Low Fidelity Research Articles

Different states of synaptic vesicle priming explain target cell type–dependent differences in neurotransmitter release

Pronounced differences in neurotransmitter release from a given presynaptic neuron, depending on the synaptic target, are among the most intriguing features of cortical networks. Hippocampal pyramidal cells (PCs) release glutamate with low probability to somatostatin expressing oriens-lacunosum-moleculare (O-LM) interneurons (INs), and the postsynaptic responses show robust short-term facilitation, whereas the release from the same presynaptic axons onto fast-spiking INs (FSINs) is ~10-fold higher and the excitatory postsynaptic currents (EPSCs) display depression. The mechanisms underlying these vastly different synaptic behaviors have not been conclusively identified. Here, we applied a combined functional, pharmacological, and modeling approach to address whether the main difference lies in the action potential-evoked fusion or else in upstream priming processes of synaptic vesicles (SVs). A sequential two-step SV priming model was fitted to the peak amplitudes of unitary EPSCs recorded in response to complex trains of presynaptic stimuli in acute hippocampal slices of adult mice. At PC-FSIN connections, the fusion probability (Pfusion) of well-primed SVs is 0.6, and 44% of docked SVs are in a fusion-competent state. At PC-O-LM synapses, Pfusion is only 40% lower (0.36), whereas the fraction of well-primed SVs is 6.5-fold smaller. Pharmacological enhancement of fusion by 4-AP and priming by PDBU was recaptured by the model with a selective increase of Pfusion and the fraction of well-primed SVs, respectively. Our results demonstrate that the low fidelity of transmission at PC-O-LM synapses can be explained by a low occupancy of the release sites by well-primed SVs.

Asynchronous Multi-Information Source Bayesian Optimization

Abstract Resource management in engineering design seeks to optimally allocate while maximizing the performance metrics of the final design. Bayesian optimization (BO) is an efficient design framework that judiciously allocates resources through heuristic-based searches, aiming to identify the optimal design region with minimal experiments. Upon recommending a series of experiments or tasks, the framework anticipates their completion to augment its knowledge repository, subsequently guiding its decisions toward the most favorable next steps. However, when confronted with time constraints or other resource challenges, bottlenecks can hinder the traditional BO’s ability to assimilate knowledge and allocate resources with efficiency. In this work, we introduce an asynchronous learning framework designed to utilize idle periods between experiments. This model adeptly allocates resources, capitalizing on lower fidelity experiments to gather comprehensive insights about the target objective function. Such an approach ensures that the system progresses uninhibited by the outcomes of prior experiments, as it provisionally relies on anticipated results as stand-ins for actual outcomes. We initiate our exploration by addressing a basic problem, contrasting the efficacy of asynchronous learning against traditional synchronous multi-fidelity BO. We then employ this method to a practical challenge: optimizing a specific mechanical characteristic of a dual-phase steel.

Transition of care from post-acute services for the older adults in Quebec: a pilot impact evaluation

