Supplier Evaluation and Selection System of Embedded E-Commerce Platform Based on Big Data

For e-commerce companies, it is easier to obtain a large amount of aggregated data about user behavior with the help of embedded network platforms, which contains valuable information that helps to form effective decision-making. This article first gives a detailed introduction to the evaluation and selection of e-commerce and suppliers; then puts forward the analytic hierarchy process and entropy method; finally, the AHP analytic method is used to build a supplier evaluation system and a selection system. The experimental results of this paper show that after obtaining the entropy AHP weights through the analytic hierarchy process, these 8 suppliers can be ranked and selected. Using the ABC classification method, classification is based on the ranking of suppliers. Among them, Class A suppliers account for 12.5%, which plays a key role in the construction of the evaluation and selection system of e-commerce suppliers.

Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas.

Incidence of Acute Respiratory Infections during Disasters in the Absence and Presence of COVID-19 Pandemic

Introduction: Japan recently experienced two major heavy rain disasters: the West Japan heavy rain disaster in July 2018 and the Kumamoto heavy rain disaster in July 2020. Between the occurrences of these two disasters, Japan began experiencing the wave of the coronavirus disease 2019 (COVID-19) pandemic, providing a unique opportunity to compare the incidence of acute respiratory infection (ARI) between the two disaster responses under distinct conditions. Sources for Information: The data were collected by using the standard disaster medical reporting system used in Japan, so-called the Japan-Surveillance in Post-Extreme Emergencies and Disasters (J-SPEED), which reports number and types of patients treated by Emergency Medical Teams (EMTs). Data for ARI were extracted from daily aggregated data on the J-SPEED form and the frequency of ARI in two disasters was compared. Observation: Acute respiratory infection in the West Japan heavy rain that occurred in the absence of COVID-19 and in the Kumamoto heavy rain that occurred in the presence of COVID-19 were responsible for 5.4% and 1.2% of the total consultation, respectively (P <.001). Analysis of Observation and Conclusion: Between the occurrence of these two disasters, Japan implemented COVID-19 preventive measures on a personal and organizational level, such as wearing masks, disinfecting hands, maintaining social distance, improving room ventilation, and screening people who entered evacuation centers by using hygiene management checklists. By following the basic prevention measures stated above, ARI can be significantly reduced during a disaster.

Aggregate-based Training Phase for ML-based Cardinality Estimation

Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 90 with our aggregate-based training phase and thus outperform indexes.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Objective Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but a manual analysis workflow hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. Results We introduce a package ( dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.

LIVESTOCK IN KAZAKHSTAN: DEVELOPMENT PROBLEMS

The research was carried out in order to highlight the main problems that impede the increase in the competitiveness of Kazakhstani animal husbandry. The indicators of productivity (milk yield, shearing of wool from one sheep, etc.), as well as aggregated data (production volumes, indices of the physical volume of gross production) were used as criteria for assessing the development of the industry. In Kazakhstan, the share of beef pedigree cattle in 2019 accounted for only 11.5% of the total cattle population. The average live weight of cattle was 336 kg, the average slaughter weight was 175 kg, which is 2 times lower than world standards, the average live weight of 1 bird was 2.2 kg. The republic has a weak base for the production of basic feed for the fattening contingent, due to which its supply with such feed is at the level of 57.8% of the scientifically grounded norm. The share of breeding stock of dairy cattle (as of January 1, 2018) is 2.8%, birds of all types - 12.3% of the total livestock, sheep - 14.8%. In modern conditions, in order to increase competitiveness, it is necessary to focus efforts on solving such problems as providing highly productive breeding cattle and poultry; improving the fodder base by expanding the crops of corn, soybeans, alfalfa, chickpea; strengthening of preventive work against especially dangerous animal diseases; adaptation of scientific developments in the field of genetics, selection and fodder production to the current economic conditions in animal husbandry; accelerating the transfer of animal husbandry to new technologies; implementation of international standards for product quality and management

Uptake of Screening Mammogram in West-central Illinois During COVID-19 Pandemic: Lessons Learned

