Special Sessions

This special session explores the transformative role of Community Energy Systems (CES) in addressing some of the most urgent societal challenges of our time, including decarbonization, energy justice, and grid resilience. With the accelerating shift toward decentralized, participatory energy models, Energy Communities (ECs) have emerged as vital frameworks for empowering citizens, promoting collective self-consumption, and integrating distributed renewable energy sources into the wider energy system.

To fully unlock the potential of CES, significant advancements are needed at the intersection of technological innovation, system optimization, and human-centered design. This includes the development of intelligent control architectures, data-driven decision-making frameworks, and equitable operational strategies that reflect both technical constraints and social values.

This session invites contributions that investigate how emerging technologies, optimization techniques, and socio-technical approaches can work in synergy to create smart, inclusive, and sustainable energy-sharing systems. Interdisciplinary work and applications across urban, peri-urban, and rural contexts are particularly welcome.

This special session explores the data-driven transformation of cities aimed at creating urban environments that are healthier, safer, more inclusive, and environmentally responsible. The growing availability of urban data—ranging from mobility traces and sensor networks to administrative records and citizen-generated inputs—enables advanced analytics and decision-support systems capable of improving quality of life (QoL) while promoting sustainability, resilience, inclusiveness, and trust in city governance.

However, the promise of data-driven urbanism relies on robust analytical methods, responsible digital technologies, and viable governance and financing models that align technological innovation with societal needs and long-term stewardship. Addressing these challenges remains a key concern for both research and real-world implementation.

This session invites contributions demonstrating how computing, electrical, and industrial systems—combined with urban policy and management perspectives—can transform smart city functions such as mobility, energy, environment, public services, finance, and safety into measurable improvements in residents' quality of life. Empirical studies, methodological advances, prototypes, and case analyses across European and global contexts are particularly welcome.

The rapid evolution of Artificial Intelligence (AI) presents unprecedented opportunities to address global sustainability challenges, particularly in alignment with the United Nations Sustainable Development Goals (SDGs). This special session focuses on the integration of AI technologies within Computer Science and Engineering (CSE) to foster sustainable development. As environmental, social, and economic pressures mount, AI-driven innovations in areas such as energy-efficient computing, smart resource management, and ethical data systems can significantly contribute to goals such as SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 12 (Responsible Consumption and Production).

The session will explore cutting-edge research and applications, including AI for environmental monitoring, predictive analytics in agriculture and healthcare, green computing algorithms to minimize carbon footprints, sustainable IoT ecosystems, and bias-mitigated AI for equitable growth. Emphasis will be placed on challenges such as algorithmic energy consumption, ethical AI deployment in resource-limited settings, and interdisciplinary approaches to ensure that AI contributes positively without exacerbating inequalities or environmental harm.

The transition toward Industry 5.0 necessitates the integration of advanced technological frameworks and methodological approaches to enable proactive and adaptive optimization of resources within interconnected manufacturing ecosystems. Contemporary manufacturing paradigms increasingly incorporate intelligent system architectures, data-driven digital manufacturing processes, rule-based automated control systems, adaptive process planning and scheduling mechanisms, physically-grounded and cognitively-informed task execution, and dynamic decision-making strategies.

Despite these advances, many existing approaches remain predominantly user-centric, with limited integration between physical and cognitive dimensions of collaborative systems. In these evolving systems, in fact, the embodied intelligence represents a key enabling paradigm, involving multiple interconnected domains: foundation model architecture, machine learning methodologies, motion planning and measurement techniques, dynamic control mechanisms, human ergonomic and cognitive assessment, system reliability engineering, and adaptive product and process design within closed-loop operational environments.

This special session aims to consolidate scientific contributions addressing embodied intelligence in manufacturing systems, with particular emphasis on Human-Robot integration design and control principles, including:

• Cognitive sensing and reasoning capabilities grounded on situational awareness;
• Control strategies and system architectures supporting adaptive, safe and inclusive system behavior
• Human-technology collaborative interaction with shared autonomy and bidirectional control and adaptation
• Reliability-driven design principles for resilient, sustainable and goal-oriented manufacturing

Thematic Dimensions

This session invites research contributions addressing the following dimensions of embodied intelligence in manufacturing systems:

Manufacturing Process and Control:

• Embodied intelligence applications in smart production systems
• Intelligent robotic systems for autonomous assembly and disassembly operations
• Digital twin technologies for embodiment-aware predictive performance monitoring and optimization
• Adaptive and reconfigurable manufacturing pathways integrating human individual feedback

Human-Machine Integration:

• Human-machine embodied interaction frameworks
• Bidirectional human-robot collaboration for dynamic, context-aware system adaptation
• Cognitive modeling for safety-critical systems
• Ergonomic and cognitive load assessment methodologies supporting inclusive and human-centered design

System Optimization and Quality:

• Intelligent learning systems for quality assessment and maintenance planning
• Intelligent Logistics for circular enhancement
• Human diversity and inclusivity in collaborative workspace design
• Reliability analysis and performance prediction mechanisms for adaptive, sustainable and resilient systems

Over the past few decades, digital innovation has become an inseparable part of both business and society. Digital technologies are no longer peripheral tools; they have evolved into foundational elements that shape our daily lives, interactions, and organizational practices. As businesses, industries, and societies increasingly rely on digital infrastructures, traditional structures and processes are undergoing profound transformation. This shift is not merely technological but also social, cultural, operational, and strategic.

