A Markov chain location-allocation meta-model for hurricane relief

An emergency is a deviation from a planned course of events that endangers people, properties, or the environment. It can be described as an unexpected event that causes economic damage, destruction, and human suffering. When a disaster happens, Emergency Managers are expected to have a response plan to most likely disaster scenarios. Unlike earthquakes and terrorist attacks, a hurricane response plan can be activated ahead of time, since a hurricane is predicted at least five days before it makes landfall. This research looked into the logistics aspects of the problem, in an attempt to develop a hurricane relief distribution network model. We addressed the problem of how to efficiently and effectively deliver basic relief goods to victims of a hurricane disaster. Specifically, where to preposition State Staging Areas (SSA), which Points of Distributions (PODs) to activate, and the allocation of commodities to each POD. Previous research has addressed several of these issues, but not with the incorporation of the random behavior of the hurricane's intensity and path. This research presents a stochastic meta-model that deals with the location of SSAs and the allocation of commodities. The novelty of the model is that it treats the strength and path of the hurricane as stochastic processes, and models them as Discrete Markov Chains. The demand is also treated as stochastic parameter because it depends on the stochastic behavior of the hurricane. However, for the meta-model, the demand is an input that is determined using Hazards United States (HAZUS), a software developed by the Federal Emergency Management Agency (FEMA) that estimates losses due to hurricanes and floods. A solution heuristic has been developed based on simulated annealing. Since the meta-model is a multi-objective problem, the heuristic is a multi-objective simulated annealing (MOSA), in which the initial solution and the cooling rate were determined via a Design of Experiments. The experiment showed that the initial temperature (T0) is irrelevant, but temperature reduction (δ) must be very gradual. Assessment of the meta-model indicates that the Markov Chains performed as well or better than forecasts made by the National Hurricane Center (NHC). Tests of the MOSA showed that it provides solutions in an efficient manner. Thus, an illustrative example shows that the meta-model is practical.

The Philippines in the Aerospace Global Value Chain

This report uses the Duke CGGC Global Value Chain (GVC) framework to examine the role of the Philippines in the global aerospace industry and identify opportunities for the country to upgrade. The Philippines is a newcomer to the growing global aerospace manufacturing industry. Although the country has been host to a major flight controls manufacturer since 1985, the industry really only began to expand within the past five to ten years. During this recent period (2007-2014), the country has rapidly ramped up its aerospace manufacturing exports, reaching US$604 million in 2014 and more than tripling employment. The industry now employs 3,000 full time and 3,000 part time workers. Although still a very small player, accounting for less than 0.15% of the global industry, this incipient growth is promising. Both foreign firms and local suppliers that have established operations in the industry have already achieved some degree of upgrading within a short timeframe. These include expanding the product lines served, obtaining essential process certifications and upgrading beyond basic assembly operations to undertake additional manufacturing processes such as machining as well as initiating procurement and engineering functions in country.

Bayesian alignment of continuous molecular shapes using random fields

Statistical methodology is proposed for comparing molecular shapes. In order to account for the continuous nature of molecules, classical shape analysis methods are combined with techniques used for predicting random fields in spatial statistics. Applying a modification of Procrustes analysis, Bayesian inference is carried out using Markov chain Monte Carlo methods for the pairwise alignment of the resulting molecular fields. Superimposing entire fields rather than the configuration matrices of nuclear positions thereby solves the problem that there is usually no clear one--to--one correspondence between the atoms of the two molecules under consideration. Using a similar concept, we also propose an adaptation of the generalised Procrustes analysis algorithm for the simultaneous alignment of multiple molecular fields. The methodology is applied to a dataset of 31 steroid molecules.

