Regulatory Data Science for Medical Devices

Regulations that cover the legal obligations that manufacturers are bound to are essential for keeping the general public safe. Companies need to follow the regulations in order to bring their products to market. A good understanding of the regulations and the regulatory pathway defines how fast and at what cost the manufacturer can introduce innovations to the market. Regulatory technology and data science can lead to new regulatory processes and evidence in the medical field. It can equip stakeholders with unique tools that can make regulatory decisions more objective, efficient, and accurate. This book describes the latest research within the broader domain of Medical Regulatory Technology (MedRegTech). It covers concepts such as the complexity and user-friendliness of medical device regulations, novel algorithms for regulatory navigation, descriptive datasets from a health service provider, regulatory data science techniques, and considerations of the environmental impacts within a national health service. This book brings all these aspects together to offer an introduction into MedRegTech research. In the long term, these technologies and methods will help optimize the regulatory strategy for individual healthcare innovations and revolutionize the way we engage with regulatory services

Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems

Decision trees and decision rule systems play important roles as classifiers, knowledge representation tools, and algorithms. They are easily interpretable models for data analysis, making them widely used and studied in computer science. Understanding the relationships between these two models is an important task in this field. There are well-known methods for converting decision trees into systems of decision rules. In this paper, we consider the inverse transformation problem, which is not so simple. Instead of constructing an entire decision tree, our study focuses on a greedy polynomial time algorithm that simulates the operation of a decision tree on a given tuple of attribute values.Comment: arXiv admin note: substantial text overlap with arXiv:2305.01721, arXiv:2302.0706

Bounds on Depth of Decision Trees Derived from Decision Rule Systems

Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse transformation is a more difficult task. In this paper, we study unimprovable upper and lower bounds on the minimum depth of decision trees derived from decision rule systems depending on the various parameters of these systems

Observation and Measurement of the Higgs Boson Produced in Association with a Vector Boson and Decaying to a Pair of Bottom Quarks with the ATLAS Detector at LHC

A search of the Higgs boson decaying into a pair of bottom quarks using the WH and ZH associated production is performed with the ATLAS detector. The analyzed data were collected in 2015, 2016, 2017 and 2018 LHC Run-2 at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 139 fb−1. Final states that contains 0, 1, and 2 charged leptons (electrons or muons, denoted as l) are considered, to target the processes of Z→νν, W→lν and Z→ll. Object reconstruction, event selection, and signal and control region definition are included. Systematic uncertainty and statistical analysis used to extract the final results are presented. This analysis observes the production pp→(W,Z)H in H→bb channel at a significance of 6.7 standard deviations (where the expectation is 6.7 standard deviations) and represents the most precise observation to date of this physics process. Cross-sections of VH (WH and ZH) with V→leptons and H→bb are measured as a function of the vector boson transverse momentum in the kinematic fiducial volumes and found to be consistent with the Standard Model predictions

An Exploration Study of using the Universities Performance and Enrolments Features for Predicting the International Quality

Quality ranking systems are crucial in the assessment of the academic performance of an institution because these assessment systems give details about how different learning institutions deliver their services. Education quality is also of paramount importance to the students because it is through quality education that these students develop skills that are needed in the job market. Besides, education enhances a student\u27s academic and reasoning capacities. When universities are subjected to ranking systems, they are likely to improve their quality to be ranked high in the system. When the university administrators are exposed to ranking, competition gears up. Through competition, the quality of education also improves and through that the general education system improves. In addition, with rapid technological progress, increased human mobility and economic growth, the concept of quality assessment at the national level has shifted to an international level and now the evaluation of higher education quality is being conducted on the basis of international standards and comparisons. In the present context, a global ranking of a university has a significant influence on attracting research funding and academic talent. Universities are expected to collaborate and compete on an international level, and it is no longer enough to achieve excellence within any national group. It is therefore, not surprising that there is a rising tendency among universities to become centres of World class excellence . The findings of this study indicated that teaching, citations, income, number of students are key predictors for predicting the international outlook of universities. Also, it showed that geography is a significant contributor that recognized when it was added to the models for assessing the quality of the worldwide universities

A real options model for the financial valuation of infrastructure systems under uncertainty

Build-Operate-Transfer (BOT) is a form of Public-Private Partnerships that is commonly used to close the growing gap between the cost of developing and modernizing transportation infrastructure systems and the financial resources available to governments. When assessing the feasibility of a BOT project, private investors consider revenue risk - which is stemmed from the uncertainty about future traffic demand - as a critical factor. A potential approach to mitigating the revenue risk is the offering of revenue risk sharing mechanisms such as Minimum Revenue Guarantee options by the government. In addition to Minimum Revenue Guarantee options, a mechanism known as Traffic Revenue Cap options may also be negotiated, which makes the government entitled to a share of revenue when it grows beyond a specified threshold. Financial valuation of investments in BOT projects should take into account uncertainty about future traffic demand, as well as Minimum Revenue Guarantee and Traffic Revenue Cap options. The conventional valuation methods including Net Present Value (NPV) analysis are not capable of integrating the uncertainty about future traffic demand in the valuation of BOT projects and properly pricing Minimum Revenue Guarantee and Traffic Revenue Cap options. Real options analysis can be used as an alternative approach to valuation of investments in transportation projects under uncertainties. However, the appropriate application of real options analysis to valuation of investments in transportation projects is conditioned upon overcoming specific theoretical challenges. Current real options models do not provide a systematic method for estimating the project volatility, which measures the variability of investment value. Existing models do not provide a method for calculating the market value of Minimum Revenue Guarantee and Traffic Revenue Cap options. Also, current models are not able to characterize the impact of Minimum Revenue Guarantee and Traffic Revenue Cap options on private investors' financial risk profile. The overarching objective of this research is to apply the real options theory in order to price Minimum Revenue Guarantee and Traffic Revenue Cap options under the uncertainty about future traffic demand. To achieve this objective, a real options model is created that characterizes the long-term traffic demand uncertainty in BOT projects and determines investors' financial risk profile under uncertainty about future traffic demand. This model presents a novel method for estimating the project volatility for real options analysis. This model devises a market-based option pricing approach to determine the correct value of Minimum Revenue Guarantee and Traffic Revenue Cap options. An appropriate procedure is created for characterizing the impact of Minimum Revenue Guarantee and Traffic Revenue Cap options on the investors' financial risk profile. The proposed real options model is applied to a BOT project to illustrate the valuation process. The limitations of the proposed real options model, as well as the barriers to its implementation, are identified and recommendations for future research are offered. This research contributes to the state of knowledge by presenting a new method for estimating the project volatility, which is required for the real options analysis of transportation investments. It also introduces a risk-neutral valuation method for pricing the market value of Minimum Revenue Guarantee and Traffic Revenue Cap options in BOT projects. The research also contributes to the state of practice by introducing a novel class of assessment tools for decision makers that characterize the investors' financial risk profile under uncertainty about future traffic demand. Proper methods for pricing of Minimum Revenue Guarantee and Traffic Revenue Cap options are useful to public and private investors, in order to avoid wasting capital in transportation projects.PhDCommittee Chair: Baabak Ashuri; Committee Member: Adjo A. Amekudzi; Committee Member: Daniel Castro-Lacouture; Committee Member: Darryl VanMeter; Committee Member: Kathy O. Roper; Committee Member: Keith R. Molenaa

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

A study of the phonological and syntactic processes in the standardisation of Limbum

PN6519.L56, ISO 639-3 : lmp, Limbum languag