Sampling and statistics in assessment of fresh produce

New measurement technologies are facilitating new approaches to the improvement of safety and quality in agri-food supply chains. However, measurement uncertainty and choice of sampling strategy can influence the outcomes of assessment programmes. This chapter provides a sampler of calculations of population statistics, required sample sizes and approaches to developing and implementing a sampling strategy. While examples from the fresh produce sector are given, the sampler is of relevance to both the student and the practitioner operating in agri-supply chains more generally.

Population Statistics for Particle Swarm Optimization on Problems Subject to Noise

<p>Particle Swarm Optimization (PSO) is a metaheuristic where a swarm of particles explores the search space of an optimization problem to find good solutions. However, if the problem is subject to noise, the quality of the resulting solutions significantly deteriorates. The literature has attributed such a deterioration to particles suffering from inaccurate memories and from the incorrect selection of their neighborhood best solutions. For both cases, the incorporation of noise mitigation mechanisms has improved the quality of the results, but the analyses beyond such improvements often fall short of empirical evidence supporting their claims in terms other than the quality of the results. Furthermore, there is not even evidence showing the extent to which inaccurate memories and incorrect selection affect the particles in the swarm. Therefore, the performance of PSO on noisy optimization problems remains largely unexplored. The overall goal of this thesis is to study the effect of noise on PSO beyond the known deterioration of its results in order to develop more efficient noise mitigation mechanisms. Based on the allocation of function evaluations by the noise mitigation mechanisms, we distinguish three groups of PSO algorithms as: single-evaluation, which sacrifice the accuracy of the objective values over performing more iterations; resampling-based, which sacrifice performing more iterations over better estimating the objective values; and hybrids, which merge methods from the previous two. With an empirical approach, we study and analyze the performance of existing and new PSO algorithms from each group on 20 large-scale benchmark functions subject to different levels of multiplicative Gaussian noise. Throughout the search process, we compute a set of 16 population statistics that measure different characteristics of the swarms and provide useful information that we utilize to design better PSO algorithms. Our study identifies and defines deception, blindness and disorientation as three conditions from which particles suffer in noisy optimization problems. The population statistics for different PSO algorithms reveal that particles often suffer from large proportions of deception, blindness and disorientation, and show that reducing these three conditions would lead to better results. The sensitivity of PSO to noisy optimization problems is confirmed and highlights the importance of noise mitigation mechanisms. The population statistics for single-evaluation PSO algorithms show that the commonly used evaporation mechanism produces too much disorientation, leading to divergent behaviour and to the worst results within the group. Two better algorithms are designed, the first utilizes probabilistic updates to reduce disorientation, and the second computes a centroid solution as the neighborhood best solution to reduce deception. The population statistics for resampling-based PSO algorithms show that basic resampling still leads to large proportions of deception and blindness, and its results are the worst within the group. Two better algorithms are designed to reduce deception and blindness. The first provides better estimates of the personal best solutions, and the second provides even better estimates of a few solutions from which the neighborhood best solutions are selected. However, an existing PSO algorithm is the best within the group as it strives to asymptotically minimize deception by sequentially reducing both blindness and disorientation. The population statistics for hybrid PSO algorithms show that they provide the best results thanks to a combined reduction of deception, blindness and disorientation. Amongst the hybrids, we find a promising algorithm whose simplicity, flexibility and quality of results questions the importance of overly complex methods designed to minimize deception. Overall, our research presents a thorough study to design, evaluate and tune PSO algorithms to address optimization problems subject to noise.</p>

Population Statistics for Particle Swarm Optimization on Problems Subject to Noise

