Aggregate Data Research Articles

Effective Hybrid Deep Learning Model of GAN and LSTM for Clustering and Data Aggregation in Wireless Sensor Networks

Background: Wireless Sensor Networks (WSNs) have emerged as a crucial technology for various applications, but they face a lot of challenges relevant to limited energy resources, delayed communications, and complex data aggregation. To address these issues, this study proposes novel approaches called GAN-based Clustering and LSTM-based Data Aggregation (GCLD) that aim to enhance the performance of WSNs. Methods: The proposed GCLD method enhances the Quality of Service (QoS) of WSN by leveraging the capabilities of Generative Adversarial Networks (GANs) and the Long Short-Term Memory (LSTM) method. GANs are employed for clustering, where the generator assigns cluster assignments or centroids, and the discriminator distinguishes between real and generated cluster assignments. This adversarial learning process refines the clustering results. Subsequently, LSTM networks are used for data aggregation, capturing temporal dependencies and enabling accurate predictions. Results: The evaluation results demonstrate the superior performance of GCLD in terms of delay, PDR, energy consumption, and accuracy than the existing methods. Conclusion: Overall, the significance of GCLD in advancing WSNs highlights its potential impact on various applications.

An Efficient Lightweight Crypto Security Module for Protecting Data Transmission Through IOT Based Electronic Sensors

The Internet of Things (IoT) devices are advanced nanoelectronics devices which has recently witnessed an explosive expansion in the field of communication and electronics, becoming ubiquitous in various applications. However, the rapid growth of IoT applications makes them prone to security threats and data breaches. Hence, cryptographic techniques are developed to ensure data confidentiality and integrity in IoT and many of the applications from optoelectronics. However, the existing cryptographic algorithms face challenges in securing the data from threats during transmission, as they lack effective key management. Therefore, we proposed a novel optimized lightweight cryptography (LWC) to resolve this challenge using the combined benefits of Grey Wolf Optimization and Hyper Elliptic Curve Cryptography (GW-HECC). The proposed LWC algorithm protects the data from attacks during data exchange by optimizing the key management process and aims to deliver greater Quality of Service (QoS) in IoT networks. An IoT network was initially created with multiple sensor devices, IoT gateways, and data aggregators. The proposed framework includes a Quantum Neural Network (QNN)-based attack prediction module to predict the malicious data entry in the IoT network. The QNN learns the attack patterns from the historical IoT data and prevents incoming malicious data entries, ensuring that only normal data is transmitted to the cloud. For secure data transmission, the sensed data from the IoT network are encrypted using the proposed GW-HECC. The presented work was designed and implemented in Python software; the experimental results demonstrate that the proposed method offers greater data confidentiality of 97.9%, improved attack prediction accuracy of 99.8%, and a reduced delay of 0.37 s. Furthermore, a comparative analysis was made with existing cryptographic algorithms, manifesting that the proposed algorithm acquired improved results.

Improved VoWiFi cell capacity using A‐MPDU for frame aggregation in sixth‐generation WLAN standard

AbstractThe rapid progress in wireless communication technology and proliferation of multimedia applications, including voice over WiFi (VoWiFi), demand exploration of innovative approaches to enhance the network performance and quality of service. We propose a technique for enhancing the cell capacity of wireless local area network (WLAN) access point that provides VoWiFi service in the sixth‐generation WLAN standard. The proposed technique uses the aggregate media access control protocol data unit (A‐MPDU) for frame aggregation with constant bit rate (CBR) traffic in the WiFi 6 standard (i.e., IEEE 802.11ax). On the other hand, the retransmission of voice packets substantially deteriorates the VoWiFi cell capacity. We compare the results obtained from the use of WiFi 6 with currently existing WLAN standards, such as IEEE 802.11b/g/n/ac. This comparison focuses on distributed coordination function interframe spacing (DIFS) and arbitration interframe spacing (AIFS) using CBR traffic. Using our technique, we can increase the VoWiFi cell capacity for CBR traffic by 24.25% and 25.20% when using DIFS and AIFS, respectively, while considering the A‐MPDU frame aggregation technique.

