Seasonal effects on microorganisms in the rhizosphere of two tropical plants in a polyculture agroforestry system in Central Amazonia, Brazil

A software tool was developed to determine what is accomplished as the result of truck weight enforcement efforts. Traditionally applied measures (e.g., numbers of trucks weighed and citations issued) have simply provided indications of enforcement effort. These previously applied measures failed to provide results in terms of real enforcement objectives, such as deterring overweight trucks and minimizing pavement wear and tear. Consequently the need exists to develop and validate truck weight enforcement measures of effectiveness (MOE). MOEs were developed via a series of analytical procedures. They were subsequently validated in a comprehensive four-state field evaluation. Matched (weigh-in-motion) (WIM) data sets, collected under controlled baseline and enforcement conditions, were analyzed to determine the sensitivity of candidate MOEs to actual enforcement activity. Data collection conditions were controlled in order to avoid contamination from hour-of-day, day-of-week, and seasonal effects. The following MOEs, were validated on the basis of their demonstrated sensitivity to truck weight enforcement objectives and the presence of enforcement activity: (1) severity of overweight violations, (2) proportion of overweight trucks, (3) average equivalent single-axle load (ESAL), (4) excess ESALs, and (5) bridge formula violations. These measures are sensitive to legal load-limit compliance objectives of truck weight enforcement procedures as well as the potential for overweight trucks to produce pavement deterioration. The software User Guide that statistically compares calculated MOEs between observed enforcement conditions is described in this paper. The User Guide also allows users to conduct an automated pavement design life analysis estimating, the theoretical pavement-life effect resulting from the observed enforcement activity.

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Faculty Opinions recommendation of Biosynthetic potential of phylogenetically unique endophytic actinomycetes from tropical plants.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.4036956.3803054 ◽

2010 ◽

Author(s):

Jean-Jacques Sanglier

Keyword(s):

Tropical Plants ◽

Endophytic Actinomycetes

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Regional and Seasonal Effects on Contributions by US Households: An Analysis of the 2004 Consumer Expenditure Survey

SSRN Electronic Journal ◽

10.2139/ssrn.916100 ◽

2006 ◽

Author(s):

Diane Hite ◽

Jeanne K. Lindsey

Keyword(s):

Seasonal Effects ◽

Consumer Expenditure ◽

Consumer Expenditure Survey ◽

Expenditure Survey

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The Seasonal End of Human Coronavirus Hospital Admissions with Implications for SARS-CoV-2 (Preprint)

10.2196/preprints.21223 ◽

2020 ◽

Author(s):

Alan T Evangelista

Keyword(s):

Hospital Admissions ◽

Disease Incidence ◽

Influenza Viruses ◽

Seasonal Effects ◽

Human Coronavirus ◽

Enveloped Virus ◽

Multiplex Pcr Assay ◽

Contact Transmission ◽

Human Coronaviruses

UNSTRUCTURED The seasonality of influenza viruses and endemic human coronaviruses was tracked over an 8-year period to assess key epidemiologic reduction points in disease incidence for an urban area in the northeast United States. Patients admitted to a pediatric hospital with worsening respiratory symptoms were tested using a multiplex PCR assay from nasopharyngeal swabs. The additive seasonal effects of outdoor temperatures and indoor relative humidity (RH) were evaluated. The 8-year average peak activity of human coronaviruses occurred in the first week of January, when droplet and contact transmission was enabled by the low indoor RH of 20-30%. Previous studies have shown that an increase in RH to 50% has been associated with markedly reduced viability and transmission of influenza virus and animal coronaviruses. As disease incidence was reduced by 50% in early March, to 75% in early April, to greater than 99% at the end of April, a relationship was observed from colder temperatures in January with a low indoor RH to a gradual increase in outdoor temperatures in April with an indoor RH of 45-50%. As a lipid-bound, enveloped virus with similar size characteristics to endemic human coronaviruses, SARS-CoV-2 should be subject to the same dynamics of reduced viability and transmission with increased humidity. In addition to the major role of social distancing, the transition from lower to higher indoor RH with increasing outdoor temperatures could have an additive effect on the decrease in SARS-CoV-2 cases in May. Over the 8-year period of this study, human coronavirus activity was either zero or >99% reduction in the months of June through September, and the implication would be that SARS-Cov-2 may follow a similar pattern. INTERNATIONAL REGISTERED REPORT RR2-doi.org/10.1101/2020.05.15.20103416

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Physiological Ecology of Tropical Plants. By U. LUTTGE. 24x16 cm. Pp. 387. Berlin and Heidelberg: Springer-Verlag, 1997. Price h/b: f26.00, ISBN 3 540 611 614.

New Phytologist ◽

10.1046/j.1469-8137.1998.00131.x ◽

1998 ◽

Vol 138 (3) ◽

pp. 563-566

Author(s):

C. B. Osmond

Keyword(s):

Physiological Ecology ◽

Tropical Plants

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Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

International Journal of Wildland Fire ◽

10.1071/wf01025 ◽

2001 ◽

Vol 10 (2) ◽

pp. 241 ◽

Cited By ~ 27

Author(s):

Jon B. Marsden-Smedley ◽

Wendy R. Catchpole

Keyword(s):

Prediction Model ◽

Regression Model ◽

Fire Management ◽

Prediction Models ◽

Dew Point ◽

Seasonal Effects ◽

Experimental Program ◽

Fuel Moisture ◽

Fire Behaviour ◽

Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

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