Pareto Law Research Articles

Research on enhancing the efficiency of food safety sampling inspections in China based on Pareto's law.

Food safety is pivotal for public welfare and directly impacts consumer health. Food safety sampling inspections (FSSIs) are essential in detecting unqualified food products and non-compliant manufacturers, which form an integral part of government regulatory frameworks. However, given the constraints on budgetary resources, improving the efficiency of food safety sampling inspections (EFSSIs) remains a considerable challenge in China's food quality and safety supervision. This study aims to apply Pareto's law, starting from the examination of food sample testing items and major hazard types, to theoretically analyze methods for improving the EFSSIs. Following the theoretical analysis, the research employs provincial food sampling data from China in 2022 to empirically validate the proposed improvement strategies. The research findings indicate that applying Pareto's law significantly reduces the number of items that should be tested for each food subcategory, effectively lowering testing costs for each batch of food samples. Theoretically, employing Pareto's law in sampling inspections can increase the EFSSIs to 2.78 times the current observed level. Furthermore, empirical validation using food sampling data confirms that EFSSIs can be improved to 2.12 times the existing level, consistent with theoretical predictions. Implementing Pareto's law in FSSIs facilitates the detection of more unqualified food products and non-compliant manufacturers without additional financial burden, significantly enhancing the EFSSIs. This approach provides an innovative strategy for government to bolster their food safety management efforts. © 2024 Society of Chemical Industry.

Inequality in the Distribution of Wealth and Income as a Natural Consequence of the Equal Opportunity of All Members in the Economic System Represented by a Scale-Free Network

The purpose of this work is to examine the nature of the historically observed and empirically described by the Pareto law inequality in the distribution of wealth and income in an economic system. This inequality is presumed to be the result of unequal opportunity by its members. An analytical model of the economic system consisting of a large number of actors, all having equal access to its total wealth (or income) has been developed that is formally represented by a scale-free network comprised of nodes (actors) and links (states of wealth or income). The dynamic evolution of the complex network can be mapped in turn, as is known, into a system of quantum particles (links) distributed among various energy levels (nodes) in thermodynamic equilibrium. The distribution of quantum particles (photons) at different energy levels in the physical system is then derived based on statistical thermodynamics with the attainment of maximal entropy for the system to be in a dynamic equilibrium. The resulting Planck-type distribution of the physical system mapped into a scale-free network leads naturally into the Pareto law distribution of the economic system. The conclusions of the scale-free complex network model leading to the analytical derivation of the empirical Pareto law are multifold. First, any complex economic system behaves akin to a scale-free complex network. Second, equal access or opportunity leads to unequal outcomes. Third, the optimal value for the Pareto index is obtained that ensures the optimal, albeit unequal, outcome of wealth and income distribution. Fourth, the optimal value for the Gini coefficient can then be calculated and be compared to the empirical values of that coefficient for wealth and income to ascertain how close an economic system is to its optimal distribution of income and wealth among its members. Fifth, in an economic system with equal opportunity for all its members there should be no difference between the resulting income and wealth distributions. Examination of the wealth and income distributions described by the Gini coefficient of national economies suggests that income and particularly wealth are far off from their optimal value. We conclude that the equality of opportunity should be the fundamental guiding principle of any economic system for the optimal distribution of wealth and income. The practical application of this conclusion is that societies ought to shift focus from policies such as taxation and payment transfers purporting to produce equal outcomes for all, a goal which is unattainable and wasteful, to policies advancing among others education, health care, and affordable housing for all as well as the re-evaluation of rules and institutions such that all members in the economic system have equal opportunity for the optimal utilization of resources and the distribution of wealth and income. Future research efforts should develop the scale-free complex network model of the economy as a complement to the current standard models.

