Development, Implementation and Evaluation of Medical Decision Support Systems Based on Mortality Prediction Algorithms from an Operations Research Perspective.

Wide implementation of electronic health record systems provides rich data for personalized medicine. One topic of great interest is to develop methods to assist physicians in prognosis for example mortality. While many studies have reported on various new prediction models and algorithms there is relatively little literature on if and how these new prediction methods translate into actual benefits. My dissertation consists of three theses that aims at filling this gap between prognostic predictions and clinical decisions in end-of-life care and intensive care settings. In the first thesis, we develop an approach to using temporal trends in physiologic data as an input into mortality prediction models. The approach uses penalized b-spline smoothing and functional PCA to summarize time series of patient data. we apply the methodology in two settings to demonstrate the value of using the shapes of health data time series as a predictor of patient prognosis. The first application a mortality predictor for advanced cancer patients that can help oncologists decide which patients should stop aggressive treatments and switch to palliative care such as that provided in hospice. The second one is a real-time near term mortality predictor for MICU patients that can work as an early alarm system to guide timely interventions. In the second thesis, we investigate the integration of a prediction algorithm with physician decision making, focusing on the advanced cancer patient setting. We design a retrospective study to compare prognoses made by doctors and those that would be recommended by the IMPAC algorithm developed in Chapter 1. We used the doctor\u27s discharge decision as a proxy of what they predict the patient as dying in 90 days and show that doctor\u27s predictions tend to very conservative. Although IMPAC on its own does not perform better than doctors in terms of precision and recall, we find that IMPAC and doctors identify significantly different group of positive cases. IMPAC and doctors are also good at identifying very different groups of patients in terms of survival time. We propose a new way to augment decisions of doctors with IMPAC. At the same recall, the augment method identifies 43\% more patients close to death than the doctors do. We also estimate potential hospitalizations and hospital length of stays avoided if the doctors use augmented procedure instead of acting on their own beliefs. In the third thesis, we look at the integration of a prediction algorithm with physician decision making, focusing on the ICU setting. We use a POMDP framework to evaluate how decision support systems based on ICU mortality predictions can help physicians allocate time to inspect the patients at highest risk of death. We assume physicians have limited time and seek to optimally allocate it to patients in order to minimize their mortality rate. Physicians can do Bayesian updates on observations of patient health state. A prediction algorithm can augment this process by sending alerts to physicians. We represent the algorithm by an arbitrary point on an ROC curve representing a particular alert threshold. We study two approaches to using the algorithm input: (1) Belief based policy (BBP) that integrates algorithm outputs using Bayesian updating; (2) Alarm triggered policy (ATP) where the physician responds only to the algorithm without updating, and compare them to benchmarks that do not rely on the algorithm at all. By running simulations, we explore how the accuracy of predictions can translate into lower mortality rates

The Social Acceptability of Personal Carbon Trading in China

Whether personal carbon trading (PCT) can be successfully implemented, as a public policy, critically depends on its social acceptability. For China, the biggest carbon emission country, public attitude towards personal carbon trading has a significant impact on its energy strategy or even the world’s. The purpose of this research is to investigate the social acceptability of personal carbon trading in China with the method of focus group and questionnaire. In order to understand Chinese citizens’ evaluations on personal carbon trading and how these attitudes are generated, researchers interviewed 32 individuals in four groups in-depth. Before and after the interview, questionnaires of almost the same content were sent to investigate whether more information on personal carbon trading will change interviewees’ attitudes towards it. A comparison was then made between public attitudes towards personal carbon trading and those towards carbon tax. Two significant conclusions are drawn from this study. First, personal carbon trading has gained more popularity compared with carbon tax. Second, the introduction of personal carbon trading to China still faces great challenges from the social atmosphere. Keywords: Personal carbon trading; PCT; Carbon tax; social acceptability

