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1
Oner, M. D., L. E. Erickson, and S. S. Yang. "Analysis of exponential growth data for yoghurt cultures." Biotechnology and Bioengineering 28, no. 6 (1986): 895–901. http://dx.doi.org/10.1002/bit.260280617.
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Ahmad, W. M. A. W., R. A. A. Rohim, Y. Norhayati, N. A. Aleng, and Z. Ali. "Developing A New Dimension of an Applied Exponential Model: Application in Biological Sciences." Engineering, Technology & Applied Science Research 8, no. 4 (2018): 3130–34. https://doi.org/10.5281/zenodo.1450630.
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Modeling of exponential growth or decay is a nonlinear regression technique. In the real world, the exponential growth is often used to model population growth while the exponential decay is often used to a model declining population or a decreasing size. In this study, we try to improve the performance of exponential growth by adding bootstrap and fuzzy techniques. This gives us the option to perform analysis even when there is not enough data. The aim of the current work is to develop a new dimension of an applied exponential analysis with improved results. The suggested method was tested and applied to biological data. The gathered data was compared by measuring the average width of the predicted interval using least squares method and fuzzy method. The result shows that the average width of the predicted interval using least squares method was 0.522 while using fuzzy method was 0.082. This indicated the superiority of the fuzzy regression methodology. Besides that, this paper provides the algorithm for the prediction of cell growth and inferences.
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Sulma, Sulma, and Nursamsi Nursamsi. "Application of Exponential and Logistic Models in Estimating the Population of Bulukumba Regency in 2020-2030." Journal of Mathematics and Applied Statistics 1, no. 2 (2023): 43–50. http://dx.doi.org/10.35914/mathstat.v1i2.72.
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Population data is useful as planning material in making various policies, including avoiding imbalances between the number of health facilities and services and the population in an area as well as other facilities such as schools, markets, and other public facilities. Ordinary differential equations of exponential and logistic models are used in modeling population dynamics in Bulukumba Regency to obtain population estimates until 2030. The determination of the future population of Bulukumba Regency is based on the growth rate and capacity obtained using the exponential and logistic approaches. The results obtained show that the estimation using the exponential model and the logistic model estimation for 2015-2019 are close to the data from the Central Bureau of Statistics. However, the logistic model is more accurate than the exponential model which is more significantly close to the data from BPS. So that the results of the logistic model are better than the exponential. The logistic model assumes that Bulukumba Regency has a capacity of K = 450000, while the exponential model assumes that the population increases exponentially.
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Abhijit, Joshi. "Architectural Paradigms in Data Management: Evaluating Data Lakes and Data Warehouses for Enterprise Data Ecosystems." Journal of Scientific and Engineering Research 6, no. 4 (2019): 221–28. https://doi.org/10.5281/zenodo.11820647.
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In the digital era, the exponential growth of data has necessitated the evolution of robust architectures for efficient data management, storage, and analysis. Data lakes and data warehouses represent two fundamentally different approaches to data storage and utilization. This whitepaper delves into the technical nuances of each architecture, assessing their structural, operational, and functional distinctions. By comparing the two in terms of data integration, scalability, flexibility, and performance, the document aims to furnish businesses with a clear understanding of how each architecture aligns with diverse business needs, focusing on large-scale, real-time analytics versus structured, query-intensive operations. We conclude with strategic advice on selecting the most suitable architecture for various types of data-driven decision-making processes in an enterprise environment.
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KAREV, GEORGY P. "DYNAMICS OF INHOMOGENEOUS POPULATIONS AND GLOBAL DEMOGRAPHY MODELS." Journal of Biological Systems 13, no. 01 (2005): 83–104. http://dx.doi.org/10.1142/s0218339005001410.
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The dynamic theory of inhomogeneous populations developed during the last decade predicts several essential new dynamic regimes applicable even to the well-known, simple population models. We show that, in an inhomogeneous population with a distributed reproduction coefficient, the entire initial distribution of the coefficient should be used to investigate real population dynamics. In the general case, neither the average rate of growth nor the variance or any finite number of moments of the initial distribution is sufficient to predict the overall population growth. We developed methods for solving the heterogeneous models and explored the dynamics of the total population size together with the reproduction coefficient distribution. We show that, typically, there exists a phase of "hyper-exponential" growth that precedes the well-known exponential phase of population growth in a free regime. The developed formalism is applied to models of global demography and the problem of "population explosion" predicted by the known hyperbolic formula of world population growth. We prove here that the hyperbolic formula presents an exact solution to the Malthus model with an exponentially distributed reproduction coefficient and that "population explosion" is a corollary of certain implicit unrealistic assumptions. Alternative models of world population growth are derived; they show a notable phenomenon, a transition from protracted hyperbolical growth (the phase of "hyper-exponential" development) to the brief transitional phase of exponential growth and, subsequently, to stabilization. The model solutions are consistent with real data and produce relatively accurate forecasts.
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Fernandes, Vítor, Gonçalo Carvalho, Vasco Pereira, and Jorge Bernardino. "Analyzing Data Reduction Techniques: An Experimental Perspective." Applied Sciences 14, no. 8 (2024): 3436. http://dx.doi.org/10.3390/app14083436.
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The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.
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Arinze, Echegu Darlington. "Unravelling Exponential Growth Dynamics: A Comprehensive Review." IAA Journal of Scientific Research 11, no. 3 (2024): 19–26. http://dx.doi.org/10.59298/iaajsr/2024/113.1926.
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Exponential growth refers to the rapid change of natural and man-made systems, characterised by unbounded growth, sensitivity to starting conditions, and self-perpetuating character. Technological developments, network effects, population changes, market dynamics, and environmental factors all contribute to exponential growth, which has economic, social, and environmental consequences in a variety of areas and businesses. Public health concerns, moral dilemmas, and economic, environmental, and social consequences arise from exponential growth, despite promoting innovation and progress. Emerging trends in exponential growth dynamics include artificial intelligence, biotechnology, healthcare, sustainable technologies, demographic changes, urbanisation trends, and digital transformation. These patterns provide insight into how exponential development can affect ecosystems, economies, and society in the future. This paper aims to provide a comprehensive understanding of the dynamics of exponential development, highlighting its broad implications and pervasive impact in a rapidly increasing world. We must implement the latest technological developments and ensure environmental sustainability to fully harness its potential. Encouraging multidisciplinary cooperation, ecologically friendly methods, and ethical issues can accelerate innovation, economic expansion, and social well-being. This growth’s collective benefit is critical for current and future generations. We utilised relevant published data (2004–2014) from diverse, reliable databases. The review suggests that to harness its exponential growth potential, we must take proactive measures, such as utilising new technology and addressing environmental sustainability. Collaborating with professionals, implementing sustainable methods, and upholding ethical standards can lead to innovation, prosperity, and societal well-being. Keywords: Dynamics, exponential, growth, comprehensive, unravelling
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Siepmann, Dietmar. "Cardinal interpolation by polynomial splines: Interpolation of data with exponential growth." Journal of Approximation Theory 53, no. 2 (1988): 167–83. http://dx.doi.org/10.1016/0021-9045(88)90064-0.