Continuous service-based process monitoring using Pareto analysis and start-end case diagram

PurposeThe purpose of this study is to offer a straightforward, cost-effective, and feasible resolution for managers to assess their processes in a live manner using the process mining technique and to identify anomalies in cases that deviate from the standard. Consequently, the findings of this research can be utilized by organizational managers, while process mining vendors can also leverage it as a feature for their solutions.Design/methodology/approachOur two-step method is designed to initially evaluate the level of standardization within the process, followed by identifying its underlying cause. These two steps are aimed at helping managers effectively evaluate their business processes. The steps are: (1). Start-End Case Diagram: This diagram allows for the evaluation of the lead time trend and identification of cases that deviate from the standard trend line in a service-based process. (2). Happy Path Analysis: Pareto law is suggested to identify the most frequent process variants.FindingsThis approach enables organizations to easily identify problematic cases and investigate bottlenecks when deviations from the standards occur.Originality/valueThe novelty of the paper lies in the introduction and utilization of the start-end case diagram, as well as the combination of this diagram with the Pareto law for the identification of happy path and root cause analysis.

Identifikasi Material Yang Berpotensi Menjadi Waste Material Pada Proyek Gedung Kuliah Terpadu B Universitas Palangka Raya

Construction projects are activities calculated to obtain maximum results in the form of buildings or infrastructure within limited time, costs and resources. Waste material in construction projects can result in material waste in construction project work. Therefore, it is necessary to identify materials that have the potential to cause waste material. This research aims to identify consumable materials in architectural work and the upper structure used which has a percentage of around (20%) which has value or has an impact of around (80%) on the construction of the Integrated Lecture Building B project at Palangka Raya University, Central Borneo. In this research, materials were identified in architectural work and upper structures that have the potential to cause material waste using the Pareto method (Pareto's law). The results of this research show the identification and grouping of material types that have the greatest rupiah value (80%) compared to other types of material and are materials that have the potential to cause material waste. The results obtained were 2 materials for upper structural work and 14 materials for architectural work. Accumulatively, the weight percentage required for the 2 materials in the upper structure work in this project is 72.25% compared to other types of upper structure materials. And the cumulative weight percentage required for the 14 materials in architectural work in this project is 79.43% compared to other types of architectural materials.

Assessment of the functional behaviour of precision engineering workers using ultradian rhythms of HRV indicators in the prevention of man-made disasters

Understanding the functional behavior of precision engineering workers was the aim of studying the ultradian rhythms of HRV indicators to prevent man-made disasters. It was revealed that the higher the voltage of the functional state, the lower the heart rate indicators. The results established specify activation of the vagus associated with cognitive activity. Precision engineers used ultradian rhythms of HRV indicators and the Pareto law to assess the functional behavior of men. The results revealed that work activity negatively impacts the functional behavior and health of 58% of men, placing them in a group associated with human factors and man-made disasters.

Measuring the Risk of Vulnerabilities Exploitation

Modeling the vulnerabilities lifecycle and exploitation frequency are at the core of security of networks evaluation. Pareto, Weibull, and log-normal models have been widely used to model the exploit and patch availability dates, the time to compromise a system, the time between compromises, and the exploitation volumes. Random samples (systematic and simple random sampling) of the time from publication to update of cybervulnerabilities disclosed in 2021 and in 2022 are analyzed to evaluate the goodness-of-fit of the traditional Pareto and log-normal laws. As censoring and thinning almost surely occur, other heavy-tailed distributions in the domain of attraction of extreme value or geo-extreme value laws are investigated as suitable alternatives. Goodness-of-fit tests, the Akaike information criterion (AIC), and the Vuong test, support the statistical choice of log-logistic, a geo-max stable law in the domain of attraction of the Fréchet model of maxima, with hyperexponential and general extreme value fittings as runners-up. Evidence that the data come from a mixture of differently stretched populations affects vulnerabilities scoring systems, specifically the common vulnerabilities scoring system (CVSS).

Universal Pareto laws in agent-based exchange models: debt and varying initial-money distributions

Universal Pareto laws in agent-based exchange models: debt and varying initial-money distributions

Seeing through noise in power laws.

Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto's law of wealth and the Gutenberg-Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov-Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.