Two-Phase Flow Visualization of Evaporating Liquid Fuels at Atmospheric Pressure

Two-phase flow visualization of fuel sprays is important for the design of better engines because it determines the efficiency and emissions of the combustion process. Simultaneous two-phase flow imaging using techniques such as planar laser-induced fluorescence (PLIF) has been a challenge due to the large variation in LIF signals from the gas and liquid phases. After laser excitation, the liquid signal initially overwhelms the gas phase signal due to its higher number density. However, the liquid signal quenches dramatically due to quenching effects that dominate the liquid LIF signal. By applying the novel concept of temporal filtering, separation of liquid and vapor signal can be achieved using different time delayed camera systems. The optical measurement provides a non-intrusive means of obtaining the liquid and vapor distributions in a spray. The experiment is performed using an ultraviolet beam from a burst-mode Nd:YAG laser in combination with two intensified cameras that are timed to maximize either the liquid or vapor phase signal. The setup is complemented by a drop generator and vaporizer flow system to allow studies of aviation fuels such as Jet-A or JP10, as well as reciprocating engine fuels such as diesel or toluene (as a surrogate for gasoline)

Energy efficient tdma sleep scheduling in wireless sensor networks

Abstract—Sleep scheduling is a widely used mechanism in wireless sensor networks (WSNs) to reduce the energy consumption since it can save the energy wastage caused by the idle listening state. In a traditional sleep scheduling, however, sensors have to start up numerous times in a period, and thus consume extra energy due to the state transitions. The objective of this paper is to design an energy efficient sleep scheduling for low data-rate WSNs, where sensors not only consume different amounts of energy in different states (transmit, receive, idle and sleep), but also consume energy for state transitions. We use TDMA as the MAC layer protocol, because it has the advantages of avoiding collisions, idle listening and overhearing. We first propose a novel interference-free TDMA sleep scheduling problem called contiguous link scheduling, which assigns sensors with consecutive time slots to reduce the frequency of state transitions. To tackle this problem, we then present efficient centralized and distributed algorithms that use time slots at most a constant factor of the optimum. The simulation studies corroborate the theoretical results, and show the efficiency of our proposed algorithms

Experimental Progress on Layered Topological Semimetals

We review recent experimental progresses on layered topological materials, mainly focusing on transitional metal dichalcogenides with various lattice types including 1T, Td and 1T' structural phases. Their electronic quantum states are interestingly rich, and many appear to be topological nontrivial, such as Dirac/Weyl semimetallic phase in multilayers and quantum spin hall insulator phase in monolayers. The content covers recent major advances from material synthesis, basic characterizations, angle-resolved photoemission spectroscopy measurements, transport and optical responses. Following those, we outlook the exciting future possibilities enabled by the marriage of topological physics and two dimensional van der Waals layered heterostructures.Comment: 2D Materials (2019

Chemical constituents, and pharmacological and toxicological effects of Cynomorium songaricum: An overview

Purpose: To review the chemical constituents, and the pharmacological and toxicological effects of Cynomorium songaricum (C. songaricum) and explore its potentials for further development as an alternative medicine.Methods: A large number of research articles related to “Cynomorium songaricum” “pharmacological effects”, “toxicological effects” and “chemical composition” in English and Chinese language were retrieved through an extensive literature review using various electronic databases including Medline(1966 - 2017) and EMBASE (1980 - 2017).Results: Ethyl acetate and aqueous extracts of C. songaricum have promising pharmacological activities, due to the presence of various flavonoids, triterpenes and polysaccharides. In addition to promising effects against inflammation, aging, fatigue, viruses and cancer,/ihas a protective effect on the nervous system and regulates hormones and immune functions. Oxidative regulation of hormone levels has a certain correlation with its pharmacological activities, e.g., cognitive functions, but its mechanism is not yet known, indicating the need for further research. Toxicity studies on C. songaricum have shown that it is not genotoxic to animals, but further toxicological studies are required to ascertain its safety in clinical use.Conclusion: C. songaricum is a biologically important plant which has many proven bioactivities; however, it requires further studies to determine the mechanistic aspects of its pharmacological effects.Keywords: Cynomorium, Chemical constituents, Inflammation, Aging, Fatigue, Virus, Tumor, Toxicological effec

Provably secure and efficient audio compression based on compressive sensing

The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security