Miracle-dz/UniFAR-FTUs huggingface dataset:
Topics in generalized linear mixed models and spatial subgroup analysis

In this thesis, two topics are studied, generalized linear mixed models and spatial subgroup analysis. Within the topic of generalized linear mixed models, this thesis focuses on three aspects. First, estimation of link function in generalized linear models is studied. We propose a new algorithm that uses P-spline for nonparametrically estimating the link function which is guaranteed to be monotone. We also conduct extensive simulation studies to compare our nonparametric approach with various parametric approaches. Second, a spatial hierarchical model based on generalized Dirichlet distribution is developed to construct small area estimators of compositional proportions in the National Resources Inventory survey. At the observation level, the standard design based estimators of the proportions are assumed to follow the generalized Dirichlet distribution. After proper transformation of the design based estimators, beta regression is applicable. We consider a logit mixed model for the expectation of the beta distribution, which incorporates covariates through fixed effects and spatial effect through a conditionally autoregressive process. Finally, convergence rates of Markov chain Monte Carlo algorithms for Bayesian generalized linear mixed models are studied. For Bayesian probit linear mixed models, we construct two-block Gibbs samplers using the data augmentation (DA) techniques and prove the geometric ergodicity of the Gibbs samplers under both proper priors and improper priors. We also provide conditions for posterior propriety when the design matrices take commonly observed forms. For Bayesian logistic regression models, we establish that the Markov chain underlying Polson et al.’s (2013) DA algorithm is geometrically ergodic under a flat prior. For Bayesian logistic linear mixed models, we construct a two-block Gibbs sampler using Polson et al.’s (2013) DA technique under proper priors and prove the uniform ergodicity of this Gibbs sampler.
Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics

Generalized linear mixed models are used to model clustered and longitudinal data in which the distribution of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying generalized linear mixed models. Generalized linear mixed models consist of two sets of parameters: fixed effects parameters that associate covariates to the response at the population level, and random effects parameters that associate covariates to the response at the individual level. We introduce a method that identifies homogeneous groups in the data based on similarities among random effects parameters that are obtained when homogeneous groups are modeled using generalized linear mixed models. We achieve this by placing a Dirichlet Process prior on random effects parameters, which induces clustering of random effects and subsequently the clustering of profiles. As a result, our method simultaneously groups profiles into clusters and estimates model parameters of each cluster without assuming that the number of clusters is known in advance. We have tested our method on both simulated data and data from public health domain. In simulations, we have shown that the method manages to recover the correct number of clusters, successfully clusters profiles and correctly estimates model parameters. In public health clustered data, our method produces parameter estimates that are very close to those obtained by a frequentist maximum likelihood method, while identifying groups of homogeneous health regions that reveal certain properties of the underlying survey population that cannot be easily obtained using other methods. Similar methods have been proposed for longitudinal data with continuous responses. This thesis extends these models in novel ways to clustered and longitudinal data, where the distribution of the response variable can be any member of the exponential family.
OPTIMIZATION OF THE RECURSIVE ONE-SIDED HYPOTHESIS TESTING TECHNIQUE FOR AUTONOMOUS THRESHOLD ESTIMATION IN COGNITIVE RADIO

In this paper, an optimized Recursive One-Sided Hypothesis Testing (ROHT) threshold estimation algorithm for energy detection based on Cognitive Radio (CR) application is presented. The ROHT algorithm is well known to compute and correct threshold values based on the choice of the parameter values; namely the coefficient of standard deviation (z-value) and the stopping criteria ( ). A fixed computational process has been employed in most cases to estimate these parameter values, thus rendering them non-adaptive under different sensing conditions. Also, this fixed (manual tuning) process requires prior knowledge of some noise level to enable pre-configuration of a predefined target false alarm rate. This renders the parameter estimation process cumbrous and unworkable for real-time purposes, particularly for autonomous CR applications. Furthermore, using wrong parameter values may lead to either too high or too low false alarms or detection rates of the algorithm. Sequel to aforementioned mentioned constraints, we propose a new mechanism for instantaneous parameter optimization of the ROHT algorithm using Particle Swarm Optimization (PSO) algorithm. Our PSO-ROHT model design was extensively tested under different conditions and results were compared to the non-optimized ROHT. The results obtained show that the proposed design effectively adapts the parameter values of the Recursive One-Sided Hypothesis Testing algorithm in accordance with the input dataset under consideration. Also, that the proposed optimized model outperforms its non-optimized counterpart following the estimated detection probability and false alarm probability of both schemes, particularly in detecting Orthogonal Frequency-Division Multiplexing signals. In detecting the Orthogonal Frequency-Division Multiplexing signals at signal-to-noise ratio of 3dB and above, the proposed model achieved a higher detection rate of 96.23% while maintaining a low false alarm rate below 10%, which complies with the IEEE 802.22 standard for Cognitive Radio application. Our PSO-ROHT algorithm is shown to be highly effective for autonomous and full blind signal detection in CR, with strong potentials for application in other areas requiring automatic threshold estimation.
Vehicle Detection in Remote Sensing Images Leveraging on Simultaneous Super-Resolution