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Ahmad, W. M. A. W., R. A. A. Rohim, Y. Norhayati, N. A. Aleng, and Z. Ali. "Developing A New Dimension of an Applied Exponential Model: Application in Biological Sciences." Engineering, Technology & Applied Science Research 8, no. 4 (2018): 3130–34. http://dx.doi.org/10.48084/etasr.2124.
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Modeling of exponential growth or decay is a nonlinear regression technique. In the real world, the exponential growth is often used to model population growth while the exponential decay is often used to a model declining population or a decreasing size. In this study, we try to improve the performance of exponential growth by adding bootstrap and fuzzy techniques. This gives us the option to perform analysis even when there is not enough data. The aim of the current work is to develop a new dimension of an applied exponential analysis with improved results. The suggested method was tested and applied to biological data. The gathered data was compared by measuring the average width of the predicted interval using least squares method and fuzzy method. The result shows that the average width of the predicted interval using least squares method was 0.522 while using fuzzy method was 0.082. This indicated the superiority of the fuzzy regression methodology. Besides that, this paper provides the algorithm for the prediction of cell growth and inferences.
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DE JESÚS, ANTONIO J., and RICHARD C. WHITING. "Modeling the Physiological State of the Inoculum and CO2 Atmosphere on the Lag Phase and Growth Rate of Listeria monocytogenes." Journal of Food Protection 71, no. 9 (2008): 1915–18. http://dx.doi.org/10.4315/0362-028x-71.9.1915.
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In previous studies, the growth of L. monocytogenes has been modeled under different CO2 headspace concentrations; however, the inoculum cells were always in the stationary phase. In this study, the growth of L. monocytogenes under different CO2 concentrations as affected by the physiological state of the cells was investigated. Exponential-growth-phase, stationary-phase, dried, and starved cells were prepared and inoculated at 5°C into brain heart infusion broths that had been preequilibrated under atmospheres of 0, 20, 40, 60, or 80% CO2 (the balance was N2). Lag-phase duration times (LDTs) and exponential growth rates were determined by enumerating cells at appropriate time intervals and by fitting the data to a three-phase linear function that has a lag period before the initiation of exponential growth. Longer LDTs were observed as the CO2 concentration increased, with no growth observed at 80% CO2. For example, the LDTs for exponential-phase, stationary-phase, starved, and dried cells were 2.21, 8.27, 9.17, and 9.67 days, respectively, under the 40% CO2 atmosphere. In general, exponential-growth-phase cells had the shortest LDT followed by starved cells and stationary-phase cells. Dried cells had the longest LDT. Exponential growth rates decreased as the CO2 concentrations increased. Once exponential growth was attained, no retained differences among the various initial physiological states of the cells for any of the atmospheres were observed in the exponential growth rates. The exponential growth rates under 0, 20, 40, 60, and 80% CO2 averaged 0.39, 0.37, 0.23, 0.23, and 0.0 log CFU/day, respectively. Dimensionless factors were calculated that describe the inhibitory action of CO2 on the LDTs and exponential growth rates for the various physiological states.
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Dr., Devidas Thosar, Sontakke Shubham, Aher Dnyanraj, Pethe Mahesh, and Patre Tanisha. "Smart Data Hub: Comprehensive Tool for Visualization, Mining, Cleaning & Prediction." Journal of Network Security and Data Mining 8, no. 2 (2025): 18–23. https://doi.org/10.5281/zenodo.15267546.
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<em>The exponential growth of data has led to an increased demand for platforms that streamline data processing and analysis. Smart Data HUB is a comprehensive solution that integrates data visualization, data mining, data cleaning, and predictive analysis in a unified system. This paper presents the architecture, implementation, and key functionalities of Smart Data HUB. The platform enables users to upload files in various formats (XML, CSV, Word, Text) and apply multiple data processing techniques efficiently. The experimental results indicate that the system enhances workflow efficiency by reducing manual effort and improving accuracy in data-driven decision-making.</em>
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Spouge, John L. "A comprehensive estimation of country-level basic reproduction numbers R0 for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data." PLOS ONE 16, no. 7 (2021): e0254145. http://dx.doi.org/10.1371/journal.pone.0254145.
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In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R0 on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R0 estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R0 estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R0 estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R0 estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available.
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Cañete, Rodríguez Ana M., Inés M. Santos-Dueñas, Hornero Jorge Jimenez, María J. Torija-Martínez, Albert Mas, and García Isidoro García. "An approach for estimating the maximum specific growth rate of Gluconobacter japonicus in strawberry purée without cell concentration data." Biochemical Engineering Journal 105 (January 15, 2016): 314–20. https://doi.org/10.1016/j.bej.2015.10.005.
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The estimation of the maximum specific growth rate ( max) for non-readily culturable bacteria, growing on complex media containing suspended solids, is a difficult task considering the important problems in obtaining reliable measures of cell concentration. An example of this situation can be a culture of Gluconobacter japonicus growing in strawberry purée for producing gluconic acid. Based on the dependency between energy requirements of the genus Gluconobacter and substrate uptake as well as its constant relationship between gluconic acid production and total substrate uptake, the total substrate concentration profile during the exponential growth phase could be used for estimating max without cell concentration measures. In this case, the high selectivity of the strain for glucose in comparison to fructose resulted in no fructose consumption during the batch; so, just using the glucose concentrations data during the exponential phase allow us to obtain an estimation of mumax. Additionally, a rough estimation of the apparent and stoichiometric yields of cell on glucose is also possible.
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Shi, J. Q. Q., and S. Durucan. "Exponential Growth in San Juan Basin Fruitland Coalbed Permeability With Reservoir Drawdown: Model Match and New Insights." SPE Reservoir Evaluation & Engineering 13, no. 06 (2010): 914–25. http://dx.doi.org/10.2118/123206-pa.
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Summary The exponential-growth behavior of coalbed permeability with reservoir pressure depletion has been observed previously at the Fairway wells in the San Juan basin. More recently, the exponential trend has also been confirmed for a group of 10 wells in a region northeast of Fairway. Increase in the absolute permeability of coalbeds is a result of matrix shrinkage caused by gas desorption, which becomes a dominant factor on cleat permeability over the effective-stress effect during reservoir production. A permeability model previously developed by the authors has been revisited in an effort to match the recently published field-permeability data. The Shi and Durucan (S&D) permeability model (2004, 2005a) makes use of a stress permeability relationship reported in the literature for an idealized geometry (matchsticks), in which the permeability varies exponentially with changes in the effective horizontal stress through the use of a constant cleat (pore) compressibility parameter. In this study, a stress-dependent cleat volume compressibility defined by an initial compressibility with a negative exponential decline rate was adopted. By tuning these two parameters, it was possible to achieve a close match to all the field-permeability data by use of a common set of reservoir properties (elastic properties and matrix-shrinkage parameters) that are consistent with the available field and laboratory data in the literature. The matching of the field-permeability data in these two regions of the San Juan basin in this study has further validated the updated S&D permeability model (now with variable cleat-volume compressibility). It has also provided theoretical support for the observed near-exponential growth behavior of coalbed permeability in a producing reservoir. Furthermore, the model has shed new light on the permeability behavior: (1) The exponential growth occurs when the reservoir pressure is reduced to a level generally below the permeability-recovery pressure (the reservoir pressure at which the initial permeability is recovered); (2) The reservoir permeability in these two regions would remain relatively flat at reservoir pressures above the permeability recovery pressure.
