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The Pirate Cats have had their plans foiled by Geronimo Stilton time and time again, due to the help from Geronimo's friend Professor Volt. The Pirate Cats decide to better their odds by getting rid of Professor Volt; kidnapping him and leaving him in the Cretaceous Period - a time when the earth was occupied by dinosaurs!

This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplified the dynamics of the locomotor networks, our model includes detailed simulations of the classes of neurons that are considered responsible for generating movement patterns. The locomotor circuits, modeled as a spiking neural network of adaptive leaky integrate-and-fire neurons, are coupled to a three-dimensional mechanical model of a salamander with realistic physical parameters and simulated muscles. In open-loop simulations (i.e., without sensory feedback), the model replicates locomotor patterns observed in-vitro and in-vivo for swimming and trotting gaits. Additionally, a modular descending reticulospinal drive to the central pattern generation network allows to accurately control the activation, frequency and phase relationship of the different sections of the limb and axial circuits. In closed-loop swimming simulations (i.e. including axial stretch feedback), systematic evaluations reveal that intermediate values of feedback strength increase the tail beat frequency and reduce the intersegmental phase lag, contributing to a more coordinated, faster and energy-efficient locomotion. Interestingly, the result is conserved across different feedback topologies (ascending or descending, excitatory or inhibitory), suggesting that it may be an inherent property of axial proprioception. Moreover, intermediate feedback strengths expand the stability region of the network, enhancing its tolerance to a wider range of descending drives, internal parameters’ modifications and noise levels. Conversely, high values of feedback strength lead to a loss of controllability of the network and a degradation of its locomotor performance. Overall, this study highlights the beneficial role of proprioception in generating, modulating and stabilizing locomotion patterns, provided that it does not excessively override centrally-generated locomotor rhythms. This work also underscores the critical role of detailed, biologically-realistic neural networks to improve our understanding of vertebrate locomotion.

Large language model (LLM)–powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate “human-like” features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.

Background Advance care planning (ACP) and advance directives (AD) are tools for supporting person-centered decision-making. In dementia care, the progression of cognitive decline, complex family dynamics and variability in healthcare systems pose unique challenges to effective ACP/AD implementation for people with dementia (PWD). Methods We conducted a scoping review of the literature related to ACP/AD in dementia care between 2014 and 2024. Studies were screened and thematically analyzed to identify current approaches, gaps and recommendations for dementia-specific ACP/AD. We identified key stakeholders involved in decision-making and highlighted procedural components for ACP/AD according to stakeholder groups. Results Forty studies were included. Key stakeholders included healthcare professionals (HCPs); family members and caregivers; PWD; dyads (PWD and their caregivers); the broader public; policymakers; and researchers. Prominent findings included: the role and training of HCPs; educational and decision-support needs; early and ongoing engagement of PWD; development and evaluation of dementia-specific tools; ethical and procedural challenges in end-of-life decision-making; and the importance of outreach and cultural sensitivity. Promising interventions include structured communication models, psychoeducational programs and tools, although few have been fully adapted for dementia. Conclusion Dementia-specific ACP/AD require a relational, flexible and ethically grounded approach that evolves with the individual’s condition. While ACP/AD should reflect the autonomous preferences of the PWD, during late-stage dementia, shared decision-making becomes central to providing care that aligns with the person’s goals and preferences. Future research should focus on inclusive tools and training; timing and process facilitation; and public health strategies to improve access and equity.

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs.

The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Our consensus process identified key contextual factors of trust, namely, an AI system's environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.

How does the brain process sensory stimuli, and decide whether to initiate locomotor behaviour? To investigate this question we develop two whole body computer models of a tadpole. The “ Central Nervous System ” ( CNS ) model uses evidence from whole-cell recording to define 2300 neurons in 12 classes to study how sensory signals from the skin initiate and stop swimming. In response to skin stimulation, it generates realistic sensory pathway spiking and shows how hindbrain sensory memory populations on each side can compete to initiate reticulospinal neuron firing and start swimming. The 3-D “ Virtual Tadpo le” ( VT ) biomechanical model with realistic muscle innervation, body flexion, body-water interaction, and movement is then used to evaluate if motor nerve outputs from the CNS model can produce swimming-like movements in a volume of “water”. We find that the whole tadpole VT model generates reliable and realistic swimming. Combining these two models opens new perspectives for experiments.