Objectives. The study aims to reveal the trend of mammogram uptake in seventeen rural counties in Illinois to understand how the COVID-19 pandemic is influencing breast cancer screening in the area.Methods. Aggregated data on mammography screening for west central Illinois was provided by the Illinois Hospital Association. Data for 2018 and 2019 was used to determine the typical monthly and annual screenings for the two years before the onset of COVID-19. Then, the two years' data was compared to the 2020 data. The monthly mean values for the aggregated 2018 and 2019 data were generated as the base "year" to compare with the monthly value for 2020. Paired T-Test analysis was used to find if there were any statistically significant differences the years and between the base year and 2020.Results. January 2020 revealed an uptick to 2,921, which is more than the uptake for January 2018 (2700) and January 2019 (2488), and 13% greater than the mean value of 2,594 for the previous two years. This was followed by a gradual decrease in uptake in February 2020 by 4% compared to previous years at a mean of 2518 and a further decline in March (44%), with a drastic fall (98%) by April 2020 at 56 screening mammograms in all 17 counties. The lowest uptake in any three months occurred from March through May 2020. Compared to previous years, increase in uptake was noted across the region in 2020 June (8%) and July (4%) after the pandemic restrictions were relaxed. Overall, the total uptake in 2020 was 15% less than the average annual uptake for 2018-2019 with a deficit of 5,537. There was no statistically significant difference in mammogram uptake across the three years.Conclusion. The findings reveal that there was a significant reduction in uptake during the pandemic restriction period. However, increased uptake during the rest of the year effectively mitigated this reduction to such an extent that there was no statistically significant downturn compared to each of the previous two years. A rising trend in total annual uptake noted in preceding years could have continued without the COVID-19 event.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but a manual analysis workflow hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data. A tutorial in bookdown format with code, diagnostics, plots and synthetic data is available here: https://neelsoumya.github.io/dsSurvivalbookdown/ All code is available from the following repositories: https://github.com/neelsoumya/dsSurvivalClient/ https://github.com/neelsoumya/dsSurvival/

An Online Tool for Estimating Return-on-investment for Water Recycling at Nurseries

Water availability, quality, and management, particularly under climate change constraints and fierce competition for water resources, are challenging the sustainability of intensively irrigated nursery crops. We created an online tool to estimate costs and benefits of a water recycling investment at a commercial nursery, given data on the operation input by the user. The online tool returns a “regulatory risk score” based on the user’s drought and pollution risk. Then, using a partial budget approach, it returns net present value of the investment, upfront capital cost, and expected change in annual cash flow. The present article seeks to cross-validate this computer model with results reported in the case study literature. We aggregated data on 38 nurseries and greenhouses profiled in five published studies into a meta study dataset. These data validated the computer tool’s assumptions about the relationship of operation size to total capital cost. Separate simulations on the profitability effects of varying public water rates and price premia due to green marketing corroborated the findings of earlier studies. A major finding of the simulation analysis not previously emphasized in the literature is that capital cost and profit vary significantly with the precise method that is used to size the recapture pond. A “minimalist” approach to this decision is likely to be the most cost-effective, but growers should also keep stormwater runoff and other issues of environmental best practices in mind.

Assymmetric effect exchange rate to Indonesian agriculture subsector

This study explores the asymmetric effect on the rupiah exchange rate on every subsector agriculture export in Indonesia during 2006-2020. The non-linear ARDL method is used in this study to analyze the asymmetric relationship between exchange rate and export. NARDL method includes short-run and long-run coefficient estimates and embraces the asymmetric effect. The previous studies generally used the linear models on the aggregated data and ignored the differences in each export of the agricultural sub-sector, then they offered ambiguous results. The latest studies have preferred to use the method of NARDL on the agricultural sector in general data. Instead of using agricultural export data for each subsector, this paper considers subsector export data of agriculture. The estimated NARDL results indicate an asymmetric effect of the rupiah exchange rate on exports of the agricultural sub-sector in the long run. In general, there is no asymmetric effect in the short run. Generally, depreciation and appreciation of the Rupiah have a negative effect on exports of the agricultural sub-sector in the long run. However, rupiah appreciation positively impacts lag 2, and depreciation caused a different effect on each sub-sector. The NARDL results suggest that positive movements have lesser impacts than those of negative movements in the exchange rate on the agriculture sector both in the short and long run