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Particularly, the relational database management systems and the big data systems (e.g., Key-Value stores, Document stores, Graph stores and Graph Computation Systems, Spark, MapReduce/Hadoop, or Data Stream Processing Systems) have evolved with novel additions and extensions. The paper by Harish Kumar Harihara Subramanian et al. on supporting DBMS GPU-based operations with out-of-the-box libraries describes the ongoing research in the database community regarding the use of GPUs for query processing. Data management systems have evolved in terms of functionality, performance characteristics, complexity, and variety during the last 40 years. [Extracted from the article]

In the paper by Dwivedi et al., a compact size wideband circularly polarized 2-port MIMO antenna is designed for 5G with (<= - 10 dB) impedance bandwidth of 900 MHz in the frequency band 3.3-4.2 GHz and 100% 3-dB axial ratio bandwidth. 5G wireless standards will play a vital role in ensuring security, enabling automation on many fronts such as connected vehicles, and facilitating data rates that are orders of magnitude greater than 4G LTE and thereby enabling rapid access to information. [Extracted from the article]

We document the growth in published papers on behavioral genetics for 5-year intervals from 1960 through 2014. We used 1861 papers published in Behavior Genetics to train our search strategy which, when applied to Ovid PsychINFO, selected more than 45,000 publications. Five trends stand out: (1) the number of behavioral genetic publications has grown enormously; nearly 20,000 papers were published in 2010-2014. (2) The number of human quantitative genetic (QG) publications (e.g., twin and adoption studies) has steadily increased with more than 3000 papers published in 2010-2014. (3) The number of human molecular genetic (MG) publications increased substantially from about 2000 in 2000-2004 to 5000 in 2005-2009 to 9000 in 2010-2014. (4) Nonhuman publications yielded similar trends. (5) Although there has been exponential growth in MG publications, both human and nonhuman QG publications continue to grow. A searchable resource of this corpus of behavioral genetic papers is freely available online at and will be updated annually. [ABSTRACT FROM AUTHOR]

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis. [ABSTRACT FROM AUTHOR]

Intelligent algorithms have excellent performance in managing large databases and mining effective knowledge, and many researchers have introduced these algorithms into research to improve the performance of book management systems. This article introduced the application of machine learning algorithms in electronic book database management systems. A linear classifier (linear regression algorithm is one of machine learning algorithms) was used to analyze the empirical loss value. This paper studied the application of machine learning algorithm in the database management of library special collection resources, aiming to optimize the management of library special collection resources database through machine learning algorithm, and provided students with better experience of using special collection resources. This paper verified the effectiveness of the machine learning algorithm from three aspects: the retrieval time before and after the special resource database, the number of crashes in a month and the number of library staff. The average time of the five sets using the machine learning algorithm was about 150% faster than the average search time of the five sets of special resource databases. The number of crashes after using machine learning algorithm was 2, 1, 1, 2, 1, which was much lower than that of special resource database. After using the machine learning algorithm, the five groups of workers were 7, 5, 9, 6, and 4, respectively, far fewer than the five groups of special collectors. Finally, by comparing the linear regression model, Support vector machine model, and random forest model, it was found that the accuracy of the linear regression model reached 98.2%, an increase of 8.2% compared to the random forest model, the precision rate reached 96.7%, and the recall rate reached 98.2%. 97.8%, F1 value reached 97.5%.It can be seen from the experimental data that the machine learning algorithm plays a good role in the database management of the library special collection resources.Article Highlights: Through linear regression algorithm, it can be identified by features, so this paper establishes an attribute linear classifier for each attribute. This paper found that machine learning algorithm can reduce the crash problem of special resource database. This paper found that machine learning algorithms improve the efficiency of a database dedicated to collecting resources, thereby saving human resources. [ABSTRACT FROM AUTHOR]

Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching. [ABSTRACT FROM AUTHOR]

Relying on network technology, the integration of personalized learning and Internet technology has become another trending industry. This paper explores a new strategy for education management, that is, a personalized information push system based on recommendation algorithms. The system can push personalized learning resources for teachers and students and help them quickly locate interest points and learning directions by analyzing their usage history and tag attribute characteristics. The personalized information push algorithm achieves data fidelity by pre-cleaning or pre-processing the data. In addition, after the clustering algorithm is integrated into the system, its computing efficiency and mining depth are greatly improved than before. At the same time, based on collaborative filtering technology, this paper introduces information entropy and standard deviation to optimize the core algorithm, so as to distinguish the similarity between users, and further push recommendation accuracy and precision to a higher level. Finally, the existing problems in the current development of big data education management are analyzed, and future development strategies are proposed. To sum up, the personalized information recommendation system proposed in this study has a lower MAE value, so this has forward-looking significance for enhancing the depth of interactive learning and changing the inherent learning mode. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

