Non-thermal WIMP baryogenesis

We propose a WIMP baryogensis achieved by the annihilation of non-thermally produced WIMPs from decay of heavy particles, which can result in low reheating temerature. Dark matter (DM) can be produced non-thermally during a reheating period created by the decay of long-lived heavy particle, and subsequently re-annihilate to lighter particles even after the thermal freeze-out. The re-annihilation of DM provides the observed baryon asymmetry as well as the correct relic density of DM. We investigate how wahout effects can affect the generation of the baryon asymmetry and study a model suppressing them. In this scenario, we find that DM can be heavy enough and its annihilation cross section can also be larger than that adopted in the usual thermal WIMP baryogenesis.Comment: 5 pages, 6 figure

Risk Analysis and Uncertainty Quantification in Insurance Ratemaking

Insurance ratemaking, which is the process of setting an adequate amount of premium for an insured entity, is an essential role of insurance actuaries. For the success of this process, they need to perform a delicate and sound statistical analysis of insurance data, considering all the information it contains. Recently, several works of literature that explore the Value-at-Risk (VaR) for premium calculation have been reported, such as Heras, Moreno, and Vilar-Zan´on (2018). Motivated by the importance of risk forecast in insurance ratemaking, this dissertation proposes diverse approaches to making inferences about risk measures and quantifying uncertainty. Specifically, I start by disputing the argument in Heras, Moreno, and Vilar-Zan´on (2018) that their two-step inference method with quantile regression at the second stage with categorical variables can make a better forecast of VaR of aggregate losses than usual simple nonparametric estimates. By constructing a confidence interval using a novel empirical likelihood method, I provide sound evidence of my disputing argument. I further expand the risk analysis in more general settings to make an inference about VaR using both categorical and continuous explanatory variables and to quantify uncertainty using a random weighted bootstrap method. Lastly, I propose a three-step inference method for forecasting quantile risk measures, such as VaR and Expected Shortfall (ES), at a high-risk level. I adopt a Generalized Pareto Distribution (GPD) with a dynamic threshold for modeling excess losses and prove that I have made an efficient and robust risk forecast. Empirically, I use a well-known Australian automobile insurance dataset to illustrate the developed methods

Abstraction, aggregation and recursion for generating accurate and simple classifiers

An important goal of inductive learning is to generate accurate and compact classifiers from data. In a typical inductive learning scenario, instances in a data set are simply represented as ordered tuples of attribute values. In our research, we explore three methodologies to improve the accuracy and compactness of the classifiers: abstraction, aggregation, and recursion;Firstly, abstraction is aimed at the design and analysis of algorithms that generate and deal with taxonomies for the construction of compact and robust classifiers. In many applications of the data-driven knowledge discovery process, taxonomies have been shown to be useful in constructing compact, robust, and comprehensible classifiers. However, in many application domains, human-designed taxonomies are unavailable. We introduce algorithms for automated construction of taxonomies inductively from both structured (such as UCI Repository) and unstructured (such as text and biological sequences) data. We introduce AVT-Learner, an algorithm for automated construction of attribute value taxonomies (AVT) from data, and Word Taxonomy Learner (WTL), an algorithm for automated construction of word taxonomy from text and sequence data. We describe experiments on the UCI data sets and compare the performance of AVT-NBL (an AVT-guided Naive Bayes Learner) with that of the standard Naive Bayes Learner (NBL). Our results show that the AVTs generated by AVT-Learner are compeitive with human-generated AVTs (in cases where such AVTs are available). AVT-NBL using AVTs generated by AVT-Learner achieves classification accuracies that are comparable to or higher than those obtained by NBL; and the resulting classifiers are significantly more compact than those generated by NBL. Similarly, our experimental results of WTL and WTNBL on protein localization sequences and Reuters newswire text categorization data sets show that the proposed algorithms can generate Naive Bayes classifiers that are more compact and often more accurate than those produced by standard Naive Bayes learner for the Multinomial Model;Secondly, we apply aggregation to construct features as a multiset of values for the intrusion detection task. For this task, we propose a bag of system calls representation for system call traces and describe misuse and anomaly detection results on the University of New Mexico (UNM) and MIT Lincoln Lab (MIT LL) system call sequences with the proposed representation. With the feature representation as input, we compare the performance of several machine learning techniques for misuse detection and show experimental results on anomaly detection. The results show that standard machine learning and clustering techniques using the simple bag of system calls representation based on the system call traces generated by the operating system\u27s kernel is effective and often performs better than approaches that use foreign contiguous sequences in detecting intrusive behaviors of compromised processes;Finally, we construct a set of classifiers by recursive application of the Naive Bayes learning algorithms. Naive Bayes (NB) classifier relies on the assumption that the instances in each class can be described by a single generative model. This assumption can be restrictive in many real world classification tasks. We describe recursive Naive Bayes learner (RNBL), which relaxes this assumption by constructing a tree of Naive Bayes classifiers for sequence classification, where each individual NB classifier in the tree is based on an event model (one model for each class at each node in the tree). In our experiments on protein sequences, Reuters newswire documents and UC-Irvine benchmark data sets, we observe that RNBL substantially outperforms NB classifier. Furthermore, our experiments on the protein sequences and the text documents show that RNBL outperforms C4.5 decision tree learner (using tests on sequence composition statistics as the splitting criterion) and yields accuracies that are comparable to those of support vector machines (SVM) using similar information