Owing to the relatively small size of vehicles in remote sensing images, lacking sufficient detailed appearance to distinguish vehicles from similar objects, the detection performance is still far from satisfactory compared with the detection results on everyday images. Inspired by the positive effects of super-resolution convolutional neural network (SRCNN) for object detection and the stunning success of deep CNN techniques, we apply generative adversarial network frameworks to realize simultaneous SRCNN and vehicle detection in an end-to-end manner, and the detection loss is backpropagated into the SRCNN during training to facilitate detection. In particular, our work is unsupervised and bypasses the requirement of low-/high-resolution image pairs during the training stage, achieving increased generality and applicability. Extensive experiments on representative data sets demonstrate that our method outperforms the state-of-the-art detectors. (The source code will be made available after the review process.)
Unitary Correlation Sets and their Applications

We relate Connes’ embedding problem in operator algebras to the Brown algebra Unc(n), which is defined as the universal unital C∗-algebra generated by the entries of an n × n unitary matrix. In particular, we show that the embedding problem is equivalent to determining whether or not the algebra Unc(n) has the weak expectation property for any (equivalently, all) n ≥ 2. From this perspective, we develop a theory of what we call unitary correlation sets. These sets are analogous to the usual sets of bipartite probabilistic correlations arising from the typical models in quantum information theory. We show that the analogue of the weak Tsirelson problem for unitary correlation sets is again equivalent to Connes’ embedding problem. Moreover, we show that as long as Alice and Bob’s unitaries are of size at least 2, the set of spatial unitary correlations is never a closed set. This result is analogous to a recent theorem of Slofstra, which states that the set of quantum probabilistic correlations is not closed, so long as the input and output sets are large enough. We also discuss several applications of the theory of unitary correlation sets. First, we show that the class of extended non-local games known as quantum XOR games is a rich enough class to detect the validity of Connes’ embedding problem. That is, the embedding problem has a positive answer if and only if the value of every quantum XOR game in the commuting model agrees with the value of the game in the approximate finitedimensional model. Second, we use a C∗-algebraic analogue of the quantum teleportation and super-dense coding maps from quantum information theory to obtain separations between the tensor product model (or “quantum spatial” model) and the approximate finite-dimensional model (or “quantum approximate” model), for matrix-valued generalizations of the usual Tsirelson corrleation sets. We use some of the intermediate results to also obtain separations between the matrix versions of the finite-dimensional model (or “quantum model”) and the tensor product model.
Pharmacological and non- pharmacological treatment of hypertension: A review article

BACKGROUND: Hypertension is a worldwide epidemic disease. It is more common and more severe in elderly persons. Various studies however have estimated 41.9 million men and 27.8 million women to have prehypertension. Diagnosis and early treatment of prehypertension are of utmost importance. Although hypertension is usually divided into 2 general categories of essential (primary) and secondary hypertension, the initial treatment for hypertension often depends on its stage which is determined by systolic and diastolic blood pressure. Lifestyle modification is the first step in treating stage one hypertension. Pharmaceutical treatments including diuretics, angiotensin converting enzyme (ACE) inhibitors, calcium blockers, beta blockers, and angiotensin receptor blockers will be recommended if lifestyle modification fails to control blood pressure. METHODS: The PubMed database was searched by a number of keywords including hypertension, pharmaceutical treatment, and non-pharmaceutical treatment. The results were limited by determining a date range of 2008-11. RESULTS: High blood pressure causes major health problems for many people around the world. It should be controlled because of its high mortality and morbidity. However, in order to select an appropriate treatment modality, it is initially important to diagnose the kinds and stages of hypertension. Pharmaceutical or non-pharmaceutical treatments can then be employed to control this serious disease. CONCLUSION: Treating hypertension depends on the kinds and stages of this disease. Several tips should be considered when selecting a method of treatment. Keywords: Hypertension, Pharmacological treatment, Non-pharmacological treatment
Response of Orographic Precipitation to Subsaturated Low-Level Layers

