Background: Advances in digital technology have transformed contemporary implant prosthodontics, offering new approaches to the design and fabrication of implant overdentures (IODs). Computer-aided design and manufacturing (CAD/CAM), additive manufacturing, and computer-assisted implant surgery have improved clinical accuracy, efficiency, and patient satisfaction. Understanding current digital workflows, manufacturing methods, and clinical implications as well as their limitations is essential to optimize treatment outcomes in IODs. Objectives: To provide a concise but comprehensive overview of the state of the art of digital implant overdentures focusing on the workflow, manufacturing, clinical outcomes, and associated complications. Conclusion: Digital technologies have improved precision and efficiency in removable implant prosthodontics. While CAD/CAM and additive manufacturing enhance fit and workflow, fully digital protocols still remain a challenge, especially in complex cases. Hybrid approaches combining conventional and digital methods currently provide the most predictable results, with future advances expected to expand clinical applicability.

Structural-coupling driven cobalt metalloporphyrin⊂MIL-101(Fe) hybrids for efficient photocatalytic CO2 reduction.

Modeling strategies for CGM data: A scoping review of mechanistic, machine learning, and hybrid approaches in diabetes management.

Fuzzy modeling of uncertain time-fractional Whitham–Broer–Kaup model using a hybrid approach

Two-month outcomes of advanced pneumatic compression device (APCD) and usual care (UC) in Head and Neck Cancer survivors with previously untreated lymphedema were compared. Participants in this multisite, randomized clinical trial were randomized to APCD or UC. The primary endpoint was severity of lymphedema symptoms. Secondary endpoints were anatomical lymphedema changes, biopsychosocial outcomes, and barriers to care. Two hundred thirty-six participants were enrolled (119 APCD, 117 UC). Analysis was intention-to-treat. Lymphedema-associated symptom burden measured using the VHNSS and LSIDS was improved to a similar degree in both groups. APCD demonstrated a statistically significant improvement in external soft tissue swelling assessed by digital photography. No difference in CT imaging measures of lymphedema was noted. UC participants experienced barriers to care. APCD is an effective treatment for lymphedema in HNCS. The APCD addresses clinically significant barriers to therapist guided treatment. A hybrid approach may be complementary and optimize patient outcomes. NCT04797390.

Heart rate (HR) is a major determinant of cardiac output (CO). During tachycardia, adequate left ventricular (LV) filling becomes important; however, excessive tachycardia reduces CO by shortening filling time. The impact of HR on hemodynamics differs according to the underlying cardiac pathological conditions, making HR management challenging. The atrioventricular (AV) time interaction, which represents the temporal coordination between atrial and ventricular functions for optimal LV filling, is key to effective HR management during tachycardia. This study aimed to elucidate tachycardia-induced hemodynamic deterioration by focusing on AV time interaction. A hybrid approach combining animal experiments and computer simulations was employed. In animal experiments, hemodynamics and mitral valve (MV) flow at HR of 90-180bpm were recorded in eight beagles. The relationship among LV end-diastolic pressure (LVEDP), left atrial pressure (LAP), and changes in MV E-A waves were evaluated. In simulation experiments, hemodynamics at HR of 50-150bpm were analyzed using a cardiovascular mathematical model across five pathological conditions. In animal experiments, CO peaked at HR of 130-140bpm, coinciding with MV E-A wave fusion and a decreased LVEDP/LAP ratio. Simulation studies showed effective tachycardic compensation in systolic and diastolic dysfunction. However, prolonged relaxation time and prolonged AV delay led to decreased CO at high HR. AV time interaction manifests as E-A wave fusion and decreased LVEDP/LAP ratio. Simulation studies suggest that conditions directly affecting filling time significantly impair AV time interaction and lead to circulatory deterioration during tachycardia. These findings highlight the importance of individualized HR management.

Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches

Gambling-related harm (GRH) significantly impacts health-related quality of life (HRQoL), though its measurement remains challenging due to varied methodologies. This study synthesises existing research on HRQoL effects of gambling, highlighting the complementary strengths of direct and indirect elicitation methods. We reviewed studies measuring GRH's impact on HRQoL, focusing on the maximum effect of severe problems and the shape of the impact curve across the harm spectrum. Results revealed consistent maximum HRQoL disutility of approximately 0.45 for severe gambling problems in direct elicitation studies, while indirect methods showed scaling that implies an arguably implausible maximum. We propose a novel hybrid approach that leverages the strengths of both methods: using direct elicitation to establish the maximum impact and indirect methods to determine the relative impacts across the harm spectrum. This approach mitigates attribution biases in direct measures for lower levels of harm while still benefiting from their ability to accurately capture the impact of gambling at severe levels. To assess lower levels of impact, it uses relative scaling from indirect methods that better reflect how quality of life changes as harms accumulate. Our final best-of-both-worlds estimate synthesises available evidence of GRH's impact on HRQoL, grounded in recognised public health metrics.

Background: Accurately distinguishing between authentic and forged handwritten signatures is crucial for ensuring document authenticity and preventing fraud. Existing offline signature verification methods often suffer from limitations in terms of precision, time efficiency, and labour intensity. To address these challenges, this research proposes a novel hybrid approach, Deep DenseNet-169 and CNN for Signature Forgery (DDCF), to improve classification reliability, accelerate verification, and reduce dependency on manual evaluation. Methods: The proposed methodology comprises three key components: feature scaling, the DenseNet-169 architecture, and transfer learning classifiers. Feature scaling optimizes the extraction of critical signature characteristics, while the DenseNet-169 model enhances feature representation. Transfer learning classifiers further refine classification performance. The approach was evaluated using the Kaggle dataset Handwritten Signature Forgery Detection and Verification to assess its effectiveness. Results: The proposed DDCF model attained 92.00% accuracy, 91.45% precision, 90.78% recall, and 91.11% F1-score on the SFD-I dataset. For the SFD-II dataset, the model achieved 95.12% accuracy, 94.37% precision, 93.89% recall, and 94.13% F1-score. On the most complex benchmark, SFD-III, the model obtained 97.34% accuracy, 96.98% precision, 96.34% recall, and 96.66% F1-score. These results demonstrate a consistent performance gain of 3–7% over existing state-of-the-art approaches, underscoring the robustness, reliability, and generalizability of the proposed framework. Conclusion: The proposed DDCF framework surpasses previous research in both accuracy and efficiency. By combining feature scaling, the DenseNet-169 architecture, and transfer learning classifiers, the study presents a robust and precise method for handwritten signature verification.

Advancing precision psychiatry: Machine learning integration with neuroimaging for early detection and diagnosis of Obsessive-Compulsive Disorder.

Extracorporeal photopheresis (ECP) is an established immunomodulatory therapy that is traditionally delivered using either "inline" systems, which integrate mononuclear cell (MNC) collection, photoactivation, and reinfusion, or "offline" platforms that allow greater processing flexibility but require dedicated irradiation equipment. Access to standalone UVA irradiators may be limited in clinical and research settings where cryopreservation of ECP-treated cells is required. In this study, we describe a practical hybrid approach that adapts the inline Amicus Blue Photopheresis System into an inline-to-offline workflow, enabling larger total blood volume processing, partial reinfusion, and cryopreservation of photoactivated MNCs. Using a fixed 200-mL MNC collection, followed by standard 8-methoxypsoralen administration and UVA irradiation, 50 mL was reinfused inline, while the remaining product was aliquoted for offline cryopreservation. This method has been implemented in the OPERA clinical trial (NCT05413005) and preserves procedural safety, product integrity, and operational simplicity. In summary, this approach enables offline capabilities within an inline ECP framework without requiring an independent UVA irradiator and expands flexibility for clinical programs and translational research. Trial Registration: NCT05413005.

