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Ambition setting through climate services to drive climate resilient development
Full text linkPublisher Copyright: © 2025 The AuthorsClimate change adaptation efforts need to accelerate and scale-up to deal with increasing climate change impacts worldwide in order to safeguard the resilience of societies. Currently adaptation action is merely following a risk-based planning approach, going from identifying a climate related risk to directly finding solutions. This has resulted into largely fragmented, local, and incremental adaptation actions up to present. There is a need for transformational change, and combining adaptation with other policy objectives, to speed up action towards climate resilient development. However, this integration alone may not be sufficient to address the systemic transformation required to tackle the root causes of existing challenges and underlaying vulnerabilities. A broader perspective is needed to envision the “future we want” and defining key goals and actions to achieve these futures. We believe that such an ambition setting process is critical, and commonly missing in adaptation planning. With ambition setting we mean a policy process that entails developing visions coupled with identifying goals and actions that work towards these visions. Ambition setting builds upon understanding the desired transformations in the system and the root cause of present challenges, including risks and vulnerabilities. To put ambition setting into practice climate services and tools can be employed. We identify key criteria supporting the selection of such tools and provide four examples showcasing how the tools support ambition setting. A tradition of ambition setting should be fostered, as well as tools and services should be further developed in parallel to accelerate transformations towards climate resilient development
Enhancing Citizen Participation in Citizen-Centered Smart Cities: Insights from Two European Case Studies
Full text linkPublisher Copyright: © 2025 by the authors.Citizen participation plays a critical role in the transformation towards citizen-centered smart cities, ensuring resilience, inclusivity, and responsiveness to community needs. Smart cities, while often associated with technological infrastructures and digital tools, also adopt a human-cepntered perspective that emphasizes the social and participatory dimensions of smart urban development. Engaging residents in these initiatives not only facilitates the acquisition of valuable insights but also strengthens the foundation for equitable urban development. However, the participatory process often encounters significant barriers that hinder its effectiveness, posing challenges to the creation of truly inclusive and citizen-centered smart cities. This paper analyzes the participatory processes and outcomes of two case studies, URBANAGE and drOp, both of which follow a Human-Centered Design approach and have implemented targeted actions to address participation challenges. This article explains the methodologies and processes followed in these projects and identifies key lessons learnt from their experiences and examining the impact of participatory processes on project outcomes. Lastly, it proposes practical guidelines to enhance the effectiveness of citizen involvement in future smart city initiatives. Despite their focus on different citizen groups and objectives, both case studies faced similar obstacles in fostering meaningful participation and awareness.Peer reviewe
Monitoring of blind rivets installations: Contributions from the manufacturing chain and time-series imaging
Full text linkPublisher Copyright: © 2025 The Author(s)Fastening is a crucial operation in the aircraft manufacturing cycle, and the demand for automated solutions has grown in recent years. Blind rivets are particularly suitable for automation due to their ease of use. However, fastening with blind rivets requires indirect evaluation of the formed head for in-line quality monitoring. This study presents two approaches to address this problem. Firstly, an analysis of the drilling-riveting chain assesses the impact of the previous operation on riveting outcomes. Secondly, time-dependent signals from the riveting process are coded into images and analysed using deep learning techniques. Despite some limitations, both methods for monitoring blind riveting have demonstrated high precision and accuracy values above 0.9, with 1 indicating perfect precision or accuracy, suggesting that they can reliably predict the quality of rivet installations.Peer reviewe
High-Purity Tungsten Oxide Production from Low-Grade Scheelite Concentrates at Pilot Plant Scale
Full text linkPublisher Copyright: © 2025 by the authors.Tungsten is a critical raw material with increasingly important industrial applications. It is primarily found in minerals such as scheelite and wolframite (0.5% W), which are extracted and processed at the mine site to produce a high-grade scheelite concentrate (60% W). This process results in significant tungsten losses in the form of tailings, currently not utilized at the EU level. Deep eutectic solvents and imidazolium-based ionic liquids have been shown to possess excellent utility for recovering tungsten from low-grade concentrates, achieving tungsten oxide (96% purity) at high global yields (80%). In this study, an optimized ionic liquid-based process (involving leaching, solvent extraction, crystallization, and calcination) was developed at the laboratory scale. Important issues such as solvent flammability or the commercial availability of ionic liquids were addressed to ensure the safety and industrial feasibility of the process. Furthermore, a pilot plant was designed, constructed, and operated for a significant period (3 days). Tungsten oxide was produced with improved purity (>99%) and global yield (91.6%) in continuous operation.Peer reviewe
Design and User Validation of Novel Hybrid Electrodes for Emg Recording and Electrotactile Stimulation
