A trend extraction method based on logistic functions and envelopes

A method of step characteristic trend extraction based on logistic functions and envelopes (LFEs) is proposed in this paper. Compared with the existing trend extraction methods, the LFE method can determine the starting position of the step trend using a logistic function and extract the local trend using upper and lower envelopes. This method enhances the extraction accuracy and reduces the computation time. To verify the effectiveness of the LFE method, a simulated signal with a step trend feature was compared with the five-spot triple smoothing method, wavelet transform method and empirical mode decomposition-based method. All of these methods were applied to a real shock signal. The results demonstrate that the LFE method can reliably and accurately extract the trends with step characteristics based on less prior knowledge. Moreover, the proposed technique is suitable for industrial online applications.

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.

Forecasting the Growth of Complexity and Change

In the spirit of punctuated equilibrium, complexity is quantified relatively in terms of the spacing between equally important evolutionary turning points (milestones). Thirteen data sets of such milestones, obtained from a variety of scientific sources, provide data on the most important complexity jumps between the big bang and today. Forecasts for future complexity jumps are obtained via exponential and logistic fits on the data. The quality of the fits and common sense dictate that the forecast by the logistic function should be retained. This forecast stipulates that we have already reached the maximum rate of growth for complexity, and that in the future, complexity's rate of change (and the rate of change in our lives) will be declining. One corollary is that we are roughly halfway through the lifetime of the universe. Another result is that complexity's rate of growth has built up to its present high level via seven evolutionary subprocesses, themselves amenable to logistic description.

Fractal aspects of natural growth

The work presented here puts forward a fractal aspect of natural growth. The S-shaped pattern of a logistic function is analyzed in terms of several constituent logistic functions. The approach sheds light on two familiar phenomena: the undulatory evolution of growth, which gives rise to an alternation between high-and low-growth periods, and the increasingly noticeable shrinking life cycle of products. There are some economic and political implications for the European nations. A quantitative example is given for computer sales from Digital Equipment Corporation. The approach is further generalized to suggest that any growth process can be analyzed in terms of natural-growth subprocesses. Applied to human growth this analysis yields precise definitions for the timing of transitions such as babyhood to childhood, and childhood to adolescence.

Learning from experience in positioning new computer products

The correct positioning of new computer products has become crucially important as markets saturate and competition intensifies. The logistic function can provide an aid to product positioning. The method presented her addresses questions of price and performance only, and involves determination of learning curves from data on past successful product launches. It assumes that companies learn like individuals and that variables such as performance/price grow according to logistic curves limited by the basic technologies at hand.Digital's experience shows that its VAX family of computers is amenable to such an analysis, which also provides insights on the overall evolution of that technology. Besides offering guidelines for product positioning, this approach provides a means for estimating price drops and/or performance enhancements necessitated from delays in product delivery.

Competition and forecasts for Nobel prize awards

The evolution of Nobel prize awards is studied as a learning/growing process. A simple logistic function describes the data well and accounts for the competition, be it between individuals or between nations. The American niche appears to be 64% exhausted by 1987, implying a diminishing expected rate of laureates in the future. Europeans have been losing ground continuously from the beginning while the remaining world has recently received more awards. Projections to the year 2000 and beyond are given. Correlations with age support the Darwinian nature of the competition for Nobel prizes. Awards to women show peaks coincidental with outbursts of feminism.

SRAM-Based PUF Reliability Prediction Using Cell-Imbalance Characterization in the State Space Diagram

This work proposes a methodology to estimate the statistical distribution of the probability that a 6T bit-cell starts up to a given logic value in SRAM memories for PUF applications. First, the distribution is obtained experimentally in a 65-nm CMOS device. As this distribution cannot be reproduced by electrical simulation, we explore the use of an alternative parameter defined as the distance between the origin and the separatrix in the bit-cell state space to quantify the mismatch of the cell. The resulting distribution of this parameter obtained from Monte Carlo simulations is then related to the start-up probability distribution using a two-component logistic function. The reported results show that the proposed imbalance factor is a good predictor for PUF-related reliability estimation with the advantage that can be applied at the early design stages.

A phenomenological theory-based viscosity model for shear thickening fluids

A viscosity model for shear thickening fluids (STFs) based on phenomenological theory is proposed. The model considers three characteristic regions of the typical material properties of STFs: a shear thinning region at low shear rates, followed by a sharp increase in viscosity above the critical shear rate, and subsequently a significant failure region at high shear rates. The typical S-shaped characteristic of the STF viscosity curve is represented using the logistic function, and suitable constraints are applied to satisfy the continuity of the viscosity model. Then, the Levenberg–Marquardt algorithm is introduced to fit the constitutive model parameters based on experimental data. Verification against experimental data shows that the model can predict the viscosity behavior of STF systems composed of different materials with different mass concentrations and temperatures. The proposed viscosity model provides a calculation basis for the engineering applications of STFs (e.g., in increasing impact resistance and reducing vibration).

The Logistic Function of Cooperation in the Agro-Food Supply with Digitalization in the Russian Federation

Currently, digital solutions are increasingly penetrating consumer cooperation. The need for the use of innovative technologies is increasing to improve the efficiency of logistics of consumer societies of the Centrosoyuz of Russia. Consumer societies have a solid opportunity to combine their economic activities into a single system based on a digital platform and become the undisputed leader in the segment of national food security. Operational processes are developing and improving in the digital economy simultaneously with the development of scientific and technological progress, the introduction of innovations, and the modernization of production. Cooperative formations participate in replenishing the resources of agricultural products to ensure the food security of the country. Consumer societies of the Centrosoyuz of Russia play a significant role in providing a guaranteed channel for the sale of agricultural products and raw materials for small agricultural businesses. Various approaches to the participation of consumer cooperation in food security are revealed in the conditions of the digital economy with the industrialization of production. Therewith, the relevance of consumer cooperation in replenishing food supplies is increasing. Its role in the development of small agricultural business in rural areas is revealed, the need for more complete use of the resources available in consumer cooperation in the organization of procurement activities in the field of procurement of agricultural products and raw materials in the households of the population, individual entrepreneurs and peasant farms is argued. The use of the achievements of the digital economy in the system of consumer cooperation is considered as one of its practical applications in modern conditions to increase competitiveness in the domestic and international food market, improving the quality of life of the working population of rural areas.

Nonsubmodular Constrained Profit Maximization in Attribute Networks

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.