Decision Making Effectiveness in Wildfire Incident Management Teams

During large scale wildfires, suppression activities are carried out under the direction of an Incident Management Team (IMT). The aim of the research was to increase understanding of decision processes potentially related to IMT effectiveness. An IMT comprises four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to study IMT processes: computer simulation experiments; analyses of wildfire reports; interviews with IMT members; and cognitive ethnographic studies of IMTs. Three processes were important determinants of IMT effectiveness: information management and cognitive overload; matching component function goals to overall goals; and team metacognition to detect and counter task‐disruptive developments. These processes appear to be complex multi‐person analogues of individual Incident Command processes identified previously. The findings have implications for issues such as: creating IMTs; training IMTs; managing IMTs; and providing decision support to IMTs.     

Straightforward modeling of dynamic I–V characteristics for microwave FETs

This work presents a straightforward approach aimed at modeling the dynamic I–V characteristics of microwave active solid‐state devices. The drain‐source current generator represents the most significant source of nonlinearity in a transistor and, therefore, its correct modeling is fundamental to predict accurately the current and voltage waveforms under large‐signal operation. The proposed approach relies on using a small set of low‐frequency time‐domain waveform measurements combined with numerical optimization‐based estimation of the nonlinear model parameters. The procedure is applied to a gallium nitride HEMT and silicon FinFET. The effectiveness of the modeling procedure in terms of prediction accuracy and generalization capability is demonstrated by validation of the extracted models under operating conditions different than the ones used for the parameters estimation. Good agreement between measurements and model simulations is achieved for both technologies and in both low‐ and high‐frequency range. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:109–116, 2014.     

Computational Aspects of Uncertainty Profiles and Angel-Daemon Games

We analyze the complexity of equilibria problems for a class of strategic zero-sum games, called angel-daemon games. Those games were introduced to asses the performance of the execution of a web orchestration on a moderate faulty or under stress environment. Angel-daemon games are a natural example of zero-sum games whose representation is naturally succinct. We show that the problems of deciding the existence of a pure Nash equilibrium or of a dominant strategy for a given player are ${\Sigma}^{p}_{2}$ -complete. Furthermore, computing the value of an angel-daemon game is EXP-complete. Thus, our results match the already known classification of the corresponding problems for the generic families of succinctly represented games with exponential number of actions.     

Classification of honeys by principal component analysis on the basis of chemical and physical parameters

Summary Eighteen chemical and physical parameters of nectar and honeydew honeys were determined and the results were analysed statistically by the method of principal component analysis. On the plots of principal component loadings, the honeys were divided into the following groups: (1) acacia honey; (2) rape honey; (3) linden, floral (nectar coming from various plants) and heather honeys; (4) honeydew honey. The samples which were a blend of nectar and honeydew honeys may be considered as a separate group between 3 and 4. The most important first principal component was strongly associated with the value of electrical conductivity, the contents of ash, free acids and proline, as well as with the pH and the diastase number. The principal component loadings and linear correlation suggested that these parameters contributed much more to the classification of honeys than apparent reducing sugars, apparent sucrose, mono-, di-, and trisaccharides, glucose and fructose. The classification of honeys by the method of principal component analysis may serve as an additional tool in specifying samples on the basis of their chemical composition.

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Klassifizierung von Honigen durch chemische und physikalische Parameter mit Hilfe der Hauptkomponentenanalyse

Zusammenfassung 18 chemische und physikalische Parameter der Nektar- und Honigtauhonige wurden bestimmt und die Ergebnisse statistisch mit Hilfe der Hauptkomponentenanalyse ausgewertet. Danach konnten die Honige in folgende Gruppen unterteilt werden: 1) Akazienhonige, 2) Rapshonige, 3) Linden-, Blüten-und Heidehonige, 4) Honigtauhonige. Zwischen dem Nektarhonig und Honigtauhonig lagen wahrscheinlich die Proben von Mischhonigen aus Blüten- und Waldtracht. Die bedeutendste erste Hauptkomponente war mit folgenden Parametern verbunden: elektrischer Leitfähigkeitswert und Aschegehalt, Gehalt an freien Säuren und Prolin sowie der pH-Wert und Diastasezahl. Aufgrund der Hauptkomponentenladungen und der linearen Korrelation kann angenommen werden, daß diese Parameter für die Honigklassifizierung von größerer Bedeutung sind als der Gehalt an reduzierenden Zuckern, scheinbarer Saccharose, Mono-, Di- und Trisaccharide, Glucose und Fructose. Die Klassifizierung der Honige mit Hilfe der Hauptkomponentenanalyse kann bei der Spezifikation einer Honigprobe aufgrund ihrer chemischen Zusammensetzung von Nutzen sein.

