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Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.

This commentary discusses the paper by Reardon et al. (2024; this issue) entitled, “Overcoming implementation challenges through using a train-the-trainer approach to teach numeracy in a special school setting.” This commentary outlines the necessary contribution this paper adds to the substantive area of research it is couched within while also identifying potential areas of future research to expand the understanding of this phenomenon and its impact upon practice.

What is fidelity of implementation, how do we measure it and how does it impact our interpretations of experimental findings? This commentary focuses specifically on the nebulous construct of fidelity in experimental studies and how this impacts experimental findings’ internal and external validity.

Although fidelity is frequently referenced as an important aspect to consider, the measurement of the construct has been critiqued in experimental studies. To understand if an intervention was “implemented as intended,” the core dimensions of the intervention must be considered in the measurement process, as well as potential confounding variables.

With an increased need for experimental work to inform what works, for whom and under what conditions, there becomes a need to better investigate the implementation of the intervention in these contexts – thus, fidelity must be reconceptualized. This commentary provides an overview of this dilemma with potential ideas to investigate moving forward.

Recent research suggests that more than two-thirds of people use online reviews to find a new primary care physician (PCP). However, it is unclear what role review content plays when a patient uses online reviews to decide about a new PCP. This paper aims to understand how a review's content, related to competence (communication and technical skills) and benevolence (fidelity and fairness), impacts patients’ trusting intentions to select a PCP. The authors build the model around information diagnosticity, construal level theory and valence asymmetries and use review helpfulness as a mediator and review valence as a moderator in this process.

The authors use two experimental studies to test their hypotheses and collect data through prolific.

The authors find that people have a harder time making inferences about the technical and communication skills of a PCP. Reviews about fidelity are perceived as more helpful and influential in building trust than reviews about fairness. Overall, reviews about the communication skills of a PCP have stronger effects on trusting intentions than other types of reviews. The authors also find that positive reviews are perceived as more helpful for the readers than negative reviews, but negative reviews have a stronger impact on patients' trust intentions than positive ones.

The authors identify how online reviews about a PCP’s competency and benevolence affect patients’ trusting intentions to choose the PCP. The implication of findings of this study for primary medical practice and physician review websites is discussed.

Based on the key dimensions of the Metaverse environment (immersiveness, fidelity and sociability), this paper aims to develop the concept of sensory word-of-mouth (WOM) in Metaverse – the metaWOM. It attempts to upgrade the Reviewchain model and suggests the utilization of non-transferable tokens (NTTs) in curbing the explosion of fake WOM.

Following Macinnis’ (2011) approach to conceptual contributions, the authors browsed the currently available literature on WOM, Metaverse and NTT to portray the emergence of metaWOM.

By relying on Metaverse’s three building blocks, the authors map out the persuasiveness of metaWOM in the Metaverse-like environment. By incorporating NTT in the Reviewchain model, the authors upgraded it to provide a transparent, safe and trusted review ecosystem. An array of emerging research directions and research questions is presented.

This paper comprehensively analyzes the implications of a Metaverse-like environment on WOM and debates on technologies that can enhance the metaWOM persuasiveness. The proposed model in this paper can assist various stakeholders in understanding the complex nature of virtual information-seeking and giving.

This is the original attempt to delineate the sensory aspect of WOM in the Metaverse based on three crucial aspects of the Metaverse environment: immersiveness, fidelity and sociability. This paper extends the discussion on the issue of fake reviews and offers viable suggestions to curb the ever-growing number of fraudulent WOM.

This study was steered to establish how buyer–supplier collaboration's commitment attributes serve as an antecedent for procurement performance in large manufacturing entities in Tanzania.

A parallel, concurrent, mixed method was used in the study. Quantitatively, 52 firms were surveyed from Temeke Municipality, Tanzania, using questionnaire that specified 1 procurement manager and 1 store manager from those firms, totaling a sample size of 104 respondents. Qualitatively, expressive opinions to supplement the numeric data were gathered from supply chain managers using the saturation principle. Explanatory design analyzed the existing cause–effect relationship, and the null hypotheses were tested using binary logistic regression at p values < 0.05 and ExpB > 1.

Fidelity and enthusiasm to suggest improvements to suppliers and the duration of the collaboration antecede the procurement performance of the manufacturing firms in Tanzania, while devotion to invest resources and initiatives on joint problem solving have no significant impact.