Bibliometric Analysis: Technology Studies in Science Education

It is thought that the results of a study examining the articles published in peer-reviewed journals on technology in science education in terms of many criteria will provide important information to researchers. For this purpose, bibliometric network analysis was chosen as a method. The purpose of choosing this research method is to clearly summarize the relationship between science education and technology in order to identify technology in science education. In order to answer the research questions, bibliometric data consisting of 8511 articles in the Scopus database were evaluated using the bibliographic data obtained with the VOSviewer program. In addition, Pareto Law, Price Law, Lotka Law were used within the scope of citation analysis in the research. The results obtained from the research are presented.

Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China

Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.

Forest Management Plan Based on Comprehensive Benefit Model

Based on a well-documented analysis of influencing factors, this paper provides an effective and referenced forest management scheme to better balance deforestation and comprehensive benefits. First,construct a Carbon Sequestration Model by dividing it into two parts: forest carbon sequestration and forest-derived products carbon sequestration. For forest carbon sequestration, this paper take into account the Net Primary Productivity and estimate based on the Thornthwaite Memoral Model. For forest-derived products, this paper used K-means clustering to analyze the lifespan of wood and combined the conversion factor from wood volume to carbon tons to calculate. this paper analyzed the carbon sequestration model and fitted the relationship between the carbon sequestration per unit of existing forest area and the deforestation, and combined with the peak points to get the forest management plan: the peak point is the largest carbon sequestration per unit area, and you should prepare to cut down trees at this time. In order to prevent the carbon sequestration from becoming lower after the peak value; on the left side of the peak point, the tree needs to continue to grow; on the right side of the peak point, the tree needs to be cut down immediately. Second, based on the carbon sequestration model, this paper consider more factors and use the Entropy Weight Method to build a Comprehensive Benefit Model. this paper analyzed the comprehensive benefit model and fitted the relationship between the comprehensive benefit per unit of existing forest area and deforestation. this paper model is applicable to any forest situation including natural disasters subject to force majeure. when it is on the left side of the peak, it is not necessary to cut down the tree. Next,this paper combine the peak points, namely transition points, to arrive at the forest management plan. Third, this paper apply the comprehensive benefit model to various forests. this paper used a Markov Model to predict carbon sequestration after 100 years, and created a three-dimensional map of carbon sequestration in forests around the world. this paper combine the peak points to derive the forest management plan. this paper combine peak points with Pareto's law to optimize forest management plans.

Examining Pareto Law across department store shoppers

Department stores invest in loyalty strategies that largely focus on retaining current high value customers in response to increasing competition in retail shopping. In this study, we examine the contribution of the top 20% customers for transaction frequency and value (“heavy buyers”) to the total sales, and the consistency of this contribution across departments within a store. We also investigate the heavy buyer stability over time across 3 years, from over 550 million transactions from a department store chain in East Asia. The results show that the Pareto ratio of the top 20% spenders account for 71% of revenue (and 52% of the total transactions), and the top 20% transactors represent 58% of revenue (and 62% of total transactions), which may signal the role of such heavy buyers to overall stores sales. At each department level, the heavy buyers (by value) contribute from 65% to 86% of the department revenue. Despite this, the stability of the top 20% segment over time varies greatly by department from 11% to 74%. Finally, whether high value customers in one department store also translate across departments, depends mainly on the department size in terms of its shopper penetration. The research furthers our knowledge on Pareto Law, with important implications for customer retention strategies and loyalty programs especially for retailers.

Wealth distribution for agents with spending propensity, interacting over a network.

Wealth distribution in an economic system is studied by means of an agent model, where agents have a certain spending propensity and they interact over a given network. When the network is random, or scale-free ( ∼k-α) with α below 1, approximately, results are equivalent to having all agents allowed to interact with any other agent. However, values of α>1 affect both the wealth distribution and the behavior at the tail. These results hold both in the absence of spending propensity and when the spending propensity follows a power-law. Results suggest that Pareto's law is a very robust phenomenon with respect to the details of the connectivity of the agents and that the ubiquity of Pareto's law in actual systems may have implications on the topological properties of the underlying networks of interaction.