Web data analysis using a hybrid approach of DOM processing and deep learning models

Exploring the sustainable development path of global digital service trade stability: A hybrid approach perspective

Regenerative medicine promises the possibility of custom-made, ready-to-use human organs without the risk of immune rejection. Human pluripotent stem cells (hPSCs) are the workhorses of stem cell-based tissue engineering. With inherent capabilities to adopt nearly any cellular form, they are supposed to solve the soaring demand for transplantable organs. Technically, PSCs are converted into cells of interest using a stepwise approach (differentiation) mimicking embryonic development. Animal models have been crucial in advancing our understanding of human embryology, mainly due to the widespread conservation of the mammalian regulome. Differentiation protocols have evolved with time from two-dimensional (2D) monocultures, which are relatively easy to maintain, to more complex three-dimensional (3D) organoids that enhance the capacity for staging multilineage assemblies. The appeal of 3D systems lies in their operational resemblance to the actual morphology of tissues. While each platform has pros and cons, its specific strengths can be leveraged to tell a more compelling story of development and how complex pathologies take root. Here, we reviewed key methodologies for the in vitro production of human functional cell lineages from hPSCs. We have connected the most recent science to the work that came before and analyzed where the trends we see now might lead. We examined the shift from 2D cell monolayers to 3D organoids. Additionally, we highlighted hybrid approaches and innovative discoveries that support the reliable generation of physiologically mature cells, enabling the study of development and disease at new depths.

A novel physics–data hybrid approach for slope stability assessment considering future rainfall patterns

A hybrid approach to CFD modeling of the combustion chamber and the platen superheater

Environmental performance of bovine leather and alternatives: a hybrid approach combining life cycle screening and assessment

This study investigates recent advancements in machine learning (ML) algorithms for Big Data analytics, with a focus on scalability, real-time processing, and ethical considerations. A qualitative literature review was performed, examining recent ML developments through thematic analysis of peer-reviewed publications and industry reports. The findings highlight notable improvements in scalability via distributed computing frameworks such as Apache Spark and Hadoop, as well as enhanced real-time processing achieved through online learning techniques. Nevertheless, challenges persist in maintaining model accuracy in the presence of noisy data and mitigating algorithmic bias. Ethical issues concerning fairness, transparency, and accountability were also identified. This research advances understanding of ML's role in Big Data applications and provides practical insights for deploying scalable, interpretable, and ethically responsible models across industries. Future work should focus on refining hybrid approaches and evaluating their applicability in real-world scenarios.

ABSTRACT Rapid technological change increases managerial uncertainty about how technological capabilities should be configured to generate distinct innovation outcomes. Prior research often assumes relatively uniform returns from technological resources. Using survey data from 1,122 firms across eight industries in Vietnam, this study examines how differentiated managerial capability investments produce asymmetric incremental and disruptive innovation trajectories across diverse industry knowledge bases. A hybrid approach combining structural equation modeling with Random Forest and SHAP analysis enables theory driven testing alongside robustness diagnostics. The findings reveal structurally differentiated capability productivity. Incremental innovation is primarily driven by skills capability and digitally enabled coordination, reflecting exploitation-oriented investment patterns. Disruptive innovation is mainly enabled by collaboration capability and advanced digital maturity, indicating exploration-oriented strategies. Capability effectiveness also varies systematically across analytical, symbolic, and synthetic knowledge bases. By conceptualizing technological capabilities as industry conditioned investment configurations with asymmetric innovation effects, the study advances a contingent theory of capability productivity under technological uncertainty and highlights how firm level configurations interact with industry knowledge architectures to shape innovation trajectories.

External evidence from prior trials, registries, and fit-for-purpose real-world data can improve drug development efficiency. Hybrid-controlled designs are particularly appealing for reducing concurrent control enrollment while simultaneously providing internal validity with a randomized control arm.Yet regulatory adoption is limited due to major concerns aroundbias due to possible differences in characteristics and outcomes between the external data and the trial. To realize the benefits of the hybrid approach without compromising credibility, methodological guardrails are crucial for mitigating bias and enabling valid inference. We assessed eight statistical methods which proactively address differences between external data and trial data.We apply these methods to both a large clinical trial as a case study, as well as within a comprehensive simulation study with continuous outcomes that varied the amount of measured versus unmeasured confounding, the severity of the between-data-source heterogeneity, and the number of external data sources. Results show that two-step strategy, propensity score-based balancing followed by Bayesian dynamic borrowing, consistently delivered the most favorable trade-off between precision gain and bias control. This approach when used with fit-for-purpose external datacan provide a robust implementation of the hybrid trial design beyond the narrow set of conditions where there is currently precedent.