In situations where the cost/benefit analysis of using physics-based damage propagation algorithms is not favorable and when sufficient test data are available that map out the damage space, one can employ data-driven approaches. In this investigation, we evaluate different algorithms for their suitability in those circumstances. We are interested in assessing the trade-off that arises from the ability to support uncertainty management, and the accuracy of the predictions. We compare here a Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), and a Neural Network-based approach and employ them on relatively sparse training sets with very high noise content. Results show that while all methods can provide remaining life estimates although different damage estimates of the data (diagnostic output) changes the outcome considerably. In addition, we found that there is a need for performance metrics that provide a comprehensive and objective assessment of prognostics algorithm performance.

Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

This data package includes information about List and Rating of Home Health Care Agencies, Health Care for Patient Survey data and State data of several home health agency quality measures as well as State Averages for Home Health Agency (HHA) Quality Measures. It also provides datasets over Hospice General Information, Provider data and CASPER or ASPEN Information about hospice agencies.

In our everyday lives, we are required to make decisions based upon our statistical intuitions. Often, these involve the comparison of two groups, such as luxury versus family cars and their suitability. Research has shown that the mean difference affects judgements where two sets of data are compared, but the variability of the data has only a minor influence, if any at all. However, prior research has tended to present raw data as simple lists of values. Here, we investigated whether displaying data visually, in the form of parallel dot plots, would lead viewers to incorporate variability information. In Experiment 1, we asked a large sample of people to compare two fictional groups (children who drank ‘Brain Juice’ versus water) in a one-shot design, where only a single comparison was made. Our results confirmed that only the mean difference between the groups predicted subsequent judgements of how much they differed, in line with previous work using lists of numbers. In Experiment 2, we asked each participant to make multiple comparisons, with both the mean difference and the pooled standard deviation varying across data sets they were shown. Here, we found that both sources of information were correctly incorporated when making responses. Taken together, we suggest that increasing the salience of variability information, through manipulating this factor across items seen, encourages viewers to consider this in their judgements. Such findings may have useful applications for best practices when teaching difficult concepts like sampling variation.

Database Comparison Software Market Outlook

The global database comparison software market size was valued at approximately USD 850 million in 2023 and is projected to reach around USD 1,750 million by 2032, growing at a CAGR of 8.5% during the forecast period. The market's growth is driven by the increasing need for efficiency and accuracy in database management across various industries. As businesses increasingly rely on data-driven decision-making, the demand for tools that can compare, synchronize, and manage databases effectively is surging, which in turn is propelling the market growth.

One of the primary growth factors for the database comparison software market is the rising adoption of cloud-based solutions. Cloud computing offers numerous advantages, such as scalability, cost-efficiency, and accessibility, which are crucial for modern enterprises. As more organizations migrate their operations to the cloud, the need for robust database comparison tools that can ensure data integrity and consistency across different environments becomes paramount. Additionally, the integration of advanced technologies like AI and machine learning into these tools is enhancing their capabilities, further driving market expansion.

Another significant factor contributing to the market's growth is the increasing complexity of database systems. With the proliferation of big data, IoT, and advanced analytics, databases have become more intricate and voluminous. This complexity necessitates the use of specialized software that can efficiently compare and synchronize vast amounts of data. The growing emphasis on data accuracy and compliance with regulatory standards also underscores the need for reliable database comparison tools, as they help organizations maintain data integrity and avoid costly errors.

The expanding use of database comparison software in various industry verticals, such as BFSI, healthcare, retail, and IT and telecommunications, is also a key driver of market growth. These industries generate and handle massive volumes of critical data, making efficient database management essential. For example, in the healthcare sector, maintaining accurate patient records across different systems is vital for delivering quality care. Similarly, in the BFSI sector, ensuring data consistency is crucial for regulatory compliance and risk management. As these industries continue to evolve and generate more data, the demand for advanced database comparison solutions will likely increase.