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Zhou, Huajun, Binchen Mao, and Sheng Guo. "Abstract 2743: Tumor growth modeling and its applications in preclinical pharmacology studies to improve translatability of animal models." Cancer Research 82, no. 12_Supplement (2022): 2743. http://dx.doi.org/10.1158/1538-7445.am2022-2743.
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Abstract Successful oncology drug development hinges on proper use of preclinical xenograft and allograft mouse models that faithfully reflect histopathology and genomic features of patient tumors and exhibit clinically similar drug response. For in vivo pharmacology studies, tumor-bearing mice are treated by drugs and efficacy is monitored by continuingly measuring tumor volumes over weeks or even months, resulting in mouse-specific tumor growth data. In our previous study, we used linear mixed model (LMM) to fit tumor growth data and demonstrated its superiority to simple readouts, such as tumor growth inhibition (TGI), in discovering biomarkers and exploring mechanism of action for drugs. Here we extended the study to more parametric statistical models on an expanded dataset with over 100 thousand tumor growth curves. Six parametric models (exponential, exponential square, Gompertz, Logistic, Monomolecular, and von Bertalanffy) were fit to tumor growth curves within a single-treatment group, by the least square method after variance stabilization transformation. Best models were selected by Akaike information criterion (AIC)-based confidence set. In the Irinotecan case study, logarithmic transformation of tumor volume converts exponential or exponential square models to mixed-effects linear models that integrate random effect of mouse difference within a treatment group, and are used to identify predictive biomarkers for drug efficacy. We compared three variance stabilization transformations and found that log transformation of tumor volume is appropriate for majority of tumor growth curves. In log transformed data, exponential or exponential square model can satisfactorily fit nearly all tumor growth curves, as 96.1% of best model confidence sets contain exponential or exponential square model, followed by von Bertalanffy (92.5%), Gompertz (92.1%), Logistic (90.5%), and Monomolecular (58.9%). The percentage of studies, in which all treatment groups can be best fit by exponential or exponential square model, is 91.3% (82.4% for von Bertalanffy, 81.4% for Gompertz, 79.1% for Logistic, 37.3% for Monomolecular). Thus, exponential or exponential square model can be applied to most in vivo mouse studies, and should be preferred for their mathematical simplicity and interpretability. We further demonstrated the utility of the modeling method on a 16-PDX-model study for Irinotecan to identify biological processes and pathways related to its mechanism of action. Citation Format: Huajun Zhou, Binchen Mao, Sheng Guo. Tumor growth modeling and its applications in preclinical pharmacology studies to improve translatability of animal models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2743.
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Volpert, Vitaly, Malay Banerjee, and Sergei Petrovskii. "On a quarantine model of coronavirus infection and data analysis." Mathematical Modelling of Natural Phenomena 15 (2020): 24. http://dx.doi.org/10.1051/mmnp/2020006.
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Attempts to curb the spread of coronavirus by introducing strict quarantine measures apparently have different effect in different countries: while the number of new cases has reportedly decreased in China and South Korea, it still exhibit significant growth in Italy and other countries across Europe. In this brief note, we endeavour to assess the efficiency of quarantine measures by means of mathematical modelling. Instead of the classical SIR model, we introduce a new model of infection progression under the assumption that all infected individual are isolated after the incubation period in such a way that they cannot infect other people. Disease progression in this model is determined by the basic reproduction number R0 (the number of newly infected individuals during the incubation period), which is different compared to that for the standard SIR model. If R0 > 1, then the number of latently infected individuals exponentially grows. However, if R0 < 1 (e.g. due to quarantine measures and contact restrictions imposed by public authorities), then the number of infected decays exponentially. We then consider the available data on the disease development in different countries to show that there are three possible patterns: growth dynamics, growth-decays dynamics, and patchy dynamics (growth-decay-growth). Analysis of the data in China and Korea shows that the peak of infection (maximum of daily cases) is reached about 10 days after the restricting measures are introduced. During this period of time, the growth rate of the total number of infected was gradually decreasing. However, the growth rate remains exponential in Italy. Arguably, it suggests that the introduced quarantine is not sufficient and stricter measures are needed.
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Mohamad Radzi, Nurul Ashikin, Haliza Abd Rahman, Shariffah Suhaila Syed Jamaludin, and Arifah Bahar. "Exponential Growth Model and Stochastic Population Models: A Comparison via Population Data." Malaysian Journal of Fundamental and Applied Sciences 18, no. 1 (2022): 60–69. http://dx.doi.org/10.11113/mjfas.v18n1.2402.
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A population dynamic model explains the changes of a population in the near future, given its current status and the environmental conditions that the population is exposed to. In modelling a population dynamic, deterministic model and stochastic models are used to describe and predict the observed population. For modelling population size deterministic model may provide sufficient biological understanding about the system, but if the population numbers do become small, then a stochastic model is necessary with certain conditions. In this study, both types of models such exponential, discrete-time Markov chain (DTMC), continuous-time Markov chain (CTMC) and stochastic differential equation (SDE) are applied to goat population data. Results from the simulations of stochastic realisations as well as deterministic counterparts are shown and tested by root mean square error (RMSE). The SDE model gives the smallest RMSE value which indicate the best model in fitting the data.
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Matthew, N. O. Sadiku, C. Chukwu Uwakwe, Ajayi-Majebi Abayomi, and M. Musa Sarhan. "Big Data in Government: An Introduction." Journal of Scientific and Engineering Research 8, no. 8 (2021): 135–43. https://doi.org/10.5281/zenodo.10612329.
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<strong>Abstract</strong> Data may be regarded as the new form of currency. It is now the lifeblood of twenty-first century government. Data is a critical asset for government agencies. It can have an enormous impact on government at local, state, national, and global levels. It allows governments to make faster decisions, monitor those decisions and make changes if necessary. Big data is a term that is widely used to describe the exponential growth of data. Big data technology is vitally important for governments all over the world. It can transform government and society. This paper is an overview on the application of big data in governments. Although the material covered in this paper is limited to US government, the information is applicable to other nations.
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Rypdal, Kristoffer. "Empirical growth models for the renewable energy sector." Advances in Geosciences 45 (July 25, 2018): 35–44. http://dx.doi.org/10.5194/adgeo-45-35-2018.