Biomass is one of the most widespread and accessible energy source and steam gasification is one of the most important processes to convert biomass into combustible gases. However, to date the difference of results between the main models used to predict steam gasification producer gas composition have been not analyzed in details. Indeed, gasification, involving heterogeneous reactions, does not reach thermodynamic equilibrium and so thermodynamic models with experimental corrections and kinetic models are mainly applied. Thus, this paper compares a 1-D kinetic model developed in MATLAB, combining hydrodynamics and reaction kinetics, and a 0-D thermodynamic model developed in Aspen Plus, based on Gibbs free energy minimization applying the quasi-equilibrium approach, calibrated by experimental data. After a comparison of the results of the models against experimental data at two S/B ratios, a sensitivity analysis for a wide range of S/B ratios has been performed. The experimental comparison and sensitivity analysis shows that the two models provide sufficiently similar data in terms of the main components of the syngas although the thermodynamic model shows, with increasing S/B, a greater increase of H2 and CO2 and lower decrease of CH4 and CO respect to the kinetic one and the experimental data. Thus, the thermodynamic model, despite being calibrated by experimental data, can be used mainly to analyze global plant performance due to the reduced importance of the discrepancy from a global energy and plant perspective. Meanwhile, the more complex kinetic model should be used when a more precise gas composition is needed and, of course, for reactor design.

Despite being commercially available, solid oxide fuel cell (SOFC) technology requires further study to understand its physicochemical processes for diagnostics, prognostics, and quality assurance purposes. Electrochemical impedance spectroscopy (EIS), a widely used characterization technique for SOFCs, is often accompanied by the distribution of relaxation times (DRT) as a method for deconvoluting the contribution of each physicochemical process from the aggregated impedance response spectra. While EIS yields valuable information for the operation of SOFCs, the quantitative analysis of the DRT and its shifts remains cumbersome. To address this issue, and to create a replicable benchmark for the assessment of DRT results, a custom tool was developed in MATLAB to numerically analyze the DRT spectra, identify the DRT peaks, and assess their deviation in terms of peak frequency and DRT amplitude from nominal operating conditions. The preliminary validation of the tool was carried out by applying the tool to an extensive experimental campaign on 23 SOFC button-sized samples from three production batches in which EIS measurements were performed in parametric operating conditions. It was concluded that the results of the automated analysis via the developed tool were in accordance with the qualitative analysis of previous studies. It is capable of providing adequate additional quantitative results in terms of DRT shifts for further analysis and provides the basis for better interoperability of DRT analyses between laboratories.

Hydrogen Refueling Stations (HRS) are a key infrastructure to the successful deployment of hydrogen mobility. Their cost-effectiveness will represent an increasingly crucial issue considering the foreseen growth of vehicle fleets, from few captive fleets to large-scale penetration of hydrogen vehicles. In this context a detailed, component-oriented cost model is important to assess HRS costs for different design concepts, layout schemes and possible customizations, respect to aggregate tools which are mostly available in literature. In this work an improved version of a previously developed component-oriented, scale-sensitive HRS cost model is applied to 5 different European HRS developed within the 3Emotion project with different refueling capacities (kg H2 /day), hydrogen supply schemes (in-situ production or delivery), storage volumes and pressures and operational strategies. The model output allows to assess the upfront investment cost (CAPEX), the annual operational cost (OPEX) and the Levelized Cost of Hydrogen (LCOH) at the dispenser and identify the most crucial cost components. The results for the five analyzed HRS sites show an LCOH at the nozzle of around 8-9 €/kg for delivery based HRSs, which are mainly dominated by the H 2 retail price and transport service price and around 11-12 €/kg for on-site producing HRS, for which the electrolyzer CAPEX and electricity price plays a key role in the cost structure. The compression, storage, and dispensing sections account for between 1-3 €/kg according to the specific design & performance requirements of the HRS. The total LCOH values are comparable with literature, standard market prices for similar scale HRSs and with the 3Emotion project targets.