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The data collected from the real world are diverse and include categorical data , numerical data, incomplete data and noisy data. In addition, many real data sets may dynamically vary, and dynamic data display characteristics with multi-dimensional variations. However, for mixed incomplete data systems, most of the existing incremental methods only work well with single-dimensional dynamic data sets and are not suitable for processing specific multi-dimensional variations of objects and attributes. In this paper, we focus on researching dynamic approaches to efficiently update three-way regions based on the simultaneous variations of the object set and the attribute set in an incomplete neighborhood decision system (INDS). First, considering the complexity of data, we utilize matrix approaches to calculate three-way regions of the INDS based on a proposed neighborhood tolerance relation. Then, under the simultaneous addition of the object set and the attribute set in the INDS, we research incremental mechanisms based on the matrix to obtain three-way regions from previous knowledge. Subsequently, an incremental algorithm for updating three-way regions is proposed when the object set and the attribute set are simultaneously added to the INDS. Finally, the results of a series of experiments and comparisons based on UCI data sets show that the performance of the proposed incremental algorithm is much better than that of the traditional static algorithm, the integrated single-dimensional incremental algorithm and the single-level combined incremental algorithm.

Summary: The COVID-19 pandemic has made vivid the need for resilient, high-quality health systems and presents an opportunity to reconsider how to build such systems. Although even well resourced, well performing health systems have struggled at various points to cope with surges of COVID-19, experience suggests that establishing health system foundations based on clear aims, adequate resources, and effective constraints and incentives is crucial for consistent provision of high-quality care, and that these cannot be replaced by piecemeal quality improvement interventions. We identify four mutually reinforcing structural investments that could transform health system performance in resource-constrained countries: revamping health provider education, redesigning platforms for care delivery, instituting strategic purchasing and management strategies, and developing patient-level data systems. Countries should seize the political and moral energy provided by the COVID-19 pandemic to build health systems fit for the future.

Guyana's capacity to address the impacts of climate change on its coastal environment requires the ability to monitor, quantify and understand coastal change over short-, medium- and long- term. Understanding the drivers of change in coastal and marine environment can be achieved through the accurate measurement and critical analyses of morphologies, flows, processes and responses. This manuscript presents a strategy developed to create a central resource, database and web-based platform to integrate data and information on the drivers and the changes within Guyana coastal and marine environment. The strategy involves four complimentary work packages including data collection, development of a platform for data integration, application of the data for coastal change analyses and consultation with stakeholders. The last aims to assess the role of the integrated data systems to support strategic governance and sustainable decision-making. It is hoped that the output of this strategy would support the country's climate-focused agencies, organisations, decision-makers, and researchers in their tasks and endeavours.

E-Government implementation is the use of technology, information, and communication to realize more efficient and effective government practices in the process of implementing public services in order to facilitate public access to information and create principles of accountability, transparency and good public participation in the Indonesian government. This study aims to explain the implementation of E-Government in Indonesia which encourages the development of information and data systems and is adapted to existing bureaucratic processes. The method used in this research is qualitative research methods. The results showed that public service innovation organized by the government of the Republic of Indonesia was able to create relationships between elements in a country online, not inline, so that efficiency and speed in public services was not only a symbol but a reality.

Youth-serving organizations seek effective and cost-efficient solutions to build evidence and advance their impact. Some common challenges include choosing data systems or assessments, budgeting and planning for 3rd-party studies, and refining measurement and outcomes when programs expand or change. Evaluation advisory boards (EABs) are a low-cost solution to add evaluation capacity and can be mutually beneficial to both youth-serving organizations and evaluation experts. Previous research suggests that EABs may encourage meaningful use of data, support internal evaluators, and/or facilitate difficult conversations among stakeholders. However, there are very few examples of successful EABs in practice. This paper shares the perspectives of EAB members and organizational evaluation leaders from a large national after-school program, After-School All-Stars (ASAS), including (a) a description of the benefits of EABs, (b) how EABs may be especially helpful with the context of the COVID-19 pandemic, and (c) examples of youth-serving organizations’ EABs. The experiences and lessons learned by ASAS and its EAB are generalizable to other non-profit youth development programs. Recommendations for structuring EABs based on organizational goals are provided.

