Renormalization Group Study of the Standard Model and its Extensions: II. the Minimal Supersymmetric Standard Model

In this paper we summarize the minimal supersymmetric standard model as well as the renormalization group equations of its parameters. We proceed to examine the feasability of the model when the breaking of supersymmetry is parametrized by the soft terms suggested by supergravity theories. In such models, the electroweak symmetry is exact at tree level and is broken spontaneously at one loop order. We make the additional assumption that the GUT-inspired relation $m_b=m_\tau$ be valid at the scale where the gauge coupling constants unify, which constrains the value of the top quark mass. For all types of soft breaking terms expected in supergravity theories, we present the results of numerical runs which yield electroweak breaking at the required scale. These yield not only the allowed ranges for the soft supersymmetry breaking parameters, but also the value of the supersymmetric partner' masses. For example in the strict no-scale model, in which global supersymmetry breaking arises solely from soft supersymmetry breaking parameters, but also the value of the supersymmetric partner' masses. For example in the strict no-scale model, in which global supersymmetry breaking arises solely from soft gaugino masses, we find that $M_t$ can be no heavier than $\sim 127$ GeV.Comment: 41 pages. ReVTeX typeset. 8 figures not included but available (as well as a full postscript version of the paper including the figures) by anonymous FTP at uful07.phys.ufl.edu in the het/UFIFT-HEP-93-18 directory. Report No. UFIFT-HEP-93-1

Upper airway symptoms among workers with work-related respiratory complaints

Background Work-related rhinitis and asthma symptoms frequently co-exist. Aims To determine the prevalence and nature of nasal, pharyngeal, laryngeal and sinus symptoms among individuals with work-related respiratory symptoms. Methods Individuals referred to a tertiary occupational asthma clinic for investigations with specific inhalation challenges were evaluated using the RHINASTHMA quality of life questionnaire and a questionnaire that assessed the nature and frequency of upper airway symptoms, their relationship to the workplace and their temporal relationship with the onset of asthma symptoms. Results There were 83 study participants. At least one upper airway symptom was reported by all of these individuals: nasal in 92%; pharyngeal in 82%; laryngeal in 65% and sinus in 53% of participants. Overall, there were no significant differences in the frequencies of nasal, pharyngeal, laryngeal and sinus symptoms when comparing these with occupational asthma (OA), work-exacerbated asthma (WEA) and work-related respiratory symptoms (WRS), except that nasal bleeding was most frequent among those with WRS. The presence of laryngeal symptoms was significantly associated with rhinitis-specific quality of life impairment. Individuals with workplace exposures to high molecular weight agents had greater impaired quality of life than those who were exposed to low molecular weight agents (RHINASTMA Upper Airway sub-scores: 24.0±10.4 versus 19.8±6.8; P < 0.05). Conclusions Individuals who were referred for work-related respiratory symptoms experienced high rates of work-related nasal, pharyngeal, laryngeal and sinus symptoms, regardless of having OA, WEA or WR

Model checker execution reports

Software model checking constitutes an undecidable problem and, as such, even an ideal tool will in some cases fail to give a conclusive answer. In practice, software model checkers fail often and usually do not provide any information on what was effectively checked. The purpose of this work is to provide a conceptual framing to extend software model checkers in a way that allows users to access information about incomplete checks. We characterize the information that model checkers themselves can provide, in terms of analyzed traces, i.e. sequences of statements, and safe canes, and present the notion of execution reports (ERs), which we also formalize. We instantiate these concepts for a family of techniques based on Abstract Reachability Trees and implement the approach using the software model checker CPAchecker. We evaluate our approach empirically and provide examples to illustrate the ERs produced and the information that can be extracted

Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering

Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals

Designing peptide nanoparticles for efficient brain delivery

The targeted delivery of therapeutic compounds to the brain is arguably the most significant open problem in drug delivery today. Nanoparticles (NPs) based on peptides and designed using the emerging principles of molecular engineering show enormous promise in overcoming many of the barriers to brain delivery faced by NPs made of more traditional materials. However, shortcomings in our understanding of peptide self-assembly and blood–brain barrier (BBB) transport mechanisms pose significant obstacles to progress in this area. In this review, we discuss recent work in engineering peptide nanocarriers for the delivery of therapeutic compounds to the brain, from synthesis, to self-assembly, to in vivo studies, as well as discussing in detail the biological hurdles that a nanoparticle must overcome to reach the brain

