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In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels. Graphic abstract

Background The “Primary Immunodeficiencies (PIDs) principles of care” were published in 2014 as the gold standard for care of patients with PIDs, setting a common goal for stakeholders to ensure that patients with PID have access to appropriate care and good quality of life. Since then, IPOPI (the International Patient Organisation for Primary Immunodeficiencies), has been working with national PID patient organisations as well as collaborating with scientific and medical institutions and experts to bring these principles closer to the day-to-day life of individuals with PIDs. Method The six PID Principles of Care were revised to consider advances in the field, as well as political developments that had occurred after their initial publication in 2014. Based on this revision the list was updated, and a new principle was added. The six established principles were: diagnosis, treatment, universal health coverage, specialised centres, national patient organisations and registries. Each principle was structured and measured through a series of criteria, and was given the same weight, as they have been considered to all be equally important. Specific weights were attributed to the criteria depending on their relevance and importance to quantify the principle. The index was translated into a survey for data collection: initially involving data from selected countries for a pilot, followed by integration of data from IPOPI’s national member organisations and key countries. Results The PID Life Index was developed in 2020 to assess the status of the PID environment and the implementation of the 6 principles worldwide. The Index allows for benchmarking countries either according to a set of principles and criteria or based on the user’s preferences. This can be displayed in an interactive map or through a data visualisation system. Conclusion The PID Life Index has been developed successfully and has potential to become an important source of information for PID stakeholders, to increase awareness and information as well as support advocacy initiatives on PIDs nationally, regionally or globally.

Internet of Things (IoT) embodies a vision of merging heterogeneous objects to establish seamless interaction among physical and virtual entities. IoT has given the Internet a shift from connecting networks to interconnecting the physical world. The IoT devices are capable of sensing, processing, communicating and storing the data acquired from the physical world. Most of the applications of IoT are pervasive in nature, the pervasive IoT environment poses many challenges due to constrained resources in these miniature and unattended IoT devices. This paper presents a survey of physical and virtual resource management in IoT systems. The main focus of the paper is on resource management in pervasive IoT environment with limited resources. This paper also presents a use case of IoT based Body Area Network and proposes a model for resource management in personal and community healthcare.

OBJECTIVE: To examine how estimates of the association between nurse staffing and patient length of stay (LOS) change with data aggregation over varying time periods and settings, and statistical controls for unobserved heterogeneity. DATA SOURCES/STUDY SETTING: Longitudinal secondary data from October 2002 to September 2006 for 215 intensive care units and 438 general acute care units at 143 facilities in the Veterans Affairs (VA) health care system. RESEARCH DESIGN: This retrospective observational study used unit‐level panel data to analyze the association between nurse staffing and LOS. This association was measured over both a month‐long and a year‐long period, with and without fixed effects. DATA COLLECTION: We used VA administrative data to obtain patient data on the severity of illness and LOS, as well as labor hours and wages for each unit by month. PRINCIPAL FINDINGS: Overall, shorter LOS was associated with higher nurse staffing hours and lower proportions of hours provided by licensed professional nurses (LPNs), unlicensed personnel, and contract staff. Estimates of the association between nurse staffing and LOS changed in magnitude when aggregating data over years instead of months, in different settings, and when controlling for unobserved heterogeneity. CONCLUSIONS: Estimating the association between nurse staffing and LOS is contingent on the time period of analysis and specific methodology. In future studies, researchers should be aware of these differences when exploring nurse staffing and patient outcomes.

In Wireless Sensor Networks (WSNs), sensor nodes are generally placed in different geographical locations. The sensor nodes collect data from environment and they react based on the received query requests. It is a great challenge for sensor nodes to send data periodically to the sink node in a conflict-free way. For this, authors present a Conflict-free Periodic Data Aggregation (CPDA) technique in WSNs. CPDA also works in heterogeneous environment where some of the sensor nodes in a network suffer from less amount of energy. The authors have also presented a mathematical model for the proposed data aggregation technique. The simulation results indicate that CPDA technique works well as compared with the existing approaches. The simulation results of CPDA technique are validated through NS-2 simulations.

Vehicular sensing is advocated to perform data collection by exploiting a plethora of vehicular on-board sensors; meanwhile, with the merging of vehicular sensing and fog computing, the deployed road side units (RSUs) can act as fog nodes to collect and share vehicular sensory data at the network edge. However, there are still several problems in terms of the secure and reliable sharing of sensory data in vehicular fog. To resolve these issues, in this paper, we present an efficient, privacy-preserving and verifiable sensory data collection and sharing scheme with a permissioned blockchain in vehicular fog. During the data collection phase, by combining the homomorphic 2-DNF (Disjunctive Normal Form) cryptosystem and an identity-based signcryption scheme, our proposed scheme achieves the secure and verifiable computation of the average and variance of the collected vehicular sensory data. Meanwhile, to achieve efficient and reliable data sharing, we exploit a permissioned blockchain to maintain an immutable and tamper-proof record of the derived sensory data. Security analysis demonstrates the security properties of the proposed scheme, in terms of location privacy preservation, verifiability and immutability. Performance evaluations are conducted to validate the efficiency of the proposed scheme, i.e., improvements in computation and communication efficiency in comparison with a scheme without exploiting blockchain. This work was supported in part by a Grant 2018YFB1800800, a Grant 2018B030338001, a Grant NSFC61629101, a Grant ZDSYS201707251409055, and a Grant 2017ZT07X152.