No full textPublisher Copyright: © 2025 IEEE.This paper presents the development and validation of a novel stretchable dry hybrid electrode for electrotactile stimulation and EMG recording, designed for integration into prosthetic socket liners. Three electrode prototypes were compared: (1) dry stretchable electrode; (2) electrode with elevated pads via plastic layering beneath; (3) electrode with 3D-printed conductive plastic atop the pads. Six able-bodies subjects were recruited to assess the active stimulation range and subjective comfort and preference. Electrodes 1 and 3 showed comparable sensation and discomfort thresholds (1.1-4.1 mA), while electrode 2 exhibited a reduced dynamic range and higher pad failure rate. Four out of six participants preferred electrode 1. A prolonged 6hour test with electrode 1 confirmed stable stimulation thresholds over time in a single subject. The initial results indicate the feasibility of novel dry textile-integrated electrodes for providing feedback in closed-loop applications.Peer reviewe
Interpreting Data in the Absence of Production Time Consistency and Source Tracking
No full textPublisher Copyright: © 2025 IEEE.Data analytics is providing data features to a large range of industrial processes that provides insights that extend well beyond traditional process control. However, data analytics has its own limitations particularly when confronted with challenges such as incomplete source traceability and random process-time window variation. In this context, robust data preparation emerges as a critical prerequisite for the successful deployment of AI-driven methodologies. This paper presents a structured approach to data preparation, following logic strategies applied to normally distributed datasets. The methodology is adapted to account for constraints such as limited data variability and process gradual drift, both presented in the glass bottles manufacturing.Peer reviewe
Identification of Subject-Specific EMG Detectors for Robot-Assisted Therapy Using Rest and Move State EMG Data
Full text linkPublisher Copyright: © 2013 IEEE.In severely impaired stroke subjects without visible movements, implementing robot-assisted therapy based on movement intention decoded from electromyogram (EMG) requires both sufficient residual EMG to drive robotic assistance and a subject-specific detector to ensure accurate, low-latency detection. However, identifying such a detector is challenging, particularly when the presence of residual EMG in a given subject is unknown. This paper proposes a systematic approach to distinguish between EMG data when the subject is relaxed versus attempting a movement. We investigated six different detector types and separation measures using retrospective EMG data from a previous randomized controlled trial. The results indicate that the approximate generalized likelihood ratio (AGLR) detector, along with the modified Hodges and modified Lidierth detectors, achieved the best separation between the data when a subject is relaxed compared to when he/she attempts movements. Using a subset of clinician-annotated data to evaluate detection performance, the modified Hodges detector combined with probability difference-sum ratio measure (MH-PDSR) showed good performance in terms of both accuracy and latency. Based on the EMG data from 30 severe stroke, we propose a PDSR threshold of 0.7 with the modified Hodges detector to identify stroke subjects with sufficient residual EMG. These findings suggest that the MH-PDSR approach can be used to learn a maximally separating detector for a given subject which can be used both to screen stroke subjects for residual EMG and to provide a detector to drive robotic assistance if residual EMG is present. Further validation using larger datasets and evaluation of the resulting human machine interaction is warranted.Peer reviewe
Uncovering attempted movements of the paralyzed upper limb after stroke through EEG and EMG
Full text linkPublisher Copyright: © The Author(s) 2025.Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of signals combined. Our results reveal that: (i) EEG and residual EMG activity provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; (ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degree of impairment of the patient; and (iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.Peer reviewe
A Comparative Analysis of the Market Bidding Decisions of Operational Pv Plants during Near-Zero Prices in the Spanish Electricity Markets
No full textPublisher Copyright: © 2025 IEEE.Nowadays, Day-Ahead market price cannibalization particularly affects photovoltaic (PV) plants, as this phenomenon impacts the revenue per unit of energy. However, renewable assets can participate in balancing services to increase their market revenues. Despite many research articles proposed complex optimization techniques, the market bidding of operational PV assets remains unsophisticated nowadays. This article studies and compares the market strategies of PV plants during near-zero price days in Spain, highlighting how these PV plants, despite having similar characteristics, operate with significantly different market decisions. It concludes that participation in balancing markets increases solar value by capturing higher revenues. Solar trackers also enhance daily solar generation. In the future, market agents of PV systems should adjust their operations to multiple energy markets and trends in market prices to optimize revenue stacking.Peer reviewe
Optimisation of Multi-Energy Systems and Power-To-X Technologies: A Hydrogen-Based Industrial Case Study
No full textPublisher Copyright: © 2025 IEEE.This study presents a daily cost optimization for a hybrid renewable energy system, hydrogen and battery storage, for PtX technology implementation. Using mixed-integer linear programming, this paper aims at minimising total system cost, subject to technical system constraints. This paper is focused on the modelling and optimisation of a hydrogen-based industrial case study comparing the operating strategy between a typical summer and winter day. Although RES generation is not sufficient to fully meet the energy needs of the industry, the system operation aims to reduce the daily cost. The results show a clear priority for the direct use of hydrogen over its conversion into electricity, due to its low efficiency and cost. The system successfully manages hydrogen generation, battery and production, depending on prices, while ensuring a stable industrial supply. Hydrogen storage fluctuates and tends to reduce system cost, which requires accurate management to avoid hydrogen shortages during peak demand.Peer reviewe