Objective This study: 1) characterized the app market by EHR app gallery and type of app; 2) tracked changes in the EHR app galleries from the end of 2019 through 2020; and 3) examined how apps connect to EHR data systems, and if the apps support the HL7 FHIR standard. Materials and Methods We developed a program that gathered data from the public app galleries hosted by Allscripts, athenahealth, Cerner Corporation, Epic Systems Corporation, and SMART. Data collection for this study began in December 2019 and ended December 2020. The program was run 11 times during this period, and the data collected were used to generate the findings and trends observed in this study. Results The total number of unique apps increased from 600 to 734 during the study period. The most common types of apps marketed were intended for administrative (42%) and clinical use (38%). About 1 in 5 apps (22%) described support for the FHIR standard. Support for FHIR varied by intended functionality and gallery. Discussion This study provides early insights into the number of third-party apps that are connecting to EHRs, what services they provide, and if these connections use standards-based application programming interfaces (APIs). Conclusion It is a federal government priority to improve the access and use of electronic health information, including third-party apps that can introduce competition as well as best-of-breed functions and user experiences. This study shows that there is room for growth, and variation exists among some of the largest EHR developers.

Medicine is often thought of as a science of the body, but it is also a science of data. In some contexts, it can even be asserted that data drive health. This article focuses on a key piece of data technology central to contemporary practices of medicine: the medical record. By situating the medical record in the perspective of its history, we inquire into how the kinds of data that are kept at sites of clinical encounter often depend on informational requirements that originate well outside of the clinic, in particular in health insurance records systems. Although this dependency of today’s electronic medical records on billing requirements is widely lamented by clinical providers, its history remains little studied. Following the archaeology of medicine developed by Michel Foucault in The Birth of the Clinic and expanding his methodology in light of more recent contributions to the field of media archaeology, this article excavates some of the underexplored technological conditions that help constitute today’s electronic medical record. If in some contexts, it is true that data drive health, then an archaeology of medical records helps reveal how health insurance records often impact clinical care and, by extension, health and disease.

Administrative data in health can contain some important information, such as the identity of patients, doctors, and even health facilities. Data security is needed to maintain confidentiality and prevent parties who do not misuse the data. The purpose of this study is to answer the gap that exists with the use of QR Code Documents. Then the QR Code is divided for Administrative Management. In previous studies using QR codes based on Cryptography for administrative data systems in the health sector. In this study, confidential information data messages are embedded into a Cryptographic-based QR code using the AES Advanced Encryption Standard Method. The QR code will correct the error generated by the secret embedding procedure into the document, then the QR code can validate to display the data. The author conducted testing with brute force and AES methods to ensure the security of the application running properly in the data. The result of this research is that the QR code can enter Medical Record Administration data documents using the AES method. With this, it is expected to significantly reduce the potential for data leakage from the Medical Record Administration. The conclusion of the authors in this study aims to perform data administration by using a QR Code based on Cryptography. The author has presented an approach to accessing data using the storage technology used for authentication when performing data administration requests. The use of this AES Advanced Encryption Standard security method provides the advantages of ease of use and efficient use of resources

Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a term that describes the process of connecting computers, smart devices, and other data-generating equipment to a network and transmitting data. As a result, data is produced and updated on a regular basis to reflect changes in all areas and activities. As a consequence of this exponential growth of data, a new term and idea known as big data have been coined. Big data is required to illuminate the relationships between things, forecast future trends, and provide more information to decision-makers. The major problem at present, however, is how to effectively collect and evaluate massive amounts of diverse and complicated data. In some sectors or applications, machine learning models are the most frequently utilized methods for interpreting and analyzing data and obtaining important information. On their own, traditional machine learning methods are unable to successfully handle large data problems. This article gives an introduction to Spark architecture as a platform that machine learning methods may utilize to address issues regarding the design and execution of large data systems. This article focuses on three machine learning types, including regression, classification, and clustering, and how they can be applied on top of the Spark platform.