BackgroundEarly discharge of frail older adults from post-acute care service may result in individuals’ reduced functional ability to carry out activities of daily living, and social, emotional, and psychological distress. To address these shortcomings, the Montreal West Island Integrated University Health and Social Services Centre in Quebec, Canada piloted a post-acute home physiotherapy program (PAHP) to facilitate the transition of older adults from the hospital to their home. This study aimed to evaluate: (1) the implementation fidelity of the PAHP program; (2) its impact on the functional independence, physical and mental health outcomes and quality of life of older adults who underwent this program (3) its potential adverse events, and (4) to identify the physical, psychological, and mental health care needs of older adults following their discharge at home.MethodsA quasi-experimental uncontrolled design with repeated measures was conducted between April 1st, 2021 and December 31st, 2021. Implementation fidelity was assessed using three process indicators: delay between referral to and receipt of the PAHP program, frequency of PAHP interventions per week and program duration in weeks. A battery of functional outcome measures, including the Functional Independence Measure (FIM) and the Patient-Reported Outcomes Measurement Information System (PROMIS) Global-10 scale, as well as fall incidence, emergency visits, and hospitalizations were used to assess program impact and adverse events. The Patient’s Global Impression of Change (PGICS) was used to determine changes in participants’ perceptions of their level of improvement/deterioration. In addition, the Camberwell Assessment of Need for the Elderly (CANE) questionnaire was administered to determine the met and unmet needs of older adults.ResultsTwenty-four individuals (aged 60.8 to 94 years) participated in the PAHP program. Implementation fidelity was low in regards with delay between referral and receipt of the program, intensity of interventions, and total program duration. Repeated measures ANOVA revealed significant improvement in FIM scores between admission and discharge from the PAHP program and between admission and the 3-month follow-up. Participants also reported meaningful improvements in PGICS scores. However, no significant differences were observed on the physical or mental health T-scores of the PROMIS Global-10 scale, in adverse events related to the PAHP program, or in the overall unmet needs.ConclusionFindings from an initial sample undergoing a PAHP program suggest that despite a low implementation fidelity of the program, functional independence outcomes and patients’ global impression of change have improved. Results will help develop a stakeholder-driven action plan to improve this program. A future study with a larger sample size is currently being planned to evaluate the overall impact of this program.Clinical trial registrationRetrospectively registered NCT05915156 (22/06/2023).

Neq2X7: a multi-purpose and open-source fusion DNA polymerase for advanced DNA engineering and diagnostics PCR

Thermostable DNA polymerases, such as Taq isolated from the thermophilic bacterium Thermus aquaticus, enable one-pot exponential DNA amplification known as polymerase chain reaction (PCR). However, properties other than thermostability - such as fidelity, processivity, and compatibility with modified nucleotides - are important in contemporary molecular biology applications. Here, we describe the engineering and characterization of a fusion between a DNA polymerase identified in the marine archaea Nanoarchaeum equitans and a DNA binding domain from the thermophile Sulfolobus solfataricus. The fusion creates a highly active enzyme, Neq2X7, capable of amplifying long and GC-rich DNA, unaffected by replacing dTTP with dUTP in PCR, and tolerant to various known PCR inhibitors. This makes it an attractive DNA polymerase for use, e.g., with uracil excision (USER) DNA assembly and for contamination-free diagnostics. Using a magnification via nucleotide imbalance fidelity assay, Neq2X7 was estimated to have an error rate lower than 2 ∙ 10−5 bp−1 and an approximately 100x lower fidelity than the parental variant Neq2X, indicating a trade-off between fidelity and processivity – an observation that may be of importance for similarly engineered DNA polymerases. Neq2X7 is easy to produce for routine application in any molecular biology laboratory, and the expression plasmid is made freely available.

Ensemble learning based hierarchical surrogate model for multi-fidelity information fusion

Recently, multi-fidelity information fusion based surrogate modeling methods have made great progress in the engineering design and optimization tasks. Two main issues in this field are: (1) Since the overarching trend of the responses from the high-fidelity (HF) model is captured by the low-fidelity (LF) model, inaccurate LF model may negatively impact the final modeling results. (2) The responsiveness of multi-fidelity surrogate (MFS) models to variations in the relationship between LF and HF models leads to limited prediction performance. To this end, we propose an ensemble learning based MFS modeling method with a hierarchical framework, called EL-MFS. Specifically, to alleviate the impact of issue (1), we present an adaptive ensemble surrogate model, which aims to effectively mitigate the negative impact of inappropriate LF model selection on the HF approximation results. Furthermore, we propose to exploit the feature mapping and hierarchical framework to boost the versatility of the model as a way to mitigate the dependence of MFS performance on the relationship between HF and LF models. To assess the effectiveness of the proposed model, a sequence of numerical problems is tested, and some advanced surrogates are selected as the baseline models. Moreover, to demonstrate the potential of the proposed model in aiding intricate engineering design, one engineering case is investigated as well.