In Wireless Sensor Networks (WSNs), when multiple data packets meet during routing to the sink, redundant data can be removed through data fusion, thereby reducing the amount of data transmitted, and increasing the life of the network. However, how to increase the data fusion rate as much as possible and ensure that the delay is lower than the deadline is a challenge issue. To solve this problem, a Differentiated Threshold Configuring joint Optimal Relay Selection based Data Aggregation (DTC-ORS-DA) scheme is proposed, which can significantly reduce redundant data routing and guarantee the delay for WSN. The main innovation is as follow: (1) In DTC-ORS-DA scheme, there are two thresholds: data volume threshold $N_{y}$ and time threshold $T_{y}$ . Data routing can only be performed when $N_{y}$ or $T_{y}$ of the node meet the threshold requirements, so that the data can be fully integrated to minimize the amount of data to be transmitted. More importantly, DTC-ORS-DA adopts differentiated threshold settings based on the characteristics of unbalanced energy consumption in WSNs, and sets a smaller threshold in the far sink area with sufficient energy, so that data packets can be routed quickly. And the near sink area where the energy is tight uses a larger threshold to maximize data fusion, so that the combination can make the data fusion high, the energy is effectively used, and the delay is small. (2) We propose a priority-based relay selection algorithm, which enables child nodes to dynamically select the parent node with the highest priority based on the number of data packets, waiting time, and remaining energy. In the process of routing, the probability of nodes with many data packets or long waiting time being selected as transmission relay is high, which can either increase the data fusion rate or reduce the delay. Finally, the performance comparison with Common data collection Scheme (CS) proves that, the DTC-ORS-DA scheme reduces the average delay by 10.74%-19.91%, increases the life cycle by 9.81% at most, and the energy utilization rate is increased by 6.67%-9.48%.

With the advent of cloud computing and Internet of Things and delegation of data collection and aggregation to third parties, the results of the computations should be verified. In distributed models, there are multiple sources. Each source creates authenticators for the values and sends them to the aggregator. The aggregator combines the authenticated values and creates a verification object for verifying the computation/aggregation results. In this paper, we propose two constructions for verifying the results of countable and window-based countable functions. These constructions are useful for aggregate functions such as median, max/min, top-k/first-k, and range queries, where the distribution of values is not visible for sources but is visible to the aggregator. The proposed constructions are secure based on the RSA problem in the random oracle model and have the correctness and succinctness properties. Experimental results show that the communication and computation costs of the constructions are acceptable in practice and the proposed solution can be employed for real-world applications.

The energy consumption of the routing protocol can affect the lifetime of a wireless sensor network (WSN) because tiny sensor nodes are usually difficult to recharge after they are deployed. Generally, to save energy, data aggregation is used to minimize and/or eliminate data redundancy at each node and reduce the amount of the overall data transmitted in a WSN. Furthermore, energy-efficient routing is widely used to determine the optimal path from the source to the destination, while avoiding the energy-short nodes, to save energy for relaying the sensed data. In most conventional approaches, data aggregation and routing path selection are considered separately. In this study, we consider the degrees of the possible data aggregation of neighbor nodes when a node needs to determine the routing path. We propose a novel Q-learning-based data-aggregation-aware energy-efficient routing algorithm. The proposed algorithm uses reinforcement learning to maximize the rewards, defined in terms of the efficiency of the sensor-type-dependent data aggregation, communication energy and node residual energy, at each sensor node to obtain an optimal path. We used sensor-type-dependent aggregation rewards. Finally, we performed simulations to evaluate the performance of the proposed routing method and compared it with that of the conventional energy-aware routing algorithms. Our results indicate that the proposed protocol can successfully reduce the amount of data and extend the lifetime of the WSN.

In the face of a massive number of vehicular users, a data collection paradigm based on vehicular crowdsensing requires an effective means of attracting participants. Thus, the incentive mechanisms play a key role in crowdsensing procedure design, inevitably leading to problems related to user privacy leakage. In this article, to reduce the risk of privacy leakage in the implementation of incentive mechanisms, we propose a fog computing-based crowdsensing architecture specialized for vehicular crowdsensing and corresponding privacy-preserving solutions for the processes of data reporting, reward issuing, and trust management. Authentication and encryption technologies, such as zero-knowledge verification, one-way hashing, partially blind signature authentication, and homomorphic encryption, are utilized to achieve our goals. Finally, efficiency improvements in both privacy preservation and network response are proven through analysis and simulations. This work was supported in part by the National Natural Science Foundation of China under Grant 61571098, in part by the Sichuan Science and Technology Program under Grant 2019YGF0206, and in part by the 111 Project under Grant B14039.

