The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance.

The paper proposes a traceability framework based on methodological approach for the deployment of IOTA-Based Distributed Ledger in the mining industry, as a first step of certification and labelling of the sustainable material production. The methodology is evaluated in real-world implementation in scope of the DIG_IT project. The implementation provides significant amount of heterogeneous data originating from different processes and sources during the operation in mines, such as vehicles operations, mining and raw material extraction, workers physical parameters, environmental parameters, sensors (field, biometric, assets), market (supply chains, commodity prices), weather data, etc. Most of these datasets, especially the data about the emission, needs integrity and the ability to be audited by the public community, government and the stakeholders. The methodology for establishing the transparency for the mining operations, the requirements, technical architecture, General Data Protection Regulation (GDPR) assessment, Distributed Ledger Technology (DLT) infrastructure, as well as end-to-end traceability, are proposed and evaluated with the actual deployment. The approach advocates end to end security for the increased traceability and data integrity based on Distributed Ledger Technology. Namely, W3C Decentralised Identities and Public Key Infrastructure (PKI) should be deployed from the data source to the cloud and anchored onto the Blockchain. The approach showed promising results with the regulations compliance made possible, making the data available and advertised publicly for the emission compliance.

The purpose of this manuscript is to present the Smart Driving Service (SDS), a customized mobile application and a complex microservices framework that is intended for not only professional drivers but also for novel people that need help during the driving time in their long-distance journeys. The European regulation about driving times, breaks and rest periods for drivers engaged in the carriage of freight is implemented into the system. Additionally, it is necessary to have a feedback report to detect the behavior of the drivers and what to do differently to drive better. This issue is addressed by implementing a Route Performance Index (RPI) to measure the drivers’ compliances. The proposed service has been running in a production stage for 6 months with a reduction of the consumption of 2 liters/100km. Having into account that the company runs more than 100M km per year, the savings in fuel is very relevant apart from the environment impact reduction.

Demand forecasting is a very important feature in companies since it provides valuable information for taking decisions about planning, pricing and business growth strategies. In this paper, multi-country demand forecasting for a company of the construction industry is carried out. In the case study, we analyze different external variables from open data sources of each country in order to reduce uncertainty in demand forecasting. The analysis is carried out with different univariate and multivariate methods. The results show how the consideration of external variables improves the forecasts. Finally, forecasts of different kinds of demand are evaluated using Syntetos, Boylan and Croston classification methodology. Key words: Demand forecasting, forecast models, multivariate methods, accuracy indicators

Textile waste is all textiles and products that are dropped away. This waste comes from various sources: households, factories, and the fashion industry and significantly challenges the environmental and economic potential for sustainable waste management. This paper proposes a simulation-based optimization of textile waste collection routes, focusing on efficiency and sustainability. The model considers vehicle characteristics, collection demand, and environmental aspects. Also, it examines vehicle routes, container, and sensor technology scenarios. The results show that efficient, sustainable solutions with strategic container placement and dynamic routes can reduce travel costs and environmental impacts. As a case study, the Huesca region of Spain confirms the effectiveness of dynamic route planning and sensor technology. This method allows waste management authorities to optimize resource allocation and reduce emissions and costs, as the simulation-based method for optimizing routes can reduce costs and environmental impacts while maximizing efficiency. It highlights the urgency for waste management stakeholders to conform with recent Spanish legislation mandating. This study provides valuable insights to meet legal obligations while ensuring operational efficiency and sustainability.

Smart city mobility systems face a persistent gap between digital innovation and large-scale deployment. While Mobility-as-a-Service platforms and Mobility Living Labs have generated promising local solutions, transferring these solutions across cities remains constrained by data fragmentation, proprietary platforms, and divergent governance frameworks. This study proposes and operationalizes a policy-oriented framework for assessing the transferability of smart mobility solutions across urban contexts, drawing on the GEMINI project spanning seven European cities: Amsterdam, Copenhagen, Helsinki, Munich, Paris-Saclay, Porto, and Turin. A structured dataset covering application features, interoperability characteristics, technical specifications, and implementation challenges was developed and analysed using comparative indicators including Ease of integration, interoperability level, and replication potential. The results demonstrate that transferability depends on the interaction of technical, governance, and institutional conditions rather than technical compatibility alone. Applications built on open standards and modular architectures show significantly higher replication potential, while legacy systems, fragmented governance structures, and restrictive data-sharing arrangements remain the most persistent barriers. To operationalize the framework, a web-based Knowledge Hub was developed and deployed as a live decision-support platform, currently serving all seven Mobility Living Labs and openly accessible to cities across Europe. The framework offers a replicable model for smart city mobility governance.