Study of patterned GaAsSbN nanowires using sigmoidal model

This study presents the first report on patterned nanowires (NWs) of dilute nitride GaAsSbN on p-Si (111) substrates by self-catalyzed plasma-assisted molecular beam epitaxy. Patterned NW array with GaAsSbN of Sb composition of 3% as a stem provided the best yield of vertical NWs. Large bandgap tuning of ~ 75 meV, as ascertained from 4 K photoluminescence (PL), over a pitch length variation of 200–1200 nm has been demonstrated. Pitch-dependent axial and radial growth rates show a logistic sigmoidal growth trend different from those commonly observed in other patterned non-nitride III–V NWs. The sigmoidal fitting provides further insight into the PL spectral shift arising from differences in Sb and N incorporation from pitch induced variation in secondary fluxes. Results indicate that sigmoidal fitting can be a potent tool for designing patterned NW arrays of optimal pitch length for dilute nitrides and other highly mismatched alloys and heterostructures.

The relationship between morphology changes and antioxidant enzymes activity during somatic embryogenesis of long-term-maintained callus of Zoysia matrella [L.] Merr.

A callus line of manila grass (Zoysia matrella [L.] Merr.) has been maintained for 8 years in our lab. The present study investigated changes in ultrastructure and antioxidant enzyme activity during regeneration of the callus and examined the correlation between these changes and regeneration ability. The changes in fresh weight and diameter of the callus over time could be described by a sigmoidal growth curve with different stages. Electron microscopy revealed small embryonic callus cells, isodiametric in shape, with large, obvious nuclei and dense cytoplasm. The cellular structures and morphology changed dramatically as regeneration proceeded. Of particular note was the formation of folded scutellum-like embryos at 14 d, which might be the turning point for morphological differentiation. Catalase (CAT) and peroxidase (POD) activities were the lowest at 14 d, the same time when superoxide dismutase (SOD) activity was the highest. Thus, we speculate that the formation of the scutellum-like structures is associated with higher activity of SOD and lower activities of CAT and POD.

Impact of Relaxing Covid-19 Social Distancing Measures on Rural North Wales: A Simulation Analysis

Background: Social distancing policies aimed to limit Covid-19 across the UK were gradually relaxed between May and August 2020, as peak incidences passed. Population density is an important driver of national incidence rates; however peak incidences in rural regions may lag national figures by several weeks. We aimed to forecast the timing of peak Covid-19 mortality rate in rural North Wales.Methods: Covid-19 related mortality data up to 7/5/2020 were obtained from Public Health Wales and the UK Government. Sigmoidal growth functions were fitted by non-linear least squares and model averaging used to extrapolate mortality to 24/8/2020. The dates of peak mortality incidences for North Wales, Wales and the UK; and the percentage of predicted mortality at 24/8/2020 were calculated.Results: The peak daily death rates in Wales and the UK were estimated to have occurred on the 14/04/2020 and 15/04/2020, respectively. For North Wales, this occurred on the 07/05/2020, corresponding to the date of analysis. The number of deaths reported in North Wales on 07/05/2020 represents 33% of the number predicted to occur by 24/08/2020, compared with 74 and 62% for Wales and the UK, respectively.Conclusion: Policies governing the movement of people in the gradual release from lockdown are likely to impact significantly on areas–principally rural in nature–where cases of Covid-19, deaths and immunity are likely to be much lower than in populated areas. This is particularly difficult to manage across jurisdictions, such as between England and Wales, and in popular holiday destinations.

Mathematical Analysis of Some Reaction Networks Inducing Biological Growth/Decay Functions.

In this work, we study some characteristics of sigmoidal growth/decay functions that are solutions of dynamical systems. In addition, the studied dynamical systems have a realization in terms of reaction networks that are closely related to the Gompertzian and logistic type growth models. Apart from the growing species, the studied reaction networks involve an additional species interpreted as an environmental resource. The reaction network formulation of the proposed models hints for the intrinsic mechanism of the modeled growth process and can be used for analyzing evolutionary measured data when testing various appropriate models, especially when studying growth processes in life sciences. The proposed reaction network realization of Gompertz growth model can be interpreted from the perspective of demographic and socio-economic sciences. The reaction network approach clearly explains the intimate links between the Gompertz model and the Verhulst logistic model. There are shown reversible reactions which complete the already known non-reversible ones. It is also demonstrated that the proposed approach can be applied in oscillating processes and social-science events. The paper is richly illustrated with numerical computations and computer simulations performed by algorithms using the computer algebra system Mathematica.

A New Flexible Sigmoidal Growth Model

Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass wmax of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss–Newton or Levenberg–Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously.

ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

The present study was carried out to compare the performances of regression models and Artificial Neural Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

Evaluation of dry matter accumulation in triticale by different sigmoidal growth models in west anatolia of turkey

Monitoring biological growth of field crops is important for planning and timing agricultural practices. In order to assess biological growth pattern of dry matter accumulation in triticale Egeyildizi triticale variety were grown in ?anakkale conditions in 2012-2013 and 2013-2014 growing seasons with continuous plant samplings from seedling emergence until seed maturation. Gompertz, Logistic, Logistic Power and Richards growth models are fitted to actual growth data and their predictions were compared. Results suggested that all sigmoidal growth models successfully explained triticale dry matter accumulation over 98 % R2 values and low mean square errors, Richards model fitted best for both years with an R2 value over 99 %. Dry matter accumulation were also investigated as a result of average temperature, precipitation, growth degree days and cumulative growth degree days with stepwise regression. Rresults indicated that average weather temperature had a similar pattern across both growing seasons and had a major influence on dry matter accumulation. Since Richards sigmoidal growth model may be adequately described growth pattern of triticale by generally high R2 with lower Mean Square Error (MSE) values.