Aggregate Data Research Articles

Alternative statistical interpretation for the apparent plateaus in the duration distributions of gamma-ray bursts

The existence of a plateau in the short-duration tail of the observed distribution of cosmological long-soft gamma ray bursts (LGRBs) is posited to be the first direct evidence of collapsars. A similar plateau in the short-duration tail of the observed duration distribution of short-hard gamma ray bursts (SGRBs) has been suggested as evidence of compact binary mergers. We present an equally plausible alternative interpretation for this evidence, based on a purely statistical approach. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs may likewise result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of GRBs through extensive numerical Monte Carlo simulations. This analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs, without invoking the physics of collapsars or jet-propagation through the stellar envelope.

Data Aggregation by Web Scraping using Python

The standard data examination are based on the root and effect relationship, formed a model tiny assessment, abstract and quantitative assessment, the level headedness approach of making extrapolation assessment. The Web Scraper's scheming morals and methodology are compared, it clarifies about the working of how the scrubber is planned. The strategy of it is distributed into three pieces: the web scrubber draws the ideal connections from web, and afterward the information is removed to get the information from the source joins lastly stowing that information into a CSV record. The Python language is carried out for the completing. Thusly, connecting every one of these with the ethical information of libraries and working skill, One can have a satisfactory Scraper in our grasp to produce the ideal outcome. Because of a gigantic local area and library assets for Python and the impeccableness of coding stylish of python language, it is generally proper one for Scraping wanted information from the ideal site.

Causal associations between sarcopenia-related traits and intervertebral disc degeneration: a two-sample mendelian randomization analysis.

Sarcopenia (SP) and intervertebral disc degeneration (IVDD) have a higher incidence in the elderly population. Previous studies have indicated a potential association between SP and IVDD. The objective of this study is to elucidate the potential causal relationship between sarcopenia-related traits and IVDD through Two-sample Mendelian randomization (MR) analysis. We utilized a genome-wide association study conducted on the European population to collect aggregated data on sarcopenia and IVDD. Inverse variance weighting was primarily employed, supplemented by MR Egger, weighted median, simple model, and weighted model methods. Additionally, sensitivity analysis was performed to assess the robustness of the findings. Appendicular lean mass is positively associated with "Other intervertebral disc disorders" (OIDD) and "Prolapsed or slipped disc" (POSD) (OIDD: p = 0.002, OR = 1.120; POSD: p < 0.001, OR = 1.003), while grip strength (GS) is positively associated with POSD (left: p = 0.004, OR = 1.008; right: p < 0.001, OR = 1.010). It is worth mentioning that walking pace has significant causal relationship with "Low back pain" (LBP), "Lower back pain or/and sciatica" (LBPOAS), "Sciatica with lumbago" (SWL) and OIDD (LBP: p < 0.001, OR = 0.204; LBPOAS: p < 0.001, OR = 0.278; SWL: p = 0.003, OR = 0.249; OIDD: p < 0.001, OR = 0.256). The present study revealed the causal relationship between SP-related traits and IVDD and recommended to prevent and treat sarcopenia as a means of preventing IVDD in clinic practice.

Unveiling realistic mobility patterns with home–origin–destination data aggregation

The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Data Acquisition, Processing, and Aggregation in a Low-Cost IoT System for Indoor Environmental Quality Monitoring

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references.

An enhanced bio‐inspired energy‐efficient localization routing for mobile wireless sensor network

SummaryEnvironmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS).

Food Insecurity and Outpatient Care Utilization Among Indonesian Older Adults.

This study aims to examine the complex relationship between household food insecurity and outpatient care utilization among Indonesian older adults. Individual-level data from the 2020 National Socioeconomic Survey (SUSENAS; n = 29 950 older adults aged 60 and over in poor self-rated health who are unable to do daily activities) and environmental-level data (n = 84 096 villages in a data aggregation across 514 municipalities) from the 2021 Village Potential Data Census Collection Survey (PODES) are used in a two-level binary logistic regression model. Older adults who live in severely food insecure households (odds ratio [OR] 1.963; CI [1.658, 2.319]) and lack of health insurance (OR 1.654; CI [1.556, 1.747]) are more likely of not utilizing outpatient care. The interclass correlation coefficient value is 0.162, indicating that primary health care services in every village improve outpatient care utilization. Food insecurity is the major determinant of reduced likelihood of outpatient care utilization, which can be explained by household resource constraints.

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

This paper introduces a method that combines the K-means clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system's effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches.