Sheath parameters for non-Debye plasmas: simulations and arc damage

This paper describes the surface environment of the dense plasma arcs that damage rf accelerators, tokamaks and other high gradient structures. We simulate the dense, non-ideal plasma sheath near a metallic surface using Molecular Dynamics (MD) to evaluate sheaths in the non-Debye region for high density, low temperature plasmas. We use direct two-component MD simulations where the interactions between all electrons and ions are computed explicitly. We find that the non-Debye sheath can be extrapolated from the Debye sheath parameters with small corrections. We find that these parameters are roughly consistent with previous PIC code estimates, pointing to densities in the range $10^{24} - 10^{25}\mathrm{m}^{-3}$. The high surface fields implied by these results could produce field emission that would short the sheath and cause an instability in the time evolution of the arc, and this mechanism could limit the maximum density and surface field in the arc. These results also provide a way of understanding how the "burn voltage" of an arc is generated, and the relation between self sputtering and the burn voltage, while not well understood, seems to be closely correlated. Using these results, and equating surface tension and plasma pressure, it is possible to infer a range of plasma densities and sheath potentials from SEM images of arc damage. We find that the high density plasma these results imply and the level of plasma pressure they would produce is consistent with arc damage on a scale 100 nm or less, in examples where the liquid metal would cool before this structure would be lost. We find that the sub-micron component of arc damage, the burn voltage, and fluctuations in the visible light production of arcs may be the most direct indicators of the parameters of the dense plasma arc, and the most useful diagnostics of the mechanisms limiting gradients in accelerators.Comment: 8 pages, 16 figure

Peeling back the layers of crassulacean acid metabolism: functional differentiation between Kalanchoë fedtschenkoi epidermis and mesophyll proteomes

Crassulacean acid metabolism (CAM) is a specialized mode of photosynthesis that offers the potential to engineer improved water‐use efficiency (WUE) and drought resilience in C3 plants while sustaining productivity in the hotter and drier climates that are predicted for much of the world. CAM species show an inverted pattern of stomatal opening and closing across the diel cycle, which conserves water and provides a means of maintaining growth in hot, water‐limited environments. Recent genome sequencing of the constitutive model CAM species Kalanchoë fedtschenkoi provides a platform for elucidating the ensemble of proteins that link photosynthetic metabolism with stomatal movement, and that protect CAM plants from harsh environmental conditions. We describe a large‐scale proteomics analysis to characterize and compare proteins, as well as diel changes in their abundance in guard cell‐enriched epidermis and mesophyll cells from leaves of K. fedtschenkoi. Proteins implicated in processes that encompass respiration, the transport of water and CO2, stomatal regulation, and CAM biochemistry are highlighted and discussed. Diel rescheduling of guard cell starch turnover in K. fedtschenkoi compared with that observed in Arabidopsis is reported and tissue‐specific localization in the epidermis and mesophyll of isozymes implicated in starch and malate turnover are discussed in line with the contrasting roles for these metabolites within the CAM mesophyll and stomatal complex. These data reveal the proteins and the biological processes enriched in each layer and provide key information for studies aiming to adapt plants to hot and dry environments by modifying leaf physiology for improved plant sustainability

A method for colocating satellite X_(CO₂) data to ground-based data and its application to ACOS-GOSAT and TCCON

Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (X_(CO₂)), where X_(CO₂) data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated and compared to ground-based column X_(CO₂) measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO₂ (X_(CO₂)) with ground-based measurements typically involve locating and averaging the satellite measurements within a latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and midtropospheric temperature at 700 hPa. We apply this methodology to X_(CO₂) retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON X_(CO₂) ground-based data, and present some comparisons between our methodology and standard existing colocation methods showing that, in general, geostatistical colocation produces smaller mean-squared error