Machine learning per l'idrologia: applicazione del metodo random forests per la previsione degli eventi di piena fluviale con un approccio probabilistico

Le tecniche di Machine Learning sono molto utili in quanto consento di massimizzare l’utilizzo delle informazioni in tempo reale. Il metodo Random Forests può essere annoverato tra le tecniche di Machine Learning più recenti e performanti. Sfruttando le caratteristiche e le potenzialità di questo metodo, la presente tesi di dottorato affronta due casi di studio differenti; grazie ai quali è stato possibile elaborare due differenti modelli previsionali. Il primo caso di studio si è incentrato sui principali fiumi della regione Emilia-Romagna, caratterizzati da tempi di risposta molto brevi. La scelta di questi fiumi non è stata casuale: negli ultimi anni, infatti, in detti bacini si sono verificati diversi eventi di piena, in gran parte di tipo “flash flood”. Il secondo caso di studio riguarda le sezioni principali del fiume Po, dove il tempo di propagazione dell’onda di piena è maggiore rispetto ai corsi d’acqua del primo caso di studio analizzato. Partendo da una grande quantità di dati, il primo passo è stato selezionare e definire i dati in ingresso in funzione degli obiettivi da raggiungere, per entrambi i casi studio. Per l’elaborazione del modello relativo ai fiumi dell’Emilia-Romagna, sono stati presi in considerazione esclusivamente i dati osservati; a differenza del bacino del fiume Po in cui ai dati osservati sono stati affiancati anche i dati di previsione provenienti dalla catena modellistica Mike11 NAM/HD. Sfruttando una delle principali caratteristiche del metodo Random Forests, è stata stimata una probabilità di accadimento: questo aspetto è fondamentale sia nella fase tecnica che in fase decisionale per qualsiasi attività di intervento di protezione civile. L'elaborazione dei dati e i dati sviluppati sono stati effettuati in ambiente R. Al termine della fase di validazione, gli incoraggianti risultati ottenuti hanno permesso di inserire il modello sviluppato nel primo caso studio all’interno dell’architettura operativa di FEWS.

Statistical learning of random probability measures

The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle.

Dna Extraction From Filter-paper Spots Of Vaginal Samples Collected After Sexual Violence.

To detect the presence of male DNA in vaginal samples collected from survivors of sexual violence and stored on filter paper. A pilot study was conducted to evaluate 10 vaginal samples spotted on sterile filter paper: 6 collected at random in April 2009 and 4 in October 2010. Time between sexual assault and sample collection was 4-48hours. After drying at room temperature, the samples were placed in a sterile envelope and stored for 2-3years until processing. DNA extraction was confirmed by polymerase chain reaction for human β-globin, and the presence of prostate-specific antigen (PSA) was quantified. The presence of the Y chromosome was detected using primers for sequences in the TSPY (Y7/Y8 and DYS14) and SRY genes. β-Globin was detected in all 10 samples, while 2 samples were positive for PSA. Half of the samples amplified the Y7/Y8 and DYS14 sequences of the TSPY gene and 30% amplified the SRY gene sequence of the Y chromosome. Four male samples and 1 female sample served as controls. Filter-paper spots stored for periods of up to 3years proved adequate for preserving genetic material from vaginal samples collected following sexual violence.

Quantitative Proteomic Analysis Reveals Metabolic Alterations, Calcium Dysregulation, And Increased Expression Of Extracellular Matrix Proteins In Laminin α2 Chain-deficient Muscle.

Congenital muscular dystrophy with laminin α2 chain deficiency (MDC1A) is one of the most severe forms of muscular disease and is characterized by severe muscle weakness and delayed motor milestones. The genetic basis of MDC1A is well known, yet the secondary mechanisms ultimately leading to muscle degeneration and subsequent connective tissue infiltration are not fully understood. In order to obtain new insights into the molecular mechanisms underlying MDC1A, we performed a comparative proteomic analysis of affected muscles (diaphragm and gastrocnemius) from laminin α2 chain-deficient dy(3K)/dy(3K) mice, using multidimensional protein identification technology combined with tandem mass tags. Out of the approximately 700 identified proteins, 113 and 101 proteins, respectively, were differentially expressed in the diseased gastrocnemius and diaphragm muscles compared with normal muscles. A large portion of these proteins are involved in different metabolic processes, bind calcium, or are expressed in the extracellular matrix. Our findings suggest that metabolic alterations and calcium dysregulation could be novel mechanisms that underlie MDC1A and might be targets that should be explored for therapy. Also, detailed knowledge of the composition of fibrotic tissue, rich in extracellular matrix proteins, in laminin α2 chain-deficient muscle might help in the design of future anti-fibrotic treatments. All MS data have been deposited in the ProteomeXchange with identifier PXD000978 (http://proteomecentral.proteomexchange.org/dataset/PXD000978).