WrapToR shells: Concept introduction, experimental testing, and design space exploration

This work introduces a novel truss stiffener concept for lightweight structural panels as an alternative to sandwich panels or stringer stiffened panels. The Wrapped Tow Reinforced (WrapToR) truss concept is modified to create stiffened composite panel structures (referred to here as WrapToR Shells). A prototype WrapToR Shell unit cell is manufactured and tested under three-point bending, where it demonstrates high levels of specific stiffness and strength. This experimental result is benchmarked against idealised sandwich panels via a low fidelity comparative analysis, and the study shows that even a best-case sandwich panel of equivalent mass under a representative bending load has an 83% increase in displacement, highlighting the excellent stiffness truss reinforced panels can provide. The design space available to the truss reinforced panels is then explored using finite element analysis and a simple parameter sweep of key truss design variables. A Pareto frontier between the competing objectives of minimising mass and maximising rigidity is shown, and global trends in performance are highlighted. It is observed that increasing the size of the truss profile or longitudinal member has a near linear increase on EI/mass, whereas for increasing shear member angle the inverse is true. For increasing shear member diameter, a less obvious effect on EI/mass is seen, indicating a more complex relationship with the other design variables.

Preventing venous thromboembolism for ambulatory patients with cancer: Developing the form and content of implementation strategies

BackgroundFor ambulatory cancer patients receiving systemic chemotherapy, adherence is low to recommended venous thromboembolism (VTE) prevention interventions. Previously, we identified implementation strategies to address barriers to adherence, including (1) conducting clinician education and training; (2) developing and distributing educational materials for clinicians; (3) adapting electronic health records to provide interactive assistance; and (4) developing and distributing educational materials for patients. The objective of this study was to develop these implementation strategies’ form (i.e., how and when) and content (i.e., information conveyed) as a critical step for implementation and dissemination. MethodsTo design and develop the form and content of the implementation strategies, we conducted multidisciplinary stakeholder panels with oncology clinicians, pharmacists, and hematologists. Over several panel discussions, we developed a low fidelity prototype. Participants performed preliminary usability testing, simulating patient care encounters. We also conducted interviews with three patients who provided additional feedback. ResultsThe form and content for each strategy, respectively, included (1) concise training with a slide deck; (2) succinct summary of evidence for the interventions and support for anticoagulation management; (3) automated VTE risk-assessment and clinical decision support, including bleeding risk assessment and anticoagulation options; and (4) patient education resources. During development, audit and feedback was identified as an additional strategy, for which we created report cards to implement. ConclusionWith stakeholder input, we successfully developed the form and content needed to put the implementation strategies into practice. The next step is to study the effect on the uptake of ambulatory VTE prevention recommendations in oncology clinics.

Vortex-induced vibrations of wind turbines: From single blade to full rotor simulations

Vortex-induced vibrations (VIVs) of wind turbine blades are a phenomenon that cannot be predicted sufficiently accurate by low fidelity methods. High fidelity methods, such as computational fluid dynamics (CFD) coupled to a structural solver come at a high computational cost. Most studies of the phenomenon have so far focused on analysis of single blades. For these cases, the so-called forced-motion method of prescribing the blade motion according to structural modeshapes has proven effective.In this article, VIVs of the IEA 10 MW reference turbine is studied in a fluid–structure interaction setup, where the structural dynamics of the full turbine are represented in the structural solver and the full rotor aerodynamics are simulated in the CFD solver. Due to the high computational cost this study is limited to only a single case with the rotor in bunny configuration, 80 degrees pitch and 90 degrees yaw. Also the purely structural power dissipation of the full turbine is studied to enable full rotor forced-motion in future work. A blade tip amplitude of roughly 17 meters is reached in the full turbine simulations, which is a significantly larger limit cycle amplitude than what would be reached in a corresponding isolated single blade simulation. The analysis also shows that forced-motion simulations combining three single blades vibrating according to the structural modeshape would be feasible. It is further observed, that the power injection or extraction close to the blade tip may change drastically at very large amplitudes compared to lower amplitudes.