<p>Particle Swarm Optimization (PSO) is a metaheuristic where a swarm of particles explores the search space of an optimization problem to find good solutions. However, if the problem is subject to noise, the quality of the resulting solutions significantly deteriorates. The literature has attributed such a deterioration to particles suffering from inaccurate memories and from the incorrect selection of their neighborhood best solutions. For both cases, the incorporation of noise mitigation mechanisms has improved the quality of the results, but the analyses beyond such improvements often fall short of empirical evidence supporting their claims in terms other than the quality of the results. Furthermore, there is not even evidence showing the extent to which inaccurate memories and incorrect selection affect the particles in the swarm. Therefore, the performance of PSO on noisy optimization problems remains largely unexplored. The overall goal of this thesis is to study the effect of noise on PSO beyond the known deterioration of its results in order to develop more efficient noise mitigation mechanisms. Based on the allocation of function evaluations by the noise mitigation mechanisms, we distinguish three groups of PSO algorithms as: single-evaluation, which sacrifice the accuracy of the objective values over performing more iterations; resampling-based, which sacrifice performing more iterations over better estimating the objective values; and hybrids, which merge methods from the previous two. With an empirical approach, we study and analyze the performance of existing and new PSO algorithms from each group on 20 large-scale benchmark functions subject to different levels of multiplicative Gaussian noise. Throughout the search process, we compute a set of 16 population statistics that measure different characteristics of the swarms and provide useful information that we utilize to design better PSO algorithms. Our study identifies and defines deception, blindness and disorientation as three conditions from which particles suffer in noisy optimization problems. The population statistics for different PSO algorithms reveal that particles often suffer from large proportions of deception, blindness and disorientation, and show that reducing these three conditions would lead to better results. The sensitivity of PSO to noisy optimization problems is confirmed and highlights the importance of noise mitigation mechanisms. The population statistics for single-evaluation PSO algorithms show that the commonly used evaporation mechanism produces too much disorientation, leading to divergent behaviour and to the worst results within the group. Two better algorithms are designed, the first utilizes probabilistic updates to reduce disorientation, and the second computes a centroid solution as the neighborhood best solution to reduce deception. The population statistics for resampling-based PSO algorithms show that basic resampling still leads to large proportions of deception and blindness, and its results are the worst within the group. Two better algorithms are designed to reduce deception and blindness. The first provides better estimates of the personal best solutions, and the second provides even better estimates of a few solutions from which the neighborhood best solutions are selected. However, an existing PSO algorithm is the best within the group as it strives to asymptotically minimize deception by sequentially reducing both blindness and disorientation. The population statistics for hybrid PSO algorithms show that they provide the best results thanks to a combined reduction of deception, blindness and disorientation. Amongst the hybrids, we find a promising algorithm whose simplicity, flexibility and quality of results questions the importance of overly complex methods designed to minimize deception. Overall, our research presents a thorough study to design, evaluate and tune PSO algorithms to address optimization problems subject to noise.</p>

Effect of altitude on COVID-19 mortality in Ecuador: an ecological study

Background The SARS-CoV-2/COVID-19 pandemic has claimed nearly 900,000 lives worldwide and infected more than 27 million people. Researchers worldwide are studying ways to decrease SARS-CoV-2 transmission and COVID-19 related deaths. Several studies found altitude having a negative association with both COVID-19 incidence and deaths. Ecuadorian data was used to explore the relationship between altitude and COVID-19. Methods This is an ecological study examining province-level data. To explore a relationship between altitude and COVID-19, this study utilized publicly available COVID-19 data and population statistics. ANOVA, correlation statistics, and a multivariate linear model explored the relationship between different Ecuadorian altitudes against incidence, mortality, and case-fatality rates. Population statistics attributed to COVID-19 were included in the linear model to control for confounding factors. Results Statistically significant differences were observed in the regions of Amazónica, Sierra, Costa of Ecuador for incidence, mortality, and case fatality rates, suggesting an association between altitude and SARS-CoV-2 transmission and COVID-19 disease severity (p-value ≤0.05). In univariate analysis, altitude had a negative association to mortality rate with a 1-unit change in altitude resulting in the decrease of 0.006 units in mortality rate (p-value = 0.03). The multiple linear models adjusted for population statistics showed a statistically significant negative association of altitude with mortality rate (p-value = 0.01) with a 1-unit change in altitude resulting in the decrease in mortality rate by 0.015 units. Overall, the model helped in explaining 50% (R2 = 0.4962) of the variance in mortality rate. Conclusion Altitude may have an effect on COVID-19 mortality rates. However, based on our model and R2 value, the relationship between our variables of interest and COVID-19 mortality may be nonlinear. More research is needed to understand why altitude may have a protective effect against COVID-19 mortality and how this may be applicable in a clinical setting.