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Abstract. Three simple, empirical models for growth of power consumption in the renewable energy sector are compared. These are the exponential, logistic, and power-law models. The exponential model describes growth at a fixed relative growth rate, the logistic model saturates at a fixed limit, while the power-law model describes slowing, but unlimited, growth. The model parameters are determined by regression to historical global data for solar and wind power consumption, and model projections are compared to scenarios based on macroeconomic modelling that meet the 2∘ target. It is demonstrated that rational rejection of an exponential growth model in favour of a logistic growth model cannot be made from existing data for the historical evolution of global renewable power consumption y(t). It is also shown that the logistic model yields saturation of growth at unrealistic low levels. The power-law growth model is found to give very good fits to the data through the last decade, and the projections align very well with the scenarios. Power-law growth is equivalent to the simple law that the relative growth rate y′/y decays inversely proportional to time. It is shown that this is a natural model for growth that slows down due to various constraints, yet not experiencing the effect of a strict upper limit defined by physical boundaries. If the actual consumption follows the power-law curve in the years to come the exponential-growth null hypothesis can be correctly rejected around 2020.
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Ishikawa, Atushi, Shouji Fujimoto, Takayuki Mizuno, and Tsutomu Watanabe. "Firm Growth Function and Extended-Gibrat’s Property." Advances in Mathematical Physics 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/9303480.
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We analytically show that the logarithmic average sales of firms first follow power-law growth and subsequently follow exponential growth, if the growth-rate distributions of the sales obey the extended-Gibrat’s property and Gibrat’s law. Here, the extended-Gibrat’s property and Gibrat’s law are statistically observed in short-term data, which denote the dependence of the growth-rate distributions on the initial values. In the derivation, we analytically show that the parameter of the extended-Gibrat’s property is identical to the power-law growth exponent and that it also decides the parameter of the exponential growth. By employing around one million bits of exhaustive sales data of Japanese firms in the ORBIS database, we confirmed our analytic results.
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Komarova, Natalia L., Luis M. Schang, and Dominik Wodarz. "Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws." Journal of The Royal Society Interface 17, no. 170 (2020): 20200518. http://dx.doi.org/10.1098/rsif.2020.0518.
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We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with ‘younger’ epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.
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Researcher. "BUILDING SCALABLE DATA ARCHITECTURES FOR MACHINE LEARNING." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 308–20. https://doi.org/10.5281/zenodo.13234031.
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This comprehensive article explores the critical role of scalable data architectures in machine learning, addressing the challenges posed by exponential data growth and increasing model complexity. It delves into the core components of such architectures, including data ingestion, storage, processing, and model deployment, while examining key architectural patterns like Lambda, Kappa, and Microservices. The article discusses various technologies and tools essential for implementing scalable ML infrastructures, and emphasizes the importance of integrating machine learning with data engineering processes. A case study on predictive maintenance in manufacturing illustrates the practical impact of these architectures, demonstrating significant improvements in equipment downtime reduction and cost savings.
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Pesti, Benedek, Zsófia Nagy, László Attila Papp, Matthias Sipiczki, and Ákos Sveiczer. "Cell Length Growth in the Fission Yeast Cell Cycle: Is It (Bi)linear or (Bi)exponential?" Processes 9, no. 9 (2021): 1533. http://dx.doi.org/10.3390/pr9091533.
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Fission yeast is commonly used as a model organism in eukaryotic cell growth studies. To describe the cells’ length growth patterns during the mitotic cycle, different models have been proposed previously as linear, exponential, bilinear and biexponential ones. The task of discriminating among these patterns is still challenging. Here, we have analyzed 298 individual cells altogether, namely from three different steady-state cultures (wild-type, wee1-50 mutant and pom1Δ mutant). We have concluded that in 190 cases (63.8%) the bilinear model was more adequate than either the linear or the exponential ones. These 190 cells were further examined by separately analyzing the linear segments of the best fitted bilinear models. Linear and exponential functions have been fitted to these growth segments to determine whether the previously fitted bilinear functions were really correct. The majority of these growth segments were found to be linear; nonetheless, a significant number of exponential ones were also detected. However, exponential ones occurred mainly in cases of rather short segments (<40 min), where there were not enough data for an accurate model fitting. By contrast, in long enough growth segments (≥40 min), linear patterns highly dominated over exponential ones, verifying that overall growth is probably bilinear.
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Enyoh, Christian Ebere, Andrew Wirnkor Verla, Chidi Edbert Duru, Emmanuel Chinedu Enyoh, and Budi Setiawan. "Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria." Healthy-Mu Journal 4, no. 1 (2020): 1. http://dx.doi.org/10.35747/hmj.v4i1.545.
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Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models. The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.
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Espinoza S., Náser A., Janet S. Mostacero L., Verónica V. Espinoza A., and Erika del C. Aguilar C. "Exponential growth of pollution of the Moche River." SCIÉNDO 27, no. 1 (2024): 53–58. http://dx.doi.org/10.17268/sciendo.2024.008.
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Pollution and at the same time water scarcity is considered one of the main concerns throughout the world. In Peru, most of its hydrographic basins are affected by pollution from productive and domestic activities. The present investigation sought to analyze the significant growth in pollution of the Moche River in the period 2010 to 2020; For this purpose, a descriptive study was carried out, using documentary analysis by recording data from the years 2010 - 2020 from entities such as the National Water Authority and other related studies; thereby determining that the pollution of the Moche River has not been reduced in the study period; On the contrary, the existence of chemicals, minerals and bacteria in water have increased exponentially in recent years. Faced with this, there are no encouraging prospects for a solution in the current decade; the political will of the authorities and greater participation and leadership of other representative organizations of the region and also raising citizen awareness through teaching in educational institutions and the media.
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Smrita Borthakur. "Trends and Growth of Productivity of Agricultural Sector using Statistics Through Machine Learning in Assam." Communications on Applied Nonlinear Analysis 32, no. 3 (2024): 488–500. http://dx.doi.org/10.52783/cana.v32.2010.
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The study of trend and growth of agricultural sector by applying statistical techniques through machine learning analysis has become a powerful tool in in the long run. period, In this study Trend Analysis have been done using fitting of curves of the types, Exponential, Modified Exponential, Gompertz and Power Curves by taking secondary data for a period of 1993to 2023. The data have been collected from the official records of Directorate of Economics and Statistics Assam. Trend analysis have been done using Mann-Kendall test and Sen’s slope estimator. From the growth models modified exponential is adjudged by higher ????2 and lowest MSE and lowest AIC with significant t values.
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Kostin, Konstantin B., Ralf Fendel, and Friedrich Wild. "Comparing the Situation of FinTech Start-Ups in Russia and Germany through Equity Investments." Economies 10, no. 2 (2022): 33. http://dx.doi.org/10.3390/economies10020033.