The causality between buyer–supplier collaboration and procurement performance has been revealed. Since there might be third party logistics in collaborations, future research should center on their moderating effect.

A framework has been developed for liberating procurement performance in the context of large manufacturing firms in Tanzania.

Based on Transaction Cost Economics and Resource Dependency Theories, the study revealed the root cause of procurement performance in the context of Tanzanian manufacturing firms, while also considering commitment to buyer–supplier collaboration as a prerequisit for the commendable target.

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

The purpose of this paper is to describe the development and pilot of a novel program to support autistic adults with social media use. Social media use among autistic adults has been associated with increased happiness and closer friendships. However, autistic adults are at risk of social media challenges such as cyber-victimization. To date, no programs exist that specifically support autistic adults with safe and effective social media use. The primary aim of this study was to develop and pilot test a novel social media skills program for feasibility and acceptability. A secondary aim was to explore changes in Facebook activity as a proxy for online social engagement.

The socialization, education and learning for the internet (SELFI) program was developed by identifying relevant target skills, goals, considerations and evidence-based strategies to help autistic adults with online social communication. The developed SELFI program was then pilot tested with six autistic male young adults to explore initial feasibility (fidelity of delivery, attendance) and acceptability (attrition, enjoyment and programmatic feedback). Facebook data were collected to explore differences in the frequency of likes/reactions, comments and posts after completion of the program.

Each participant completed all procedures and attended every session. There was no attrition from the program. The average fidelity score for recorded sessions was 85.1%. Most participants endorsed agreement with program enjoyment and that it helped them maintain current relationships. Participant feedback provided valuable information regarding areas of strength and areas that needed improvement. Each participant who completed the study demonstrated increased Facebook engagement across likes/reactions, comments and posts.

Limitations include a small all-male sample, exclusion of adults with intellectual disabilities and adolescents and dependence on Facebook data. Additionally, the developed program did not incorporate feedback from relevant stakeholders, including autistic adults.

To the best of the authors’ knowledge, this is one of the first studies to explore support for autistic adults specifically related to social media use.

This study was intended to investigate a small-scale School-based Motivational Interviewing (SBMI) pilot with first-year college students. This approach honors student autonomy, supports self-determination and has the potential to impact educational outcomes in higher education. Motivational Interviewing (MI) is an evidence based conversational skill set, defined as “a collaborative conversational style for strengthening a person's own motivation and commitment to change” (Miller and Rollnick, 2013, p. 12). Student perceptions of satisfaction with the faculty-student mentoring intervention were sought. Relational aspects of MI (partnership, empathy and alliance) were also explored.

A mixed-method approach was used for the SBMI study, focused on college students with recent academic setbacks (N = 19).

The intervention was deployed with high levels of MI technical fidelity and relational quality. Participants reported high satisfaction with the intervention. The relational aspects and participant perceived alliance with their faculty were highly correlated across the intervention, adding to the discussion of the mechanisms of MI that contribute to its effectiveness.

This work is formative, yet at this point is not generalizable given the scope of the study.

Findings are encouraging for further development of this innovative pedagogical approach. Possible future applications of research are provided.

Discussed herein, SBMI has the potential to meet the needs of traditionally underrepresented student groups.

The reported study is the initial portion of a larger intervention development project.

Children with developmental disabilities (e.g. learning disability and autism) often struggle with handwriting skills. This study aims to implement an adapted handwriting programme for children with developmental disabilities to improve their handwriting skills.

Six children with developmental disabilities aged 9–15 years received an adapted Handwriting Without Tears® (HWT) programme in small groups over eight weeks. The programme was delivered by typical teaching staff (i.e. paraprofessionals) at a special education school following a brief training session and with ongoing supervision. A range of measures assessed the children’s handwriting and related skills. Social validity interviews were conducted with school staff following the intervention to evaluate the programme’s acceptability.

Typical teaching staff implemented the handwriting programme with 92.3% average fidelity and delivered a minimum of three sessions per week. Social validity interviews demonstrated the acceptability of the intervention to school staff. After eight weeks of intervention, all children improved their handwriting on various assessments. Improvements were only partially maintained at follow-up.

This study supports the feasibility of using an adapted HWT programme to teach handwriting to children with developmental disabilities in special education settings. Typical teaching staff can be trained to support the delivery of the programme to children in small groups.