<p>Orographic precipitation is, on the one hand, an important source of fresh water, and on the other hand, a potential cause of floods and other disasters. Previous studies have focused on the situation where the whole atmosphere is saturated and nearly moist-neutral. However, there are times when subsaturated low-level layers are observed to be below saturated, nearly moist-neutral, upper-level layers. </p><p>A series of idealized two-dimensional simulations are performed here to investigate the impact of this subsaturated low-level layer on orographic precipitation. It is found that the impact is mainly controlled by a nondimensional parameter and two competing effects. The nondimensional parameter is N<sub>2</sub>z<sub>t</sub>/U, where N<sub>2</sub> and z<sub>t</sub> are, respectively, the dry Brunt&#8211;V&#228;is&#228;l&#228; frequency and depth of the subsaturated low-level layer, and U the cross-mountain wind speed. When the nondimensional parameter exceeds a critical value, the decelerated region on the upwind side of the mountain moves upwind, resulting in weak surface precipitation near the mountain peak. When it is smaller than the critical value, surface precipitation occurs near the mountain peak. </p><p>The two competing effects are: 1) the vapor-transport effect, meaning that increasing z<sub>t</sub> decreases the amount of vapor transported to the mountain, and hence tends to decrease surface precipitation; and 2) the updraft width effect, meaning that increasing z<sub>t</sub> enhances flow blocking, producing a wider updraft over the upwind slope, and hence tends to increase surface precipitation. When the vapor-transport effect dominates, surface precipitation decreases with z<sub>t</sub>. When the updraft-width effect dominates, surface precipitation increases with z<sub>t</sub>. </p>
Evaluation of GNSS Data with Internet Based Services: The Case of HRUH Station

Nowadays, as the Internet services are developed, it becomes possible to offer services for many applications in the engineering field online via the Internet. One of these ser vices is the evaluation of online GPS data. The most important feature of Internet-based applications is that these services are free and easy to use. In this study, the data of Global Navigation Satellite Systems (GNSS) of different periods belonging to newly established HRUH permanent GNSS station in Harran University were evaluated through Internet-based services. The evaluation strategy of GNSS data was conducted in 1-, 2-, 6-, 12-, and 24-h campaigns, and the results were compared between different Internet site solution results. When the results obtained are examined, it can be said that the accuracy of the data obtained from these services can be used in many applications requiring precision in centimeter levels and is capable of satisfying the expectancies.
Gender differences in skin and core body temperature during exercise in a hot, humid environment.

Background. It is universally accepted that men and women regulate heat differently during exercise in hot, humid environments. Despite this common knowledge, little empirical data is available to explain how gender differences effect core/skin temperature changes. Further, the data that is available includes only limited measurement sites and/or time points. Methods. The purpose of this study was to measure the heat regulatory patterns for both men and women during 60-min of exercise in a hot, humid environment. Twenty aerobically fit subjects (10 men; 10 women) completed an acclimation session followed by a 60-min exercise protocol in an environmental chamber set at 39.9 ± 1.1 °C and 46.4 ± 4% relative humidity. Each exercise protocol consisted of four intervals of an 8-min walk (mean ± SD; men: 4.0 ± 0.3, women: 3.0 ± 0.2 mph) and 7-min light jog (men: 5.4 ± 0.5, women: 5.0 ± 0.3 mph). Subjects were confirmed to be euhydrated (urine specific gravity) prior to exercise and were encouraged to drink water to maintain hydration during exercise. Preand Post-exercise body weights did not differ, thus it appears hydration was maintained during exercise. Skin temperatures (iButton wireless loggers) were recorded every minute at seventeen sites (right and left: upper chest, mid-chest, abdomen, upper back, mid-back, lower back, upper arm, and lower arm, and back of the neck). In addition, core body temperature (rectal), rating of perceived exertion (RPE), and heart rate (wireless telemetry) were also recorded at the end of each interval of the protocol. Statistical analysis was carried out using a 2 (gender) x 60 (time) repeated measures ANOVA. Other variables were analyzed using a 2 (gender) x 8 (time) ANOVA with repeated measures on the 2nd factor. Any non-normally distributed data was log transformed. Significance was set at p<0.05 and location of effects will be determined using individual t-tests with a Bonferroni correction for multiple comparisons. Results. Analysis revealed significantly higher skin temperatures in four locations on men compared to women: right upper back (p=0.048), right mid-back (p=0.001), right lower back (p=0.001), and left upper back (p<0.001). Additionally, these changes were seen despite no significant differences between genders in core body temperature, RPE, and heart rate. This latter finding supports the conclusion that a similar degree of exercise, heat stress was applied to both men and women. Conclusions. We found gender differences at four different skin temperature locations. These changes might suggest that men retain more metabolic heat in various locations on the back when exercising in a hot, humid environment compared to women. Further research is needed to understand how these changes may affect post-exercise recovery return to baseline skin and core temperature values.
Passive Sampling as a Low-Cost Method for Monitoring Air Pollutants in the Baikal Region (Eastern Siberia)