Data systems collecting information from different sources or over long periods of time can receive multiple reports from the same indi- vidual. An important example is public health surveillance systems that monitor conditions with long natural histories. Several state-level systems for surveillance of one such condition, the human immunodeficiency virus (HIV), use codes composed of combinations of non-unique personal charac- teristics such as birth date, soundex (a code based on last name), and sex as patient identifiers. As a result, these systems cannot distinguish between several different individuals having identical codes and a unique individual erroneously represented several times. We applied results for occupancy models to estimate the potential magnitude of duplicate case counting for AIDS cases reported to the Centers for Disease Control and Prevention with only non-unique partial personal identifiers. Occupancy models with equal and unequal occupancy probabilities are considered. Unbiased estimators for the numbers of true duplicates within and between case reporting areas are provided. Formulas to calculate estimators' variances are also provided. These results can be applied to evaluating duplicate reporting in other data systems that have no unique identifier for each individual.

The agricultural sector is undergoing a period of rapid transformation, driven by the powerful and interconnected impacts of climate change, demographic transitions and uneven economic growth around the world. For governments and the international community to navigate this period of upheaval to protect vulnerable populations and ensure positive societal change will require a similar degree of transformation within agricultural data systems. While technological innovation has resulted in substantive improvements in the availability, timeliness and overall quality of agricultural data, many technical and institutional challenges remain. This paper reviews recent developments in the agricultural data landscape, highlights existing constraints to further progress and argues for agricultural economists to take responsibility for building agricultural data systems equipped to respond to the diverse needs of a changing world.

In recent years, we have seen increased interest in applying machine learning to system problems. For example, there has been work on applying machine learning to improve query optimization, indexing, storage layouts, scheduling, log-structured merge trees, sorting, compression, and sketches, among many other data management tasks. Arguably, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately, these techniques will allow us to build "instance-optimized" systems: that is, systems that self-adjust to a given workload and data distribution to provide unprecedented performance without the need for tuning by an administrator. While many of these techniques promise orders-of-magnitude better performance in lab settings, there is still general skepticism about how practical the current techniques really are. The following is intended as a progress report on ML for Systems and its readiness for real-world deployments, with a focus on our projects done as part of the Data Systems and AI Lab (DSAIL) at MIT By no means is it a comprehensive overview of all existing work, which has been steadily growing over the past several years not only in the database community but also in the systems, networking, theory, PL, and many other adjacent communities.

Quasi-cyclic low-density parity-check (QC-LDPC) codes, which have been adopted by the Consultative Committee for Space Data Systems (CCSDS), are widely used in Deep-Space (AR4JA) and Near-Earth (C2) communications. A large number of encoder architectures with CCSDS recommended standard have been proposed. But the existing architectures are not efficient enough in high-throughput implementations, many architectures have a lot of logical resources and registers. In this brief, we introduce a novel architecture with low resource utilization. A grouping algorithm is used to extract the common subexpressions (CS) of the encoding algorithm. Similar circuit structures are integrated through a two-layer architecture, which further reduces logical resources. For the special size of the generator’s matrix, we introduce a preprocessing method. In addition, configuration registers are used to replace the control unit. Implemented and verified on FPGA, the proposed architecture achieves a throughput of 4.69 Gbps using only 1658 LUTs and 1038 FFs. Compared with the previous architectures, this architecture achieves lower resource utilization and multi-Gbps throughput.

The complex and evolving picture of COVID-19–related mortality highlights the need for data to guide the response. Yet many countries are struggling to maintain their data systems, including the civil registration system, which is the foundation for detailed and continuously available mortality statistics. We conducted a search of country and development agency Web sites and partner and media reports describing disruptions to the civil registration of births and deaths associated with COVID-19 related restrictions. We found considerable intercountry variation and grouped countries according to the level of disruption to birth and particularly death registration. Only a minority of the 66 countries were able to maintain service continuity during the COVID-19 restrictions. In the majority, a combination of legal and operational challenges resulted in declines in birth and death registration. Few countries established business continuity plans or developed strategies to deal with the backlog when restrictions are lifted. Civil registration systems and the vital statistics they generate must be strengthened as essential services during health emergencies and as core components of the response to COVID-19.

