6.3 Barriers and opportunities to AE system development:insights from WaterWarmth pilots

The main research question that guides the analysis for this Work Package 6 Deliverable 6.3 report is: What are the barriers and opportunities that govern niche development and scaling of aqua thermal energy (AE) systems? In answering this research question, we map and analyze the enabling policy and opportunities that support AE system niche development, as well as policy that can also play a role in up-scaling and outscaling of AE systems and practices through demonstration pilots. The report also identifies barriers that (could potentially) hinder AE system niche development. The focus of the report is on the AE pilots of the Interreg North Sea WaterWarmth Project and is therefore primarily based on interviews and pilot text documents. We compiled a set of key questions that were designed to explore issues related to governance and implementation of renewable energy systems. For this report we particularly explored issues about the tensions and challenges faced by the pilots as well as enabling factors that lead to successes in the implementation and scaling of their projects. We studied nine pilot projects using an exploratory multi-case study approach. The first questions for mapping the AE projects were asked to the pilots in an on-line online survey April 2024. A workshop was then held in Caen May 2024 and in the next step semi-structured interviews were carried out with key respondents involved in the nine pilots during the Fall of 2024. The results are first analyzed case-by-case, and then a multi-case synthesis is presented followed by conclusions and policy recommendations.Results show that all the nine pilot projects have experienced several barriers that hinder AE niche development but also referred to important enablers. Policy and regulation barriers were observed in all cases. Lack of clear policy and regulations included complex regulations and inconsistent policies for AE systems. Working closely with policy and governance stakeholders was regarded as an enabling factor in developing the projects, especially at the early phases. Having no internal vision was considered a barrier as it leads to difficulty in formulating a business plan that in turn may hinder securing funding. Lack of financial support is in turn one of the key barriers for the projects having challenges in securing bank guarantees for equipment and operation costs as well as permitting costs. The novelty of the technology in some countries also created barriers in convincing permitting authorities and finance stakeholders about project viability. Further, lack of expertise was seen as something that could be overcome through sharing experiences with similar projects in the same regions or in other countries. Exchange of lessons lead to strengthening of the project and peer-to-peer feedback. Support of fossil fuels by governments reduces subsidies for innovations such as AE systems and leads to lack of incentives for adapting AE or large-scale collective energy systems and is therefore regarded as unfair competition for renewable energy alternatives. Reluctance and lack of acceptance of AE technology due to skepticism about renewable energy technologies is also a hindrance to AE development. Finally, lack of public awareness is a related barrier which can partly be attributed to keystakeholders withholding information that could benefit the projects through information exchange. Regarding enablers, networks and stakeholder collaboration were regarded as fundamental in the implementation andsuccess of projects. Good communication with these stakeholders was also seen as a key success and an enabling factor to ensure a full understanding of the project details and expectations. Through this analysis of barriers and enablers a solid basis is created for mapping and developing insights into how to resolve such hurdles, which is also a key component of the coming WP6 report D6.4., which will present a key vision, strategy and transition policy pathways. This may include developing and implementing specific policy instruments or policy mixes, or by using networking and participatory arrangements

The MyFlex-ζ Foot:A Variable Stiffness ESR Ankle-Foot Prosthesis

Most commercially available foot prostheses are passive ESR feet, which store and release energy to reduce metabolic costs and improve comfort but cannot adjust to varying walking conditions. In contrast, bionic feet adapt to different tasks but are hindered by high weight, power consumption, and cost. This paper presents MyFlex-ζ, an ESR foot with a variable stiffness system, as a compromise between these two categories. MyFlex-ζadjusts stiffness by varying the sagittal-plane distance between two key points, altering force interactions within the prosthesis and affecting overall stiffness. Clinical tests with three transfemoral amputees evaluated stiffness variation across two sessions: the first subjective, where participants assessed stiffness settings during different tasks, and the second biomechanical, measuring performance parameters. Two participants selected different stiffness settings for various tasks, while the third, with limited perception of stiffness changes, showed less distinction in outcomes. Greater sagittal-plane rotation and higher energy absorption were observed in most tasks with more compliant settings, although one participant's results were limited due to selecting close stiffness settings. Overall, these findings suggest MyFlex-ζoffers adaptability and performance improvements over traditional ESR feet. With further actuation and control system development, MyFlex-ζcould mark significant progress in prosthesis technology.</p

Context-specific clinical applicability of the end-expiratory occlusion test to predict fluid responsiveness in mechanically ventilated patients:A systematic review and meta-analysis