Modeling the Size of Tax Revenues by Probabilistic Laws

In order to obtain objective data based on the results of the study, the planned indicators of the budgets of all subjects of the Russian Federation for the upcoming financial year are not taken as a basis, but the actual parameters obtained for the execution of budgets for the past financial year. Practice shows that every year almost all budgets of subjects and the consolidated budget of the state receive significantly more tax funds than was provided for when adopting budgets for the coming year. It would be erroneous to attribute this excess of actual tax revenues over planned ones due to the more efficient operation of the economic sector. The subjective factor plays an important role here. Local tax and financial authorities, the Federal Tax Service and the Ministry of Finance of the Russian Federation, which are responsible for developing draft budgets for the upcoming financial period and ensuring their execution, sometimes underestimate the actual possible amount of tax revenues for pragmatic reasons. Based on the above, the analysis is based on statistical data on the execution of budgets of the constituent entities of the federation for the past year. This research is devoted to modeling the size of tax revenues to the consolidated budget of the Russian Federation. In the article, the reporting data on the size of tax revenues are approximated by the following probabilistic laws: exponential distribution, Pareto distribution, gamma distribution (Erlanger distribution), and lognormal distribution. For each class of distributions, the problem of estimating unknown parameters using maximum likelihood and moments methods is solved. As a result of the study, it was found that the distribution of tax revenues is best described by the Pareto law and the log-normal mode.

Quantitative methods to determine the student workload. I. Empirical study based on digital platforms.

We present a quantitative study of an online course developed during COVID19 sanitary emergency in Chile. We reconstruct the teaching-learning process considering the activity logs on digital platforms in order to answer the question of How do our students study? The results from the analysis evidence the complex adaptive character of the academic environment, which exhibits regularities similar to those found in financial markets (e.g., distributions of the daily time devoted to learning activities follow patterns like Pareto's or Zipf's law). Our empirical results illustrate (i) the relevance of economic notions in the understanding of the teaching-learning processes and (ii) the reliability of quantitative methods based on digital platforms to conduct experimental studies in this framework. We introduce in the present work a series of indicators to characterize the performance of professors, students' follow-up of the course, and their learning progress by crossing information with the results of assessments. In this context, the learning rate appears as a key statistical descriptor for the allocation of the student workload.

ПРИМЕНЕНИЕ МЕТОДОВ МАТЕМАТИЧЕСКОЙ СТАТИСТИКИ ДЛЯ ОЦЕНКИ УРОВНЯ ЛАВИННОЙ ОПАСНОСТИ В ГОРАХ ИЛЕ АЛАТАУ

The Institute of Geography and Water Safety adapted the world experience in assessing the avalanche danger level to the conditions of Kazakhstan. This work is based on the five-point scale method of danger level classification recommended by the WSL Institute for Snow and Avalanche Research (SLF). The methods of mathematical statistics are used to analyze data on weather conditions and avalanches. The analysis identified the main statistical parameters of data series. The cluster analysis revealed five similar groups (clusters) in the distribution of snow water equivalent and air temperature and three clusters in the distribution of precipitation amounts. The spectral analysis showed that the duration of avalanche periods is basically 2-3 days. The distribution of the number of days with different levels of avalanche danger, total volumes of avalanches and heavy precipitation obeys the Pareto law. The results are statistically significant according to the Kolmogorov-Smirnov test (α = 10%, p < 0.01).

Precipitation Extremes and Their Synoptic Models in the Northwest European Sector of the Arctic during the Cold Season

Precipitation extrema over the Barents Sea and the neighbouring locations in Europe were analysed using data obtained from station observations and a highly detailed ERA5 re-analysis dataset. These data did not always spatially coincide (on average, coincidence was ~50%). Daily amounts of precipitation were typically higher in the observation data, although there may be a reverse picture. The analysis revealed that at several stations and in many of the ERA5 grids, the set of precipitation extremes exists as a mixture of two different subsets. The cumulative distribution functions (CDF) of the largest population in the context of both the re-analysis and observational data are well described by Pareto’s law. However, very rare cases exist in which the values deviate and exceed this base distribution value in regions possessing large values. These super-large anomalies do not obey the statistical law common to all other extremes. However, this does not mean that the extremes can be arbitrarily large. They do not exceed the marginal values that are typical for this type of climate and season. The analysis confirms that extreme precipitation in the western sector of the Arctic is caused by the penetration of moist air masses from the Atlantic in the circulation systems of intense cyclones. At certain times, mesoscale convective systems are embedded in atmospheric fronts and can significantly contribute to the formation of precipitation. Intensification of such cyclones corresponding to global warming should lead to a transformation of typical CDF, as modern outliers will become regular components of the Pareto law. This change in the statistics of extreme events reflects the nonstationarity of the climate state. The influence of polar lows on the formation of large daily precipitation amounts is not felt.