Identification Of Corynebacterium Spp. Isolated From Bovine Intramammary Infections By Matrix-assisted Laser Desorption Ionization-time Of Flight Mass Spectrometry.

Corynebacterium species (spp.) are among the most frequently isolated pathogens associated with subclinical mastitis in dairy cows. However, simple, fast, and reliable methods for the identification of species of the genus Corynebacterium are not currently available. This study aimed to evaluate the usefulness of matrix-assisted laser desorption ionization/mass spectrometry (MALDI-TOF MS) for identifying Corynebacterium spp. isolated from the mammary glands of dairy cows. Corynebacterium spp. were isolated from milk samples via microbiological culture (n=180) and were analyzed by MALDI-TOF MS and 16S rRNA gene sequencing. Using MALDI-TOF MS methodology, 161 Corynebacterium spp. isolates (89.4%) were correctly identified at the species level, whereas 12 isolates (6.7%) were identified at the genus level. Most isolates that were identified at the species level with 16 S rRNA gene sequencing were identified as Corynebacterium bovis (n=156; 86.7%) were also identified as C. bovis with MALDI-TOF MS. Five Corynebacterium spp. isolates (2.8%) were not correctly identified at the species level with MALDI-TOF MS and 2 isolates (1.1%) were considered unidentified because despite having MALDI-TOF MS scores >2, only the genus level was correctly identified. Therefore, MALDI-TOF MS could serve as an alternative method for species-level diagnoses of bovine intramammary infections caused by Corynebacterium spp.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

A Metabolomic Protocol For Plant Systematics By Matrix-assisted Laser-desorption/ionization Time-of Flight Mass Spectrometry.

Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.

Improvement Of The Physical Performance Is Associated With Activation Of No/pgc-1α/mttfa Signaling Pathway And Increased Protein Expressions Of Electron Transport Chain In Gastrocnemius Muscle From Rats Supplemented With L-arginine.

To examine the influence of l-arginine supplementation in combination with physical training on mitochondrial biomarkers from gastrocnemius muscle and its relationship with physical performance. Male Wistar rats were divided into four groups: control sedentary (SD), sedentary supplemented with l-arginine (SDLA), trained (TR) and trained supplemented with l-arginine (TRLA). Supplementation of l-arginine was administered by gavage (62.5mg/ml/day/rat). Physical training consisted of 60min/day, 5days/week, 0% grade, speed of 1.2km/h. The study lasted 8weeks. Skeletal muscle mitochondrial enriched fraction as well as cytoplasmic fractions were obtained for Western blotting and biochemical analyses. Protein expressions of transcriptor coactivator (PGC-1α), transcriptor factors (mtTFA), ATP synthase subunit c, cytochrome oxidase (COXIV), constitutive nitric oxide synthases (eNOS and nNOS), Cu/Zn-superoxide dismutase (SOD) and manganese-SOD (Mn-SOD) were evaluated. We also assessed in plasma: lipid profile, glycemia and malondialdehyde (MDA) levels. The nitrite/nitrate (NOx(-)) levels were measured in both plasma and cytosol fraction of the gastrocnemius muscle. 8-week l-arginine supplementation associated with physical training was effective in promoting greater tolerance to exercise that was accompanied by up-regulation of the protein expressions of mtTFA, PGC-1α, ATP synthase subunit c, COXIV, Cu/Zn-SOD and Mn-SOD. The upstream pathway was associated with improvement of NO bioavailability, but not in NO production since no changes in nNOS or eNOS protein expressions were observed. This combination would be an alternative approach for preventing cardiometabolic diseases given that in overt diseases a profound impairment in the physical performance of the patients is observed.