EXPRESS: Robust Demand Estimation with Customer Choice-Based Models for Sales Transaction Data

We develop a novel statistical method to estimate customer choice among a firm’s portfolio of offerings when the firm cannot directly observe customers who choose not to purchase any product. This censored demand problem is prevalent in many industries such as hotels, airlines, and retail. Although several methods have been proposed to address this problem, they require some level of data aggregation across arrivals and/or choice sets, which results in information loss and potentially biased estimates. Therefore, they have limited applicability in an environment where the prices of a firm’s portfolio of offerings vary over time and sometimes even across different customers. Our proposed method combines several desirable properties, which makes it a better fit for realistic datasets where the available choice sets or attributes of the products in the choice sets change over time. We consider two additional types of information for identification of our model parameters: 1) additional mild assumptions on the customers’ utility function, and 2) external information about a firm’s market share. We then develop a robust estimation procedure that accounts for inaccuracies in either information type and let the data determine the best approach. Through Monte-Carlo simulations, we show that our approach provides promising predictions of customer choice behavior when compared with other generally used methods and clearly outperforms those methods in scenarios where the product prices change frequently over time. Utilizing a real hotel transaction dataset provided by Oracle Labs, we further illustrate the improved estimation accuracy of our method compared to benchmark methods. Relative to existing approaches for estimating customer choice-based models, our proposed methodology better suits environments employing dynamic pricing and personalized offering practices, such as hospitality or online retailing.

Characteristics and Consequences of Violent Victimization in Sexual and Gender Minority Communities: An Analysis of the 2017-2021 National Crime Victimization Survey.

Purpose: This article investigates rates of violent victimization, subsequent help-seeking, and health-related consequences within sexual and gender minority (SGM) communities. Methods: Aggregate data from the 2017-2021 National Crime Victimization Survey were examined to determine nationally representative estimates of rates and distributions of violent victimization, help-seeking, and socioemotional consequences within those 16 years of age and older. Due to sample size, most analyses aggregated sexual orientation and gender identity to allow comparison of SGM persons to non-SGM persons and examine differences within the SGM population. Results: Persons who identified as lesbian, gay, or bisexual experienced violent victimization at rates two to six times higher than straight persons. Transgender persons were victimized more than three times as often than cisgender persons. SGM persons experienced higher rates of all types of violent victimization than non-SGM persons regardless of victim-offender relationship. There were differences by victim demographic characteristics, including sex, race and Hispanic origin, age, marital status, and household income. A higher proportion of SGM victims reported only problems with work/school or problems both at work/school and with family/friends. Finally, higher proportions of SGM victims reported socioemotional consequences when they were female, older, or experienced serious violent crime. Conclusion: The findings in this study continue to highlight high levels of violence experienced by SGM persons and disproportionate socioemotional consequences. There is an evident need to develop targeted interventions and provide services to address the consequences of victimization among this population. The analyses demonstrate the necessity of continued research to better understand the impact of violence on SGM communities.

An aggregated dataset of serial morbidity and titer measurements from influenza A virus-infected ferrets

Data from influenza A virus (IAV) infected ferrets provides invaluable information towards the study of novel and emerging viruses that pose a threat to human health. This gold standard model can recapitulate many clinical signs of infection present in IAV-infected humans, support virus replication of human, avian, swine, and other zoonotic strains without prior adaptation, and permit evaluation of virus transmissibility by multiple modes. While ferrets have been employed in risk assessment settings for >20 years, results from this work are typically reported in discrete stand-alone publications, making aggregation of raw data from this work over time nearly impossible. Here, we describe a dataset of 728 ferrets inoculated with 126 unique IAV, conducted by a single research group under a uniform experimental protocol. This collection of morbidity, mortality, and viral titer data represents the largest publicly available dataset to date of in vivo-generated IAV infection outcomes on a per-ferret level.

Telehealth-Based Information Retrieval and Extraction for Analysis of Clinical Characteristics and Symptom Patterns in Mild COVID-19 Patients