High-Fidelity Gradient Inversion in Distributed Learning

Distributed learning frameworks aim to train global models by sharing gradients among clients while preserving the data privacy of each individual client. However, extensive research has demonstrated that these learning frameworks do not absolutely ensure the privacy, as training data can be reconstructed from shared gradients. Nevertheless, the existing privacy-breaking attack methods have certain limitations. Some are applicable only to small models, while others can only recover images in small batch size and low resolutions, or with low fidelity. Furthermore, when there are some data with the same label in a training batch, existing attack methods usually perform poorly. In this work, we successfully address the limitations of existing attacks by two steps. Firstly, we model the coefficient of variation (CV) of features and design an evolutionary algorithm based on the minimum CV to accurately reconstruct the labels of all training data. After that, we propose a stepwise gradient inversion attack, which dynamically adapts the objective function, thereby effectively and rationally promoting the convergence of attack results towards an optimal solution. With these two steps, our method is able to recover high resolution images (224*224 pixel, from ImageNet and Web) with high fidelity in distributed learning scenarios involving complex models and larger batch size. Experiment results demonstrate the superiority of our approach, reveal the potential vulnerabilities of the distributed learning paradigm, and emphasize the necessity of developing more secure mechanisms. Source code is available at https://github.com/MiLab-HITSZ/2023YeHFGradInv.

Learning Dense Correspondence for NeRF-Based Face Reenactment

Face reenactment is challenging due to the need to establish dense correspondence between various face representations for motion transfer. Recent studies have utilized Neural Radiance Field (NeRF) as fundamental representation, which further enhanced the performance of multi-view face reenactment in photo-realism and 3D consistency. However, establishing dense correspondence between different face NeRFs is non-trivial, because implicit representations lack ground-truth correspondence annotations like mesh-based 3D parametric models (e.g., 3DMM) with index-aligned vertexes. Although aligning 3DMM space with NeRF-based face representations can realize motion control, it is sub-optimal for their limited face-only modeling and low identity fidelity. Therefore, we are inspired to ask: Can we learn the dense correspondence between different NeRF-based face representations without a 3D parametric model prior? To address this challenge, we propose a novel framework, which adopts tri-planes as fundamental NeRF representation and decomposes face tri-planes into three components: canonical tri-planes, identity deformations, and motion. In terms of motion control, our key contribution is proposing a Plane Dictionary (PlaneDict) module, which efficiently maps the motion conditions to a linear weighted addition of learnable orthogonal plane bases. To the best of our knowledge, our framework is the first method that achieves one-shot multi-view face reenactment without a 3D parametric model prior. Extensive experiments demonstrate that we produce better results in fine-grained motion control and identity preservation than previous methods.

Physics informed deep learning for flow and force predictions in dense ellipsoidal particle suspensions

Solid-fluid multiphase systems are ubiquitous in many chemical, pharmaceutical, and energy based applications. These flows are challenging to study experimentally, thus several numerical simulation techniques have been developed. In this paper we use Particle Resolved Simulations (PRS) combined with Deep Learning (DL) to predict drag forces on each particle in suspensions of ellipsoidal particles for use in lower fidelity Euler-Lagrange and Euler-Euler simulations. Suspensions with solid fraction of 0.1, 0.2 and 0.3 are investigated at Reynolds numbers of 10, 50, 100 and 200. A UNet architecture is used to predict the velocity and pressure fields and a CNN is used to predict the drag forces. It is shown that drag force predictions using the predicted velocity and pressure fields give more accurate results than predictions that only use geometric information.

