238455 research outputs found
Sort by
Metal–Organic Frameworks for Urban Air Pollution Mitigation: A Review and Research Framework Addressing India’s AQI Crisis
No full textThis OSF project presents a comprehensive review of the rising Air Quality Index (AQI) in Indian metropolitan regions, with a focus on Delhi, and explores Metal–Organic Frameworks (MOFs) as advanced materials for air pollution mitigation.
The project examines the scientific basis of AQI escalation, the limitations of natural atmospheric purification mechanisms, current MOF-based technologies for particulate and gaseous pollutant control, and existing challenges related to scalability and stability.
Additionally, the project outlines future research pathways in MOF design, green synthesis, urban-scale air purification systems, and interdisciplinary opportunities for students and early-career researchers
The Association Between Early Chemoprophylaxis versus No Chemoprophylaxis and the Risk of Venous Thromboembolism and Bleeding Complications after Same-Day Spine Surgery for Central Cord Syndrome: A Retrospective Cohort Analysis
No full textA retrospective cohort study that compares the effect of early chemoprophylaxis (enoxaparin or heparin) versus no chemoprophylaxis on the risk of venous thromboembolism (VTE) and bleeding complications after same day spine surgery for adult patients with central cord syndrome (CCS)
Types of dentin–pulp complex protection in permanent teeth: a scoping review
No full textDespite the wide range of materials and techniques currently available, the literature remains heterogeneous regarding indications, mechanisms of action, and reported clinical and biological outcomes associated with dentin–pulp protection strategies (Mittal, Koul & Mittal, 2025; Hatipoğlu, Tekingür & Hatipoğlu, 2025). Therefore, a systematic mapping of the existing evidence is warranted to organize the different types of dentin–pulp complex protection described in the literature, identify knowledge gaps, and support future research and clinical decision-making
Study Package - Using OADAPT (LI 4)
No full textAligned to the case study goal, after the participant used OADAPT, we applied a questionnaire and interviewed to capture the developer’s perception of OADAPT to help us evaluate whether using OADAPT without its author’s intervention is feasible and whether the approach is useful.
To collect data, first, the participant completed the questionnaire. Then, an interview was conducted by the author of this work to explore the responses and collect additional comments. The questions in the interview were based on the answers given in the questionnaire.
The instruments used to collect data consisted of: (i) a consent form to participate in the study, which aims to safeguard the participant’s rights regarding the study and its results; (ii) a form presenting a questionnaire to collect feedback from the study participant about the use of the OADAPT; and (iii) a set of questions to guide the interview.
[Complementary document to the Thesis: Using Networked Ontologies to Develop Adaptive User Interface Systems
Moral Emotions Questionnaire - Study 2
No full textThe project aims at developing and validating the Moral Emotions Questionnaire (MEQ), an emotion-focused adaptation of the belief-focused Moral Foundations Questionnaire (MFQ-2, Atari et al., 2023)
Technological Surveillance and Innovation Trends in Immunoassays for Neglected Bacterial Diseases: A Scoping Review and Patent Analysis
No full textTo analyze the temporal evolution and technological trends of immunoassay technologies for the diagnosis of bartonellosis, rickettsiosis, and leptospirosis, based on evidence from scientific literature and patent
The Influence of Artificial Intelligence–Assisted Intraoral Scanning on the Accuracy of Digital Implant Impressions: A systematic Review
No full textDigital implant impressions obtained using intraoral scanners (IOS) have become integral to contemporary implant dentistry. Recently, artificial intelligence (AI)–assisted algorithms have been incorporated into IOS software to enhance scan alignment, compensate for stitching errors, fill mesh defects, and optimize scan-body recognition. Despite their rapid clinical adoption, the true influence of AI-assisted intraoral scanning workflows on the accuracy of digital implant impressions remains unclear and inconsistently reported.
Recent in vitro investigations have demonstrated that implant impression accuracy is influenced by multiple interrelated factors, including intraoral scanning coverage, scan-body geometry, scanning strategy, scanner hardware, and the activation of AI-driven features. Studies evaluating AI-assisted complete-arch implant scanning workflows have shown that AI algorithms may improve trueness and precision in certain IOS systems, while producing neutral or even adverse effects in others. Similarly, investigations on image-guided photogrammetry and AI-driven coordinate superimposition have highlighted improvements in implant position transfer accuracy, yet with significant variability depending on workflow design and reference datasets.
Additional in vitro evidence indicates that excessive intraoral scanning coverage can accumulate stitching errors, negatively affecting implant impression accuracy, whereas insufficient coverage may compromise alignment stability. Deep-learning–based models, including convolutional neural networks, have been used to predict optimal scanning coverage; however, these approaches remain scanner- and scenario-dependent. Furthermore, the design and geometry of intraoral and extraoral scan bodies have been shown to significantly influence trueness and precision, with AI-assisted scanning tools exerting device-specific effects that are not yet fully understood.
Collectively, the existing literature demonstrates methodological heterogeneity in experimental design, reference standards, AI functionalities, and reported outcome measures (e.g., trueness, precision, RMS deviation, linear and angular discrepancies). While individual studies provide valuable insights, there is currently no comprehensive synthesis that systematically evaluates how AI-assisted intraoral scanning influences the accuracy of digital implant impressions across different in vitro conditions.
Therefore, this systematic review aims to synthesize and critically appraise in vitro evidence on AI-assisted intraoral scanning workflows for digital implant impressions. The review will focus on implant position transfer accuracy, scan-body alignment, trueness, and precision, while examining the role of scanning coverage, scan-body design, scanner type, and AI algorithm activation. By consolidating current evidence, this review seeks to clarify the benefits and limitations of AI-assisted intraoral scanning and to provide evidence-based guidance for future research and clinical workflow optimization