Online Nonparametric Process Monitoring for IoT Systems Using Edge Computing

Facilitated by modern sensing and integrated circuit technology, Internet of Things (IoT) systems now have the ability to intelligently and automatically collect data, perform computational tasks on edge, and efficiently make decisions locally or remotely. However, existing multivariate statistical process control (MSPC) methods commonly assume that sensors directly transmit the raw measurements to the central server, and thus the edge computing capability of the IoT systems is not effectively harnessed. To fill this literature gap, we propose a monitoring scheme tailored for IoT systems by leveraging their edge computational power. In particular, inspired by the inherent hierarchical structure of the IoT system, we construct the monitoring statistics hierarchically. At each edge device, we first construct a nonparametric stream-level statistic for each data stream and aggregate them to derive the device-level statistics. To eliminate the inconsistency in device-level statistics across different edge devices, the device-level statistics are transformed into time-related statistics, which are then sent to the central server. At the central server, a system-level monitoring statistic is constructed, and the most informative edge devices that should transmit statistics are intelligently determined. Theoretical analyses are carried out to justify the effectiveness of the data transmission strategy. Both simulations and a case study, which is based on an IoT system we built, are conducted to evaluate the performance and validate the superiority of the proposed monitoring scheme.

Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

The Prevalence of Anemia Among Vietnamese Children: A Systematic Review and Meta-Analysis.

While anemia constitutes a prevalent health issue among children in Vietnam, comprehensive aggregate data remain scarce. Consequently, the objective of this meta-analysis is to establish the pooled prevalence of anemia in the pediatric population of Vietnam. A comprehensive systematic search of existing prevalence studies was conducted up until June 2022, encompassing databases such as PubMed, Scopus, and the Cochrane Library, as well as reference list analysis. Pooled prevalence estimates, along with 95% confidence intervals (CIs), were calculated using a random-effects model. Publication bias was assessed using the Egger regression test and funnel plot inspection. Subgroup analyses were performed to identify potential variations in anemia prevalence across study groups. The pooled prevalence estimate for anemia among children under 10-year-olds amounted to 35.5% (95% CI = [23.9, 49.1]). Notably, gender-specific analyses unveiled distinct prevalence rates, with 37.8% among males and 33.4% among females. Furthermore, variations in anemia prevalence among Vietnamese children were evident across different demographic strata, encompassing gender, age groups, and rural or poor rural residency. The calculated pooled prevalence of anemia among children in Vietnam revealed a significant burden. Addressing this issue necessitates targeted interventions within specific regions and the implementation of government policies aligned with global targets for the eradication of childhood anemia.

Accounting for sample overlap in economics meta‐analyses: The generalized‐weights method in practice

AbstractMeta‐analyses in economics frequently exhibit considerable overlap among primary samples. If not addressed, sample overlap leads to efficiency losses and inflated rates of false positives at the meta‐analytical level. In previous work, we proposed a generalized‐weights (GW) approach to handle sample overlap. This approach effectively approximates the correlation structure between primary estimates using information on sample sizes and overlap degrees in the primary studies. This paper demonstrates the application of the GW method to economics meta‐analyses, addressing practical challenges that are likely to be encountered. We account for variations in data aggregation levels, estimation methods, and effect size metrics, among other issues. We derive explicit covariance formulas for different scenarios, evaluate the accuracy of the approximations, and employ Monte Carlo simulations to demonstrate how the method enhances efficiency and restores the false positive rate to its nominal level.

Fuzzy inference logic assisted rendezvous points based effective routing strategy in wireless sensor networks

SummaryIn this proposed strategy, an effective routing strategy is introduced for routing based on Rendezvous Points assisted fuzzy inference logics. First, sensor nodes in the cluster are created using the mean‐shift clustering mechanism (MS‐ClusT). A cluster head is selected for the clusters to gather aggregated data from the nodes of clusters. The cluster head selection is done using the Manta Ray optimization‐assisted modified network‐based fuzzy inference logic (MMFiL) approach. Rendezvous points are then selected for routing to minimize the delay and energy consumption for visiting all cluster heads. The rendezvous points are used to define the shortest route for routing. The shortest path from clusters to the base station by selecting rendezvous points is implemented using an integrated neural network with an Archer‐Fish optimization algorithm (InnAFo). In the end, the performance attained by the model is compared with the results of the classical strategy and returns a 0.976563 packet delivery rate and 0.029297 loss rate in 500 nodes. Furthermore, the throughput attained by the proposed model is 0.992874 Mbps, and the delay is 67.30 ms at 500 nodes. The analysis of the proposed model will result in less time needed for cluster head selection.