Clinical characteristics of COVID-19 patients have been mostly described in hospitalised patients, yet most are managed in an outpatient setting. The COVID-19 pandemic transformed healthcare delivery models and accelerated the implementation and adoption of telemedicine solutions. We employed a modular remote monitoring system with multi-modal data collection, aggregation, and analytics features to monitor mild COVID-19 patients and report their characteristics and symptoms. At enrolment, the patients were equipped with wearables, which were associated with their accounts, provided the respective in-system consents, and, in parallel, reported the demographics and patient characteristics. The patients monitored their vitals and symptoms daily during a 14-day monitoring period. Vital signs were entered either manually or automatically through wearables. We enrolled 162 patients from February to May 2022. The median age was 51 (42–60) years; 44% were male, 22% had at least one comorbidity, and 73.5% were fully vaccinated. The vitals of the patients were within normal range throughout the monitoring period. Thirteen patients were asymptomatic, while the rest had at least one symptom for a median of 11 (7–16) days. Fatigue was the most common symptom, followed by fever and cough. Loss of taste and smell was the longest-lasting symptom. Age positively correlated with the duration of fatigue, anorexia, and low-grade fever. Comorbidities, the number of administered doses, the days since the last dose, and the days since the positive test did not seem to affect the number of sick days or symptomatology. The i-COVID platform allowed us to provide remote monitoring and reporting of COVID-19 outpatients. We were able to report their clinical characteristics while simultaneously helping reduce the spread of the virus through hospitals by minimising hospital visits. The monitoring platform also offered advanced knowledge extraction and analytic capabilities to detect health condition deterioration and automatically trigger personalised support workflows.

Considering the development of organic farming in Poland at different territorial aggregation levels

The aim of this paper is to show and assess how the level of data aggregation can change the way the development of organic agriculture in Poland in 2014–2021 is perceived. A composite indicator of eco-farming at different levels of territorial aggregation and in different time horizons has been built taking into account sub-components and individual indicators. The performance of organic agriculture with the initial effects of the pandemic (for 2020–2021) was also quantified. The results showed that, in populationally larger territorial units (NUTS-1, NUTS-2, NUTS-3), organic farming characteristics are less variable, whereas the same characteristics show greater variability in smaller territorial units (LAU-1, LAU-2). This reflects the strong regional character of eco-agriculture. It can be observed especially in northern and south-eastern Poland. Moreover, although the highest performance of organic farming was observed in 2021, the effects of the pandemic crisis were highly heterogeneous by location. The results may be crucial for farmers and policymakers in planning sustainable agricultural strategies and building resilient organic regions.

Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review.

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001). Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Global focus on ESG in the supply chain – insights into supply chain management of Environmental, Social and Governance requirements and trends both locally and globally

In our increasingly interconnected world, responsible business operation has become a normal, critical part of doing business for virtually all industries. Consumers, investors, and other company stakeholders are demanding businesses take responsibility for their current and future impacts on people and the environment. When assessing the role corporations play in a more sustainable future, many turn their focus to Environmental, Social and Governance (ESG) factors that help define and measure sustainable business. Given the wide variety of topics encompassed in ESG, companies often identify which are material, or prioritised by company stakeholders. Quantitative and qualitative information for material ESG topics is often reported using a standard or framework such as the Task Force on Climate-related Financial Disclosures; Taskforce on Nature-related Financial Disclosures; Sustainability Accounting Standards Board; the Global Reporting Initiative and the Carbon Disclosure Project (CDP), along with other region-specific standards. One reporting challenge includes extending ESG strategies and data collection to a company’s value chain. By including ESG-related requirements in service contracts or pre-qualification metrics, expectations can be clearly communicated and data aggregation becomes simpler. Engaging with vendors on the importance of material topics and providing educational tools and opportunities is necessary for accurate data collection. Benchmarking companies against their peers allows organisations to make informed decisions to continue or halt business relationships. Every company’s approach to ESG will look different, but protecting the business’s reputation and customer expectations is critical to continuous operations. Outcomes: understanding different components of ESG. Recognition of the types of reporting disclosure frameworks. Best practices with contractors and suppliers.

Ticagrelor monotherapy for acute coronary syndrome: an individual patient data meta-analysis of TICO and T-PASS trials.