Fish Biodiversity Of The Vitória-trindade Seamount Chain, Southwestern Atlantic: An Updated Database.

Despite a strong increase in research on seamounts and oceanic islands ecology and biogeography, many basic aspects of their biodiversity are still unknown. In the southwestern Atlantic, the Vitória-Trindade Seamount Chain (VTC) extends ca. 1,200 km offshore the Brazilian continental shelf, from the Vitória seamount to the oceanic islands of Trindade and Martin Vaz. For a long time, most of the biological information available regarded its islands. Our study presents and analyzes an extensive database on the VTC fish biodiversity, built on data compiled from literature and recent scientific expeditions that assessed both shallow to mesophotic environments. A total of 273 species were recorded, 211 of which occur on seamounts and 173 at the islands. New records for seamounts or islands include 191 reef fish species and 64 depth range extensions. The structure of fish assemblages was similar between islands and seamounts, not differing in species geographic distribution, trophic composition, or spawning strategies. Main differences were related to endemism, higher at the islands, and to the number of endangered species, higher at the seamounts. Since unregulated fishing activities are common in the region, and mining activities are expected to drastically increase in the near future (carbonates on seamount summits and metals on slopes), this unique biodiversity needs urgent attention and management.

Profile Of Phenolic Compounds Of Brazilian Virgin Olive Oils By Rapid Resolution Liquid Chromatography Coupled To Electrospray Ionisation Time-of-flight Mass Spectrometry (rrlc-esi-tof-ms).

In recent years, agronomical researchers began to cultivate several olive varieties in different regions of Brazil to produce virgin olive oil (VOO). Because there has been no reported data regarding the phenolic profile of the first Brazilian VOO, the aim of this work was to determine phenolic contents of these samples using rapid-resolution liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry. 25 VOO samples from Arbequina, Koroneiki, Arbosana, Grappolo, Manzanilla, Coratina, Frantoio and MGS Mariense varieties from three different Brazilian states and two crops were analysed. It was possible to quantify 19 phenolic compounds belonging to different classes. The results indicated that Brazilian VOOs have high total phenolic content because the values were comparable with those from high-quality VOOs produced in other countries. VOOs from Coratina, Arbosana and Grappolo presented the highest total phenolic content. These data will be useful in the development and improvement of Brazilian VOO.

Photo-fenton degradation of poly(Ethyleneglycol)

Polyethyleneglycol (PEG) was photooxidized in a photo-Fenton system and results compared with the dark reaction. The products were analysed using GPC and HPLC. In the absence of light, PEG samples needed 490 min to reduce their w by 50%, whereas under UV irradiation, only 10 min were necessary. The exponential decay of w with a concomitant increase in polydispersity and number of average chain scission, characterized a random chain scission mechanism. The degradation products of PEG in both systems showed the presence of lower molecular weight products, including smaller ethyleneglycols and formic acid. The mechanism involves consecutive processes, were the larger ethyleneglycols give rise, successively, to smaller ones. This suggests that the mechanism involves successive scissions of the polymer chain. Irradiated samples decomposed faster than those kept in the dark This study proves that the foto-Fenton method associated with UV-light is a good reactant for PEG photodegradation.

The UV/H2O2 - photodegradation of poly(ethyleneglycol) and model compounds

The general mechanism for the photodegradation of polyethyleneglycol (PEG) by H2O2/UV was determined studying the photooxidation of small model molecules, like low molecular weight ethyleneglycols (tetra-, tri-, di-, and ethyleneglycol). After 30 min of irradiation the average molar mass (Mw) of the degradated PEG, analysed by GPC, fall to half of its initial value, with a concomitant increase in polydispersitivity and number of average chain scission (S), characterizing a random chain scission process yielding oligomers and smaller size ethyleneglycols. HPLC analysis of the photodegradation of the model ethyleneglycols proved that the oxidation mechanism involved consecutive reactions, where the larger ethyleneglycols gave rise, successively, to smaller ones. The photodegradation of ethyleneglycol lead to the formation of low molecular weight carboxylic acids, like glycolic, oxalic and formic acids.