In the realm of digital commerce and enterprise operations, Price Comparison Software has emerged as a pivotal tool for businesses aiming to maintain competitive pricing strategies. This software enables organizations to monitor competitor pricing, analyze market trends, and adjust their pricing models accordingly. By leveraging such tools, businesses can ensure they offer competitive prices, attract more customers, and ultimately increase their market share. The integration of Price Comparison Software with database systems allows for seamless data analysis and reporting, providing businesses with actionable insights to optimize their pricing strategies. As the market becomes increasingly competitive, the adoption of such software is becoming essential for businesses looking to thrive in the digital age.

The regional outlook for the database comparison software market indicates robust growth across several key regions. North America currently holds the largest market share, driven by the high adoption of advanced technologies and the presence of major market players. Europe is also witnessing significant growth, fueled by stringent data protection regulations and the increasing digitalization of businesses. The Asia Pacific region is expected to register the highest CAGR during the forecast period, owing to rapid economic development, increased IT spending, and the growing adoption of cloud technologies in countries like China, India, and Japan. Latin America and the Middle East & Africa are also experiencing steady growth, supported by improving IT infrastructure and rising awareness about the benefits of database comparison tools.

Component Analysis

The database comparison software market is segmented by component into software and services. The software segment encompasses standalone comparison tools, integrated solutions, and platforms that offer various functionalities such as schema comparison, data comparison, and synchronization. Standalone tools ar

Physician Compare, which meets Affordable Care Act of 2010 requirements, helps you search for and select physicians and other healthcare professionals enrolled in the Medicare program.

This online application gives manufacturers the ability to compare Iowa to other states on a number of different topics including: business climate, education, operating costs, quality of life and workforce.

Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a new algorithm that enables us to compare the spectral density of a large number of time series. The new algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series.

This excel file will do a statistical tests of whether two ROC curves are different from each other based on the Area Under the Curve. You'll need the coefficient from the presented table in the following article to enter the correct AUC value for the comparison: Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839-843.

The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.

This empirical research examines the normalized difference vegetation index (NDVI) and the percent impervious surface area (%ISA) as indicators of urban heat island (UHI) effects, using the relationships between land surface temperature (LST), %ISA, and NDVI. Landsat 8 Operational Land Imager and Thermal Infrared Sensor data were used to estimate the LST in Connecticut at different times. A map of the percent impervious surface was generated using the Impervious Surface Analysis Tool developed by the Center for Land Use Education and Research and distributed through the National Oceanic and Atmospheric Administration. Strong linear relationships between LST and %ISA exist, as stated in previous studies, whereas the relationship between LST and NDVI is evidently affected by the seasons. As the patterns of LST in urban areas are influenced by the UHI, the results demonstrate that %ISA is a more reliable indicator of UHI effects than NDVI. Thus, %ISA could be a promising alternative for use in the quantitative analysis of LST when studying UHI effects.

The data-comparison extension for CKAN provides a means to compare data from CSV or XLSX files through visualization. Targeted at CKAN 2.9, this plugin enhances data analysis capabilities by allowing users to visually compare datasets directly within the CKAN environment. This facilitates a more intuitive understanding of data variations and trends. Key Features: CSV/XLSX File Comparison: Allows direct comparison of data contained in CSV and XLSX file formats. Visualization: Leverages visualization tools to present the data comparison results. Chart.js Integration: Employs Chart.js library for creating interactive and customizable charts used in the visualization process. Technical Integration: The data-comparison extension integrates with CKAN by adding a plugin that needs to be activated via the ckan.plugins setting in the CKAN configuration file (/etc/ckan/default/ckan.ini). It also requires the installation of Chart.js using npm to render the visualizations. After installing the extension and modifying the CKAN configurations file, CKAN needs to be restarted for the changes to take effect. Benefits & Impact: Implementing the data-comparison extension offers several benefits, especially for data-driven organizations. By providing visualization-based comparison of datasets, it enables quicker insights and potentially more informed decision-making.