Secure data aggregation has been widely studied in the area of smart grid. Many existing schemes have studied protecting user’s privacy in data aggregation by using advanced cryptographic tools. However, they usually introduce large computation burden to smart meters in limited computing power or require a trusted authority. How to ensure the efficiency on the user side while preserving user’s privacy still has not been well addressed. In this paper, we consider the scenario where there does not exist a trusted authority and users in the smart grid may dynamically change, and propose an efficient and robust data aggregation scheme without a trusted authority for the smart grid. Our proposed scheme not only ensures user’s privacy and efficiency, but also supports flexible dynamic user management with no need of involving a trusted authority. Analysis of security and performance shows that our scheme can guarantee stronger security features, while ensuring efficiency in terms of computation, communication, and storage overhead. IEEE Scopus

Finding a more efficient use of energy is an important problem that needs attention. Compared with the traditional power grid, a smart grid can monitor users’ electricity situation and electricity consumption instantly. However, it involves many problems of deploying network equipment. Consequently, it is vital to promote smart grids by collecting data from smart meters efficiently and keeping costs low. In this article, we propose a two-stage method of data collection for smart grids. The main contribution of this paper is to lower the number of data aggregation points (DAPs) so that the cost can be reduced. By using the K-means method, an entire smart grid can be divided into many smaller parts. In addition, the needs of transmitting and receiving data in the entire smart grid can be met by installing the least number of DAPs. Finally, the simulations show that the proposed two-stage method of data collection can use fewer DAPs to collect data than other methods which use one-stage methods, so the proposed scheme is more cost-effective.

Industrial Internet of Everything (IIoE), as the deep integration of industry 6.0, the Internet of Things (IoT) and 6G mobile communication technology, pave the way for intelligent industry, enabling industrial optimization and automation. To ensure the high quality of services (QoS) in IIoE, tremendous real-time information generated by the pervasive smart things needs to be aggregated and processed quickly and reliably. However, a large-scale disaster could damage the entire communication network and cut off data aggregation such that Qos is compromised. In this paper, an Intelligent NIB based Data Aggregation Strategy, named (IDAS), is proposed for after disaster scenarios in IIoE. Specifically, IDAS first applies both iterative cubature kalman filter and radial basis function neural network to predict the data collection rates of survived infrastructures. Then, an energy efficient task distribution mechanism is design. Next, a deep reinforcement learning method is developed for the car-carrying NIB route design to perform corresponding task. Eventually, all data are aggregated toward the rescue headquarter by NIB deployment based on Fermat tree constructions. The theoretical analysis and simulations indicate that IDAS is not only energy efficient for after disaster scenarios but requires the least NIB consumption while compared with contemporary strategies.

In recent years, due to the rapid development of information techniques and network technologies, more and more medical documents have been replaced by electronic files for sharing and transmitting in real time. However, medical data transmitted over public communication channels may suffer from security attacks and privacy threats. Blockchain technology has been gotten many attentions in different areas due to its unique properties such as anonymity, verifiability, immutability and decentralization. In order to secure patient privacy and provide more personal healthcare services, in this paper, we propose a data aggregation scheme based on Blockchain technology for medical environments. Moreover, in order to implement remote medical monitoring, we design a group authentication mechanism for multiple authorized users (such as patient, doctors, caregivers, family and friends) to freely access patient's personal health records. The authorized group members in a group will agree on a group session key and use it to protect patient's sensitive information. In case of a new member joins the medical group or an old member leaves the medical group, the group session key needs to be updated at any time. Finally, the electronic medical system will become more secure, reliable and useful by our proposed scheme.

Objective The study sought to assess the feasibility of nationwide chronic disease surveillance using data aggregated through a multisite collaboration of customers of the same electronic health record (EHR) platform across the United States. Materials and Methods An independent confederation of customers of the same EHR platform proposed and guided the development of a program that leverages native EHR features to allow customers to securely contribute de-identified data regarding the prevalence of asthma and rate of asthma-associated emergency department visits to a vendor-managed repository. Data were stratified by state, age, sex, race, and ethnicity. Results were qualitatively compared with national survey-based estimates. Results The program accumulated information from 100 million health records from over 130 healthcare systems in the United States over its first 14 months. All states were represented, with a median coverage of 22.88% of an estimated state’s population (interquartile range, 12.05%-42.24%). The mean monthly prevalence of asthma was 5.27 ± 0.11%. The rate of asthma-associated emergency department visits was 1.39 ± 0.08%. Both measures mirrored national survey-based estimates. Discussion By organizing the program around native features of a shared EHR platform, we were able to rapidly accumulate population level measures from a sizeable cohort of health records, with representation from every state. The resulting data allowed estimates of asthma prevalence that were comparable to data from traditional epidemiologic surveys at both geographic and demographic levels. Conclusions Our initiative demonstrates the potential of intravendor customer collaboration and highlights an organizational approach that complements other data aggregation efforts seeking to achieve nationwide EHR-based chronic disease surveillance.