The measured concentrations of inorganic pollutants, such as ozone (2015–2018), sulfur, and nitrogen oxides (2012–2018) at air monitoring sites in the south of Eastern Siberia were sampled, following the passive sampling method, and analyzed. The spatial inhomogeneity of atmospheric gas concentrations is presented. The ozone concentration is lower in urban areas than those in rural areas and the background level. However, the nitrogen and sulfur oxide concentrations are higher in the atmosphere over the city site. The seasonal dependence of the ozone concentration was determined using its maximum (March–April) and minimum (September–October) levels. The dynamics of the nitrogen and sulfur oxide concentrations indicate that they are at their highest in December–June and their lowest in July–August. To verify the validity of the pollutant concentration measurements sampled by passive sampling, we compared our results with those obtained following the automatic and filter pack methods. A linear regression analysis and a pairwise modification of Student’s t test evaluated the concentrations of the air pollutant, sampled and measured using different methods, and they correlate well (r = 0.7–0.9). Full validation of the passive sampling method is not possible for some sites; therefore it is necessary to remove the remaining systematic errors in future work.
Material for : Subwavelength acoustic valley-Hall topological insulators using soda cans honeycomb lattices

Subwavelength acoustic valley-Hall topological insulators using soda cans honeycomb lattices Zhiwang Zhang, 2 Ye Gu, Houyou Long, Ying Cheng, a) Xiaojun Liu, b) and Johan Christensen c) Department of Physics, MOE Key Laboratory of Modern Acoustics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China Department of Physics, Universidad Carlos III de Madrid, ES-28916 Leganés, Madrid, Spain
Traffic analysis of anonymity protocol using hidden Markov model based on the model confidence

The issue of increasing the confidentiality and stealth of users on the Internet is the most pressing issue of the day. One way to increase the secrecy of using Internet services is to install the Tor software, which protects itself from the "data flow analysis" is a type of network surveillance that threatens the privacy of users, the confidentiality of business contacts and communications implemented through routing network traffic over a distributed network of servers running volunteers from around the world that does not allow the external observer to monitor the user's Internet connection, find out which sites were visited, and also does not allow the site to know the physical location of the user. However, the software in question has vulnerabilities that result in the loss of personal user freedom. The author, through the application of general scientific methods such as analysis and synthesis, identified a list of vulnerabilities and their importance for the confidentiality of the Tor software. The author carried out the simulation of the Tor software by devices of the experimental environment and the construction of experimental procedures based on the used mathematical apparatus of the Markov chains. The results of the experiment indicate the necessity to determine the validity of the model for analysis of the anonymity protocol. In the course of this research, an algorithm for testing the anonymity of Tor software users was developed, which allows to identify possible sources of personal information of users. The effectiveness of the proposed modeling trust algorithm was demonstrated by calculating the value of a training set of data necessary for outputting a wireless access protocol, a proxy through Tor.
Sensitivity of Lake-Effect Cloud Microphysical Processes to Ice Crystal Habit and Nucleation during OWLeS IOP4

Ice crystal habit significantly impacts ice crystal processes such as growth by vapor deposition. Despite this, most bulk microphysical models disregard this natural shape effect and assume ice to grow spherically. This paper focuses on how the evolution of ice crystal shape and choice of ice nucleation parameterization in the adaptive habit microphysics model (AHM) influence the lake-effect storm that occurred during intensive observing period 4 (IOP4) of the Ontario Winter Lake-effect Systems (OWLeS) field campaign. This localized snowstorm produced total accumulated liquid-equivalent precipitation amounts up to 17.92 mm during a 16-h time period, providing a natural laboratory to investigate the ice–liquid partitioning within the cloud, various microphysical process rates, the accumulated precipitation magnitude, and its associated spatial distribution. Two nucleation parameterizations were implemented, and aerosol data from a size-resolved advanced particle microphysics (APM) model were ingested into the AHM for use in parameterizing ice and cloud condensation nuclei. Simulations allowing ice crystals to grow nonspherically produced 1.6%–2.3% greater precipitation while altering the nucleation parameterization changed the type of accumulating hydrometeors. In addition, all simulations were highly sensitive to the domain resolution and the source of initial and boundary conditions. These findings form the foundational understanding of relationships among ice crystal habit, nucleation parameterizations, and resultant cold-season mesoscale precipitation within detailed bulk microphysical models allowing adaptive habit.