Business Inferences and Risk Modeling with Machine Learning; The Case of Aviation Incidents

Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "high-impact, low-probability" (HILP) events. Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study provides an interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers. In short, we combine predictive results with simulations to interpret findings and prescribe actions

Business Inferences and Risk Modeling with Machine Learning; The Case of Aviation Incidents

Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as high-impact, low-probability (HILP) events. Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study provides an interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers. In short, we combine predictive results with simulations to interpret findings and prescribe actions

Evidence-Based Managerial Decision-Making With Machine Learning: The Case of Bayesian Inference in Aviation Incidents

Understanding the factors behind aviation incidents is essential, not only because of the lethality of the accidents but also the incidents’ direct and indirect economic impact. Even minor incidents trigger significant economic damage and create disruptions to aviation operations. It is crucial to investigate these incidents to understand the underlying reasons and hence, reduce the risk associated with physical and financial safety in a precarious industry like aviation. The findings may provide decision-makers with a causally accurate means of investigating the topic while untangling the difficulties concerning the statistical associations and causal effects. This research aims to identify the significant variables and their probabilistic dependencies/relationships determining the degree of aircraft damage. The value and the contribution of this study include (1) developing a fully automatic ML prediction based DSS for aircraft damage severity, (2) conducting a deep network analysis of affinity between predicting variables using probabilistic graphical modeling (PGM), and (3) implementing a user-friendly dashboard to interpret the business insight coming from the design and development of the Bayesian Belief Network (BBN). By leveraging a large, real-world dataset, the proposed methodology captures the probability-based interrelations among air terminal, flight, flight crew, and air-vehicle-related characteristics as explanatory variables, thereby revealing the underlying, complex interactions in accident severity. This research contributes significantly to the current body of knowledge by defining and proving a methodology for automatically categorizing aircraft damage severity based on flight, aircraft, and PIC (pilot in command) information. Moreover, the study combines the findings of the Bayesian Belief Networks with decades of aviation expertise of the subject matter expert, drawing and explaining the association map to find the root causes of the problems and accident relayed variables

Iatrogenic Mandibular Fracture Associated with Third Molar Removal

Third molar extraction is one of the most common procedures performed in oral and maxillofacial surgery units. It is sometimes accompanied by complications such as alveolar osteitis, secondary infection, hemorrhage, dysesthesia and, most severely, iatrogenic fracture. This article describes two mandibular angle fractures that occurred in two patients during the surgical extraction of one erupted and one unerupted third molar, including a brief review of the literature

Evaluation of Light-Emitting Diode (LED-660 Nm) Application over Primary Osteoblast-Like Cells on Titanium Surfaces: An In Vitro Study

Background: The goal of this study was to evaluate the behavior of neonatal rat calvarial osteoblast-like cells cultured on different implant surfaces and exposed once or three times to a 660-nm light-emitting diode (LED)

Extraction socket healing in rats treated with bisphosphonate : animal model for bisphosphonate related osteonecrosis of jaws in multiple myeloma patients

Aim: The aim of this study is to replicate both clinical and histological presentation of bisphosphonate induced osteonecrosis of the jaws (BONJ) in an animal model of the disease state. Successful recapitulation of a BONJlike indication in an animal model will be useful for studying pathogenesis, as well as prevention and treatment strategies for BONJ. Materials and Methods: Eighty (80) rats were prospectively and randomly divided into two groups; control group(40) and study group(40). All animals in study group, injected with a dose of 1 mg/kg dexamethasone (DX) subcutaneously on day 7, 14, or 21; and 1, 2, or 3 doses of 7.5 ?g/kg zoledronic acid (ZA) subcutaneously administered to coincide with the last day of DX. Half of the animals from each group underwent extraction of the left mandibular molars and the remaining animals underwent extraction of the left maxillary molars under pentobarbital-induced general anesthesia. All animals were euthanized twenty-eight (28) days following tooth extractions. Results: The amount of new bone trabecules as significantly decreased in bisphosphonate-dexamethasone (BPDX) treated sockets. Difference between both groups was found statistically significant (p=0,0001). There's no foreign body reaction in sockets of both groups and no significance difference observed for fibrosis (p=0,306). The necrosis scores were significantly higher in BP-DX treated sockets (p=0,015). The inflamation scores were significantly higher for study group (p=0,0001). Conclusion: This study provides preliminary observations for the development of an animal model of BONJ. But we think that there is need for other studies have only BP treated group and larger study population. © Medicina Oral S. L

Use of Cone-Beam Computerized Tomography for Evaluation of Bisphosphonate-Associated Osteonecrosis of the Jaws in an Experimental Rat Model

Background: Bisphosphonate-induced osteonecrosis of the jaw (BONJ) is a frequently reported complication. The aim of this study was to investigate the clinical and histopathological presentation of BONJ with the Hounsfield score and to evaluate the reliability of the score for determining necrosis in an animal model

Combined metabolic activators improve cognitive functions in Alzheimer’s disease patients: a randomised, double-blinded, placebo-controlled phase-II trial

Background: Alzheimer’s disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered\ua0combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress. Methods: Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35\ua0g L-serine (61.75%), 1\ua0g nicotinamide riboside (5%), 2.55\ua0g\ua0N-acetyl-L-cysteine (12.75%), and 3.73\ua0g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28\ua0days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients. Results: We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the\ua0CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment. Conclusion: Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration\ua0ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131