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Plant diversity may enhance stability of ecosystem function and its satellite-derived indicators. However, its potential to stabilize phenology, or seasonal changes in plant function, is little understood, especially in understudied systems with high biodiversity potential such as wetlands. Using a large sample of U.S. wetlands and a new satellite-based indicator of phenological stability, we found that plant diversity was negatively associated with interannual phenological variability after controlling for covariates representing climate, site conditions, and spectral fluctuations. Furthermore, plant diversity and covariates better explained phenological variability than stability in annually summarized satellite-based biomass indicators used by earlier studies. Last, a subsequent path analysis indicated that phenological variability could mediate plant diversity relationship with the latter stability. Our results suggest that contributions of plant diversity to seasonality of ecosystems may have a stabilizing role in their functioning and offer a new basis for assessing biodiversity-stability relationships across broad geographic extents.

A single expression for estimating the nominal pitting strength of steel materials, based on surface hardness, is developed from first principles for a reliability of 99% at 107 load cycles. It requires the hardness values to be measured in Vicker's hardness scale. The expression may be used for any steel material processed by hot rolling, cold drawing, quenching and tempering or case-hardening. The formulation incorporates a nominal design factor at 99% reliability which is estimated from a probabilistic model based on the lognormal probability density function. Pitting strength estimates from the expression are compared with those of American Gear Manufacturers Association (AGMA) estimates and data from other sources as indicated in Tables 3 and 4. The expression predicts lower values at low hardness but higher values at high hardness. The variance is between - 15.21% and 10.13% for through-hardened steels. For case-hardened steels, the variances range from 14.23% to 20.26% between the estimates and available data. These variances appear to be reasonable considering the many factors involved in pitting resistance. The main advantage of this study is that pitting strength of new steel materials may be estimated for initial design sizing without long and costly contact fatigue testing which of course is necessary for design validation. Also, the estimation method developed may be applied to other materials, metallic and non-metallic. Suggestions are made for estimating some pertinent pitting strength adjustment factors when considering field or service pitting strength.

An attempt is made to predict the pitting strength of cast iron and copper alloy materials from their compressive yield or compressive proof strength for a reliability of 99% at 107 load cycles. The compressive yield or compressive proof strength is related to the tensile strength of ductile cast iron and copper alloy materials by a proportionality factor. Two proportionality factors are used for brittle cast iron materials. The pitting strength formulation incorporates a nominal design factor at 99% reliability which is estimated from a probabilistic model based on the lognormal probability density function. Pitting strength estimates from the predictions are compared with those of American Gear Manufacturers Association (AGMA) estimates and data from other sources. The predicted values for gray cast irons had variances in the range of -11.28% to 25%. Ductile cast iron pitting strength estimates deviated from those of AGMA by -30.28% to 1.73% and 16.76% to 36.34% for Austempered ductile irons. The variances obtained for cast bronze were from 11.17% and 14.73%, but the sample size was small. These variances appear to be reasonable due to the many factors that can influence pitting resistance. Since pitting strength data for many grades of cast iron and copper alloys are not available (especially in the public domain), they may be estimated by the expressions developed in this study for initial design sizing. Also, the pitting strength of new cast iron and copper alloy materials could likewise be estimated for initial design sizing. This will eliminate long and costly contact fatigue testing at the initial design phases, which of course is necessary for design validation.

The open source Field Operation and Manipulation (OpenFOAM) software was used to investigate the performance of a fully premixed, modern high-performance 4-valve, ISO-octane, dual overhead cam (DOHC) engine with quasi-symmetric pent roof combustion chamber running at 1500 revolutions per minute. The peak pressure occurred at the TDC and had a value of about 30 bar. The results from this study show that the maximum combustion temperature occurred at approximately 95 degrees crank angle ATDC and has a volume averaged value of about 2700°K , whereas the actual computed peak temperature was found to be about 3000ºK and it occurred at grid point 12630. The other temperatures which were found to be higher than the volume averaged temperature were found to be in the range 2968.81 ° K to 2974.01 ° K and correspond to grid point positions 12630 to 12633.The flame-wrinkling factor, X = St / Su was found to be in the range 1.0 ≤ X ≤ 3.8. The dynamics of the regress variable b was accurately predicted.

A revised Lewis bending fatigue stress capacity model for spur gears is presented and used to study the influence of mesh friction on root stress. It took the original Lewis formula and made modifications for dynamic loads, shear stress, and mesh friction in spur gear design. The study reveals that mesh friction may increase bending stress by up to 6% in enclosed cylindrical gear drives when an average mesh friction coefficient of 0.07 is assumed. A possible increase of 15% in root stress may occur in open gear drives when the mesh friction coefficient is taken as 0.15, a value considered to be representative for properly maintained open drives. To account for mesh frictional load and other factors directly influencing mesh friction, a friction load factor of 1.1 is suggested and introduced to gear service load estimation for enclosed gear drives and 1.15 for open gear drives.