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients’ health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Feature analysis of weak nonlinear signals from geographic spatiotemporal data has received increasing attention. Most existing signal processing methods cannot effectively perform comprehensive feature analysis because of the multiple dimensions and unbalance of spatiotemporal data. We developed a divide–aggregate–explore method for the feature analysis of spatiotemporal data. In our method, strategies for dividing different dimensions are defined for multidimensional analysis, and the tensor–block structure is adopted to reorganize the original data and distinguish differences in dimensions. Then, information‐based data aggregation is used to weaken the impact of dimensional unbalance. Case studies based on climatic reanalysis field data released by the National Oceanic and Atmospheric Administration showed that the proposed method can effectively extract weak propagation signals such as the El Niño–Southern Oscillation and El Niño–Southern Oscillation Modoki. Our method can also reveal more detailed evolutionary characteristics of complex coupling systems in different dimensions compared with classical feature detection methods such as principal component analysis and tensor decomposition.

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.

BackgroundData quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues.ObjectivesTo explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task.MethodsWe started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods’ results and corresponding outcome data (data that indicated the data’s suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without.ResultsOur self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods’ results.ConclusionsOur data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods’ results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

International audience; The secure multidimensional data aggregation (MDA) has been widely investigated in smart grid for smart cities. However, previous proposals use heavy computation operations either to encrypt or to decrypt the multidimensional data. Moreover, previous fault-tolerant mechanisms lead to an important computation cost, and also a high communication cost when considering a separate identification phase. In this article, we propose an efficient and secure MDA scheme, named ESMA. Unlike existing schemes, the multidimensional data in ESMA are structured and encrypted into a single Paillier ciphertext and thereafter, the data are efficiently decrypted. For privacy preserving, the Paillier cryptosystem is adopted in a fog computing-based architecture, and to achieve efficient authentication, the batch verification technique is applied. Besides, ESMA is fault tolerant, i.e., even if some of the smart meters fail to send their data, the final aggregation result will not be affected. Furthermore, ESMA can be adapted to respond to other queries than the summation of data. The performance analysis demonstrates the cost efficiency of ESMA both in computation and communication and the scalability as well. For instance, with a 16-bits size for each data type and 500 reporting smart meters, 40 data types can be supported in a single Paillier ciphertext. ESMA also resists various security attacks and preserves the user’s privacy.

BackgroundAssessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.ObjectivesTo present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.MethodsWe describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool—openCQA—that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.ResultsApplying the method on the study’s dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.ConclusionsThe main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Diabetes prevalence is increasing in most places in the world, but prevalence is affected by both risk of developing diabetes and survival of those with diabetes. Diabetes incidence is a better metric to understand the trends in population risk of diabetes. Using a multicountry analysis, we aimed to ascertain whether the incidence of clinically diagnosed diabetes has changed over time.In this multicountry data analysis, we assembled aggregated data describing trends in diagnosed total or type 2 diabetes incidence from 24 population-based data sources in 21 countries or jurisdictions. Data were from administrative sources, health insurance records, registries, and a health survey. We modelled incidence rates with Poisson regression, using age and calendar time (1995-2018) as variables, describing the effects with restricted cubic splines with six knots for age and calendar time.Our data included about 22 million diabetes diagnoses from 5 billion person-years of follow-up. Data were from 19 high-income and two middle-income countries or jurisdictions. 23 data sources had data from 2010 onwards, among which 19 had a downward or stable trend, with an annual estimated change in incidence ranging from -1·1% to -10·8%. Among the four data sources with an increasing trend from 2010 onwards, the annual estimated change ranged from 0·9% to 5·6%. The findings were robust to sensitivity analyses excluding data sources in which the data quality was lower and were consistent in analyses stratified by different diabetes definitions.The incidence of diagnosed diabetes is stabilising or declining in many high-income countries. The reasons for the declines in the incidence of diagnosed diabetes warrant further investigation with appropriate data sources.US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the “best” final prediction. When experts’ performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts’ estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.

The Office of the National Coordinator for Health Information Technology estimates that 96% of all U.S. hospitals use a basic electronic health record, but only 62% are able to exchange health information with outside providers. Barriers to information exchange across EHR systems challenge data aggregation and analysis that hospitals need to evaluate healthcare quality and safety. A growing number of hospital systems are partnering with third-party companies to provide these services. In exchange, companies reserve the rights to sell the aggregated data and analyses produced therefrom, often without the knowledge of patients from whom the data were sourced. Such partnerships fall in a regulatory grey area and raise new ethical questions about whether health, consumer, or health and consumer privacy protections apply. The current opinion probes this question in the context of consumer privacy reform in California. It analyzes protections for health information recently expanded under the California Consumer Privacy Act (“CA Privacy Act”) in 2020 and compares them to protections outlined in the Health Information Portability and Accountability Act (“Federal Privacy Rule”). Four perspectives are considered in this ethical analysis: 1) standards of data deidentification; 2) rights of patients and consumers in relation to their health information; 3) entities covered by the CA Privacy Act; 4) scope and complementarity of federal and state regulations. The opinion concludes that the CCPA is limited in its application when health information is processed by a third-party data aggregation company that is contractually designated as a business associate; when health information is deidentified; and when hospital data are sourced from publicly owned and operated hospitals. Lastly, the opinion offers practical recommendations for facilitating parity between state and federal health data privacy laws and for how a more equitable distribution of informational risks and benefits from the sale of aggregated hospital data could be fostered and presents ways both for-profit and nonprofit hospitals can sustain patient trust when negotiating partnerships with third-party data aggregation companies.