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Examining and comparing the FinTech investment environments of major economies has untapped potential when it comes to comparing their innovativeness in the financial sector. Therefore, this paper examines the development of FinTech companies from Germany and Russia by investigating funding circumstances and by analyzing equity investments. The goal of the article is to analyze the growth and development of equity funding in FinTech companies in both countries. The underlying hypothesis for this investigation is the applicability of an exponential growth model for the examined funding rounds. The analysis shows that the German market has more FinTech start-ups pursuing equity funding rounds. From Pre-Seed to Series D funding, the considered investment market is about 18 times larger in Germany than it is in Russia. The German market shows strong evidence of exponentially increasing investment tickets based on the behavior of the total data set. This is further supported by testing exponential and linear models on the averages for the investment stages. In this analysis, the exponential model shows a significantly better fit than its linear counterpart. The analysis of the Russian market is not supportive of the hypothesis, as substantial evidence of the superiority of a linear model over an exponential model could be found. This, combined with comparatively compact average funding sizes, signals a more immature equity investment market in Russia.
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Feng, Jian, and Yanguang Chen. "Modeling Urban Growth and Socio-Spatial Dynamics of Hangzhou, China: 1964–2010." Sustainability 13, no. 2 (2021): 463. http://dx.doi.org/10.3390/su13020463.
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Urban population density provides a good perspective for understanding urban growth and socio-spatial dynamics. Based on sub-district data of the five national censuses in 1964, 1982, 1990, 2000, and 2010, this paper is devoted to analyzing of urban growth and the spatial restructuring of the population in the city of Hangzhou, China. Research methods are based on mathematical modeling and field investigation. The modeling result shows that the negative exponential function and the power-exponential function can be well fitted to Hangzhou’s observational data of urban density. The negative exponential model reflects the expected state, while the power-exponential model reflects the real state of urban density distribution. The parameters of these models are linearly correlated to the spatial information entropy of population distribution. The fact that the density gradient in the negative exponential function flattened in the 1990s and 2000s is closely related to the development of suburbanization. In terms of investigation materials and the changing trend of model parameters, we can reveal the spatio-temporal features of Hangzhou’s urban growth. The main conclusions can be reached as follows. The policy of reformation and opening-up and the establishment of a market economy improved the development mode of Hangzhou. As long as a city has a good social and economic environment, it will automatically tend to the optimal state through self-organization.
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Rohim, RabiatulAdawiyah Abdul, Wan Muhamad Amir W. Ahmad, Noor Huda Ismail, Muhammad Azeem Yaqoob, Mohammad Khursheed Alam, and Farah Muna Mohamad Ghazali. "Study of oral lactobacillus towards developing a comprehensive structured for integrated exponential regression model." Bangladesh Journal of Medical Science 19, no. 3 (2020): 552–57. http://dx.doi.org/10.3329/bjms.v19i3.45874.
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Introduction: Probiotics are well-defined as live microorganisms that usefully affect the host and probiotic bacteria have been used intensely. For years to target gastrointestinal disease by rebalancing the compound microflora. Besides the gastrointestinal tract also the oral cavity is highly colonized by bacteria and many different bacterial species are part of the microbiota in the mouth, as it offers ideal conditions for bacteria with a stable temperature, moist surface with a relatively stable pH and regular supply of nutrients. Probiotic bacteria like Lactobacillus are a promising treatment strategy for oral disease with a microbiological etiology. To gain better results, many researchers that study and emphasize specific methods been tried to build a new or improved methodology.
 Objectives: The aimed of this study is to improve the performance of exponential growth by adding bootstrap and fuzzy techniques (Integrated exponential regression method). The aim of the research work is to develop a comprehensive framework for an integrated exponential regression model.
 Material and Methods: The data were taken from the present data available from the recently done by a researcher for nurturing selected microorganisms. The gathered data will be used for the exponential modeling and the efficiency of the model will be compared accordingly due to the predicted interval from the exponential regression method and an integrated exponential regression method. This paper also provides the algorithm for the prediction of cell growth and inferences.
 Results: The result shows that the average width for the exponential regression model was 19.2228 while an integrated exponential regression method was 0.0075. The average width of integrated exponential regression was smaller than the exponential regression. This clearly shows that the integrated exponential regression method is more efficient than exponential regression technique.
 Conclusion: This proposed method can be applied to small sample size data, especially when limited data is obtained.
 Bangladesh Journal of Medical Science Vol.19(3) 2020 p.552-557
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Trenkenshu, R. P. "Calculation of the specific growth rate of microalgae." Marine Biological Journal 4, no. 1 (2019): 100–108. http://dx.doi.org/10.21072/mbj.2019.04.1.09.
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The work focuses on techniques of quantifying the specific growth rate of microalgae in both batch and continuous culture. It is shown, that to prove that the specific growth rate is a constant value, both the ratio of two chemical biomass characteristics and dimensional structure of cell population must be constant. Critical analysis of the correctness of using the logarithmic formula for estimating the specific growth rate (μ) of microalgae in the exponential phase of growth of batch culture is held: μ = (lnB2 – lnB1) / (t2 – t1), where B1 and B2 are densities (concentrations) of the culture at a moment of time t1 and t2, respectively. This formula is widely used by most microalgae researchers without proving exponential growth character. Availability of such proofs makes the applying of the logarithmic formula meaningless. Examples of quantitative description of the experimental data obtained for two types of marine microalgae in the exponential and linear phases of culture growth are given.
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Researcher. "MIGRATING LEGACY DATA WAREHOUSES TO CLOUD-BASED DATA LAKES: A PRACTICAL APPROACH WITH REAL-WORLD." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 238–50. https://doi.org/10.5281/zenodo.13164093.
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This comprehensive article explores the migration process from legacy data warehouses to cloud-based data lakes, addressing the growing need for scalable and flexible data management solutions in the face of exponential data growth. It covers key aspects of the migration journey, including assessment and planning, data modeling considerations, ETL process adaptation, technical implementation, performance optimization, and common challenges with their solutions. The article provides practical guidance, best practices, and real-world insights to help organizations successfully navigate this complex transition. A case study of a financial services company migrating from a SAS environment to a Hadoop/Spark ecosystem illustrates the challenges and benefits of such a migration, offering valuable lessons for other organizations undertaking similar projects.
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Herlina, Nina, Dedek Kustiawati, Denia Liza Halimi, and Andita Mayang Sari. "Proyeksi Pertumbuhan Penduduk Kecamatan Cibinong Dengan Metode Matematik." ETNIK: Jurnal Ekonomi dan Teknik 2, no. 2 (2023): 145–49. http://dx.doi.org/10.54543/etnik.v2i2.157.
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This study examines the growth model with mathematical methods used to obtain information or predict future population growth in Cibinong. In this study, researchers used data we obtained from the Central Bureau of Statistics (BPS) of West Java Province. Data were taken by researchers from the results of the 2010 – 2020 population census. The method used is a mathematical method consisting of arithmetic models, geometric models and exponential models. . Based on the results of the analysis, that large population growth is known by using the Exponential Model. Meanwhile, for the Geometry, Arithmetic model, it shows that population growth continues to occur in the Cibinong area, although it is very small.