Plant diversity safeguards wetland ecosystem functions, stability, and resilience, but is threatened by habitat loss and degradation. Remote sensing could support the cost-effective management of biodiversity by providing consistent and frequent data at large scales. While identifying individual species from remote sensing datasets with low spatial and spectral resolution is challenging, studies can focus on factors known to correlate with or promote diversity. We tested the predictive potential of such factors — maximum annual greenness as an indicator of productivity, texture (i.e., spatial arrangement of grey tones) as a proxy for habitat heterogeneity, and spatial autocorrelation — across a dataset of 1115 wetlands in the conterminous United States surveyed by the EPA’s National Wetland Condition Assessment. We used multivariate linear regressions to test whether spectral and spatial metrics derived from two open-source datasets — NASA’s Landsat 5 TM and 7 ETM+ (30 m, 16-day revisit) and USDA’s National Agriculture Inventory Program (1 m, biennial) — can predict wetland plant diversity and richness. Individual texture metrics showed different sensitivity to vegetation evenness, growth form, and spatial distribution and could together predict 35–36% of site variation in richness and diversity. This highlights the impact of habitat heterogeneity on species diversity and spectral variability. While maximum annual greenness and texture metrics had similar predictive capacity, their interactions and combined effects improved the fit of linear models by 11–14%, demonstrating their complementarity. Best results were achieved when including distance-based Moran's Eigenvector Maps (dbMEMs) describing spatial relations among sites at multiple scales and reflecting the role of spatially structured factors (e.g., climate, topography, dispersal) on diversity. Together greenness, texture, and dbMEMs could predict 59% of plant richness and 50% of plant diversity across the entire dataset and up to 71% of the richness of least disturbed sites. These results show the potential of open-source remote sensing datasets to monitor biodiversity resources at a large scale and prioritize the protection and field monitoring of wetlands.

The diversity of Anuran parasites is poorly surveyed, despite arguably being one of the most important threats to anuran populations worldwide. Additionally, parasites also interact with a number of other stressors, such as invasive species, pollution, sedimentation and changing light conditions, caused by anthropogenic disturbance in natural habitats. We aimed to explore the use of metabarcoding, a new, non-invasive tool to survey the parasite assemblages in frogs in different environments facing different levels of anthropogenic pressure. We collected fecal samples from frogs across three different transects in Ranomafana National Park, located in southeastern Madagascar, and then used the 18S metabarcoding technique to identify nematode species from the collected fecal samples. We were able to find four different putative species, which were all identified to the genus level. In comparison to the literature on previous surveys done with traditional methods, the metabarcoding approach seems to provide similar diversity estimates and taxonomical accuracy. Our results suggest that non-invasive sampling and metabarcoding can provide a suitable tool for intestinal parasite surveys in anuran host populations.

The unprecedented global biodiversity loss has massive implications for the capacity of ecosystems to maintain functions critical to human well-being, urgently calling for rapid, scalable, and reproducible strategies for biodiversity monitoring, particularly in threatened ecosystems with difficult field access such as wetlands. Remote sensing indicators of spectral variability and greenness may predict the diversity of plant communities based on their optical diversity; however, most evidence is based on narrowband spectral data or terrestrial ecosystems. We investigate how spectral greenness and heterogeneity from publicly available broadband multi-spectral Landsat satellite imagery explain variation in vegetation diversity across different wetland types, ecoregions, and disturbance levels using 1,138 sites surveyed by U.S. EPA's National Wetland Condition Assessment. We found positive correlations of plant species richness and diversity with indicators of annual maximum spectral greenness and its spatial heterogeneity, explaining up to 43% variation within the global sample, 48% within wetland types or ecoregions, and up to 61% with abiotic covariates. The combined effect of spectral greenness and heterogeneity was stronger than the best-performing model using climatic, topographic, and edaphic factors alone. When compared among major U.S. watersheds and individual states, the fit of diversity-greenness models increased when more wetland types were included within the corresponding region's boundaries, up to 61% at the watershed and 77% at the state level, respectively, for diversity models and up to 73% and 80%, respectively, for richness models. Model outliers were characterized by a significantly greater diversity of nonnative species (P < 0.0001), suggesting that changes in model performance and greenness distributions could be used as indicators of shifts in plant community composition, particularly in tidal wetlands making the majority of outliers with significantly lower than predicted diversity. This study represents a first-time national-scale effort to use publicly available remote sensing, climatic, and topographic data to predict plant diversity in wetlands, which tend to be understudied compared to terrestrial ecosystems despite being among the most stressed ecosystems on Earth. Our study suggests that multi-temporal broadband satellite imagery could provide a low-cost assessment of regional and national wetland biodiversity for prioritization of conservation efforts and early detection of biodiversity loss.

Neonatal intensive care patients are at high risk of infection because of immature immune status. Cases of illness and death have been reported as a result of formula-related problems. Powdered formulas are discouraged due to potential bacterial contamination and sterile liquid products are not available in nutrient concentrations required to support growth and development of fragile neonates. An increased number of formula errors revealed the need for training of formula room technicians. Specific training targeted areas of formula preparation that would decrease errors and improve safety and accuracy of product delivered to high-risk neonates.

Introduction. Acute adrenal crisis in relation to nasal steroid overuse has been reported very scantly in English medical literature and remains an underdiagnosed condition.Case presentation. A 55 year-old male presented with altered mental status, retrograde amnesia, fluid refractory hypotension, abdominal pain, fever, and chest pain. Physical examination revealed amnesia, bradypsychia, tachycardia, decreased muscle tone and hyporeflexia. Overuse of nasal steroid was suspected by history. Random early morning cortisol level was < 0.2 mcg/dL. The patient was started on hydrocortisone and within 24 hours he had a full recovery.Conclusion. This one-of-a-kind case describes acute adrenal crisis secondary to withdrawal from inhaled nasal corticosteroids overuse in a patient with particular risk factors. Prevention and early recognition of this disorder can significantly reduce its morbidity and mortality.