Estimation of Life Expectancy for Dependent Population in a Multi-State Context

Population statistics show that there was an increase in life expectancy during the last century. However, this fact hides that this increase was not equal for all groups of the population. One of the most problematic cases for measuring this increase is that of the dependent population because of the absence of specific statistics. This paper describes a methodology for calculating life expectancy using multistate models that take into account the diversity of situations considered by Spanish legislation. For this purpose, statistical information contained in the national survey on disability and dependency (EDAD 2008) is used. The results suggest that life expectancies are lower than those of the general population and that they differ according to gender and intensity of suffering from this contingency. The calculations were made considering the legal framework currently existing in Spain. This fact conditions the definition of dependent person and, therefore, the set of individuals, their characteristics, and therefore, their final results.

Defining the Race and Ethnic Standards for Federal Statistics and Administrative Reporting

Race describes cultural, historical, and oppressive relationships in society. The use of race in biomedical and scientific studies has been a powerful tool that can reinforce and alter society’s current assumptions about race. Some of the historical uses of race include evidence for race-based medicine, biological inferiority, and genocide. These uses have all used race as a crude proxy for genetic makeup, rather than a biological expression of the social environment that infiltrates the health and well-being of every American. By defining race and its social and cultural impacts on identity and the human experience within research, the field of biomedicine will improve clarity and integrity in addressing historical, scientific, and clinical inequalities. Currently, the Office of Management and Budget (OMB) does not contain a definition of race and uses homogeneous ethnical categories when reporting population statistics. We propose that the definition of race be added in the collection of race data as a requirement of the OMB for nationally conducted research.

Who do phone surveys miss, and how to reduce exclusion: recommendations from phone surveys in nine Indian states

Computer-assisted telephone interviews (CATI) through mobile phones are a low-cost, rapid and safe way to collect data. However, decisions for how such mobile phone surveys are designed and implemented, and their data analysed, can have implications for the sample reached, and in turn affect the generalisability of sample estimates. In this practice paper, we propose a framework for extending the use of CATI–mobile phone surveys in India, which can be applied broadly to future surveys conducted using this method. Across the stages of design, implementation and analysis, we outline challenges in ensuring that the data collected through such surveys are representative and provide recommendations for reducing non-coverage and non-response errors, thereby enabling practitioners in India to use CATI–mobile phone surveys to estimate population statistics with lower bias. We support our analysis by drawing on primary data that we collected in five mobile phone surveys across nine Indian states in 2020. Our recommendations can help practitioners in India improve the representativeness of data collected through mobile phone surveys and generate more accurate estimates.

A Porcine Congenital Single-Sided Deafness Model, Its Population Statistics and Degenerative Changes

ObjectiveTo describe and study the population statistics, hearing phenotype, and pathological changes of a porcine congenital single-sided deafness (CSSD) pedigree.MethodsClick auditory brainstem response (ABR), full-frequency ABR, and distortion product otoacoustic emission (DPOAE) were used to assess the hearing phenotype of the strain. Tympanogram was used to assess the middle ear function since birth. Celloidin embedding–hematoxylin–eosin (CE-HE) stain and scanning electron microscopy (SEM) were used to study the pathological changes of cochlear microstructures. Chi-square analysis was used to analyze the relation between hearing loss and other phenotypes.ResultsThe mating mood of CSSD with CSSD was most efficient in breeding-targeted CSSD phenotype (47.62%), and the prevalence of CSSD reached 46.67% till the fifth generation, where 42.22% were bilateral hearing loss (BHL) and 9.00% were normal hearing (NH) individuals. Hearing loss was proved to have no relation with coat color (P = 0.0841 &gt; 0.05) and gender (P = 0.4621 &gt; 0.05) by chi-square analysis. The deaf side of CSSD offspring in the fifth generation had no relation with that of their maternal parent (P = 0.2387 &gt; 0.05). All individuals in this strain exhibited congenital severe to profound sensorineural hearing loss with no malformation and dysfunction of the middle ear. The good hearing ear of CSSD stayed stable over age. The deaf side of CSSD and BHL presented cochlear and saccular degeneration, and the hair cell exhibited malformation since birth and degenerated from the apex to base turn through time. The pathology in BHL cochlea progressed more rapidly than CSSD and till P30, the hair cell had been totally gone. The stria vascularis (SV) was normal since birth and degenerated through time and finally exhibited disorganization of three layers of cells.ConclusionThis inbred porcine strain exhibited high and stable prevalence of CSSD, which highly resembled human non-syndromic CSSD disease. This porcine model could be used to further explore the etiology of CSSD and serve as an ideal tool for the studies of the effects of single-sided hearing deprivation on neural, cognitive, and behavioral developments and the benefits brought by CI in CSSD individuals.