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The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. [ABSTRACT FROM AUTHOR]

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring. [ABSTRACT FROM AUTHOR]

Aiming at the sparse problem of object point cloud based on structure from motion method, a 3D reconstruction method using different matching data is proposed. The matching points are calculated by contrast context histogram(CCH) algorithm. The M-estimation sampling consensus(MSAC) algorithm is used to calculate the fundamental matrix, the translation and rotation matrix are decomposed from fundamental matrix. The image projection matrix is obtained combining the camera internal parameters. KLT algorithm is used to update the matching data, and the point cloud is generated by triangulation principle. This method makes use of the advantage of high accuracy of CCH algorithm to make the calculation results of the basic matrix converge. Using KLT algorithm to realize the matching by displacement instead of description vector, it makes up for the deficiency of matching data in low frequency region. The experimental results show that the proposed algorithm is effective and feasible, and the reconstructed point cloud has advantages in comparison with existing algorithms, it can be used for building 3D model of objects in the real scene.

Health technology assessment (HTA) was introduced in Australia for the reimbursement of pharmaceuticals in 1992 and in the following years for procedures, diagnostic tests, and devices. The Australian health system is largely funded by the government. The Pharmaceutical Benefits Scheme is a national list of prescription pharmaceuticals for which the patient pays a small copayment. HTA submissions to the Pharmaceutical Benefits Scheme are assessed by the Pharmaceutical Benefits Advisory Committee. The Medical Benefits Scheme provides ambulatory medical services and HTA submissions are assessed by the Medical Services Advisory Committee. This article describes the processes of reimbursement in Australia as well as the special case of codependent technologies (eg, diagnostic test and a therapeutic drug) where a combined Medical Services Advisory Committee and Pharmaceutical Benefits Advisory Committee application is required. There are many future challenges for HTA in Australia, with growing pressure to provide early access to promising treatments and high cost personalized medicines looming on the horizon. However, Australia is well placed to deal with these issues as the early adoption of HTA and coexistence between industry, academia and the payer has proven to be a fertile environment for developing capacity to undertake and evaluate HTA. • Two distinct advisory bodies are charged with assessing health technology assessment reimbursement submissions for pharmaceuticals and medical services and devices: The Pharmaceutical Benefits Advisory Committee and the Medical Services Advisory Committee. • The process of submission, evaluation, reimbursement decision, and post listing or rejection is complex. • Many future challenges lie ahead which will have to be dealt with by industry, academia, and the payer. [ABSTRACT FROM AUTHOR]

Random Sample Consensus (RANSAC) is an iterative algorithm for robust model parameter estimation from observed data in the presence of outliers. First proposed by Fischler and Bolles back in 1981, it still is a very popular algorithm in the computer vision community. The primary objective of their paper was to find an effective strategy for excluding outliers from estimation process, but it did not consider the presence of noise among the inliers. A common practice among implementations of RANSAC is to take a few samples extra than the minimum required for estimation problem, but implications of this heuristic are lacking in the literature. In this paper, we present a probabilistic analysis of this common heuristic and explore the possibility of finding an optimal size for the randomly sampled data points per iteration of RANSAC. We also improve upon the lower bound for the number of iterations of RANSAC required to recover the model parameters. On the basis of this analysis, we propose an improvement in the hypothesis step of RANSAC algorithm. Since this step is shared (unchanged) by many of the variants of RANSAC, their performance can also be improved upon. The paper also presents the improvements achieved by incorporating the findings of our analysis in two of the popular variants of RANSAC. [ABSTRACT FROM AUTHOR]

Objectives: To explore experiences of, and perspectives on, health technology assessment (HTA) processes used to produce recommendations about subsidizing new medicines, and medical technologies in Australia, from the perspectives of those experienced in these processes., Methods: Semi-structured interviews with a diverse group of 18 informants currently or previously members of the Pharmaceutical Benefits Advisory Committee (PBAC) or the Medical Services Advisory Committee (MSAC). Participants were interviewed September 2021-February 2022. Transcripts were analyzed using reflexive thematic analysis., Results: 3 major themes were identified: contrasting technical and decision-making stages, resisting reductionist approaches, and navigating decision-making trade-offs. Participants discussed the complexities of the evaluative HTA process, especially when considering uncertainty in the evidence. As part of the current process, a deliberative decision-making stage was considered essential, allowing a flexible approach to decision making to consider factors beyond strength and quality of quantifiable data in the technical evaluation. Participants acknowledged these less-quantifiable factors were sometimes considered implicitly or were difficult to describe and this, paired with commercial in confidence requirements, presented challenges with respect to the desire to increase transparency., Conclusion (s): As HTA processes for new medicines and medical technologies in Australia continue to be reviewed, the balance between retaining flexibility during deliberation, confidentiality for sponsors and the public's desire for greater transparency may be a fruitful area for continuing research., Competing Interests: Declaration of Competing Interest Emily Lancsar is a member of the MSAC; Kirsten Howard is a Member of the PBAC and Chair of ESC of the PBAC. There are no other declarations of interest., (Copyright © 2023. Published by Elsevier B.V.)