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Vinaydeep, Brar, Kumar Atul, A. Patil Nitin, and Gade Santosh. "An analysis of key growth drivers and challenges in organised sector of Indian retail industry." Siddhant Management Review 2, no. 1 (2017): 29–40. https://doi.org/10.5281/zenodo.6677413.
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The Indian retail industry is considered as one among the most vibrant industries in country and one of the pillars of economy. The Indian retail industry is also one among the most contributing sectors to GDP (Gross domestic product) and employment of the country. The Indian retail industry especially organised sector has witnessed an exponential growth in last few years, at the same time faced some challenges. In this article, a modest attempt is made to critically analyse key drivers of exponential growth of organised sector of Indian retail industry, as well as key challenges faced. The article is based on exploratory research and secondary data. The data was accumulated from books, journals, magazines, websites and other published sources available. This article provides an original insight of key growth drivers and challenges in organised sector of Indian retail industry.
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Bitelli, Gabriele, and Emanuele Mandanici. "2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques." Remote Sensing 13, no. 7 (2021): 1310. http://dx.doi.org/10.3390/rs13071310.
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The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]
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Myshkovskyi, Yurii, and Mariia Nazarkevych. "Exponential data augmentation methods for improving YOLO performance in computer vision tasks." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 17 (June 2025): 189–202. https://doi.org/10.23939/sisn2025.17.189.
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The article examines data augmentation methods in the task of image recognition, specifically introducing the exponential augmentation approach to enhance the performance of deep neural networks, particularly YOLO, in object detection tasks. The proposed methodology is based on the sequential and repeated application of various transformations, including horizontal and vertical flipping, 90° rotation, Gaussian Blur, brightness and contrast adjustment. This approach ensures exponential dataset growth and significantly increases the diversity of training data, which is critical for improving the model’s generalization capability. Experimental results demonstrate that applying exponential augmentation leads to a significant improvement in detection performance, as indicated by increased mean Average Precision (mAP), Precision, and Recall, even when the initial dataset is limited. Additionally, the integration of the proposed approach with other effective augmentation techniques, such as Mosaic and MixUp, has been explored. The results indicate that combining exponential augmentation with these methods leads to more robust models that can better recognize objects under various lighting conditions, viewpoints, and noise levels. Beyond accuracy analysis, the study also investigates the impact of exponential augmentation on training stability, including the convergence speed of gradient descent and resistance to overfitting. It is shown that multiple data enrichment cycles allow neural networks to adapt more efficiently to challenging conditions and reduce the likelihood of memorizing only specific examples from the training set. The proposed method can be particularly useful in computer vision tasks with limited or imbalanced datasets, as well as in scenarios where improving model accuracy is required without significantly increasing computational costs. The obtained results confirm that exponential augmentation is a promising approach for enhancing the performance of YOLO and other modern object detectors in complex image recognition scenarios.
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Allali, Malika, Mohammed Tamali, and Mostefa Rahli. "Algeria energy production, population growth based on Olduvai Theory." Journal of Renewable Energies 21, no. 3 (2018): 445–54. http://dx.doi.org/10.54966/jreen.v21i3.703.
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During the last two centuries we have known nothing but exponential growth and in parallel we haveevolved what amounts to an exponential-growth culture, a culture so heavily dependent upon the continuance of exponential growth for its stability that it is incapable of reckoning with problems of no growth. So the Olduvai Theory is based on time-series data of world energy production and population; the data are arrays of discrete numbers year-on-year, not continuous functions of time. Hence the difference calculus must be used, not the infinitesimal calculus. Postulates 1 and 2 require that we distinguish intervals of linear growth from those of exponential growth. Global population has grown to 8.3 billion. Most, though not all, of that growth has been concentrated in South Asia and sub-Saharan Africa, in Algeria will growth to 52 million in 2050 and in 2100 will be 72 million, in Africa and in the same year 2050 will touched 1787 million and in the world will be 9655 million in 2050. The most important measure in the energy balance of Algeria is the total consumption of 42.87 billion kWh per year. Per capita this is an average of 1.065 kWh. Algeria could provide itself completely with self-produced energy. The total production of all energy producing facilities is 54 bn kWh. Thats 126 % of the countries own usage. Despite this Algeria trades its energy with foreign countries. Along with pure consumptions the production, imports and exports play an important role.
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Wan, Muhamad Amir W. Ahmad, Azlida Aleng Nor, Dasril Yosza, et al. "MODIFIED NONLINEAR MODEL FOR EXPONENTIAL GROWTH METHOD AND ITS APPLICATION IN BIOSTATISTICSS USING SAS." International Journal of Multidisciplinary Research and Modern Education 3, no. 1 (2017): 89–94. https://doi.org/10.5281/zenodo.344046.
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This paper supplied an alternative method for exponential growth modeling as a technique for regression analysis through SAS algorithm. This alternative method is a combination technique (using bootstrap and fuzzy regression for nonlinear model) for the small data set and gives the researcher an option to launch the analysis even there is not enough data set. This method current method improves the previous methodology with embedded bootstrapping and fuzzy technique to the step of nonlinear regression model. The aim of <em>this principle</em> is to propose an alternative method of doing analysis with better improved results. In our case, we applied this principle to the agriculture data and the gained results were compared by looking at the average width of predicted interval.
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M.Raihan and Muhammad Irwan Fadli Nasution. "Penerapan Sistem Basis Data Pada Minimarket Alfamart." Journal of Management and Creative Business 1, no. 3 (2023): 216–25. http://dx.doi.org/10.30640/jmcbus.v1i3.1190.
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In Indonesia, Alfamart is a large supermarket chain with many locations. In 1989, Djoko Susanto and his family launched the Alfamart supermarket chain. Originally known as PT Sumber Alfaria Trijaya Tbk (the Company), branched out into wholesale and retail in 1999. The company started exponential growth in 2002 by purchasing 141 Alfa Minimart locations and changing its name to Alfamart. Alfamart's vision is to establish itself as a superior community owned retail distribution network dedicated to driving small business growth, exceeding customer expectations, and remaining competitive on a global scale. The modern retail industry can take advantage of Indonesia's consumption of IDR 3,600 trillion in 2016.
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Hamitouche, Fella, Jean Armengaud, Luc Dedieu, and Catherine Duport. "Cysteine Proteome Reveals Response to Endogenous Oxidative Stress in Bacillus cereus." International Journal of Molecular Sciences 22, no. 14 (2021): 7550. http://dx.doi.org/10.3390/ijms22147550.
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At the end of exponential growth, aerobic bacteria have to cope with the accumulation of endogenous reactive oxygen species (ROS). One of the main targets of these ROS is cysteine residues in proteins. This study uses liquid chromatography coupled to high-resolution tandem mass spectrometry to detect significant changes in protein abundance and thiol status for cysteine-containing proteins from Bacillus cereus during aerobic exponential growth. The proteomic profiles of cultures at early-, middle-, and late-exponential growth phases reveals that (i) enrichment in proteins dedicated to fighting ROS as growth progressed, (ii) a decrease in both overall proteome cysteine content and thiol proteome redox status, and (iii) changes to the reduced thiol status of some key proteins, such as the transition state transcriptional regulator AbrB. Taken together, our data indicate that growth under oxic conditions requires increased allocation of protein resources to attenuate the negative effects of ROS. Our data also provide a strong basis to understand the response mechanisms used by B. cereus to deal with endogenous oxidative stress.