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These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare and Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare and Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

Clinical trials to compare two treatments

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions. Methods Distance measure test results were produced using R and can be replicated using scripts available on GitHub at https://github.com/shawndove/Trend_compare. Detailed information on wading bird trends can be found in Jellesmark et al. (2021) below. Jellesmark, S., Ausden, M., Blackburn, T. M., Gregory, R. D., Hoffmann, M., Massimino, D., McRae, L., & Visconti, P. (2021). A counterfactual approach to measure the impact of wet grassland conservation on U.K. breeding bird populations. Conservation Biology, 35(5), 1575–1585. https://doi.org/10.1111/cobi.13692

The Veterans Health Administration (VHA) has now collaborated with the Centers for Medicare & Medicaid Services (CMS) to present information to consumers about the quality and safety of health care in VHA. VHA has approximately 50 percent of Veterans enrolled in the healthcare system who are eligible for Medicare and, therefore, have some choice in how and where they receive inpatient services. VHA has adopted healthcare transparency as a strategy to enhance public trust and to help Veterans make informed choices about their health care.VHA currently reports the following types of quality measures on Hospital Compare:Timely and effective care.Behavioral health.Readmissions and deaths.Patient safety.*Experience of care.

Compare Analysis is a configurable app template with the ability to display and compare up to four web maps at a time. This app relates web map content side by side for visual analysis. The first web map chosen in the app controls the extent of the succeeding web maps. Use CasesCompare Analysis provides the ability to do side-by-side comparison of several maps.Use this app to present the results from a variety of different analytic methods.Show the difference between household income in multiple places, or the difference between household income and home values in a single location.Configurable OptionsCompare Analysis can be used to present content from a web maps and configured using the following options:Select up to four maps to be presented within the app.Enable a side panel that can contain custom text and a custom title. If included it can be opened or closed on load.Configure place searching and limit search to the current map extent.Select text color and side panel background color.Include a home extent button for returning to the original web map’s default extent.Supported DevicesThis application is responsively designed to support use in browsers on desktops and tablets.Data RequirementsThis application has no data requirements.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to create a web appOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The Data Comparision extension for CKAN allows users to compare data from CSV and XLSX files through visualizations. This extension aims to enhance data analysis capabilities within CKAN by providing a direct visual comparison of data sets, facilitating a better understanding of the content. The extension is compatible with CKAN 2.9, providing an extended feature set for data comparison. Key Features: CSV/XLSX Data Support: Enables the comparison of data stored in common tabular formats such as CSV and XLSX files by leveraging the extension's visualization capabilities. Visual Data Comparison: Supports visualizing the data for side-by-side comparisons, allowing users to easily identify differences and similarities between datasets. Chart.js Integration: Relies on Chart.js library for generating data visualizations, specifically for the comparison feature. This ensures compatibility and a wide selection of chart formats. Technical Integration: The extension needs to be added to the ckan.plugins setting in the CKAN configuration file (/etc/ckan/default/ckan.ini by default). It also requires installing Chart.js via npm. After these configurations and a CKAN restart, the plugin extends CKAN's user interface with data comparison features. Benefits & Impact: By facilitating a comparative view of data stored in CSV and XLSX formats, the Data Comparison extension reduces the effort needed to analyze datasets. This enables better decision-making based on clear, visually represented data comparisons. Because the data is visualized, differences and similarities are noted quicker than non-visualized data.

Social Comparison Social Comparison Theory (Festinger, 1954) is centered around the idea that individuals seek self-evaluations. The theory suggests a possible way in which these self-evaluations occur: individuals compare themselves to others to reduce uncertainty and discover the definition of the self. Self-evaluation has been proposed as one of the functions of social comparison (Thorton & Arrowood, 1966), serving as a self-enhancement process with the help of downward and upward comparisons (Wills, 1981). Downward social comparison refers to a defensive tendency of self-evaluation, and these comparisons can elevate self-regard (Gibbons, 1986). However, the intention or impact of upward social comparison is less clear. On the one hand, it has been suggested that comparing oneself to better individuals could lower self-regard (Tesser et al., 1988), but on the other hand, upward comparisons might result in self-improvement (Collins, 1995).