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Deep learning (DL) is growing in popularity for many data analytics applications, including among enterprises. Large business-critical datasets in such settings typically reside in RDBMSs or other data systems. The DB community has long aimed to bring machine learning (ML) to DBMS-resident data. Given past lessons from in-DBMS ML and recent advances in scalable DL systems, DBMS and cloud vendors are increasingly interested in adding more DL support for DB-resident data. Recently, a new parallel DL model selection execution approach called Model Hopper Parallelism (MOP) was proposed. In this paper, we characterize the particular suitability of MOP for DL on data systems, but to bring MOP-based DL to DB-resident data, we show that there is no single "best" approach, and an interesting tradeoff space of approaches exists. We explain four canonical approaches and build prototypes upon Greenplum Database, compare them analytically on multiple criteria (e.g., runtime efficiency and ease of governance) and compare them empirically with large-scale DL workloads. Our experiments and analyses show that it is non-trivial to meet all practical desiderata well and there is a Pareto frontier; for instance, some approaches are 3x-6x faster but fare worse on governance and portability. Our results and insights can help DBMS and cloud vendors design better DL support for DB users. All of our source code, data, and other artifacts are available at https://github.com/makemebitter/cerebro-ds.

It is a very difficult problem to achieve high-order functionality for graphical dependency parsing without growing decoding difficulties. To solve this problem, this article offers a way for Semantic Graphical Dependence Parsing Model (SGDPM) with a language-dependency model and a beam search to represent high-order functions for computer applications. The first approach is to scan a large amount of unnoticed data using a baseline parser. It will build auto-parsed data to create the Language-dependence Model (LDM). The LDM is based on a set of new features during beam search decoding, where it will incorporate the LDM features into the parsing model and utilize the features in parsing models of bilingual text. Our approach has main benefits, which include rich high-order features that are described given the large size and the additional large crude corpus for increasing the difficulty of decoding. Further, SGDPM has been evaluated using the suggested method for parsing tasks of mono-parsing text and bi-parsing text to carry out experiments on the English and Chinese data in the mono-parsing text function using computer applications. Experimental results show that the most accurate Chinese data is obtained with the best known English data systems and their comparable accuracy. Furthermore, the lab-scale experiments on the Chinese/General bilingual information in the bitext parsing process outperform the best recorded existing solutions.

Information systems are devices that are generally used in an organization or company in managing data or information. One of the advantages of this information system is in the search for data information needed by the industry. Logistics activities require an information system that can represent a real situation that contains all activities to expedite logistics activities as well as making logistics policies quickly and accurately. Therefore it is necessary to have an integrated data system to support all logistical activities. The purpose of this study was to analyze the application of management data systems in logistics management. The benefit is to make it easier to solve the problem of delays in sending information data. The method used in this research is descriptive qualitative research. The results of this information search will eventually be stored and then used as a database in the industry so that when the industry requires data retrieval it can be easier to do it quickly and accurately.

The development of the era where everything is instantaneous and fast as it is today must be supported by the development of increasingly sophisticated information technology. One of the functions of this technological development is to obtain accurate and fast information. But in fact, there are still companies / organizations that have not used computer technology as a work aid, such as the Bromo Rajawali Convection which still uses the manual method of writing employee data, namely data storage still using books. Therefore, the design of an employee data management information system is carried out that can help companies control the production data of each tailor employee. The method used in this research is the Waterfall method. It is called a waterfall because the steps that are passed must wait for the completion of the previous stage and run sequentially, for example the design stage must wait for the completion of the previous stage, namely the requirements stage [1]. The Waterfall method consists of 5 stages, namely Analysis, Design, Coding, Testing and Maintenance. The purpose of this study is to design an information system that can make it easier for companies to write down employee production data accurately. From the design results, there are two factors that support the running of the system, namely administration and production data, the input form available on the system that has been designed, namely the login input form and the employee data input form, while the output form contains complete data related to employee personal data and employee production data. With the creation of a management information system in writing data on employee production, the Rajawali Bromo Convection can accelerate the search for more accurate data in the future, and applications made later by the company can be used properly as one of the employee production data systems