• Synthesis and Characterization of MSAC. • Studies adsorption of Doxorubicin Hydrochloride at MSAC. • Studying the mechanism of adsorption and rate controlling steps. • Statistical and physical simulation. The mango seeds activated carbon (MSAC) nanoparticles were created because these materials have high biocompatibility and might be used as a medication carrier. The anti-cancer medication doxorubicin hydrochloride (DOX) was loaded on MSAC nanoparticles in this study was characterized by the XRD, SEM, and BET showed that the surface area being 443 m2/g while pore volume was 0.649 cm3/g. The doxorubicin technique was applied performance factors, contact period, pH, concentration, adsorbent dose, and temperature were all examined to evaluate DOX adsorption. Greatest DOX adsorption on MSAC found to be at a pH of 5. This shows that, because to the outstanding components employed in the MSAC structure, the existing difficulty of employing larger doses of doxorubicin could be effectively mitigated. The created approach significantly enhanced DOX adsorption, indicating that the produced nanocomposite might be used to deliver drugs. The adsorption process of MSAC was at its optimum of 0.25 mmol/g, and the adsorption isotherm fit to the Langmuir model, while fitted to pseudo-second-order model for adsorption kinetics. DOX adsorption on MSAC is endothermic and spontaneous, according to thermodynamic studies. [ABSTRACT FROM AUTHOR]

Abstract: TheMSAC(modifiedsilica-aluminum-carbon)was used as a new type reinforced filler of the ENR (the epoxidation national rubber). It was made from coal shale by cracking, comminuting and modifying of a compiling agent. The influence factor about the reinforced mechanical of the MSAC was discussed. The effects of the MSAC content changing on the theology and curing behavior of the ENR vulcanizes were investigated. The result showed MSAC can be as a reinforced filler of the ENR The MSAC can replace the clay or part replace carbon-black used as the reinforced filler of the ENR. It can replace the clay or part replace common carbon-black, the replace content is about 50%. [Copyright &y& Elsevier]

Objectives: In 1998, a formal process using full health technology assessments (HTAs) was implemented to determine the suitability for public subsidy of new and emerging medical technologies in the Australian private healthcare sector. This process is overseen by the Medical Services Advisory Committee (MSAC). In 2004, horizon scanning was introduced in Australia with the stated objective of identifying new and emerging medical technologies into the public healthcare sector, with consideration to the publicly subsidized private healthcare sector. How well horizon scanning works in identifying new and emerging technologies suitable for government subsidized funding in the private healthcare sector is examined in this study. Methods: A descriptive evaluation of the impact of horizon scanning as an early alert and awareness system identifying new and emerging technologies before these technologies are submitted to MSAC for a full HTA. All MSAC HTAs commenced after the introduction of horizon scanning in 2004 were cross-checked with the list of Prioritizing Summaries or Horizon Scanning Reports to determine whether a prior Prioritizing Summary or Horizon Scanning Report had been carried out. Results: Of the forty-three technologies that were the subject of a full MSAC HTAs in the time period examined, only eleven had been the subject of either a Prioritizing Summary or Horizon Scanning Report. As a result of a full MSAC HTA, twelve of the technologies that were not the subject of a Prioritizing Summary or Horizon Scanning Report were given positive recommendations for public funding. Conclusions: Horizon scanning was set up to scan the introduction of new and emerging medical technologies into the public healthcare sector, with consideration to the publicly subsidized private healthcare sector. Based on the number of new and emerging technologies that have been the subject of a full MSAC HTA without first being subjected to either a Prioritizing Summary or Horizon Scanning Report, horizon scanning in Australia does not function as an "early alert and awareness system" for funding in the publicly subsidized private healthcare sector in Australia. [ABSTRACT FROM AUTHOR]