Asymmetric Mean Annual Temperature Wavelets Surface Air Layer of Berlin for 1701–2021

The regularities of the dynamics of the average annual temperature of Berlin from 1701 to 2021 are revealed. A total of 65 wavelets were received. The temperature has a high quantum certainty, and the change in the average annual temperature of Berlin was identified by a model that contains only two components for prediction. The basis of the forecast at 320 years makes it possible to look into the future until the year 2340. The forecast confirms the conclusions made in the CMIP5 report on global warming. With an increase in the number of components in the model up to five, the forecast is possible only until 2060. Therefore, the model with only two components is workable. The trend is characterized by a modified Mandelbrot equation showing exponential growth with a high growth rate of 1.47421. The wave equation also has an amplitude in the form of the Mandelbrot law (in mathematics, the Laplace law, in biology, the Zipf-Pearl law, in econometrics, the Pareto law), when the exponential growth activity is equal to 1. For 1701, the period of oscillation was 2× 60.33333 ≈ 120.7 years. By 2021, the period decreased and became equal to 87.6 years. The trend is such that by 2340 the period of oscillation will decrease to 30.2 years. Such an increase in fluctuations indicates an imbalance in climate disturbances in temperature in Berlin. For Berlin, the last three years are characterized by sharp decreases in the average annual temperature from 11.8 °C to 10.5 °C, i.e. by 12.4% in 2021. Therefore, the forecast is still unstable, as a further decrease in the average annual temperature of Berlin in the near future may change the picture of the forecast.

Application of Failure Mode, Effects, and Criticality Analysis to the Medication-Use Process for Temperature-Sensitive Drugs in a University Hospital.

In the hospital setting, the medication-use system for temperature-sensitive drugs is a high-risk process. To analyze the risks associated with the hospital-based medication-use process and to propose corrective and preventive actions for the most critical failure modes. A multidisciplinary team was trained to analyze the medication-use process for temperature-sensitive drugs and to identify potential failures using a risk analysis method known as failure mode, effects, and criticality analysis (FMECA). The medication-use process, from initial supply to administration to patients, was investigated using "the 5 Ws and How" method (Who? What? Where? When? Why? How?), and the causes of the failure modes were analyzed using Ishikawa diagrams. The most critical failure modes were selected using the Pareto law, and relevant improvement actions were proposed. This analysis identified 41 failure modes for the 9 stages of the medication-use process, of which only 36 were deemed assessable by the participants. Eighteen (50%) of these failure modes were critical, according to the Pareto law, with criticality indices between 12 and 60. The stage of tidying up and storage in patient care units had the highest number of critical failures (n = 5). A total of 48 corrective actions were proposed. The proposed action plan prioritized 3 areas for improvement: the documentation system, staff training, and equipment acquisition. A second FMECA should be carried out to reassess the medication-use process after implementation of these improvement actions. The second FMECA, allowing detection of residual risks and identification of new risks, will be part of a continuous improvement process.

Two-class structure of Forbes Global 2000 firms sales per employee similar to countries income distributions including some COVID-19 pandemic effects

Sales per capita (per number of employees) of the two thousand Forbes Magazine top publicly-traded companies (G-2000) for years 2015, 2020 and 2021 are statistically analyzed. Employing an econophysical model, the sales distributions per capita for the three years exhibit a two-class structure: a Pareto power law in the higher part and exponential in the lower part resembling a Boltzmann-Gibbs distribution. This distribution is consistent with income distributions around the world as if a fraction of a firms' wealth goes to its employees in the form of wages and salaries. We highlight some changes in sales between 2020 and 2021 on selected industries that had the biggest negative and positive impacts partially due to the COVID-19 pandemic.