharing and local markets
• Design and management of Battery Energy Storage Systems (BESS)
• AI for autonomous microgrid management and grid stability
• Innovative business models for energy systems and Energy Communities
• Blockchain and IoT applications for secure data management in smart grids
• Electric Vehicle (EV) integration (V2G/G2V) and smart charging within energy communities
• Legal, social, and privacy frameworks for AI deployment in the energy sector
• The interplay between national legislation and technical constraints in the design and scaling of Energy Communities
• Breaking legal and bureaucratic barriers: addressing policy hurdles for AI adoption and decentralized energy markets

The rapid diffusion of wearable devices and portable sensing technologies is enabling the continuous monitoring of environmental conditions, physiological parameters, and human-environment interactions. These technologies create large amounts of data that can be used to better understand human wellbeing, comfort, safety, and productivity in indoor and outdoor environments. At the same time, it is increasingly recognized that environmental perception and comfort conditions are highly subjective and vary significantly across individuals, highlighting the need for personalized and adaptive solutions.

Wearable systems can support several application domains, including Indoor Environmental Quality monitoring, smart HVAC control, Personal Environmental Control Systems development, occupational health and safety, and productivity analysis in workplaces. The integration of wearable sensors with environmental monitoring systems allows to develop human-centred environments and improve building operation and workplace conditions. Research challenges remain in data integration and interpretation, sensor reliability and data quality, real-time decision support, and the development of predictive models linking environmental conditions, physiological responses, and human performance.

This track invites original research papers, review papers, experimental and case studies focusing on wearable systems, smart sensing, data analytics, and intelligent solutions for improving human wellbeing, safety, and productivity.

Track Topics:

• Wearable sensors for physiological and environmental monitoring
• Long-term monitoring and field studies using wearable devices
• Integration of wearable devices with environmental monitoring systems
• Wearables for smart HVAC control and building operation
• Occupational health and safety monitoring using wearable devices
• Wearable technologies for outdoor risk assessment
• Monitoring of human productivity and performance
• Data analysis and interpretation of wearable and environmental data
• Predictive models linking environmental conditions, physiology, and performance
• Human-centric and adaptive environments

The transition to sustainability is becoming urgent, with the Circular Economy recognised as a key pathway to achieve long-term economic, environmental, and social goals. Emerging and smart technologies — such as Artificial Intelligence, Blockchain, IoT, and Digital Product Passports — are implemented across industries to enable traceability, improve resource efficiency, and support data-driven decision-making.

These technologies are reshaping business processes and supply chain configurations, opening new opportunities for sustainable innovation within an Industry 5.0 perspective. However, despite their diffusion, organisations still struggle to translate technological adoption into measurable, reliable, and scalable sustainability outcomes. Challenges related to data governance and interoperability and the lack of standardised metrics and reporting frameworks increase the risk of digitally enabled greenwashing and weaken accountability.

This track invites contributions that investigate, qualitatively or quantitatively, the adoption and impact of digital technologies in enabling measurable and trustworthy sustainability performance. We welcome research on technology diffusion, digitally enabled practices, and their implications for firm sustainability and competitiveness, as well as studies on metrics and reporting. Contributions addressing Circular Economy, supply chains, and sustainable business models — from design to implementation and scaling — are especially encouraged.

Track Topics:

• Artificial Intelligence and Machine Learning for sustainability analytics, reporting, and greenwashing detection
• Blockchain-based systems for transparency, auditability, and trust in sustainable supply chains
• Digital Product Passport architectures and applications for scalable traceability and accountability
• IoT and data platforms for real-time monitoring and optimisation of resource use and environmental performance
• Data governance and interoperability for sustainability data across organisations and supply chains
• Digital innovation for sustainable and circular value creation within the Industry 5.0 and Twin Transition frameworks
• Diffusion and adoption of emerging technologies and their implications for firm sustainability, competitiveness, and value creation
• Managerial, organisational, and supply chain implications of emerging digital technologies
• Digital technologies for circular economy, waste management, Deposit-Refund Systems, and the scaling of sustainable business models
• Metrics, methodologies, and reporting frameworks for assessing environmental, social, and economic sustainability impacts

Healthcare systems are undergoing a profound digital transformation, driven by the growing adoption of advanced technologies that enable new ways of designing, managing, and improving organisational processes and healthcare services. Data generated by hospital information systems, connected medical devices, digital platforms, and interoperable infrastructures are becoming critical assets for supporting more timely, evidence-based, and adaptive decision-making.

