S-curve Model Research Articles

Impacts of agricultural intensity on soil organic carbon pools in a main vegetable cultivation region of China

Agricultural intensity, a function of agricultural input and output, impacts agricultural soil organic carbon (SOC) pools. Investigating the agricultural intensity and SOC density relationship supports understanding of anthropogenic activities on SOC pools in agricultural regions. Cangshan County, one of the most important vegetable cultivation counties in China, was selected as the study area for this investigation. By using soil survey data compiled in 1980 and 2008, 21 towns in Cangshan County were used as study units to investigate the relationship between agricultural intensity indicators and index, with SOC density. Results demonstrate that single agricultural intensity indicators could not reflect SOC densities changes well. Conversely the agricultural intensity index, a composite measure of agricultural input, output and input–output indicators (AI1, AI2 and AI3, respectively), was a more reliable measure reflecting SOC density variability. An S-curve model, SOCd (tha−1)=exp (α-β/AIi), was the best fit for the plot of SOC densities with the agricultural intensity index. Relationships between AI3 and SOC density were statistically significant for 1980, 2008 and their variation during 2008–1980 (p<0.05), indicating that AI3 had a more robust relationship with SOC density compared with AI1 and AI2. Though the relationships have low precision (R2=0.24–0.40) for not including the natural factors and more indictors relative to variation of SOC density should be selected to improve the interpretability, it is still valuable to introduce the agricultural intensity index when detecting dynamics of SOC pools at a large regional scale.

Sonophotolytic degradation of dimethyl phthalate without catalyst: Analysis of the synergistic effect and modeling

The merits of the combined process of high-frequency ultrasound (US) and catalyst-free ultraviolet irradiation (UV) have been evaluated in this study by investigating the sonophotolytic degradation of dimethyl phthalate (DMP). A 400 kHz ultrasonic system and a photolytic system at 253.7 nm were employed individually, sequentially and simultaneously to examine the details of the processes. High UV intensities and low pH conditions enhanced the sonophotolytic degradation of DMP and a clear synergy was evident from the combination of the US and UV irradiation with a synergetic index of 2.6. The role of ultrasonically generated hydrogen peroxide was examined qualitatively and quantitatively, and its generation and photo-decomposition were found to be the principal reason for the process synergy. A novel inverted S-curve model was developed and found to successfully describe the process of sonophotolysis and DMP degradation.

Uncertain Technology Evolution and Decision Making in Design

The potential for engineering technology to evolve over time can be a critical consideration in design decisions that involve long-term commitments. Investments in manufacturing equipment, contractual relationships, and other factors can make it difficult for engineering firms to backtrack once they have chosen one technology over others. Although engineering technologies tend to improve in performance over time, competing technologies can evolve at different rates and details about how a technology might evolve are generally uncertain. In this article we present a general framework for modeling and making decisions about evolving technologies under uncertainty. In this research, the evolution of technology performance is modeled as an S-curve; the performance evolves slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. We extend the existing single-attribute S-curve model to the case of technologies with multiple performance attributes. By combining an S-curve evolutionary model for each attribute with a Pareto frontier representation of the optimal implementations of a technology at a particular point in time, we can project how the Pareto frontier will move over time as a technology evolves. Designer uncertainty about the precise shape of the S-curve model is considered through a Monte Carlo simulation of the evolutionary process. To demonstrate how designers can apply the framework, we consider the scenario of a green power generation company deciding between competing wind turbine technologies. Wind turbines, like many other technologies, are currently evolving as research and development efforts improve performance. The engineering example demonstrates how the multi-attribute technology evolution modeling technique provides designers with greater insight into critical uncertainties present in long-term decision problems.

Novel real-time nonuniformity correction solution for infrared focal plane arrays based on S-curve model

Detectors of infrared focal plane arrays are, in the nature of their photoresponse, nonuniform. Response curves are nonlinear. In view of this, a novel nonuniformity correction algorithm based on the S-curve nonlinear response model and its simplified correction algorithm is proposed. The algorithm is an essential calibration and interpolation correction based on an analytical formula. It requires calibrating in advance, but possesses higher correction precision and wider applicative response range, which is particularly applicable to uncooled infrared focal plane arrays. Borrowing ideas from look-up tables, a real-time hardware correction system, which provides an experimental platform for contrasting different algorithms, is expounded. Experimental results validate that the proposed S-curve model-based algorithm does much better than the two-point correction algorithm and the simplified S-curve model-based correction algorithm.

Forecasting Development of Hybrid Rice Breeding through S-Curve Model and Community Analysis in Complex Networks

Hybrid rice breeding is one of high-technology in agriculture. This breeding technology effectively protects world food security. Using s-curve and community analysis in complex networks, development of hybrid rice breeding are explored. Results indicate that two branches of hybrid rice breeding will get to saturation between 2080’s to 2090’s in this century.