Self-Perceived Attractiveness Individuals’ beliefs about the quality of their physical appearance are referred to as self-perceived attractiveness (Belmi & Neale, 2014). Previous research has found a relationship between social comparison and self-perception, wherein women exposed to less attractive same-sex participants described themselves as being more attractive (Castro et al., 2014). Likewise, it has been shown that in females, exposure to attractive others decreased the ratings of both face and body attractiveness, yet exposure to unattractive same-sex individuals increased the ratings of self-perceived attractiveness (Little & Mannion, 2005). Similarly, when rating their own desirability as a marriage partner, women‘s self-ratings were significantly lower after exposure to physically attractive women, but unaffected by exposure to socially dominant ones. On the contrary, it has been demonstrated that men‘s self-evaluations were significantly lower after exposure to socially dominant men (Gutierres et al., 2012).

The Ideal Standards of Mate Preference The mechanism underlying these findings is claimed to be the perception of the available population of desirable members of one‘s own sex with whom one must compete. In other words, exposure to highly dominant or highly attractive same-sex individuals could draw attention to the distribution of physically attractive or dominant individuals that are available to the members of the opposite sex, which brings us to the ideals of mate preference (Gutierres et al., 2012).

The ideal standards of mate preference could also be named as demands or minimum acceptable criteria (Kenrick, Groth, Trost, & Sadalla, 1993; Kenrick et al., 1990; Regan, 1998). It has been shown that exposure to images of highly physically attractive women or dominant males might influence individuals' perceptions of their own mate value, and therefore, the awareness of an abundance of highly attractive women or, in the case of males, the abundance of highly dominant men could negatively impact their perceived mate value (Gutierres et al., 2012).

Likes-Attract and Potentials-Attract The exploration of mate preferences introduces two hypotheses: The "Likes-Attract" hypothesis and the „Potentials-Attract“ hypothesis (Buston & Emlen, 2003). The "Likes-Attract" hypothesis posits that individuals are drawn to those who share similar qualities, characteristics, or attitudes. This theory asserts that people naturally gravitate toward partners with whom they share common ground, fostering a sense of similarity and compatibility. In the context of self-perceived attractiveness, the "Likes-Attract" hypothesis suggests that individuals prioritize qualities in potential mates that align with their own self-perception. For instance, an individual who rates themselves highly in terms of physical attractiveness may accord greater importance to the physical appearance of a potential partner. Conversely, the „Potentials-Attract“ Hypothesis postulates that people are drawn to potential partners based on specific attributes they consider advantageous or enriching to their own lives. When applied to self-perceived attractiveness, individuals who regard themselves as highly attractive prioritize qualities such as wealth, status, and family commitment in prospective long-term mates.

A study on mate preferences in Western society provides strong support for the "likes-attract" hypothesis (Buston & Emlen, 2003). The findings suggest that individuals tend to seek long-term partners who share similar traits to their own self-perception across various evolutionarily relevant categories. These findings resonate with earlier research that observed women with high self-perceived physical attractiveness modifying images of male faces to align with their ideals of masculinity and symmetry (Little et al. 2000). What sets Buston and Emlen's study apart is its explicit support for the likes-attract hypothesis. While previous research demonstrated conditionality in mate choice, this study underscores the active preference for partners who resemble one's self-perceived attributes.

Social Comparison and Self-Perceived Attractiveness & Its Impact on Ideals of Mate Preference Our aim is to investigate the impact of social comparison on self-perceived attractiveness and the relationship between this well-founded effect and mate-selection criteria of female participants. Social comparisons, whether in real life or on social media, automatically occur in society. It is crucial to understand how these upward or downward comparisons impact the standards and preferences of an individual in choosing a partner. Likewise, we will investigate if women with altered high self-perceived attractiveness will place great importance on wealth and status and family commitment in a male, or if the high ratings of self-perceived attractiveness are positively related to their physically attractive mate demands. These research questions should provide us with data supporting the potentials-attract and likes-attract hypotheses in terms of physical attractiveness only, which will provide us a deeper understanding of mate selection regarding physical appearance.