A new numerical modelling framework for fixed oscillating water column wave energy conversion device combining BEM and CFD methods: Validation with experiments

The Oscillating Water Column (OWC) wave energy converter has been shown to have high potential, thus rendering extensive development in recent years. In order to further accelerate its development, highly accurate yet computationally efficient tools are necessary particularly when studying the interaction of multiple OWC devices. This paper proposes a new framework for fixed OWC devices with an orifice, that uses the input from a high fidelity non-linear numerical model to improve the accuracy of a low fidelity linear numerical model keeping computational costs low. This is done by accounting for the non-linearities in the pressure-flow of an orifice in the input to the linear numerical model. Experimental data is used to validate the framework, thus providing an accurate and computationally efficient linear numerical model, that can be used for the preliminary analysis of fixed OWC devices.

Low fidelity simulation versus standardized patient for teaching peripheral venous cannulation for nursing students: A crossover randomized trial

BackgroundThe placement of a peripheral venous cannulation is a procedure that all healthcare providers must be able to perform. AimThis study compared two simulation techniques of a peripheral venous cannulation (VC): low fidelity simulation (LFS) versus standardized patient (SP) in terms of efficiency. MethodsThis crossover randomized trial compared LFS to SP in VC placement for 2nd year nursing students from the Honoris United Universities. Each student performed the VC on both the manikin's arm and the SP in random order. The main outcome measure was successful of VC after the second attempt. The secondary outcome measures were time to successful of VC, number of attempts, adherence to asepsis (Registered protocol number: PACTR202111766112898). ResultsOne hundred sixty learners were needed (α = β = 5%, 20% difference between the two techniques and 53% for SC). We enrolled 172 students. No statistical difference was found concerning the main outcome measure: 135 (78.5%) versus 138 (80.2%) for respectively LFS versus SP (p:0.689), nor for the secondary outcome measures. Six out of nine non-technical skills variables had a statistically significant difference in favor of SP. ConclusionNo statistical difference was found concerning the main and secondary outcome measures. RegistrationRegistered protocol number: Pan African Clinical Trials register (PACTR202111766112898)—Date of first recruitment on February 2, 2022.

Framework-based qualitative analysis of free responses of Large Language Models: Algorithmic fidelity.

Today, with the advent of Large-scale generative Language Models (LLMs) it is now possible to simulate free responses to interview questions such as those traditionally analyzed using qualitative research methods. Qualitative methodology encompasses a broad family of techniques involving manual analysis of open-ended interviews or conversations conducted freely in natural language. Here we consider whether artificial "silicon participants" generated by LLMs may be productively studied using qualitative analysis methods in such a way as to generate insights that could generalize to real human populations. The key concept in our analysis is algorithmic fidelity, a validity concept capturing the degree to which LLM-generated outputs mirror human sub-populations' beliefs and attitudes. By definition, high algorithmic fidelity suggests that latent beliefs elicited from LLMs may generalize to real humans, whereas low algorithmic fidelity renders such research invalid. Here we used an LLM to generate interviews with "silicon participants" matching specific demographic characteristics one-for-one with a set of human participants. Using framework-based qualitative analysis, we showed the key themes obtained from both human and silicon participants were strikingly similar. However, when we analyzed the structure and tone of the interviews we found even more striking differences. We also found evidence of a hyper-accuracy distortion. We conclude that the LLM we tested (GPT-3.5) does not have sufficient algorithmic fidelity to expect in silico research on it to generalize to real human populations. However, rapid advances in artificial intelligence raise the possibility that algorithmic fidelity may improve in the future. Thus we stress the need to establish epistemic norms now around how to assess the validity of LLM-based qualitative research, especially concerning the need to ensure the representation of heterogeneous lived experiences.