Towards continuous innovation for regional high-tech industrial clusters

This paper explores the inherent evolutionary mechanism of continuous innovation of high-tech industrial clusters based on an S-curve model. Moreover, the relationship between life cycle of continuous innovation and high-tech industrial cluster is examined to explore the evolutionary mechanism of each development stage of high-tech clusters. The paper finds that the life cycle of continuous innovation of clusters lags behind the life cycle of clusters. The findings reveal that the evolution of continuous innovation is driven by different factors. Therefore, policies should be made based on different driving factors of clusters, and varied according to the different evolutionary stages of clusters.

The Relationship Between R&amp;D Investment and Firm Profitability Under a Three-Stage Sigmoid Curve Model: Evidence From an Emerging Economy

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The relationship between investment in research and development (R&amp;D) and firm performance has been the subject of numerous academic investigations, but the findings of these investigations have varied greatly, with research revealing a number of different patterns in the R&amp;D–performance relationship. This inconsistency may be partly attributable to the failure of the commonly used linear modeling method to capture the full dynamics of the R&amp;D–performance relationship. Based on the sigmoid (S) curve paradigm, as well as on other economic foundations, this study proposes the use of a three-stage S-curve model to help reconcile the disparities in the literature. This S-curve model shows that the relationship between R&amp;D intensity and firm profitability is nonlinear, with the slope negative at low levels (stage 1), positive at medium levels (stage 2), and negative again at high levels of R&amp;D investment (stage 3). Empirical evidence from a sample of 377 publicly listed Taiwanese high-tech manufacturing firms and 179 nonhigh-tech firms, examined during the period between 2000 and 2007, confirmed our proposed model. This study not only establishes a relationship pattern that differs from that shown in past studies, but also has important managerial implications for R&amp;D managers and important policy implications for governments. </para>

S Marks the Spot

This study highlights that wind and geothermal power are now a better investment than coal. Biofuels startups are tapping millions in venture capital funds; and techniques for drawing power from wind, ocean, and solar energy have also seen an influx of R&amp;D dollars. According to Schilling, both wind and geothermal power are poised to become more economical than fossil fuel, needing just a relatively small infusion of additional capital. When people teach innovation and strategy in economics, the S-curve is given. Although electricity-generating technologies are subject to different economic forces than, say, disk drives or semiconductors, Schilling says the S-curve model should apply to them as well. When Schillings fit the performance versus investment data on an S-curve, the technology appeared just at the beginning of a long period of improvement. In the near term, wind and geothermal power have the best potential for performance growth, according to Schilling’s analysis, and make the best sense for additional R&amp;D.

Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis

Patents contain much significant technical information which can serve as an indicator of technological and economical development. This study attempts to forecast the development of the biped robot walking technique in Japan by use of the patent data obtained from the Japan Patent Office. The study applies linear regression to the patent data using three S-curve models developed by Loglet Lab, Pearl, and Gompertz individually. Various parameters inherent to each model including the least sum of modulus error and the least mean of square error of the model are analyzed. The most appropriate model for measuring the inflection point, the growth and the saturation time of the technique is described. Based on the Gompertz model analysis, this study finds that the biped robot walking technique will continue to develop for several decades in Japan and the saturation period is estimated to be around the year 2079–2082. This finding can help related researchers and managers in the robot field to foresee the development trend of the biped robot walking technique in this century.

Simplified Relationships for Particle-Size Distribution and Permeation Groutability Limits for Soils

The particle-size distribution of soil with mean particle size and fines content are used not only in soil classifications but also in a number of other soil property relationships. In this study, two simple relationships (hyperbolic [tan h(x)] and S-curve) were investigated to represent the particle size distribution of soils. The parameters of the hyperbolic model were correlated to various soil parameters such as the mean particle size, particle size range, and fines content. There was no direct correlation between Fredlund (four-parameter model) and S-curve model parameters and the soil parameters. The predictions of the two (hyperbolic) and three (S-curve) parameter models were compared to the four-parameter model (unimodal) using limited soil data from the literature and the agreements were good. The hyperbolic model was used to map the Unified Soil Classification System. A recent study had quantified the relationship between the grouting pressure and the fines content in nonplastic soils. Also in the current practice, upper and lower particle-size distribution limits are used in determining the groutability of soils. In this study, the relationship between grouting pressure and fines contents of the soil was generalized using the hyperbolic particle-size distribution model and verified with a groutability study using an acrylamide grout. Based on limited data in the literature, the groutability of soils was defined using a new set of parameters, grouting pressure, fines content, and mean particle size diameter of the soil.

Characterization of Error Reduction in Mobile Liquid Crystal Display Using Distinct &lt;italic&gt;XYZ&lt;/italic&gt; Electro-Optical Transfer Functions for Each Channel and Interchannel Components