Biological adhesion in wet environments: adaptations and mechanisms

Physiochemical conditions in water are fundamentally different to those in air; hence, organisms require special adaptations to adhere in wet environments. In my thesis, I have investigated three study systems to elucidate mechanisms for adhesion under wet conditions. In Chapters 2 and 3, I explore an aquatic insect (Diptera: Blephariceridae) that uses suction organs to attach to rocks in raging alpine torrents. Suction-based attachment is driven by physical processes requiring a pressure difference and a seal to maintain it. Through my investigations, I have identified several key principles for biological suction attachments to wet and rough surfaces. Using three-dimensional reconstructions of blepharicerid suction organs and in vivo visualisation of the adhesive contact zone, I found several internal and external morphological adaptations that are important for strong adhesion under water. Moreover, I characterised a mechanism for rapid detachment which is the first detailed account of an actively controlled detachment system in biological suction organs. In Chapter 4, I investigate the contribution of physical and chemical mechanisms to the powerful attachment of common limpets (Patella vulgata) to rocks in the intertidal zone. I demonstrate that suction is not the primary contributor to their attachment forces; rather, it is their adhesive pedal mucus that is responsible. This adhesive mucus comprises of a complex mixture of glycans and proteins, many of which share homology with adhesive secretions from other marine invertebrates, such as sea stars, sea anemones, and flatworms. In Chapters 5 and 6, I study the physical and chemical properties of sticky secretions from carnivorous pitcher plants (Nepenthes) that help to capture, retain, and digest insects. I show that the viscoelastic pitcher fluid readily adheres to but not easily dewets from insect cuticle, and forms stable filaments as the insect attempts to escape that require significant work to overcome. In addition, the surface tension is reduced in pitcher fluid compared to water, making insects sink more easily into the former and facilitating further wetting of the cuticle. Chemical characterisation of the pitcher fluid revealed that its sticky filamentous property is caused by a polysaccharide with a glucurono-mannan backbone structure, which is chemically stable and contains carboxylic groups for strong interactions. Glucurono-mannan are an understudied group of plant polysaccharides that are present in mucilaginous secretions from across the plant kingdom, including sticky capture fluids from other carnivorous plants. My findings show that pitcher plant fluid can be used as a study system for future investigations into the origins and functional role of glucurono-mannan in carnivorous plants. In summary, my thesis has identified novel adaptations and principles for biological adhesion under wet conditions using three selected study systems, hence expanding our understanding of the underlying physical and chemical mechanisms and providing inspiration for biomimetic adhesives with improved performance in wet environments.EU Horizon 2020 under Marie Skłodowska-Curie grant agreement No. 64286

Ignition improvers for aqueous ammonia as marine fuel

The potential of three molecules NH4NO2, H2O2, and O3 to ignite aqueous solutions of ammonia (25% by mass) as fuel, was investigated using chemical kinetic simulations at conditions representative of a two-stroke marine diesel engine. The purpose was to address two of the most prominent issues with making ammonia a practical fuel for marine applications: the difficulty of igniting ammonia, and the safety concerns regarding its volatility and toxicity. The ignition simulations carried out to this end used a two-zone reactor model of the engine, representing the ignition zone into which fuel was injected, and the bulk cylinder gases, respectively. The results suggested that all three ignition improving molecules were able to ignite aqueous ammonia reliably and at high combustion efficiency with acceptable levels of NO, N2O and NH3 emissions. Among the three fuel formulations investigated, H2O2 in 12% aqueous solution by mass, promised the lowest emissions of NO and N2O in the exhaust gases. This fuel blend added in a mole fraction of 0.15 to 0.85 aqueous ammonia at 25% by mass and promised to be the most practical solution, since it is stable and can be stored safely in a separate tank until injected into the engine

Mechanical Behaviors of Wire-woven Metals Composed of Two Different Thickness of Wires

AbstractWire-weaving is virtually only a practical method to fabricate multi-layered truss type cellular metals, except for stacking multiple single layered structures. To date, the wire-woven metals have been fabricated of wires of a uniform thickness. In this work, variations of wire-woven metals fabricated of two different thickness wires in out-of-plane and in-plane directions are introduced. The mechanical properties subjected to compressive or shear loading are investigated