In this paper, we discuss two aspects concerning terrestrial laser scanners (TLSs) - error model calibration and performance evaluation. Error model calibration is the process of determining parameters of an error model to improve the accuracy of TLSs. Performance evaluation refers to a series of tests to determine if a TLS meets specifications provided by the manufacturer. Both procedures can be accomplished using a network of stationary targets whose locations are known from a prior calibration using another method/instrument. This paper explores the question of whether the network (i.e., target locations) must be calibrated using an instrument of higher accuracy such as a laser tracker (LT) or whether the TLS under study is itself suitable for network calibration. Regardless of whether an LT or a TLS is used, the calibration is performed from target measurements made from multiple locations of the instrument to average out systematic errors and reduce the uncertainties in target coordinates. Such multi-position measurements on stationary targets is referred to as the network method. We provide guidance on when the TLS is sufficient for network calibration and when an LT may be necessary for performance evaluation purposes.
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http://dx.doi.org/10.1016/j.optlaseng.2020.106298 | DOI Listing |
J Health Organ Manag
January 2025
Amrita School of Business - Amritapuri Kollam Campus, Kollam, India.
Purpose: This paper aims to delve into the critical aspect of supplier selection in the healthcare sector, emphasizing the significance of strategic sourcing in enhancing operational efficiency and quality of services. The primary aim is to develop a comprehensive framework for supplier evaluation that aligns with the unique requirements of hospitals, ultimately improving procurement processes and patient care outcomes.
Design/methodology/approach: The study leverages the renowned Carter's 7 C model as a foundational framework for supplier assessment, supplemented by insights gathered from interviews with experts in the New Product Introduction, Purchasing and Procurement departments of a leading hospital in India.
J Cancer Educ
January 2025
Department of Pharmacy, Al Rafidain University College, 10001, Baghdad, Iraq.
Chemotherapy-drug interactions (CDIs) pose significant challenges in oncology, affecting treatment efficacy and patient safety. Despite their importance, there is a lack of validated tools to assess oncologists' knowledge of CDIs. This study aimed to develop and validate a comprehensive questionnaire to address this gap and ensure the reliability and validity of the instrument.
View Article and Find Full Text PDFLasers Med Sci
January 2025
Universidade Federal de Pelotas, Pelotas, Brazil.
This systematic review aimed to compare postoperative pain in endodontic treatments using PIPS Er: YAG laser-activated irrigation (LAI) versus conventional needle irrigation. An electronic search was conducted to identify randomized clinical trials (RCT) investigating postoperative pain in patients who underwent root canal treatments in permanent teeth using PIPS Er: YAG laser-activated irrigation or conventional needle irrigation. Two reviewers performed study selection, data extraction, risk of bias assessment (RoB 2.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Fujian Key Laboratory of Oral Diseases & Stomatological Key lab of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian Province, 350002, China.
Objective: Both the Masquelet technique (MT) and concentrated growth factors (CGF) reduce early graft loss and improve bone regeneration. This study aims to explore the efficacy of combining MT with CGF for mandibular defect repair by characterizing the induced membrane and assessing in vivo osteogenesis.
Materials And Methods: Three experimental groups were compared: negative control (NC), MT, and Masquelet combined with CGF (MTC).
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
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