Novel data visualization and risk analytic algorithm in a low-middle income country.

To discuss the use of T3™, a data aggregation, visualization, and risk analytic platform in a single centre and its framework for implementation of such a tool in clinical care. We share experience of a tool implemented in a tertiary care Intensive Care Unit (ICU) with limited resources. Superusers were identified and trained. Implementation involved monitoring, evaluation, and user engagement data for continuous emphasis on the use of this tool. Persistent display of T3 data enhanced nursing operational efficiency. Its use was expanded to use in nurses rounds and handover, mortality and morbidity meetings, clinical team teaching through selected teaching cases and analysis of stored data with different research questions. However, lack of infrastructure and technological comprehension, paucity of multidisciplinary teams makes it a challenge in its implementation. Clear framework of implantation and pre-designed studies to determine the clinical usage and effectiveness are important for wide-spread use of such tools.

Cancer incidence and digital information seeking in Germany: a retrospective observational study

Awareness is vital for cancer prevention. US studies show a strong link between web searches and cancer incidence. In Europe, the relationship remains unclear. This study characterizes regional and temporal relationships between cancer incidence and web searches and investigates the content of searches related to breast, cervical, colorectal, lung, prostate, and testicular cancer, brain tumors, and melanoma in Germany (July 2018–December 2019). Aggregate data from Google Ads Keyword Planner and national cancer registry data were analyzed. Spearman’s correlation coefficient (rS) examined associations between cancer incidence and web search, repeated measures correlation (rrm) assessed time trends and searches were qualitatively categorized. The frequency of malignancy-related web searches correlated with cancer incidence (rS = 0.88, P = 0.007), e.g., breast cancer had more queries than the lower-incidence cervical cancer. Seasonally, incidence and searches followed similar patterns, peaking in spring and fall, except for melanoma. Correlations between entity incidence and searches (0.037 ≤ rrm ≤ 0.208) varied regionally. Keywords mainly focused on diagnosis, symptoms, and general information, with variations between entities. In Germany, web searches correlated with regional and seasonal incidence, revealing differences between North/East and South/West. These insights may help improve prevention strategies by identifying regional needs and assessing impact of awareness campaigns.

MIDAS and dividend growth predictability: Revisiting the excess volatility puzzle

AbstractWe examine dividend growth predictability and the excess volatility puzzle across a large sample of international equity markets using a mixed‐frequency data sampling (MIDAS) regression approach. We find that accounting for dividend seasonality under the MIDAS framework significantly improves dividend growth predictability compared to simple regressions with annually aggregated data. Moreover, variance bounds tests that allow for nonstationary dividends consistently fail to reject the market efficiency hypothesis across all countries. Our findings suggest that the common rejection of market efficiency in the literature is most likely driven by the annual aggregation of dividend data as well as by the assumption of stationary dividends.

Balancing data privacy and sharing in IIoT: Introducing the GFL-LFF aggregation algorithm

As the Industrial Internet of Things (IIoT) continues to evolve, the need for effective data aggregation schemes that balance the imperatives of data sharing and individual data privacy becomes paramount. Traditional data aggregation methods often entail risks of sensitive information exposure during data transmission and storage. This paper proposes a novel Graphical Federated Learning-based Local Fennec Fox (GFL-LFF) algorithm to ensure secure aggregation by minimizing the revelation of individual data. In this study, a Graph Neural Network (GNN) with federated learning is employed to enhance privacy protection for IIoT's data aggregation scheme. A Local search-based fennec fox optimization is utilized for tuning the parameters of GNN. The GFL and LFF approaches are integrated to enhance the security of the data that can be shared through the IIoT system. The MNIST dataset, CIFAR-10 dataset, and LFW dataset were taken to conduct the experimentation results, and the evaluation measures, such as throughput, end-to-end delay, network lifetime, latency, and energy consumption, are utilized to evaluate the proposed GFL-LFF method, and these results are compared with other approaches. The GFL-LFF method achieved a throughput of 0.98 Mbps, an end-to-end delay of 1.2 s, a network lifetime of 5610 rounds, a latency of 1.6 s, and an energy consumption of 0.2 mJ. The experimental results illustrate the effectiveness of the proposed GFL-LFF method for IIoT's data aggregation scheme in privacy protection.

Robust language-based mental health assessments in time and space through social media

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.