Problems of mathematical modeling of onboard air data systems errors are of paramount importance in pitot-static sources errors determination, air data systems evaluation in flight tests. The problems of development, identification and assessment of the mathematical models of errors adequacy acquire main importance in the modern technology of the air parameters true values determination, air data systems evaluation using satellite navigation systems, developed and applied in the practice of flight tests at JSC "FRI n.a. M. M. Gromov". This paper gives a general description of an air data systems estimation technology using satellite navigation systems. The principles of solving problems of aircraft data systems aerodynamic errors mathematical modeling are stated. The structure of mathematical models, factors of the aerodynamic errors, relationship of the solving problems of errors modeling within the framework of technology with a flight experiment plan are shown. Mathematical models parameters identification are based on a complex solving of the problems of a true air data parameters values and aerodynamic errors determination in flight tests. New results of mathematical modeling of errors in tests at high angles of attack in 2018 year of medium-range and short-range aircraft are presented. The results illustrate the technology effectiveness in solving the problems of flight tests at high angles of attack information support, aerodynamic errors modeling, air data systems estimation. The applied modeling methods make it possible to allocate in the mathematical models of pitot-static sources aerodynamic errors even the factors of very weak aerodynamic influence, comparable with the minimum pressure sensors instrumental errors.

The Ovarian-Adnexal Reporting and Data System (O-RADS) is a lexicon and risk stratification tool designed for the accurate characterization of adnexal lesions and is essential for optimal patient management. O-RADS is a recent addition to the American College of Radiology (ACR) reporting and data systems and consists of ultrasound (US) and MRI arms. Since most ovarian or adnexal lesions are first detected with US, O-RADS US is considered the primary assessment tool. Application of O-RADS US is recommended whenever a nonphysiologic lesion is encountered. Lesion characterization may be streamlined by use of an algorithmic approach focused on relevant features and an abbreviated version of the lexicon. Resources to expedite O-RADS US categorization and determination of a management recommendation include easy online access to the ACR color-coded risk stratification scorecards and an O-RADS US calculator that is available as a smartphone app. Reporting should be concise and include relevant features for risk stratification that adhere to lexicon terminology. Technical considerations include optimization of gray-scale and color Doppler technique and performance of problem-solving maneuvers to help avoid common pitfalls. This review provides a user-friendly summary of O-RADS US with practical tips for everyday clinical use.

Objective The study sought to develop an in-depth understanding of how hospitals with a long history of health information technology (HIT) use have responded to the COVID-19 (coronavirus disease 2019) pandemic from an HIT perspective. Materials and Methods We undertook interviews with 44 healthcare professionals with a background in informatics from 6 hospitals internationally. Interviews were informed by a topic guide and were conducted via videoconferencing software. Thematic analysis was employed to develop a coding framework and identify emerging themes. Results Three themes and 6 subthemes were identified. HITs were employed to manage time and resources during a surge in patient numbers through fast-tracked governance procedures, and the creation of real-time bed capacity tracking within electronic health records. Improving the integration of different hospital systems was identified as important across sites. The use of hard-stop alerts and order sets were perceived as being effective at helping to respond to potential medication shortages and selecting available drug treatments. Utilizing information from multiple data sources to develop alerts facilitated treatment. Finally, the upscaling/optimization of telehealth and remote working capabilities was used to reduce the risk of nosocomial infection within hospitals. Discussion A number of the HIT-related changes implemented at these sites were perceived to have facilitated more effective patient treatment and management of resources. Informaticians generally felt more valued by hospital management as a result. Conclusions Improving integration between data systems, utilizing specialized alerts, and expanding telehealth represent strategies that hospitals should consider when using HIT for delivering hospital care in the context of the COVID-19 pandemic.