Search Results (583)

In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and discusses the critical aspects of the CHIL implementation process, emphasizing the relevance of the control delays that arise from sampling, computation, and pulse width modulation (PWM), which also adversely affect system stability, accuracy, and performance. Time and frequency domains are used to validate the modeling of the system, either to represent large-signal or small-signal models. This work shows multiple representations of the system under study: the fundamental frequency model, the switched model, and the switched model controlled by the DSP, are used to validate the nonlinear model, whereas the impedance-based modeling is followed to validate the linear representation. The results demonstrate a strong correlation among the models, confirming that the delay effects are accurately captured in the different simulation approaches. This comparison provides valuable insights into configuration practices that improve the fidelity of CHIL-based validation and supports impedance-based stability analysis in power electronic systems. The findings are particularly relevant for wideband modeling and real-time studies in electromagnetic transient analysis. Full article

Traditional hybrid RANS/LES methods often struggle to accurately capture both the boundary layer transition and flow separation simultaneously due to their reliance on fully turbulent RANS models. To address this limitation, the present study first evaluates three transitional RANS models (γ-Re_θt-SST, γ-SST, and Kγ-SST) on the E387 airfoil. The results demonstrate that the γ-SST model offers the best balance of accuracy and computational efficiency in predicting laminar separation bubbles (LSBs) and transition points. Building on this, we implement the γ-SST-IDDES model into OpenFOAM-v2312, which integrates the γ-SST transitional RANS model with the Improved Delayed Detached Eddy Simulation (IDDES) approach. This coupling allows for the simultaneous prediction of the laminar-turbulent transition and high-fidelity resolution of separated flows. The γ-SST-IDDES model is rigorously validated across three airfoil cases with distinct separation characteristics: E387 (small separation), DBLN-526 (moderate separation), and NACA 0021 (massive separation). The results show that the γ-SST-IDDES model outperforms conventional methods, capturing leading-edge LSBs with high accuracy compared to fully turbulent IDDES. Additionally, it successfully resolves complex 3D vortical structures in separated regions, whereas unsteady URANS provides only quasi-2D results. Full article

Emerging research has shown that pharmacist-led comprehensive medication management (CMM) can be an effective strategy for controlling hypertension. A synthesis of the evidence on the overall effects of CMM on clinical, quality, and economic outcomes could help inform and contribute to improvements in programming and practice. Presently, such a synthesis is limited in the literature. To address this gap, we conducted a scoping review of CMM effects on these outcomes, organized by 4 domains: therapeutic, humanistic, safety and economic. Using predefined search terms for articles on studies published between 2010 and 2024, we performed a literature search utilizing these terms to search the MEDLINE, Cochrane Library and CINAHL databases. For each of the identified studies, we applied a multi-stage screening process to extract data, chart results, and synthesize findings. The process took into account methodology of study design, patient population involved, CMM implementation, relevance of outcomes to clinical improvement, and factors that were deemed relevant to study selection. In total, 49 experimental, observational, and simulation-based studies were included in the scoping review. The synthesis focused on outcomes most frequently reported and those rigorously evaluated by the studies in the review. They included clinical measures of blood pressure reduction and control, frequency and duration of healthcare visits, and changes in medication therapy regimen and medication adherence. Overall, CMM interventions were found to have significantly favorable effects on systolic blood pressure reduction, hypertension control, and medication changes. Other outcomes, which showed positive effects, included self-reported patient experience and behaviors, emergency department visits, hospitalizations, mortality, and program costs and related savings from implementing a CMM program. Some results, however, were mixed. For example, a number of studies reported outcomes data without significance testing and many generally lacked consistent characterization of their programming and implementation processes. Future research and practice evaluations should include these elements in their documentation. Furthermore, a more consistent approach to implementing CMM in the field may lead to better support of program delivery fidelity, helping to optimize CMM, moving it from demonstrated efficacy to intervention effectiveness in the real world. Full article

Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical domains, including radiology, oncology, neurology, and critical care. Model-agnostic techniques such as visualization models like Gradient-weighted Class Activation Mapping (Grad-CAM) and attention mechanisms dominated in imaging and sequential data tasks. Results: However, there are still gaps in user-friendly evaluation, methodological transparency, and ethical issues, as seen by the absence of research that evaluated explanation fidelity, clinician trust, or usability in real-world settings. In order to enable responsible AI implementation in healthcare, our analysis emphasizes the necessity of longitudinal clinical validation, participatory system design, and uniform interpretability measures. Conclusions: This review offers a thorough analysis of the state of XAI practices in CDSSs today, identifies methodological and practical issues, and suggests a path forward for AI solutions that are open, moral, and clinically relevant. Full article

Caregiver coaching is an essential component of caregiver-mediated interventions for young autistic children. Previous research evaluating usual practice in early intervention (EI) has found that EI providers often do not use caregiver coaching. Increasing the use of caregiver coaching strategies is critical to improving the outcomes of EI. We used a community-partnered process to develop a toolkit of implementation strategies to improve the use of caregiver coaching in EI. This study presents findings from a preliminary evaluation of the toolkit using a non-concurrent multiple-baseline design across groups of providers and caregiver–child dyads. The results indicate that providers’ caregiver coaching fidelity improved following the introduction of the toolkit. Caregivers demonstrated moderate growth in their use of supportive parenting techniques. All providers rated the toolkit as acceptable, appropriate, and feasible. The findings suggest that a toolkit of implementation strategies tailored to support the needs of community-based providers shows promise for improving caregiver coaching in EI. Full article

Background: Globally, socioeconomic disparities persist across the trajectory of chronic kidney disease and are pronounced among people with kidney failure. Unmet health-related social needs contribute to these disparities, but limited guidance exists about how best to address them. To guide implementation, we conducted a rapid scoping review to identify and characterize interventions that address health-related social needs among people with kidney failure. Methods: We adapted established scoping review methods to conduct a rapid review. We searched Embase, PubMed, CINAHL, SCOPUS, and PsychInfo for articles and conference abstracts published since 2013 that described interventions to address health-related social needs as identified in the Centers for Medicare and Medicaid Services’ Accountable Health Communities Health-Related Social Needs Screening Tool. We applied the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) to synthesize findings and characterize intervention components. Results: Our review identified three articles and five conference abstracts that described diverse interventions to address health-related social needs among people with kidney failure. Six targeted social support, one addressed food insecurity, and one addressed transportation needs. Two pilot studies to address social support reported high recruitment and retention rates. One study formally tested an intervention to address social support among adolescents with kidney failure and reported negative findings (no change in social exclusion). The level of detail about intervention implementation varied across studies, but none described excluded participants or intervention fidelity, adaptations, or cost. Conclusions: Despite recent attention, there remains a lack of evidence to guide interventions addressing health-related social needs among people with kidney failure. From limited available data, interventions to address social support may be feasible and acceptable. Full article

High-dynamic airborne gravimetry faces critical challenges from platform-induced noise contamination. Conventional filtering methods exhibit inherent limitations in simultaneously achieving dynamic tracking capability and spectral fidelity. To overcome these constraints, this study proposes a Kalman–FIR fusion filtering (K-F) method, which is validated through engineering implementation on the GIPS-1A airborne gravimeter platform. The proposed framework employs a dual-stage strategy: (1) An adaptive state-space framework employing calibration coefficients (S_x, S_y, S_z) continuously estimates triaxial acceleration errors to compensate for gravity anomaly signals. This approach resolves aliasing artifacts induced by non-stationary noise while preserving low-frequency gravity components that are traditionally attenuated by conventional FIR filters. (2) A window-optimized FIR post-filter explicitly regulates cutoff frequencies to ensure spectral compatibility with downstream processing workflows, including terrain correction. Flight experiments demonstrate that the K-F method achieves a repeat-line internal consistency of 0.558 mGal at 0.01 Hz—a 65.3% accuracy improvement over standalone FIR filtering (1.606 mGal at 0.01 Hz). Concurrently, it enhances spatial resolution to 2.5 km (half-wavelength), enabling the recovery of data segments corrupted by airflow disturbances that were previously unusable. Implemented on the GIPS-1A system, K-F enables precision mineral exploration and establishes a noise-suppressed paradigm for extreme-dynamic gravimetry. Full article