     

A Fast Explicit Operator Splitting Method for Passive Scalar Advection

The dispersal and mixing of scalar quantities such as concentrations or thermal energy are often modeled by advection-diffusion equations. Such problems arise in a wide variety of engineering, ecological and geophysical applications. In these situations a quantity such as chemical or pollutant concentration or temperature variation diffuses while being transported by the governing flow. In the passive scalar case, this flow prescribed and unaffected by the scalar. Both steady laminar and complex (chaotic, turbulent or random) time-dependent flows are of interest and such systems naturally lead to questions about the effectiveness of the stirring to disperse and mix the scalar. The development of reliable numerical methods for advection-diffusion equations is crucial for understanding their properties, both physical and mathematical. In this paper, we extend a fast explicit operator splitting method, recently proposed in (A. Chertock, A. Kurganov, G. Petrova, Int. J. Numer. Methods Fluids 59:309–332, 2009), for solving deterministic convection-diffusion equations, to the problems with random velocity fields and singular source terms. A superb performance of the method is demonstrated on several two-dimensional examples.     

Prognostic Analysis of Human Pluripotent Stem Cells Based on Their Morphological Portrait and Expression of Pluripotent Markers

The ability of human pluripotent stem cells for unlimited proliferation and self-renewal promotes their application in the fields of regenerative medicine. The morphological assessment of growing colonies and cells, as a non-invasive method, allows the best clones for further clinical applications to be safely selected. For this purpose, we analyzed seven morphological parameters of both colonies and cells extracted from the phase-contrast images of human embryonic stem cell line H9, control human induced pluripotent stem cell (hiPSC) line AD3, and hiPSC line HPCASRi002-A (CaSR) in various passages during their growth for 120 h. The morphological phenotype of each colony was classified using a visual analysis and associated with its potential for pluripotency and clonality maintenance, thus defining the colony phenotype as the control parameter. Using the analysis of variance for the morphological parameters of each line, we showed that selected parameters carried information about different cell lines and different phenotypes within each line. We demonstrated that a model of classification of colonies and cells by phenotype, built on the selected parameters as predictors, recognized the phenotype with an accuracy of 70–75%. In addition, we performed a qRT-PCR analysis of eleven pluripotency markers genes. By analyzing the variance of their expression in samples from different lines and with different phenotypes, we identified group-specific sets of genes that could be used as the most informative ones for the separation of the best clones. Our results indicated the fundamental possibility of constructing a morphological portrait of a colony informative for the automatic identification of the phenotype and for linking this portrait to the expression of pluripotency markers.     

Replacing the Burden of the Gluten Free Diet: Then,Now, and the Future

Without a doubt, a majority of diseases are food-pattern-related. However, one disease stands out as an increasingly more common autoimmune-mediated enteropathy triggered by the ingestion of gluten. Celiac disease (CD) is an old disease, with changing clinical patterns, affecting any age, including infancy and adolescence, and becoming more frequent among the elderly. The gluten-free diet (GFD) has been the sole provider of clinical, serological, and histological improvement for patients with CD for more than seven decades. Nowadays, complete avoidance of dietary gluten is rarely possible because of the wide availability of wheat and other processed foods that contain even more gluten, to the detriment of gluten-free products. Undeniably, there is a definite need for replacing the burdensome GFD. An add-on therapy that could control the dietary transgressions and inadvertent gluten consumption that can possibly lead to overt CD should be considered while on GFD. Nevertheless, future drugs should be able to provide patients some freedom to self-manage CD and increase food independence, while actively reducing exposure and mucosal damage and alleviating GI symptoms. Numerous clinical trials assessing different molecules have already been performed with favorable outcomes, and hopefully they will soon be available for patient use.     

Mitochondrial DNA Together with miR-142-3p in Plasma Can Predict Unfavorable Outcomes in Patients after Acute Myocardial Infarction

Myocardial infarction is one of the leading causes of death worldwide, despite numerous efforts to find efficient prognostic biomarkers and treatment targets. In the present study, we aimed to assess the potential of six microRNAs known to be involved in cardiovascular diseases, cell-free DNA (cfDNA), and mitochondrial DNA (mtDNA) circulating in plasma to be used as prognostic tools for the occurrence of unfavorable outcomes such as major adverse cardiovascular events (MACE) after acute ST-segment elevation myocardial infarction (STEMI). Fifty STEMI patients were enrolled and monitored for 6 months for the occurrence of MACE. Plasma was collected at three time points: upon admission to hospital (T₀), at discharge from hospital (T₁), and 6 months post-STEMI (T₆). Plasma levels of miR-223-3p, miR-142-3p, miR-155-5p, miR-486-5p, miR-125a-5p, and miR-146a-5p, as well as of cfDNA and mtDNA, were measured by RT-qPCR. Results showed that the levels of all measured miRNAs, as well as of cfDNA and mtDNA, were the most increased at T₁, compared to the other two time points. In the plasma of STEMI patients with MACE compared to those without MACE, we determined increased levels of miRNAs, cfDNA, and mtDNA at T_1. Hence, we used the levels of all measured parameters at T₁ for further statistical analysis. Statistical analysis demonstrated that all six miRNAs and cfDNA plus mtDNA levels, respectively, were associated with MACE. The minimal statistical model that could predict MACE in STEMI patients was the combination of mtDNA and miR-142-3p levels, as evidenced by ROC analysis (AUC = 0.97, p < 0.001). In conclusion, the increased plasma levels of mtDNA, along with miR-142-3p, could be used to predict unfavorable outcomes in STEMI patients.