Experimental study of the Sb-Sn-Zn alloy system

experimental description of the SbSn-Zn system by methods scanning electron microskope and differetial scanning calorimetryexperimentální popis ternární soustavy Sb-Sn-Zn metodami skenovací elektronové mikroskopie a diferenční skenovací kalorimetrieexperimental description of the SbSn-Zn system by methods scanning electron microskope and differetial scanning calorimetr

Search for black holes and other new phenomena in high-multiplicity final states in proton-proton collisions at root s=13 TeV

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Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

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Search for high-mass diphoton resonances in proton-proton collisions at 13 TeV and combination with 8 TeV search

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Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Measurement of the mass difference between top quark and antiquark in pp collisions at root s=8 TeV

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Search for leptophobic Z ' bosons decaying into four-lepton final states in proton-proton collisions at root s=8 TeV

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Measuring Cigarette Smoking Risk Perceptions

Risk perception is an important construct in many health behavior theories. Smoking risk perceptions are thoughts and feelings about the harms associated with cigarette smoking. Wide variation in the terminology, definition, and assessment of this construct makes it difficult to draw conclusions about the associations of risk perceptions with smoking behaviors. To understand optimal methods of assessing adults' cigarette smoking risk perceptions (among both smokers and nonsmokers), we reviewed best practices from the tobacco control literature, and where gaps were identified, we looked more broadly to the research on risk perceptions in other health domains. Based on this review, we suggest assessments of risk perceptions (1) about multiple smoking-related health harms, (2) about harms over a specific timeframe, and (3) for the person affected by the harm. For the measurement of perceived likelihood in particular (ie, the perceived chance of harm from smoking based largely on deliberative thought), we suggest including (4) unconditional and conditional items (stipulating smoking behavior) and (5) absolute and comparative items and including (6) comparisons to specific populations through (7) direct and indirect assessments. We also suggest including (8) experiential (ostensibly automatic, somatic perceptions of vulnerability to a harm) and affective (emotional reactions to a potential harm) risk perception items. We also offer suggestions for (9) response options and (10) the assessment of risk perception at multiple time points. Researchers can use this resource to inform the selection, use, and future development of smoking risk perception measures. Implications: Incorporating the measurement suggestions for cigarette smoking risk perceptions that are presented will help researchers select items most appropriate for their research questions and will contribute to greater consistency in the assessment of smoking risk perceptions among adults