Lung Computed Tomography (CT) is an imaging technique useful to assess the severity of COVID-19 infection in symptomatic patients and to monitor its evolution over time. Lung CT can be analysed with the support of deep learning methods for both aforementioned tasks. We have developed a U-net based algorithm to segment the COVID-19 lesions. Unfortunately, public datasets populated with a huge amount of labelled CT scans of patients affected by COVID-19 are not available. In this work, we first review all the currently available public datasets of COVID-19 CT scans, presenting an extensive description of their characteristics. Then, we describe the design of the U-net we developed for the automated identification of COVID-19 lung lesions. Finally, we discuss the results obtained by using the different publicly available datasets. In particular, we trained the U-net on the dataset made available within the COVID-19 Lung CT Lesion Segmentation Challenge 2020, and we tested it on data from the MosMed and the COVID-19-CT-Seg datasets to explore the transferability of the model and to assess whether the image annotation process affects the detection performances. We evaluated the performance of the system in lesion segmentation in terms of the Dice index, which measures the overlap between the ground truth and the predicted masks. The proposed U-net segmentation model reaches a Dice index equal to 0.67, 0.42 and 0.58 on the independent validation sets of the COVID-19 Lung CT Lesion Segmentation Challenge 2020, on the MosMed and on the COVID-19-CT-Seg datasets, respectively. This work focusing on lesion segmentation constitutes a preliminary work for a more accurate analysis of COVID-19 lesions, based for example on the extraction and analysis of radiomic features. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

Organizations may be related in terms of similar operational procedures, management, and supervisory agencies coordinating their operations. Supervisory agencies may be governmental or non-governmental but, in all cases, they perform oversight functions over the activities of the organizations under their control. Multiple organizations that are related in terms of oversight functions by their supervisory agencies, may differ significantly in terms of their geographical locations, aims, and objectives. To harmonize these differences such that comparative analysis will be meaningful, data about the operations of multiple organizations under one control or management can be cultivated, using a uniform format. In this format, data is easily harvested and the ease with which it is used for cross-population analysis, referred to as data comparability is enhanced. The current practice, whereby organizations under one control maintain their data in independent databases, specific to an enterprise application, greatly reduces data comparability and makes cross-population analysis a herculean task. In this paper, the collocation data model is formulated as consisting of big data technologies beyond data mining techniques and used to reduce the heterogeneity inherent in databases maintained independently across multiple organizations. The collocation data model is thus presented as capable of enhancing data comparability across multiple organizations. The model was used to cultivate the assessment scores of students in some schools for some period and used to rank the schools. The model permits data comparability across several geographical scales among which are: national, regional and global scales, where harvested data form the basis for generating analytics for insights, hindsight, and foresight about organizational problems and strategies.

To ensure the driving safety in vehicular network, it is necessary to construct a local dynamic map (LDM) for an extended range. Using the standard vehicular communication protocols, however, vehicles can construct the LDM for only one-hop range. Constructing large-scale LDM is highly challenging because vehicles randomly change their position. This paper proposes a dynamic map propagation (DMP) method, which builds a large aggregated LDM data using a multi-hop communication. To reduce the data overhead, we introduce an efficient clustering method based on a half-circle of the forwarder&rsquo, s wireless range. The DMP elects one forwarder per cluster, which constructs LDM and forwards it to a neighbor cluster. The inter-cluster interference is minimized by allocating a different transmit window to each cluster. DMP copes with a dynamic environment by frequently re-electing the forwarders and their associated transmission windows. Simulation results reveal that DMP enhances the forwarders&rsquo, reception ratio by 20%, while extending LDM dissemination range by 29% over a previous work.

Many wireless sensor network (WSN) applications require both security and quality-of-service (QoS) to be guaranteed simultaneously. However, ensuring both security and QoS is not trivial in WSNs since security normally has bad impact on QoS. In this paper, we propose an energy-efficient secure forwarding method that minimizes energy consumption while meeting both security and QoS requirements at the same time. To accomplish this goal, we alternatively use hop-by-hop security for conserving energy through data aggregation and end-to-end security to satisfy the QoS requirement. We further analyze why hop-by-hop security with data aggregation provide better energy efficiency than end-to-end security without aggregation in detail. The simulation result shows that our proposed method outperforms other scheme in terms of energy efficiency while meeting both QoS and security.