Most display characterization methods, such as the gain- offset-gamma model and S-curve model, generally assume that dis- plays have two fundamental characteristics, channel-chromaticity constancy and channel independence. Consequently, based on the assumption of channel-chromaticity constancy, only one electro- optical transfer function (EOTF) is used for each channel to estab- lish the relation between the digital input values and the output lu- minance levels. Meanwhile, based on the channel-independence assumption, the channel color values are simply summed to acquire mixed color values. However, these assumptions are not so appli- cable in the case of liquid crystal-based mobile displays. Accord- ingly, modifications are required to enable the application of conven- tional display characterization methods to mobile displays. Therefore, this study proposes the modeling of distinct EOTFs in terms of the X, Y, and Z values for each channel to consider the differences among the EOTFs resulting from channel-chromaticity inconstancy. In addition, to overcome the poor additivity property among the channels due to channel interaction, the proposed method also models and uses the EOTFs of the X, Y, and Z values for the interchannel components cyan, magenta, yellow, and gray. Experimental results confirm that the mobile display color values predicted by the proposed characterization method are more accu- rate than those predicted by other characterization methods due to considering the channel-chromaticity inconstancy and/or channel dependence of the display. © 2006 Society for Imaging Science and Technology. DOI: 10.2352/J.ImagingSci.Technol.200650:4349

Solution for the nonuniformity correction of infrared focal plane arrays

Based on the S-curve model of the detector response of infrared focal plan arrays (IRFPAs), an improved two-point correction algorithm is presented. The algorithm first transforms the nonlinear image data into linear data and then uses the normal two-point algorithm to correct the linear data. The algorithm can effectively overcome the influence of nonlinearity of the detector's response, and it enlarges the correction precision and the dynamic range of the response. A real-time imaging-signal-processing system for IRFPAs that is based on a digital signal processor and field-programmable gate arrays is also presented. The nonuniformity correction capability of the presented solution is validated by experimental imaging procedures of a 128 x 128 pixel IRFPA camera prototype.

Nomograms for the Sonographic Measurement of the Fetal Philtrum and Chin

Objective: To generate nomograms for the sonographic measurement of the fetal philtrum and chin during pregnancy. Design: A prospective, cross-sectional study in normal singleton pregnancies. Subjects: One hundred and fifty-three fetuses between 13 and 42 weeks of gestation were studied. Methods: The philtrum was measured from the base of the columella to the upper lip. The chin was measured from the tip of the lower lip to the skin under the lower tip of the mandible. Predictive models were evaluated to generate graphic description of the 5th, 50th and 95th centiles for the fetal philtrum and chin. Results: Fetal philtrum length increased with gestational age. The regression equation for the philtrum length (y) according to gestational age in weeks (x) is best predicted by the S-curve (Gompertz) model, as described by the following equation: y = exp(a + b/x), where a = 2.778577, and b = –23.476723 (R² = 85.3%, p < 0.0001). The fetal chin length increased with gestational age. The regression equation for the mean chin length (y) according to gestational age in weeks (x) is best predicted by the S-curve model as described in the following equation. y = exp(a + b/x), where a = 3.7922, b = –28.043, (R² = 89.0%, p < 0.0001). Conclusions: The nomograms generated in this study for the fetal philtrum and chin during pregnancy can be used in confirming subjective impression of facial dysmorphism.

The S-Curve Relation Between Per-Capita Income and Insurance Penetration

Models that assume a constant income elasticity of demand for insurance have the unrealistic implication that insurance penetration grows without constraint. This article introduces a logistic function that allows income elasticity to 0vary as the economy matures. Econometric estimations yield a so-called S-curve, for which the income elasticity of demand is equal to one at specific low and high levels of income, but may reach two or more at intermediate income levels. Long-term forecasts for insurance premiums based on GDP projections are possible for countries that either conform to the S-curve model or deviate consistently from it. Analysing deviations from the S-curve allows the identification of outlier countries, in which factors other than GDP drive insurance demand.

Moving up the automation S‐curve: The role of the laboratory automation supportfunction in successful pharmaceutical R&amp;D

The political and economic climate that exists today is a challenging one for the pharmaceutical industry. To effectively compete in today's marketplace, companies must discover and develop truly innovative medicines. The R&D organizations within these companies are under increasing pressure to hold down costs while accomplishing this mission. In this environment of level head count and operating budgets, it is imperative that laboratory management uses resources in the most effective, efficient ways possible. Investment in laboratory automation is a proven tool for doing just that. This paper looks at the strategy and tactics behind the formation and evolution of a central automation/laboratory technology support function at the Glaxo Research Institute. Staffing of the function is explained, along with operating strategy and alignment with the scientific client base. Using the S-curve model of technological progress, both the realized and potential impact on successful R&D automation and laboratory technology development are assessed.

Behind the “bathtub”-curve A new model and its consequences

The hitherto commonly accepted background model for the “bathtub” curve is rejected due to lack of experimental evidence. A new model — the S-curve model — based on bimodal or trimodal failure distribution functions and the concepts of “strong”, “freak” and “infant mortality” components is presented and its fitting with practical experience is demonstrated. The impact of the new model with regard to burn-in of components and systems and the use of reliability indicators to screen for built-in “flaws” in components are described.

Is there a reliable screen for VHF power transistors?

The hitherto commonly accepted background model for the “bathtub” curve is rejected due to lack of experimental evidence. A new model — the S-curve model — based on bimodal or trimodal failure distribution functions and the concepts of “strong”, “freak” and “infant mortality” components is presented and its fitting with practical experience is demonstrated. The impact of the new model with regard to burn-in of components and systems and the use of reliability indicators to screen for built-in “flaws” in components are described.