Parametric shape optimization of pin fin arrays using a multi-fidelity surrogate model based Bayesian method

The current work aims to develop a multi-fidelity Bayesian optimization methodology to reduce computation time in CFD based design optimization of an internal cooling channel of a gas turbine blade. To build energy efficient gas turbine engines, it is important to increase the turbine inlet temperature, while preventing material failure of hot gas path components. Power lost due to pumping compressed air in turbine blade internal cooling channels can affect the overall efficiency of a power cycle. While designing such a cooling channel, it is important to ensure high heat transfer within a given pressure drop budget. Design optimization using CFD typically needs a sufficient number of computationally expensive high fidelity simulations to search for an optimum design. In the current work, a multi-fidelity Gaussian process surrogate based Bayesian optimization framework has been used to design a pin fin array channel, typically used for gas turbine blade trailing edge cooling, with the goal of maximizing Nusselt number at a constrained pressure loss. Data from a cheaper, low fidelity computation model has been leveraged, along with an expensive high fidelity one, in order to search for a design configuration that maximizes heat transfer at a given pressure drop constraint. The multi-fidelity approach improves upon single fidelity optimization results, at a low additional computational cost due to usage of simulation data from the cheap lowfidelity CFD model. A multifidelity approach outperformed the previous single fidelity case, with 6% higher objective while satisfying the pressure constraint, with 2% lower computational expense and 8 fewer high fidelity data point computations.

Wings of Knowledge: Navigating Learner Confidence and Cognitive Load in Avian Radiography with a Low Fidelity Model.

In veterinary first opinion practice, radiography is an important diagnostic tool for avian patients. Teaching of such diagnostic skills to learners is usually conducted using teaching models in clinical skills laboratories. The aim of this work is to evaluate the impact of using a teaching model for avian radiography positioning by measuring learner's cognitive load, confidence, satisfaction, and assessing learning by OSCE assessment. An avian radiography positioning model was created and evaluated with pre- and post-Likert questions on confidence, a pre and post 9-point cognitive load scale, an OSCE assessment (max score = 20), and post Likert questions on satisfaction. Thirty-two undergraduate veterinary medicine and veterinary nursing students participated in the study. The results showed the cognitive load of participants was high and did not change with the use of a physical model (p = .882). Participants exhibited increased confidence in avian radiography positioning (pre; M = 2, post; M = 4, p < .001) and expressed high overall satisfaction with the model (x̄ = 4.6, no negative or neutral Likert responses). The OSCE results demonstrated a higher pass rate mean (82%) for the positioning tasks compared to the collimation and centering tasks (53%). Overall, the model was well received by learners with increased confidence and a satisfactory learning experience in a clinical skill for exotics species. These findings suggest the avian radiography positioning model is an effective model to train students to position avian patients for radiography.

Incremental learning with multi-fidelity information fusion for digital twin-driven bearing fault diagnosis

Digital twin (DT)-driven intelligent fault diagnosis (IFD) has been a hot topic, which can support personalized monitoring of critical machinery. A central challenge is that diagnostic models using deep learning (DL) suffer from the problem of catastrophic forgetting if personalized faults occur in dynamic environments. To deal with this issue, this article presents a class-incremental learning method with multi-fidelity information fusion (MFIF-CIL) for the continuous diagnosis of key faults in rolling bearings. First, an effective bearing DT model is constructed to generate enough low-fidelity (LF) simulation data. Second, feature boosting is developed to fit the residuals with distribution drifts between old classes and new classes, which helps prevent the problem of catastrophic forgetting. Last, the MFIF module is proposed for multi-fidelity knowledge transfer and fusion to leverage LF simulation data to improve the class-incremental learning ability of feature boosting with limited high-fidelity (HF) physical data. The testing datasets consisting of the measured signals are utilized as testing datasets of optimal incremental neural networks for fault diagnosis. The proposed MFIF-CIL-1 (using 15 HF data and 100 LF data as exemplars) and MFIF-CIL-2 (using 20 HF data and 100 LF data as exemplars) obtain the average diagnostic accuracies of 96.87% and 98.10%, respectively. The MFIF-CIL-2 only uses 41.93% of the training time required by the joint training method. These satisfying results demonstrate that the MFIF-CIL can effectively diagnose different health conditions over time and provide a tradeoff between relatively low experimental costs and high accuracy.