BACKGROUND The emergence of context-specific clinical evidence from the end-expiratory occlusion test (EEOT) may change the perception of its operative performance to predict fluid responsiveness. OBJECTIVE(S) Assessment of predictive performance of the EEOT in the intensive care unit (ICU) and operating room. DESIGN Systematic review of observational diagnostic test accuracy studies with meta-analysis. DATA SOURCES MEDLINE, Embase and Scopus were used as data sources for relevant publications until February 2024. ELIGIBILITY CRITERIA Prospective clinical studies in which the EEOT was used to predict fluid responsiveness in mechanically ventilated adults, regardless of the clinical care context. The operative performance characteristics must also have been reported. RESULTS Twenty-four studies involving 1073 adult patients (588 receiving intensive care and 485 in the operating room) were systematically reviewed, and 22 studies comprising 1049 volume expansions were meta-analysed. The pooled sensitivity [95% confidence interval (CI)] of the EEOT was 0.87 (0.81 to 0.92), and the pooled specificity was 0.90 (0.85 to 0.94); the median [interquartile range] cardiac index (CI) threshold for a positive test was a 5.0 [3.3 to 5.3] increase. The clinical context, the method used for haemodynamic monitoring, the ratio of the averaging time of the monitoring method to the occlusion time, the levels of positive end-expiratory pressure and the choice of cardiac output marker were identified as significant sources of heterogeneity. However, the occlusion duration and tidal volume did not significantly affect its performance. A novel insight is that performance was notably lower in the operating room setting. The likelihood ratios were 14 (positive) and 0.12 (negative) for the ICU, both better than 3.1 and 0.21 for the operating room. The overall quality of the evidence was assessed to be very low, mainly due to high heterogeneity and risk of bias; however, no publication bias was detected. CONCLUSION The EEOT for predicting fluid responsiveness in critical care performs acceptably well overall and is a confirmative test. In the operating room and/or with specific technical settings, its performance and clinical utility are reduced, driving the need for more context-specific and patient-specific fluid responsiveness assessments.</p

Cycle Patterns and Mean Payoff Games

We introduce the concept of a \emph{cycle pattern} for directed graphs as functions from the set of cycles to the set $\{-,0,+\}$. The key example for such a pattern is derived from a weight function, giving rise to the sign of the total weight of the edges for each cycle. Hence, cycle patterns describe a fundamental structure of a weighted digraph, and they arise naturally in games on graphs, in particular parity games, mean payoff games, and energy games. Our contribution is threefold: we analyze the structure and derive hardness results for the realization of cycle patterns by weight functions. Then we use them to show hardness of solving games given the limited information of a cycle pattern. Finally, we identify a novel geometric hardness measure for solving mean payoff games (MPG) using the framework of linear decision trees, and use cycle patterns to derive lower bounds with respect to this measure, for large classes of algorithms for MPGs

Measuring contextuality in investment preferences

This study investigates the role of contextuality in investment preference formation, specifically in global investments and major US stocks. Utilizing the "Contextuality by Default" framework (Dzhafarov and Kujala in PLoS ONE 8:e61712, 2013; J Math Psychol 74:11–33, 2016), we measure contextuality within a cyclic system of random variables. Through two experiments, each with four context stimuli, we assess contextuality across various decision-making scenarios. Our results indicate the presence of "true" contextuality in global investment decisions, but not in US stock selections. We investigate the determinants of contextual preference formation, focusing on factors such as future return expectations, risk perception, and familiarity with stock markets and individual US stocks. Employing logistic regression analysis for the first two contexts, we find that preferences in foreign stock markets exhibit instability, indicating context-specific drivers. On the other hand, familiarity with individual companies and future return expectations consistently influence investment preferences in specific US stocks.</p

Dropout and completion in iCBT for university students:Insights from a thematic analysis

Online cognitive behavioral therapy (iCBT) is a promising treatment for depression and anxiety among university students but faces high dropout rates. Understanding the reasons behind dropout or completion can help improve the implementation of iCBT in educational settings. Semi-structured phone interviews were conducted with 32 students who dropped out early (n = 9), midway (n = 12), or completed (n = 11) guided or unguided iCBT in the context of a randomized controlled trial. Data were analyzed using Braun and Clarke's (2012) thematic analysis. Common themes among dropouts included personal factors (like competing priorities), perceived difficulty or redundancy of the intervention, and lack of human interaction. Early dropouts uniquely cited disbelief in the intervention's efficacy and preference for other mental health support. Midway dropouts mentioned issues with the interactivity, feedback, content, perceived effectiveness, and lack of personalization. Completers had positive initial impressions, valued the online format, found the exercises and guidance helpful, and felt cared for. The themes identified among participants who dropped out from or completed the iCBT intervention provide valuable insights into factors which may be of importance for retention. Implications regarding setting expectations, participant selection, interactive functionalities, personalized feedback, and the role of therapist guidance are discussed.</p

