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With the exception of renal cell carcinoma, studies assessing the association between hypertension and other cancers are inconsistent. We conducted a meta-analysis to assess this evidence. We included observational studies investigating the association between any definition of hypertension or systolic and diastolic blood pressure and risk of any cancer, after searching PubMed until November 2017. We calculated summary relative risks (RR) and 95% confidence intervals (CI) using inverse-variance weighted random effects methods. A total of 148 eligible publications were identified out of 39,891 initially screened citations. Considering only evidence from 85 prospective studies, positive associations were observed between hypertension and kidney, colorectal and breast cancer. Positive associations between hypertension and risk of oesophageal adenocarcinoma and squamous cell carcinoma, liver and endometrial cancer were also observed, but the majority of studies did not perform comprehensive multivariable adjustments. Systolic and diastolic blood pressure were positively associated with risk of kidney cancer but not with other cancers. In addition to the previously well-described association between hypertension and risk of kidney cancer, the current meta-analysis suggested that hypertensive individuals may also be at higher risk of colorectal and breast cancer. However, careful interpretation is required as most meta-analyses included relatively small number of studies, several relative risks had weak or moderate magnitude and maybe affected by residual confounding.

As a growing number of wireless services with high performance demands is offered, their intelligent planning and efficient management becomes more urgent. To that end, channel propagation models are indispensable. Such models can be used to optimize the position of wireless access points, assess interference from and towards neighboring systems, and facilitate network-level performance evaluation studies.Propagation modeling has been widely dependent on time-consuming and arduous measurement surveys. Alternatively, fast and easy-to-use empirical models have also been utilized. Yet, these models lack predictive accuracy. A partial solution to the problem is offered by the use of computational methods based on electromagnetic theory, such as finite difference time domain, vector parabolic equation (VPE) and ray tracing (RT) methods. However, the higher accuracy of these methods is often combined with computationally expensive simulations.Motivated by the need to create accurate, yet computationally efficient and flexible propagation models, this thesis explores a data-driven approach, leveraging recent advances in machine learning (ML). That learning is achieved over a training phase, by specifying a set of input features and the expected output the ML-driven propagation model has to produce. ML models are powerful and efficient tools that can capture highly complex and non-linear functions at their output. After such models are trained, they can use their learned parameters to generate fast and accurate predictions for new cases of considerable complexity.To enhance the accuracy of the proposed ML-based propagation models, we use physics-based training data generated by high-fidelity solvers, such as VPE and RT solvers. The cost for simulating the training data is significantly outweighed by the ability of the trained ML models to rapidly simulate new test cases. This is achieved by utilizing physics-informed input features, such as information regarding the complexity of the modeling environment and the antenna specifications of the communication system, and physics-driven cost functions. In addition, to further improve the computational efficiency of our models, we train using small sets of data, and utilize efficient, yet simple, ML network structures. Thus, our main goal is to design efficient and accurate ML-based surrogate models for the VPE and RT solvers, for a diverse set of propagation scenarios. We successfully demonstrate this accuracy and computational efficiency by evaluating our models on a wide range of test cases, of significant difficulty and diversity.The proposed models promise to overcome the classical dichotomy between computational efficiency and accuracy that has dominated the area of propagation modeling. This can have a profound impact in many large-scale computations usually encountered in wireless propagation scenarios, such as providing real-time and accurate localization services, and efficiently selecting the positions of transmitters in electrically large environments.

We are presenting examples of artificial intelligence enabled algorithms for radiowave propagation modeling and their application to practical problems of interests. We use computational electromagnetics techniques to generate physics-based training data for neural networks. Emphasis is given on ray-tracing and the vector parabolic equation method, for indoor and tunnel propagation problems, respectively. For the latter, the use of artificial neural networks for the efficient computation of uncertainties inherent in propagation models is discussed.

To systematically evaluate the use of Framingham Risk Score (FRS) in the medical literature and specifically examine the use of FRS in different populations and settings and for different outcomes than the ones originally developed for. We identified all the citations to the article by Wilson et al. (1998), in which FRS was originally described through ISI Web of Science until April 2011. We selected studies that stated in their abstract that they calculated or used the FRS for any reason and extracted information on publication date, population studied, outcome, or disease risk factor with which FRS was associated and study design. We identified 375 eligible articles corresponding to 471 analyses using the FRS in cohort (n = 141), case–control (n = 16), or cross-sectional (n = 314) settings. Only a minority of the cohort studies had as a primary aim to externally validate the FRS (n = 45). The studied population was different (from general or healthy) in 35 (25%) and 133 (42%) of the cohort and cross-sectional analyses, respectively. All case–control studies examined healthy controls. The studied outcome was different (from coronary heart disease) in 79 (56%) of the cohort analyses and 10 (63%) of the case–control studies. Overall, only 46 (33%) of the 141 cohort analyses examined the same outcome and population as FRS was originally developed for. A large number of studies use FRS in populations and for outcomes other than the ones it has been developed for and therefore for which its performance is unknown and nonvalidated.

We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with machine learning-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area.