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user&rsquo, s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

BackgroundIntegrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.ObjectiveThe architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.MethodsWe reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.ResultsDespite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).ConclusionsIDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

In the current era of patient empowerment and precision medicine, access to timely information is critical to decision-making. Unfortunately, we currently lack patient-specific, real-time data about clinical presentation, risk of thrombotic or hemorrhagic events, key risk factors, and adverse outcomes in patients with venous thromboembolism (VTE). Accordingly, the Registro Informatizado Enfermedad TromboEmbólica (RIETE) investigators developed a tool to provide an open-source, real-time graphic representation of VTE-related data derived from over 90 000 patients with confirmed VTE. This information is intended to facilitate discussion in the informed decision-making process. The current article describes the aims, rationale, methods, and ongoing and future efforts of the real-time VTE infographics developed by the RIETE registry collaborators.

Data aggregation, understood as the process of gathering and combining data in order to prepare datasets that might be useful for specific business or other purposes, is not per se forbidden. However, some forms of it can be considered anticompetitive. In the Decision B6-22/16 of the German Federal Cartel Office (Bundeskartellamt) data aggregation, which included the collection of data from sources outside of Facebook’s social network (from Facebook-owned services such as WhatsApp and Instagram and from third party websites or mobile applications) and their combination with the information connected with a particular Facebook user account without that user’s consent, constituted an abuse of Facebook’s dominant position on the German market for social networks. The Bundeskartellamt found that the processing of user’s personal data by Facebook has, to some extent, been carried out in a way which infringed GDPR provisions. In the same decision, the Bundeskartellamt also identified the exclusionary nature of Facebook’s anticompetitive behaviour. According to the Bundeskartellamt, the illegal data aggregation formed a barrier to entry for Facebook’s competitors which, through compliance with data protection standards, found themselves in a worst position. Facebook, through its inappropriate data aggregation gained a competitive advantage. The Bundeskartellamt’s decision is, therefore, reflecting the anticompetitive dangers that data aggregation might pose. Nevertheless, it is debated whether the Bundeskartellamt, as a competition authority, is competent to determine the compliance or lack of compliance of business terms with the provisions of the GDPR. This paper analyzes the Bundeskartellamt’s decision as to where an anticompetitive nature of data processing has been identified, and tries to answer the question why it is problematic that it was the Bundeskartellamt and not a data protection supervisory authority that has issued such a decision.

Nowadays, robots (including non-humanoid ones, like self-driving cars) are part of the most promising technologies, and they rise various computing requirements. Some of those requirements came from the fact that the robots are involved in highly dynamic environments and have to execute complex decision algorithms in real-time, while other requirements ask for batch processing of big data compatible datasets. In this paper, we propose a cloud architecture for optimizing data processing using a cloud-edge infrastructure. Besides the computational architecture, we develop a mathematical model for each type of entity in our proposal and a formal description of a data capsule, which represents a generic and flexible representation for unstructured units of data in time series databases. The architecture includes multiple processing platforms. We evaluate the proposed model in edge-cloud computing platforms designed for robots that run machine learning tasks.

Network lifetime maximization is the primary challenge in the wireless sensor network (WSN) due to its resource limitations. E2R2 routing algorithm uses a hierarchical WSN model to minimize energy consumption in the WSN. However, it increases complexity, time consumption, and energy consumption due to the hierarchical model. To resolve these problems, this paper proposes a novel ring partitioned based MAC (RP-MAC) protocol for the energy-efficient WSN with a mobile sink node. Energy efficiency is achieved by the following phases: clustering phase, MAC scheduling phase, data aggregation phase, and routing phase. Clustering phase is initiated by a weighted Voronoi diagram (WVD) algorithm by assigning a weight value for each node. Energy consumption due to idle listening is minimized by enabling novel RP-MAC scheduling in each cluster. Involvement of RP-MAC protocol also achieves collision-free data transmission in the network. A two-fold data aggregation (TFDA) scheme is proposed for the data aggregation phase to minimize energy consumption by reducing the number of transmissions. Routing phase supports both intra-cluster routing and inter-cluster routing. For intra-cluster routing, a hybrid chicken swarm optimization algorithm is proposed. For inter-cluster routing, position-based routing tree is constructed based on the sink node’s position. Our proposed RP-MAC protocol minimizes energy consumption in all possible ways for improving the network lifetime. Extensive simulation in ns-3 shows that the RP-MAC protocol achieves promising results in the following performance metrics: the number of dead nodes, average energy consumption, network lifetime, and throughput.