To ensure these transformations deliver long-term benefits without causing environmental or societal harm, sustainable digitalization must be prioritized. This involves integrating digitally responsible practices, sustainable design principles, ethical standards, and resource efficiency into every stage of digital development.

Digital technologies present boundless opportunities across virtually every sector. Innovations such as the Internet of Things (IoT), social media platforms, and artificial intelligence (AI) generate unprecedented volumes of data, creating new possibilities for value creation and competitive advantage. However, each organization faces unique constraints and complexities, making it essential to design digital strategies that align with specific business models, resources, and long-term objectives, while ensuring sustainable digitization.

In this session, we aim to develop a research roadmap that critically examines the aspects of digital innovation, particularly focusing on the tension between business model innovation and the rapidly evolving digital technology landscape.

One of the key challenges in contemporary industrial ecosystems is enabling trusted collaboration and coordination among actors, including firms, workers, platforms, and institutions. As industrial systems become increasingly distributed and interconnected, collaboration relies not only on data exchange, but also on the sharing, validation, and governance of knowledge and decisions across organizational boundaries.

Blockchain technology offers a promising foundation for addressing these challenges by providing decentralized, secure, and transparent infrastructures that support trust without requiring centralized authorities. Within industrial contexts, blockchain-enabled systems can support new forms of collaborative governance, including shared decision-making processes, decentralized coordination mechanisms, and trusted data and knowledge. These capabilities are particularly relevant in complex industrial networks such as supply chains, industrial symbiosis networks, manufacturing platforms, and Industry 5.0-oriented systems, where collaboration, sustainability, and resilience are central objectives.

This special session aims to explore blockchain-enabled collaboration networks that can strengthen industrial ecosystems by supporting trustworthy interactions, preserving and governing knowledge assets, and fostering sustainable and resilient industrial systems where humans and organizations collaborate effectively within socio-technical infrastructures.

Objectives

• To investigate the role of blockchain as an enabling digital infrastructure for trusted collaboration and coordination in industrial ecosystems;
• To examine how blockchain technology can support the preservation, traceability and governance of industrial knowledge, including tacit and experience-based knowledge;
• To identify challenges, best practices, and future research agendas for designing and deploying blockchain-enabled collaborative systems in real industrial settings;
• To promote sustainable, trusted and resilient industrial ecosystems through decentralized and transparent digital collaboration mechanisms.

One of the central challenges in Industry 5.0 (I5.0) is ensuring that industrial transformation remains human-centric by preserving and effectively leveraging human tacit knowledge—the experience-based, context-aware expertise embedded in workers' skills and decision-making. As industrial systems become increasingly digital and automated, this critical knowledge risks being undervalued, underutilized, or lost, which can negatively affect operational resilience, safety, and long-term sustainability.

To respond to this challenge, technological innovation must move beyond automation-driven efficiency toward human augmentation, where advanced systems are designed to strengthen human capabilities rather than replace them. In this context, Decision Support Systems (DSS) play a pivotal role by capturing, structuring, and contextualizing human expertise while integrating it with real-time data and analytics. When designed to complement human judgment, DSS can enhance situational awareness, reduce cognitive workload, support complex decision-making, and enable faster and more reliable responses in dynamic industrial environments.

Beyond DSS, a range of enabling technologies can further promote human augmentation in I5.0, including artificial intelligence and machine learning, digital twins, industrial Internet of Things (IIoT), and human-machine interfaces such as augmented reality (AR), wearable devices, and collaborative robotics. Together, these technologies can support continuous learning, improve task execution, facilitate knowledge transfer across teams and generations, and strengthen worker autonomy and well-being.

Aligned with the vision of I5.0, this research theme explores how decision support systems and complementary augmentation technologies can enhance human-centric production by safeguarding tacit knowledge, improving decision quality, and fostering resilient, inclusive, and sustainable industrial ecosystems where humans remain at the core of innovation and value creation.

Objectives

• To investigate the role of AI-driven Decision Support Systems (DSS) in enhancing industrial decision-making by integrating human expertise with data-driven intelligence in I5.0 contexts;
• To examine human augmentation approaches (physical, cognitive, and organizational) that strengthen workers' capabilities, safety, well-being, and performance in industrial environments;
• To identify challenges, best practices, and future research agendas for designing and deploying AI-enabled and human-centred augmentation technologies in real industrial settings;
• To promote sustainable, resilient, and human-centric industrial ecosystems by leveraging advanced digital technologies that keep humans at the core of production and innovation.