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Nsoesie, Elaine Okanyene, Nina Cesare, Martin Müller, and Al Ozonoff. "COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study." Journal of Medical Internet Research 22, no. 12 (2020): e24425. http://dx.doi.org/10.2196/24425.
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Background The epidemic of misinformation about COVID-19 transmission, prevention, and treatment has been going on since the start of the pandemic. However, data on the exposure and impact of misinformation is not readily available. Objective We aim to characterize and compare the start, peak, and doubling time of COVID-19 misinformation topics across 8 countries using an exponential growth model usually employed to study infectious disease epidemics. Methods COVID-19 misinformation topics were selected from the World Health Organization Mythbusters website. Data representing exposure was obtained from the Google Trends application programming interface for 8 English-speaking countries. Exponential growth models were used in modeling trends for each country. Results Searches for “coronavirus AND 5G” started at different times but peaked in the same week for 6 countries. Searches for 5G also had the shortest doubling time across all misinformation topics, with the shortest time in Nigeria and South Africa (approximately 4-5 days). Searches for “coronavirus AND ginger” started at the same time (the week of January 19, 2020) for several countries, but peaks were incongruent, and searches did not always grow exponentially after the initial week. Searches for “coronavirus AND sun” had different start times across countries but peaked at the same time for multiple countries. Conclusions Patterns in the start, peak, and doubling time for “coronavirus AND 5G” were different from the other misinformation topics and were mostly consistent across countries assessed, which might be attributable to a lack of public understanding of 5G technology. Understanding the spread of misinformation, similarities and differences across different contexts can help in the development of appropriate interventions for limiting its impact similar to how we address infectious disease epidemics. Furthermore, the rapid proliferation of misinformation that discourages adherence to public health interventions could be predictive of future increases in disease cases.
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Rhoads, Kathryn, and James A. Mendoza Alvarez. "Data Modeling Using Finite Differences." Mathematics Teacher 110, no. 9 (2017): 709–13. http://dx.doi.org/10.5951/mathteacher.110.9.0709.
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The Common Core State Standards for Mathematics (CCSSM) states that high school students should be able to recognize patterns of growth in linear, quadratic, and exponential functions and construct such functions from tables of data (CCSSI 2010). Accordingly, many high school curricula include a method that uses finite differences between data points to generate polynomial functions. That is, students may examine differences between successive output values (called first differences), successive differences of the first differences (second differences), or successive differences of the (n - 1)th differences (nth-order differences), and rely on the following:
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Thangaraju, Dinesh. "Edge data governance: Frameworks and approaches for distributed data management at the network edge." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 4 (2024): 1353–61. https://doi.org/10.54660/.ijmrge.2024.5.6-1353-1361.
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The exponential growth of edge computing has shifted data processing closer to its source, enabling low-latency applications and real-time decision-making. However, this distributed architecture introduces significant challenges in data governance, including data security, compliance, and policy enforcement across disparate environments. This paper explores the need for edge data governance frameworks, examines the challenges posed by decentralized data environments, and proposes approaches to enable robust governance at the edge. It also highlights future opportunities in advancing edge data governance, along with potential challenges to adoption and scalability.
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Sattarov, M.A. "PARALLELIZATION METHODS OF DATA MINING ALGORITHMS: ENHANCING PERFORMANCE IN THE AGE OF BIG DATA." RESEARCH AND EDUCATION 3, no. 12 (2024): 34–38. https://doi.org/10.5281/zenodo.14567380.
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<em>The exponential growth of data in recent years has presented significant challenges for traditional data mining algorithms. These algorithms, often designed for sequential processing, struggle to handle the massive datasets common in modern applications. Parallelization offers a solution by distributing the computational workload across multiple processors or machines, leading to significant improvements in efficiency and scalability. This article explores the importance of parallelization in data mining, examines common parallelization techniques, and discusses their application to popular algorithms like k-means clustering and DBSCAN, including their mathematical foundations.</em>
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Deepika, Ompal Singh, Adarsh Anand, and Jyotish N. P. Singh. "Testing Domain Dependent Software Reliability Growth Models." International Journal of Mathematical, Engineering and Management Sciences 2, no. 3 (2017): 140–49. http://dx.doi.org/10.33889/ijmems.2017.2.3-013.
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Software Reliability Growth Models (SRGMs) are supporting software industries in expecting and scrutinizing quality of software. Numerous SRGMs have been proposed; majority of which concentrate on testing period of software. For testing, domain specific knowledge plays a very crucial role. Based on necessity condition, a set of programmes are in testing phase of software development. “Domain testing is a software technique in which small number of test cases is selected for trial. These sets of testing paths, all of which are to be eventually influenced by designed test cases are called the testing domain which expands with the progress of testing”. Keeping this concept in mind, we propose SRGMs with the concept of testing domain with exponential coverage. Utility of proposed framework has been emphasized in this paper through some models pertaining to different distribution i.e Exponential, Logistic, Weibull and Rayleigh. Moreover, the data analysis is performed to find the estimates of parameters by fitting the models on authentic data sets.
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Ambhore, Rajashree. "Governance of Unstructured Data: Managing Data Quality in Non-Traditional Data Sources." International Journal of Research 11, no. 12 (2024): 19–37. https://doi.org/10.5281/zenodo.14283398.
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<em>In an era defined by exponential data growth, unstructured data now constitutes over 80% of all data generated globally, including diverse formats like text, video, audio, and social media posts. Despite its potential value, unstructured data presents unique governance challenges due to its complexity and lack of standardization. This study explores the importance of governing unstructured data, particularly from non-traditional sources like IoT devices and social media, emphasizing strategies for maintaining data quality, integrity, and security. Through an analysis of current frameworks and practices, this paper identifies gaps in traditional governance models and proposes a structured approach to address these challenges. Key findings highlight the importance of integrating AI and machine learning tools for data standardization and leveraging cross-departmental collaboration to manage data silos effectively.</em>
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Journal, Baghdad Science. "Correlation between Paris function parameters to crack velocity for Alumina ceramics." Baghdad Science Journal 8, no. 2 (2011): 326–32. http://dx.doi.org/10.21123/bsj.8.2.326-332.
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The question about the existence of correlation between the parameters A and m of the Paris function is re-examined theoretically for brittle material such as alumina ceramic (Al2O3) with different grain size. Investigation about existence of the exponential function which fit a good approximation to the majority of experimental data of crack velocity versus stress intensity factor diagram. The rate theory of crack growth was applied for data of alumina ceramics samples in region I and making use of the values of the exponential function parameters the crack growth rate theory parameters were estimated.