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis of 23 national policy frameworks and a stakeholder survey (n = 28). The results reveal that while many policies reference spatial data conceptually, critical components such as standardised datasets, spatial dashboards, and institutional coordination mechanisms remain underdeveloped. Spatial references are largely confined to early policy stages, with limited integration in evaluation and maintenance, thereby limiting adaptive governance. Conversely, survey findings reflect strong recognition of NSDI’s value across governance principles, policy integration, and spatial awareness dimensions. The composite endorsement score highlights institutional demand for geospatial tools, data standards, and capacity-building platforms. The study concludes that embedding NSDI within policy and planning systems can bridge critical governance gaps, enhance implementation fidelity, and support inter-agency coordination for long-term policy effectiveness. Full article

Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide a quantitative means to assess this alignment in terms of geometry, behavior, and performance. Among these, 3D shape descriptors play a central role in evaluating geometric fidelity, offering computational tools to measure shape similarity between physical and digital entities. This paper presents a comprehensive review of 3D shape descriptor methods and their applicability to geometric fidelity assessment in DTw systems. We introduce a structured taxonomy encompassing classical, structural, texture-based, and deep learning-based descriptors, and evaluate each in terms of transformation invariance, robustness to noise, computational efficiency, and suitability for various DTw applications. Building upon this analysis, we propose a conceptual fidelity metric that maps descriptor properties to the specific fidelity requirements of different application domains. This metric serves as a foundational framework for shape-based fidelity evaluation and supports the selection of appropriate descriptors based on system needs. Importantly, this work aligns with and contributes to the emerging ISO 30138 standardization initiative by offering a descriptor-driven approach to fidelity assessment. Through this integration of taxonomy, metric design, and standardization insight, this paper provides a roadmap for more consistent, scalable, and interoperable fidelity measurement in digital twin environments—particularly those demanding high precision and reliability. Full article

The structured integration of analytical methods, numerical simulations, and emerging data science techniques enables a highly efficient and robust modeling approach for manufacturing processes. To successfully implement advanced analytical strategies, numerical methods, and data-driven tools within digital twin or digital shadow frameworks for next-generation metal processing, several critical requirements must be addressed. This paper discusses the foundational elements necessary for the seamless integration of these technologies, with a focus on achieving impactful optimization and precise control of material processes. The research highlights the outcomes of combining data-driven models with high-fidelity numerical simulations, emphasizing their complementary roles in process control and data generation for future-oriented manufacturing modeling. Full article

Multisystemic Therapy (MST) is an intensive family and community-based treatment targeting antisocial behaviours in adolescents. Treatment fidelity has proved crucial for successful implementation of the MST intervention, with prior research demonstrating a strong association with positive and enduring treatment outcomes. The Therapist Adherence Measure (TAM) is a standardised measure reported by caregivers and comprised of 28 items based on the nine treatment principles of MST. Several randomised control trials have confirmed that therapist adherence to the MST model is a valid predictor for a reduction of antisocial behaviours in adolescents. However, there is limited understanding of mechanisms by which therapist model adherence is related to positive changes in family relations and association with decreased adolescent behavioural problems. In this retrospective study, we evaluated effects of therapist adherence on changes in parental factors (e.g., parental mental well-being, monitoring and discipline approach) which in turn were associated with decreased behavioural problems in adolescents. We extracted data collected from 186 families engaged with the MST research program operating within the Western Australian Child and Adolescent Mental Health Service (CAMHS) during 2018–2024. Data for TAMs were collected monthly during treatment, and family outcome measures were collected at pre-treatment and post-treatment. The finding highlights the importance of therapists maintaining treatment fidelity and addressing treatment barriers throughout MST intervention to ensure the desired therapeutic outcomes. Full article