Wireless sensor networks (WSNs) have been broadly used in various areas of commercial applications as well as public services. Furthermore, emerging unmanned aerial vehicles (UAVs) have been combined into many existing technologies. By employing UAVs in WSNs, sensor data can be effectively collected and the network lifetime is prolonged with reduced energy consumption. In the UAV-aided WSNs (UWSNs), medium access control (MAC) is very important because it affects not only the system performance but also the energy efficiency in battery-powered sensor nodes. It should be noted that the tiny sensor nodes are typically disposable and their batteries are not replaceable in most applications. Thus, a number of MAC protocols with different objectives have been designed for UWSNs. To the best of the authors' knowledge, there is no survey on MAC protocols for UWSNs in the literature. In this paper, the various MAC protocols for UWSNs are extensively investigated and qualitatively compared with each other in terms of major features, characteristics, and pros and cons. Moreover, open issues and challenges in the design of the MAC protocols for the UWSNs are discussed.

Machine type communication (MTC) is a fundamental technology to realize the concept of fully connected world in fifth generation (5G) Internet of Things (IoT). The massive roll out of the MTC devices is a serious challenge for cellular networks from operational and management perspective, including massive access and network congestion. Many proposals have been put forward by the research community to cater to the nuisance of massive MTC (mMTC) access in cellular networks. Recently, data aggregation has attracted a lot of research attention owing to its robust ability to resolve the above-mentioned challenges. In this paper, we review the recent development in data aggregation techniques, including their application scenarios, design, and limitations. Commencing with the application scenarios and current challenges in the MTC network, the classification of various proposed solutions for massive access along with the family of data aggregation techniques is discussed in detail. By doing so, it provides an insight about the future design trends that can propel the current research efforts to curb the mMTC access in a cellular network.

Biomedical research using data from participants’ mobile devices borrows heavily from the ethos of the “citizen science” movement, by delegating data collection and transmission to its volunteer subjects. This engagement gives volunteers the opportunity to feel like partners in the research and retain a reassuring sense of control over their participation. These virtues, in turn, give both grass-roots citizen science initiatives and institutionally sponsored mHealth studies appealing features to flag in recruiting participants from the public. But while grass-roots citizen science projects are often community-based, mHealth research ultimately depends on the individuals who own and use mobile devices. This inflects the ethos of mHealth research towards a celebration of individual autonomy and empowerment, at the expense of its implications for the communities or groups to which its individual participants belong. But the prospects of group harms — and benefits — from mHealth research are as vivid as they are in other forms of data-intensive “precision health” research, and will be important to consider in the design of any studies using this approach.

This paper proposes an efficient data aggregation and dynamic billing system that it uses anonymous communication for exchanging information between smart meters and CC. Moreover, the given scheme consists of a permissioned blockchain. This blockchain contains the ledger that keeps users’ anonymized identities and electricity consumption for predefined time ranges. Using consumption data of users, a billing mechanism can bill the users accordingly. In the construction, since all the parties in the system have the ledger, every party has the aggregated usage of the electricity without using very heavy cryptographic operations such as homomorphic encryption, bilinear pairing, etc. Using the ledger in our model, the aggregation of the users’ electricity consumption can be computed by anyone in the system. Moreover, users can verify their bills and check any data using a signature scheme. This results in that the integrity of all data is going to be preserved. The proposed approach mainly uses hash functions to provide the same functionality (aggregation of the users’ data consumption, data integrity check, and dynamic pricing and billing) with preserving data privacy of the users.

Background and objectives The Cochrane Central Register of Controlled Trials (CENTRAL) is compiled from a number of sources, including PubMed and Embase. Since 2017, we have increased the number of sources feeding into CENTRAL and improved the efficiency of our processes through the use of application programming interfaces, machine learning, and crowdsourcing.Our objectives were twofold: (1) Assess the effectiveness of Cochrane's centralized search and screening processes to correctly identify references to published reports which are eligible for inclusion in Cochrane systematic reviews of randomized controlled trials (RCTs). (2) Identify opportunities to improve the performance of Cochrane's centralized search and screening processes to identify references to eligible trials. Methods We identified all references to RCTs (either published journal articles or trial registration records) with a publication or registration date between 1st January 2017 and 31st December 2018 that had been included in a Cochrane intervention review. We then viewed an audit trail for each included reference to determine if it had been identified by our centralized search process and subsequently added to CENTRAL. Results We identified 650 references to included studies with a publication year of 2017 or 2018. Of those, 634 (97.5%) had been captured by Cochrane's Centralised Search Service. Sixteen references had been missed by the Cochrane's Centralised Search Service: six had PubMed-not-MEDLINE status, four were missed by the centralized Embase search, three had been misclassified by Cochrane Crowd, one was from a journal not indexed in MEDLINE or Embase, one had only been added to Embase in 2019, and one reference had been rejected by the automated RCT machine learning classifier. Of the sixteen missed references, eight were the main or only publication to the trial in the review in which it had been included. Conclusion This analysis has shown that Cochrane's centralized search and screening processes are highly sensitive. It has also helped us to understand better why some references to eligible RCTs have been missed. The CSS is playing a critical role in helping to populate CENTRAL and is moving us toward making CENTRAL a comprehensive repository of RCTs.