Small-scale movements and site fidelity of two sympatric sea turtle species at a remote atoll

Understanding natural movement patterns and ecological roles of marine megafauna is a research priority best studied in areas with minimal human impact. The spatial distribution patterns specifically for immature turtles at foraging grounds have been highlighted as a research gap for effective management and conservation strategies for sea turtle populations. Capture–mark–recapture (CMR) records (n = 2287) of 1672 immature green (Chelonia mydas) (n = 1158) and hawksbill turtles (Eretmochelys imbricata) (n = 514) from a long-term (1981–2021) in-water CMR program at Aldabra Atoll, Seychelles, were analyzed for 10 sites (0.35–25 km apart). Site fidelity was not correlated with either season or turtle size. Green turtles had lower site fidelity than hawksbill turtles. Green turtles showed avoidance (i.e., opposite of fidelity) of three sites, while hawksbill turtles displayed high fidelity to two sites. Sites displaying non-random behavior (avoidance and/or fidelity) did not share the same benthic habitat types. Results indicate that fidelity can be detected at a fine scale with CMR, but that further exploration into the habitat characteristics of the sites and the ecological roles of both species at the atoll is needed.

Evaluating a targeted person-centred pain management intervention programme in lumbar spine surgery - a controlled segment-specific before-and-after interventional design

BackgroundPostoperative pain management in lumbar spine surgery care remains a challenge. The aim of this study was to evaluate the impact of a person-centred postoperative pain management intervention programme on lumbar spine surgery patients on postoperative pain, shared decision-making, and satisfaction with postoperative pain management.MethodsThe study was performed with a controlled before-and-after interventional design in an orthopaedic unit at a university hospital. Person-centred pain management for patients undergoing spine surgery was developed in co-creation by a multi-professional team and implemented throughout the care pathway. The usual care group (pre-intervention) served as a comparison to the intervention group. Pain intensity, shared decision-making in pain management, and patient satisfaction with results of pain management, served as patient-reported measures, collected using the International Pain Outcomes questionnaire and analysed using descriptive statistics.ResultsThe intervention showed no benefit for patients’ pain and satisfaction, while shared decision-making in pain management was significant lower in the intervention group than in the conventional group. The per-protocol analysis showed no significant differences between groups.ConclusionThe initial assumption of the study, that the implementation of a co-created structured person-centred care pathway would improve patient-reported outcomes, was not confirmed. The periodically low fidelity to the intervention due to organizational constraints (due to sub-optimal organizational conditions and managerial support) may have affected the results.

Validation of tidal turbine wake simulations using an open regional-scale 3D model against 1MW machine and site measurements

Full-scale tidal turbines deployed in tidal channels are subjected to complex flow due to the effects of local bathymetry and coastline shape which modify the flow directionality, shear, twist and speed of the underlying tidally forced flow. In response to these effects, the wake of an operational tidal turbine will vary spatially and temporally. In order to predict wake form, which is a key step for scaling up tidal energy as developments move from single devices to arrays, we have developed and demonstrated an open source turbine-embedded regional three-dimensional (3D) hydrodynamic model for power and wake prediction. Simulation outputs have been validated against in-situ measurements acquired via ADCPs positioned downstream of and adjacent to the rotor-plane of an operating tidal turbine. Model predictive performance is parameterised by the relative difference between modelled and measured power-weighted rotor averaged velocity using speed binning and temporal-averaging. In the absence of the turbine representation, the difference between model and measurement for ebb and flood was found to be 0.81% and 1.04% respectively at the flow speed of 2.5 m/s. With the turbine represented as an actuator disc in the model, the averaged error of the velocity profiles in the wake is 4.4% and 5.2% at ADCP locations that are 3.7D and 6.2D downstream of the rotor plane during ebb. The model has been designed for use on moderately-priced workstations and the promising results, in terms of capturing key 3D flow features and predictions of wake velocity deficits, is a starting point for further development and may provide baseline data for alternative (e.g., higher fidelity, slower performance, or lower fidelity including 2D models with much faster performance).