Influence of Gait Speed on Inter-Joint Coordination in People with and Without Parkinson’s Disease

Background: The influence of gait speed on lower-extremity coordination while walking in people with Parkinson’s disease (pwPD) is poorly understood. This study sought to investigate the relationship between gait speed and hip–knee coordination and coordination variability in older adults and pwPD. Methods: A total of 27 pwPD and 21 healthy older adults were recruited. Participants walked in a straight line at slow, preferred, and fast walking speeds. Gait data were collected using inertial measurement units, and the kinematics of the hip and knee were calculated. Coordination and coordination variability at the hip–knee joint pair were determined using continuous relative phase. A repeated measures two-way ANCOVA tested the impact of gait speed on coordination and coordination variability, while group differences were evaluated using statistical parametric mapping (SPM). Results: Neither the healthy older adults nor the pwPD adjusted their hip–knee coordination in response to changes in gait speed. pwPD also displayed a trend towards restricted hip and knee joint excursion compared to older adults, which may further limit their ability to adapt gait strategies. Conclusions: These findings suggest that interventions addressing both joint excursion and motor adaptability may be important for improving gait function in individuals with Parkinson’s disease. Real-world applicability can be found in the potential of wearable sensors to become a valuable tool in routine clinical practice for both diagnosis and ongoing management. Trial registration: The study is registered in the German Clinical Trials Register (DRKS00022998).</p

Doubled-Frequency Triangular Current Mode Modulation with Sinusoidal Frequency Profile for a Three-Level ANPC Converter

Triangular current mode (TCM) modulation is a well-researched technique for optimizing the trade-off between converter power or gravimetric density and power efficiency. However, its application in multilevel ac–dc converters has been limited. This article extends the TCM concept to a three-level active-neutral-point-clamped converter and addresses key implementation challenges. First, two fundamental TCM commutation sequences are introduced. Building on these, a doubled-frequency TCM (DF-TCM) modulation method is proposed to offer additional benefits. A major drawback of conventional TCM is its complex switching frequency profile, which imposes significant computational burdens and can easily excite circuit resonance. To address this, this article proposes replacing it with sinusoidal frequency profiles. In addition, considerations for parameter design are discussed, and a model for semiconductor conduction losses is developed. Finally, a hardware prototype is constructed to validate the feasibility of the proposed DF-TCM strategy.</p

Explainable AI (XAI) for Arrhythmia Detection in ECG

Background: Advancements in deep learning have enabled highly accurate arrhythmia detection in electrocardiogram (ECG) signals. However, these models are often considered "black-box" systems, limiting their clinical adoption due to a lack of interpretability. Explainable AI (XAI) techniques aim to bridge this gap by providing insights into model decisions. Despite the progress in XAI, most techniques are designed for image and feature-based models rather than time series data like ECG signals. This study aims to explore explainability of AI models for arrhythmia detectionMethods: A model was developed utilizing the MIT-BIH arrhythmia dataset. The study employs deep learning techniques, specifically Long Short-Term Memory (LSTM) networks. Data preprocessing includes segmentation based on R-peak, detected using Pan-Tompkins algorithm, and handling class imbalance through Synthetic Minority Over-sampling Technique (SMOTE). To assess performance on a large dataset, an additional 12-lead ECG dataset was included. Various eXplainable AI (XAI) techniques were analyzed andtested, leading to the use of SHapley Additive exPlanations (SHAP), a method based on game theory. Four different SHAP-based techniques were implemented, tested, and compared (Permutation importance, KernelSHAP, gradients, and Deep Learning Important FeaTures (DeepLIFT). Additionally, medical professionals were consulted to gather their perspectives on the explainability of these models.Findings: The deep learning model achieved a high validation accuracy of 98.3% on the MIT-BIH dataset. However, performance dropped significantly when trained on the combined dataset, highlighting the importance of consistent measurement environments. Of the four tested methods, permutation importance and KernelSHAP exhibited similar behavior, producing cluttered output. The remaining two methods produced more promising results, showing the points of interests more clearly. These methods provided clear indications of important waveform segments used in classification, aligned with clinical knowledge. However, the patterns they highlighted differed between the two methods and also varied across samples, raising concerns about reliability. Additionally, medical professionals preferred heatmap-style visualizations for interpretability.Discussion: This study highlights the potential of XAI in improving transparency in arrhythmia detection models. While the deep learning model demonstrated strong classification performance, its reliance on single-heartbeat segmentation limited the detection of arrhythmias that require rhythm context. The study underscores the need for domain-specific adaptations in XAI methods for ECG analysis. While the explainable methods sometimes produced incorrect highlight areas, the arrhythmias were still correctly identified. Future work should focus on refining XAI techniques for time-series data and validating their clinical applicability.<br/