Logistic Growth Curve Model Research Articles

Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies

This study presents the technological S-curves that integrates the Bibliometric and patent analysis into the Logistic growth curve model for hydrogen energy and fuel cell technologies and identifies the optimal patent strategy for the fuel cell industry, including PEMFC, SOFC, and DMFC/DAFC. Empirical analysis is via an expert survey and Co-word analysis using the United States Patent and Trademark Office database to obtain useful data. Analytical results demonstrate that the S-curves is a highly effective means of quantifying how technology forecasting of cumulative publication patent number. Analytical results also indicate that technologies for generating and storing hydrogen have not yet reached technological maturity; thus, additional R&D funding is needed to accelerate the development of hydrogen technology. Conversely, fuel cell technologies have reached technological maturity, and related patent strategies include freedom to operate, licensing, and niche inventions. The proposed model can be applied to all high-technology cases, and particularly to new clean technologies. The study concludes by outlining the limitations of the proposed model and directions for further research.

Technology forecasting of new clean energy: The example of hydrogen energy and fuel cell

Due to energy shortage, global warming and climate change, balanced development of energy security, economic growth, and environmental protection (3Es) has become a major energy policy issue and prompted the development of low-carbon economies. The goals of exploiting new clean energies, improving the efficiency of conventional energy sources, and improving renewable energy technologies have gathered considerable attention of governments worldwide. Among the many clean energies, hydrogen energy plays an important part in new clean energy fields nowadays. However, little has been done in discussing the technology forecasting for the hydrogen energy development. Therefore, this study predicts the technological S-curves for hydrogen energy and fuel cell technologies by integrating bibliometric and patent analysis into the logistic growth curve model, which includes generation, storage, proton exchange membrane fuel cell (PEMFC), solid oxide fuel cell (SOFC) and direct methanol fuel cell/direct alcohol fuel cell (DMFC/DAFC). Empirical analysis is via an expert survey and co-word analysis using the USPTO database to obtain useful data. The results demonstrate that technologies for generating and storing hydrogen have not yet reached technological maturity, and the fuel cell technology is either in the mature stage or approaching maturity.

Mixed-effects growth curves in the valuation of Nellore sires

Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.

The trend of small hydropower development in China

The paper makes an analysis of the status quo and existing issues of small hydropower (SHP) in China and based on the logistic growth curve model forecasts the installed capacity of SHP and cost of newly built SHP in the future. It also explores the opportunity of the clean development mechanism (CDM) in SHP projects and puts forward suggestions and recommendations on enhancing the SHP market competitiveness.

A Natural Goodness-of-Fit Testing Procedure for the Logistic Growth Curve Model

Growth curve models are frequently used in a wide range of disciplines such as biology, ecology, demography, population dynamics etc. Living organisms exhibit different types of growth patterns. To analyze these curves investigators need adequate parametric models. A proper identification of the growth model is very important for the appropriateness of the subsequent analysis. In this paper we develop a natural goodness of fit test for the logistic growth curve model. Bhattacharya et al. (2004, 2008) have provided some interesting approaches based on Hill's method of finite differences (Hill 1968) in case of the exponential and exponential polynomial growth curve models. But in case of the logistic model their approach leads to very complicated and cumbersome mathematics. Basu and Bhattacharj6ee (2006) have presented an alternative method to test the goodness of fit for the exponential growth curve model by directly modeling the relative growth rate rather than the size variable itself. In this paper we extend that approach for testing goodness of fit in case of the logistic growth curve model.

Simental x Güney Anadolu Kırmızısı sığırlarına ait beden ölçüleri için basit doğrusal ve lojistik büyüme modeli

Bu çalışmada, Simental x Güney Anadolu Kırmızısı G1 ve F1xG genotiplerine ilişkin beden ölçüleri için doğrusal ve doğrusal olmayan lojistik büyüme modelleri oluşturulmuştur. Doğrusal ve lojistik büyüme modellerine ait artıklarda ortaya çıkabilecek özilişki sorunu incelenmiştir. Modellerinin uyum iyiliği, hata kareler ortalaması ve belirleme katsayısı değerleri kullanılarak yapılmıştır. Sonuç olarak, beden ölçülerinin tanımlanmasında doğrusal olmayan lojistik modelinin doğrusal modelden daha başarılı olduğu uyum iyiliği ölçütleri ile tespit edilmiştir

Characterization of voltage‐gated sodium channels in ovine gonadotrophs: relationship to hormone secretion.

1. The properties of whole-cell Na+ currents (INa) were studied in immunocytochemically identified ovine gonadotrophs using the patch clamp technique. 2. Voltage recording under current clamp revealed that gonadotrophs did not fire spontaneously, and fired only a single action potential in response to a depolarizing current clamp step. 3. Under voltage clamp, INa was found to be sensitive to tetrodotoxin (TTX) and had an activation threshold of about -75 mV, with peak current occurring at -20 to -30 mV. 4. Using a two-pulse protocol a delay in the onset of inactivation was observed, suggesting that inactivation is dependent on and preceded by the activation phenomenon. 5. Kinetics of recovery from inactivation of the Na+ channels were studied with test pulses applied at various times after a depolarizing pre-pulse. Recovery from inactivation showed an initial delay, in contrast to the predictions of the Hodgkin-Huxley equations. 6. Recovery from inactivation was examined by using a repetitive pulse protocol, showing approximately 1 s is required for the channels to achieve a 95% recovery. 7. The steady-state inactivation (h infinity -V) curve was sigmoidal and fitted by a logistic growth curve model. The half-inactivation value of the Na+ current occurred at a membrane potential of -70 +/- 8 mV. 8. Noise power spectra derived from fluctuations of INa could be fitted with a single Lorentzian function, and the time constant value was slower at more depolarizing potentials. 9. The single-Na+-channel conductance was estimated from fluctuation analysis under conditions of reduced Na+ current amplitude by depolarizing pre-pulses. The single-channel conductance derived by the above method (approximately equal to 11 pS) corresponded to the single-channel conductance derived from single-channel current measurements using the outside-out version of the patch clamp technique (approximately equal to 13 pS). 10. Inactivation of INa was slowed by including 15 mM-iodate in the pipette. Ensemble fluctuation analysis of INa under these conditions was carried out using the steady state portion of the inactivation phase of the modified INa records, revealing a process best fitted by a double Lorentzian power spectrum, consistent with inactivation kinetics involving both a fast and a slow process. The time constant values correlated well with those obtained from a double-exponential fit to the decaying inactivation phase of the iodate-modified INa.(ABSTRACT TRUNCATED AT 400 WORDS)