In this context, digital technologies such as telemedicine, IoT-enabled monitoring, digital platforms, process mining, analytics, automation, and artificial intelligence offer significant opportunities to improve the delivery, coordination, and management of care. They support more effective service delivery, continuous monitoring, data-driven decision-making, and more efficient use of resources, while also contributing to better quality of care, patient safety, and operational performance. More broadly, these technologies enable service innovation, stronger integration across care settings, and more proactive and adaptive healthcare management approaches.

This track aims to explore how digital technologies, process analytics and AI can support the transformation of healthcare processes from managerial, organisational, and technological perspectives. We welcome contributions addressing the adoption, integration, and impact of digital solutions in complex healthcare environments, with particular attention to approaches that connect data, processes, technologies, and organisational change to support operational performance, organisational effectiveness, and strategic decision-making.

Track Topics:

• Health service redesign through digital technologies
• Data-driven decision support in healthcare
• Process mining in healthcare
• Predictive analytics and AI for healthcare operations
• Patient safety and quality improvement through digital tools
• Telemedicine
• Process automation and workflow optimisation in healthcare
• Digital twins and simulation in healthcare
• Organisational impact of digital transformation in healthcare
• Digital platforms in healthcare
• Implementation challenges of digital health technologies

Many real-world decisions — such as supplier selection, technology adoption, and public procurement — are inherently multidimensional and cannot be fully captured through traditional financial metrics (e.g., NPV or IRR). As a result, decision processes involve heterogeneous, qualitative, and context-dependent elements that are difficult to formalize and analyse, characterized by limited, fragmented, or non-standardized data, making decision-making difficult to structure and support effectively.

This track explores the role of Artificial Intelligence (AI) in structuring and supporting multidimensional decision-making through digitalization across public and private organizations, enhancing rather than replacing human judgment by transforming fragmented, case-specific evaluation practices into more consistent, evidence-based, and transferable decision-support structures. It welcomes contributions on AI-driven approaches for both predictive decision-making (anticipating outcomes and optimizing choices) and retrospective analysis (understanding how decisions were made and which factors most significantly shaped final outcomes). Emphasis is placed on methods that compare AI-generated outputs with human decisions, highlighting alignments and divergences to support reflexive evaluation and improve robustness.

Applications include procurement and tendering, supply chain management, strategic planning, and technology evaluation. The track encourages contributions supporting both decision-makers and evaluated actors, framing AI not only as a tool for evaluating alternatives but also for making decision logics more explicit, enabling actors to better understand, anticipate, and respond to multidimensional decision processes. The goal is to advance AI-enabled decision intelligence systems that enhance consistency, traceability, and accountability, while complementing human judgment in socio-technical environments.

Track Topics:

• AI for multi-criteria decision-making under uncertainty
• Explainable AI for decision transparency, traceability, and auditability
• Predictive analytics for strategic and operational decision support
• AI-enabled structuring of heterogeneous data for evidence-based decision-making
• Retrospective analysis of past decisions and the key factors shaping them using machine learning
• AI in supplier selection, vendor evaluation, and procurement systems
• Decision intelligence in public procurement and tendering processes
• Natural language processing for analyzing calls, bids, and technical documents
• Hybrid qualitative-quantitative decision models beyond financial metrics
• AI tools supporting bidders and suppliers in competitive selection processes
• Data-driven decision-making in complex organizational environments

Achieving sustainability in the built environment poses a challenge that spans multiple levels of analysis, from individual buildings to the complex dynamics of entire urban systems. Managing the numerous interacting parameters related to environmental, economic, and social dimensions across these levels is difficult without advanced digital integration solutions. Accordingly, this track investigates the role of digital technologies in leading and supporting the shift towards a more sustainable construction sector. Specifically, it aims to gather workflows, case studies, and methodological contributions involving specific digital tools for sustainability. Key areas of interest include, but are not limited to, the use of digital tools to support urban and building design phases, the assessment of life cycle impacts, and tools for urban-scale simulations testing phenomena such as urban heat islands. This track aims to foster interdisciplinary dialogue among researchers, engineers, data scientists, and practitioners working on digital technologies for building sustainability.

Track Topics:

• BIM interoperability for Life Cycle Assessment and Costing
• Urban-scale simulation and microclimate modelling
• Design tools enabling circularity evaluation
• Building and urban digital twins
• Digital Building Notebook implementation
• Digital Product Passports and material traceability across the construction supply chain
• GIS for mapping urban resource flows
• Digital tools supporting Material Flow Analysis at the urban and building level
• Parametric design for building and urban energy/climate evaluations
• Digital tools fostering communication across the actors of the construction supply chain
• Digital tools for sustainable urban redevelopment and building renovation