Extreme ultraviolet (EUV) ptychography is a promising actinic mask metrology technique capable of providing aberration-free images with subwavelength resolution. However, its performance is fundamentally constrained by the strong absorption of EUV light and the limited detection of high-frequency diffraction signals, which are critical for resolving fine structural details. In this study, we demonstrate significant improvements in EUV ptychographic imaging by implementing an upgraded EUV source system with reduced source etendue and applying an illumination aperture to spatially refine the probe. This approach effectively enhances the photon flux and spatial coherence, resulting in an increased signal-to-noise ratio of the high-frequency diffraction components and an extended maximum detected spatial frequency. Simulations and experimental measurements using a Siemens star pattern confirmed that the refined probe enabled more robust phase retrieval and higher-resolution image reconstruction. Consequently, we achieved a half-pitch resolution of 46 nm, corresponding to a critical dimension of 11.5 nm at the wafer plane. These findings demonstrate the enhanced capability of EUV ptychography as a high-fidelity actinic metrology tool for next-generation EUV mask characterization. Full article

Suicide is a leading cause of death among construction workers, particularly younger and lower-skilled employees. Barriers such as stigma, low mental health literacy, and traditional masculine norms hinder help-seeking in this male-dominated sector. Few mental health interventions are tailored to this context. This study developed a co-designed, theory-informed training to improve mental health literacy, reduce stigma, and increase help-seeking among construction workers in Ireland. Using the Medical Research Council’s framework, the training was developed with the Theory of Planned Behavior (TPB), Behavior Change Techniques, and extensive stakeholder co-design. Two systematic reviews, a broad literature review, and focus groups with industry managers informed the content and structure. The training will be pilot-tested using validated measures: the Literacy of Suicide Scale (LOSS), the Stigma of Suicide Scale (SOSS), and the General Help-Seeking Questionnaire (GHSQ), the results of which will be the subject of a separate study. CAIRDE is a promising, evidence-based training that addresses key mental health barriers in Irish construction. Embedding the TPB within a co-design methodology has resulted in the development of a training program that is underpinned by theoretical fidelity and cultural relevance and provides a framework for other male-dominated industries to draw upon. Future work should address remaining challenges related to stigma and help-seeking, and explore broader implementation through integration into mandatory safety training. Full article

This work presents a high-fidelity two-way coupled Fluid-Structure Interaction (FSI) simulation framework for wind turbine blades, developed using the Arbitrary Hybrid Turbulence Modelling (AHTM) implemented through Very Large Eddy Simulation (VLES) in the DAFoam solver. By integrating VLES with the Toolkit for the Analysis of Composite Structures (TACS) structural solver via the OpenMDAO/MPhys framework, this work aims to accurately model the complex aeroelastic characteristics of wind turbines, specifically focusing on the NREL Phase VI wind turbine. The numerical model accounts for the effects of transient, turbulent, and unsteady aerodynamic loading, incorporating the impact of structural deflections. A comparison of the calculated results with experimental data demonstrates strong agreement in key performance metrics, including blade tip displacements, power output, and pressure distribution. This alignment confirms that the proposed model is effective at predicting wind turbine performance. One of the significant advantages of this study is the integration of advanced turbulence modeling with shell element structural analysis, enhancing the design and performance predictions of modern wind turbines. Although computationally intensive, this approach marks a significant advancement in accurately simulating the aeroelastic response of turbines, paving the way for optimized and more efficient wind energy systems. Full article

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article