Wireless Sensor Networks (WSNs) is a fast-emerging technology which has become an integral part of the research. WSNs have various applications covering military, environment monitoring, health care, surveillance, national security, etc. Due to the inherent nature of wireless communication, such types of networks are more vulnerable to security attacks, and the authentication and confidentiality of wireless networks are much more critical. WSNs needs include efficient clustering methods, data aggregation methods, data compression methods, data encryption and authentication methods, and data gathering methods. WSNs are more vulnerable to attacks due to their ad hoc nature, so the design of a good key management scheme to provide security is necessary. In this paper, different methods of clustering, data aggregation, data compression, data encryption and authentication, and data gathering are analyzed. A survey is conducted on the key management schemes of WSNs.

Wound healing is a highly complex life-important repair process triggered by plenty of local and/or systemic organ and tissue damaging events, such as an acute surgical invasion, accidental organ and tissue damages, acute and chronic diseases, aggressive local and systemic therapeutic approaches (e.g. irradiation and systemic chemotherapy). Individual health condition determines over the quality of wound healing. Impaired wound healing, in turn, may lead, for example, to post-surgical complications frequently observed in elderly, chronic ulcers in diabetic patients, hindered and ineffective pain management, etc. However, these well-acknowledged examples are just the tip of the iceberg. The entire spectrum of potential consequences is much broader. Therefore, all the aspects of wound healing need to receive a dedicated attention of many specialised medical fields and healthcare as a whole. In contrast, there is still strongly limited knowledge collected regarding the molecular and cellular mechanisms underlying the physiological versus impaired wound healing. The contents of this article might be of great importance for multi-professional considerations as well as for the experts working in specific fields such as clinical proteomics, general practice, laboratory medicine, surgery including plastic surgery and aesthetic medicine, gerontology, psychology, diabetology, endocrinology, oncology, cardiovascular disease, radiology, and healthcare economy. Significance The contents of this article are strongly motivated by the particular value of wound healing quality for medical care and might be of great importance for multi-professional considerations and experts working in specialised fields: predictive and preventive medicine, general practitioners, laboratory medicine, surgery including plastic surgery and aesthetic medicine, gerontology, psychology, diabetology, endocrinology, oncology, cardiovascular disease, radiology, and healthcare economy. The article is aiming at both educational and scientific purposes: on one side it summarises comprehensive information available regarding wound healing mechanisms and molecular pathways involved. On the other side the article provides highly innovative hypotheses for multi-professional considerations relevant for several research fields which may potentially advance medical services in the close future such as clinical proteomics and multi-omics.

In order to accelerate the understanding of pathophysiological mechanisms and clinical biomarker discovery and in psychiatry, approaches that integrate multiple -omics platforms are needed. We introduce a workflow that investigates a narrowly defined psychiatric phenotype, makes use of the potent and cost-effective discovery technology of gene expression microarrays, applies Weighted Gene Co-Expression Network Analysis (WGCNA) to better capture complex and polygenic traits, and finally explores gene expression findings on the proteomic level using targeted mass-spectrometry (MS) technologies. To illustrate the effectiveness of the workflow, we present a proteomic analysis of peripheral blood plasma from patient's remitted major depressive disorder (MDD) who experience ongoing cognitive deficits. We show that co-expression patterns previous detected on the transcript level could be replicated for plasma proteins, as could the module eigengene correlation with cognitive performance. Further, we demonstrate that functional analysis of multi-omics data has the potential to point to cellular mechanisms and candidate biomarkers for cognitive dysfunction in MDD, implicating cell cycle regulation by cyclin D3 (CCND3), regulation of protein processing in the endoplasmatic reticulum by Thioredoxin domain-containing protein 5 (TXND5), and modulation of inflammatory cytokines by Tripartite Motif Containing 26 (TRI26). Significance: This paper discusses how data from multiple -omics platforms can be integrated to accelerate biomarker discovery in psychiatry. Using the phenotype of cognitive impairment in remitted major depressive disorder (MDD) as an example, we show that the application of a systems biology approach - weighted gene co-expression network analysis (WGCNA) - in the discovery phase, and targeted proteomic follow-up of results, provides a structured avenue towards uncovering novel candidate markers and pathways for personalized clinical psychiatry. Refereed/Peer-reviewed

In dynamic network conditions, most routing protocols periodically send additional control messages, which allow the network to quickly adjust itself to topological changes and keep the routing table up to date. In resource-constrained devices, these communication overheads should be controlled in order to conserve battery. In this study, the authors focused mainly on routing protocol for low power and lossy network (RPL), IPv6 RPL networks, which is fully compatible with limited resources and Internet of things expectations. They proposed a dynamic method in order to extract the most suitable value for the frequency of executing the objective function which are rules governing how to construct a network graph. In this method, first nodal data is collected and aggregated along the path to the sink, and then a novel metric is used to determine the degree of the environmental changes and control the signalling overhead in RPL routing protocol. They evaluated their scheme with the Contiki operating system and Cooja simulator. They aimed to decrease the packet loss ratio, routing overhead and energy consumption. The simulation results showed the effectiveness of the proposed method.