In patients with acute coronary syndrome (ACS), dual antiplatelet therapy (DAPT) with aspirin and a potent P2Y12 inhibitor is recommended for 12 months after drug-eluting stent (DES) implantation. Monotherapy with a potent P2Y12 inhibitor after short-term DAPT is an attractive option to better balance the risks of ischaemia and bleeding. Therefore, this study evaluated the efficacy and safety of ticagrelor monotherapy after short-term DAPT, especially in patients with ACS. Electronic databases were searched from inception to 11 November 2023, and for the primary analysis, individual patient data were pooled from the relevant randomized clinical trials comparing ticagrelor monotherapy after short-term (≤3 months) DAPT with ticagrelor-based 12-month DAPT, exclusively in ACS patients undergoing DES implantation. The co-primary endpoints were ischaemic endpoint (composite of all-cause death, myocardial infarction, or stroke) and bleeding endpoint [Bleeding Academic Research Consortium (BARC) type 3 or 5 bleeding] at 1 year. Individual patient data from two randomized clinical trials including 5906 ACS patients were analysed. At 1 year, the primary ischaemic endpoint did not differ between the ticagrelor monotherapy and ticagrelor-based DAPT groups [1.9% vs. 2.5%; adjusted hazard ratio (HR) 0.79; 95% confidence interval (CI) 0.56-1.13; P = .194]. The incidence of the primary bleeding endpoint was lower in the ticagrelor monotherapy group (2.4% vs. 4.5%; adjusted HR 0.54; 95% CI 0.40-0.72; P < .001). The results were consistent in a secondary aggregate data meta-analysis including the ACS subgroup of additional randomized clinical trials which enrolled patients with ACS as well as chronic coronary syndrome. In ACS patients undergoing DES implantation, ticagrelor monotherapy after short-term DAPT was associated with less major bleeding without a concomitant increase in ischaemic events compared with ticagrelor-based 12-month DAPT. PROSPERO (ID: CRD42023476470).

Untangling multi-species fisheries data with species distribution models

AbstractLong-term trends in fisheries catch are useful to monitor effects of fishing on wild populations. However, fisheries catch data are often aggregated in multi-species complexes, complicating assessments of individual species. Non-target species are often grouped together in this way, but this becomes problematic when increasingly common shifts toward targeting incidental species demand closer management focus at the species level. Species distribution models (SDMs) offer an under-utilised tool to allocate aggregated catch data among species for individual assessments. Here, we present a case study of two shovel-nosed lobsters (Thenus spp.), previously caught incidentally and recorded together in logbook records, to illustrate the design and use of catch allocation SDMs to untangle multi-species data for stock assessments of individual species. We demonstrate how catch allocation SDMs reveal previously masked species-specific catch trends from aggregated data and can identify shifts in fishing behaviour, e.g., changes in target species. Finally, we review key assumptions and limitations of this approach that may arise when applied across a broad geographic or taxonomic scope. Our aim is to provide a template to assist researchers and managers seeking to assess stocks of individual species using aggregated multi-species data.

Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Cluster based Wireless Sensor Network

Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. DOI: https://doi.org/10.52783/pst.387

Retraction Note: Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

Retraction Note: Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

VMEMDA: Verifiable Multidimensional Encrypted Medical Data Aggregation Scheme for Cloud-Based Wireless Body Area Networks

VMEMDA: Verifiable Multidimensional Encrypted Medical Data Aggregation Scheme for Cloud-Based Wireless Body Area Networks

Privacy‐preserving data aggregation achieving completeness of data queries in smart grid

AbstractIn smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine‐grained data. To address these challenges, we propose a privacy‐preserving data aggregation scheme supporting data query (PAQ). We designed a multi‐level data aggregation mechanism based on Paillier semi‐homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency.

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.

Crop yields fail to rise in smallholder farming systems in sub-Saharan Africa

Drawing on a harmonized longitudinal dataset covering more than 55,000 smallholder farms in six African countries, we analyze changes in crop productivity from 2008 to 2019. Because smallholder farmers represent a significant fraction of the world's poorest people, agricultural productivity in this context matters for poverty reduction and for the broader achievement of the UN Sustainable Development Goals. Our analysis measures productivity trends for nationally representative samples of smallholder crop farmers, using detailed data on agricultural inputs and outputs which we integrate with detailed data on local weather and environmental conditions. In spite of government commitments and international efforts to strengthen African agriculture, we find no evidence that smallholder crop productivity improved over this 12-y period. Our preferred statistical specification of total factor productivity (TFP) suggests an overall decline in productivity of -3.5% per year. Various other models we test also find declining productivity in the overall sample, and none of them finds productivity growth. However, the different countries in our sample experienced varying trends, with some instances of growth in some regions. The results suggest that major challenges remain for agricultural development in sub-Saharan Africa. They complement previous analyses that relied primarily on aggregate national statistics to measure agricultural productivity, rather than detailed microdata.

Data Aggregation by Web Scraping using Python

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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.