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Abbo, Abd-Allah A., Mohammd A. Saalih, and Aqeel M. Ja'afer. "Correlation between Paris function parameters to crack velocity for Alumina ceramics." Baghdad Science Journal 8, no. 2 (2011): 326–32. http://dx.doi.org/10.21123/bsj.2011.8.2.326-332.
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The question about the existence of correlation between the parameters A and m of the Paris function is re-examined theoretically for brittle material such as alumina ceramic (Al2O3) with different grain size. Investigation about existence of the exponential function which fit a good approximation to the majority of experimental data of crack velocity versus stress intensity factor diagram. The rate theory of crack growth was applied for data of alumina ceramics samples in region I and making use of the values of the exponential function parameters the crack growth rate theory parameters were estimated.
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Sardaraz, Muhammad, Muhammad Tahir, and Ataul Aziz Ikram. "Advances in high throughput DNA sequence data compression." Journal of Bioinformatics and Computational Biology 14, no. 03 (2016): 1630002. http://dx.doi.org/10.1142/s0219720016300021.
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Advances in high throughput sequencing technologies and reduction in cost of sequencing have led to exponential growth in high throughput DNA sequence data. This growth has posed challenges such as storage, retrieval, and transmission of sequencing data. Data compression is used to cope with these challenges. Various methods have been developed to compress genomic and sequencing data. In this article, we present a comprehensive review of compression methods for genome and reads compression. Algorithms are categorized as referential or reference free. Experimental results and comparative analysis of various methods for data compression are presented. Finally, key challenges and research directions in DNA sequence data compression are highlighted.
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Mozzherin, Dmitry, and Deborah Paul. "Preservation strategies for biodiversity data." Biodiversity Information Science and Standards 7 (August 22, 2023): e111453. https://doi.org/10.3897/biss.7.111453.
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We are witnessing a fast proliferation of biodiversity informatics projects. The data accumulated by these initiatives often grows rapidly, even exponentially. Most of these projects start small and do not foresee the data achitecture challenges of their future. Organizations may lack the necessary expertise and/or money to strategically address the care and feeding of this expanding data pile. In other cases, individuals with the expertise to address these needs may be present, but lack the power or status or possibly the bandwidth to take effective actions. Over time, the data may increase in size to such an extent that handling and preserving it becomes an almost insurmountable problem. The most common technical challenges include migrating data from one physical data storage to another, organizing backups, providing fast disaster recovery, and preparing data to be accessible for posterity. Some sociotechnical and strategic hurdles noted when trying to address data stewardship include funding, data leadership (Stack and Stadolnik 2018) and vision (or lack thereof), and organizational structure and culture. The biodiversity data collected today will be indispensable for future research, and it is our collective responsibility to preserve it for current and future generations.Some of the most common information loss risk factors are the end of funding, retirement of a researcher, or the departure of a critical researcher or programmer. More risk factors, such as hardware malfunction, hurricanes, tornadoes, and severe magnetic storms, can destroy the data carefully collected by large groups of people.The co-location of original data and backups create a "Library of Alexandria" where a single disaster at this location can lead to permanent data loss and poses an existential threat to the project.Biodiversity data becomes more valuable over time and should survive for several centuries. However, SSD (solid-state drive) and HDD (hard disk drive) storage solutions have an expiration date of only a few years. We propose the following solutions (Fig. 1) to provide long-term data security:Technical tacticsUse an immutable file storage for everything that is not entered very recently.Most of the biodiversity "big data" are files that are written once and never changed again. We suggest separating storage into a read-only part and small read/write sections. Data from the read/write section can be moved to the read-only part often, for example, daily.Use a Copy-On-Write file system, such as ZFS (Zettabyte File System).The ZFS file system is widely used in industry and is known for its robustness and error resistance. It allows efficient incremental backups and much faster data transfer than other systems. Regular incremental backups can work even with slow internet connections. ZFS provides real-time data integrity checks and uses powerful tools for data healing.Split data and its backups into smaller chunks.Dividing backups into cost-effective 2–8 terabyte chunks allows running backups using cheap hardware. Assuming that the data is read-only, such data organization always splits the backup into chunks, with hardware costs changing from tens of thousands of dollars (US) to less than two thousand dollars. We recognize that with time data storage costs drop, and larger chunks will be used.Split the data even further to the size of the largest available long-term storage unit (currently an optical M-disc).The write-once optical M-DISC is analogous to a Sumerian clay tablet. Data written on such discs does not deteriorate for hundreds of years. This option addresses the need for last resort backups because the storage does not depend on magnetic properties and is impervious to electromagnetic disasters. Optical discs can be easily and cheaply copied and distributed to libraries worldwide. In the future, discs' data can be transferred to a different long-term storage medium. We also trust these discs can be deciphered by those in the future, just like clay tablets.Sociotechnical insightsThe above example of a comprehensive strategy to preserve data epitomizes "LOCKSS" (lots of copies keep stuff safe) and makes it clear that these copies need to be in multiple media types. Our suggestions here focus on projects that experience data growth pains. Such projects often look to see how others address these data needs. Recently, The Species File Group (SFG) did this exercise to evaluate and address our data growth needs (Mozzherin et al. 2023). We recognize and emphasize here the need forpersonnel with the knowledge and skills to build, maintain, and evolve robust strategies and infrastructure to make data accessible and preserve it,funding to back the most suitable architectural strategies to do so, andpeople with expertise in long-term data security to have a seat at the leadership table in our organizations.We encourage our colleagues to evaluate the status of data leadership at your organizations (Stack and Stadolnik 2018, Kalms 2012). Implementing these suggestions will help ensure the survival of the data and accompanying software for hundreds of years to come.
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Neale, Michael C., and John J. McArdle. "Structured latent growth curves for twin data." Twin Research 3, no. 3 (2000): 165–77. http://dx.doi.org/10.1375/twin.3.3.165.
Full textAbstract:
AbstractWe describe methods to fit structured latent growth curves to data from MZ and DZ twins. The well-known Gompertz, logistic and exponential curves may be written as a function of three components – asymptote, initial value, and rate of change. These components are allowed to vary and covary within individuals in a structured latent growth model. Such models are highly economical, requiring a small number of parameters to describe covariation across many occasions of measurement. We extend these methods to analyse longitudinal data from MZ and DZ twins and focus on the estimation of genetic and environmental variation and covariation in each of the asymptote, initial and rate of growth factors. For illustration, the models are fitted to longitudinal Bayley Infant Mental Development Scale data published by McArdle (1986). In these data, all three components of growth appear strongly familial with the majority of variance associated with the shared environment; differences between the models were not great. Occasion-specific residual factors not associated with the curve components account for approximately 40% of variance of which a significant proportion is additive genetic. Though the growth curve model fit less well than some others, they make restrictive, falsifiable predictions about the mean, variance and twin covariance of other (not yet measured) occasions of measurement. Twin Research (2000) 3, 165–177.