In the process of continuous development of rural economy, big data management plays an important role. Rural economic data mainly include rural labor force, added value of agriculture, forestry, fishery and animal husbandry, agricultural product output and per capita net income. Using big data to manage economic data can not only integrate and analyze a large number of data, build an economic management analysis database, but also predict and warn related risks in time and provide reasonable and effective economic management suggestions, thus ensuring the smooth operation of the economy. However, there are some shortcomings in data reliability, data quality and data management efficiency in rural regional economic data management, which have a great impact on the long-term stable development of the economy. This paper makes a systematic analysis of rural regional economic data, uses the blockchain consensus mechanism to quickly manage and analyze different types of economy in rural areas, and improves the speed of economic data analysis through smart contracts. This paper makes a comparative analysis of rural regional economic data management before and after adopting blockchain technology. In view of this phenomenon, according to the development demand of data management, this paper uses blockchain technology to effectively analyze and study the big data management of rural regional economy, and makes a comparative analysis through the tests of data operation efficiency, data quality, data management security and sharing degree. The experimental data show that the efficiency of data operation is as high as 1100 times per minute after the combination of blockchain technology, and the minimum manual modification ratio of node data is 0.24. This shows that the data quality of technical means is guaranteed. The data leakage rate per hour is at least 66%, and the data sharing volume is maintained at about 8000 views/hour and 7000 references/hour. The improvement of quality and efficiency can ensure the effectiveness of data management and improve the speed of economic development in rural areas, thus promoting good economic development. This paper uses blockchain technology to manage rural economic data, which has certain reference value for rural economic development. [ABSTRACT FROM AUTHOR]

Deep learning aims to learn the internal laws and representation levels of sample data. The information obtained in the learning process is of great help in the interpretation of data such as text, images and sounds. With the continuous development of modern technology, vision-based autonomous navigation technology, as the core of UAV technology, has received extensive attention worldwide. However, in the complex flight environment of drones, the capability of the airborne image sensor is affected by weather factors and defocusing diffraction phenomena, which will result in low image resolution. In response to this problem, this paper studies deep learning super-resolution reconstruction algorithms (SRRAs) for aerial images and proposes a super-resolution reconstruction method based on deep learning. Through the experimental design of the algorithm in this paper, we can conclude that the method proposed in this paper not only increases the sharpness of aerial images by approximately 10% but also increases the speed of image reconstruction by approximately 20% compared with the traditional depth reconstruction algorithm. [ABSTRACT FROM AUTHOR]

Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future. [ABSTRACT FROM AUTHOR]

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models. [ABSTRACT FROM AUTHOR]

In June 2021, FDA released a Draft Guidance on Sponsor Responsibilities for IND Safety Reporting and cited components of a recommended Safety Surveillance Plan (SSP). To meet the expectations of the 2021 FDA guidance, sponsors should document their plan for aggregate safety assessment. The Drug Information Association-American Statistical Association Interdisciplinary Safety Evaluation scientific working group has proposed an Aggregate Safety Assessment Plan (ASAP) that addresses this recommendation. The 2021 FDA guidance also discusses potential strategies for unblinded review of safety data from ongoing studies by an independent Assessment Entity, which could occur via planned periodic evaluations or "triggered" reviews based on blinded data assessments. The Assessment Entity reviewing unblinded data makes recommendations as to whether the threshold has been met for submission of an aggregate IND safety report. In this paper, we discuss how the ASAP supports IND aggregate safety reporting decisions, including elements to be included in a proposed SSP appendix to the ASAP. In addition, the authors advocate for the benefits of developing a charter (or specific section of the Data Monitoring Committee charter, if applicable) that describes the responsibilities and conduct of the Assessment Entity. With these components in place, study sponsors will meet the objective of having clearly defined processes for the monitoring of clinical trial safety data in aggregate and making IND safety reporting decisions. [ABSTRACT FROM AUTHOR]

The paper is aimed at establishing a corrosion risk assessment model for the buried gas pipeline. To realize the forecast of the gas pipeline corrosion damage risks, the adverse factors of the safe operation were classified, and the index evaluation system of possibility of gas pipeline failure was established based on five first-level indicators and six second-level indicators. According to the index evaluation system, improved analytic hierarchy process (I-AHP) method was used to determine the weight of each indicator, and then the fuzzy comprehensive evaluation was proposed based on improved AHP. The calculation and analysis were carried out, and the membership function of the fuzzy comprehensive evaluation suitable for gas pipeline corrosion risk assessment was obtained, which had improved the practicability. Taking the buried gas pipeline network of Suzhou Industrial Park, China, as an example, the assessment was carried out. Some buried gas pipelines were randomly selected for excavation to verify the degree of conformity between the observation results and the calculation results. The results show that: the evaluation results of this method are consistent with the field detection results. In addition, the evaluation model established in this paper can provide a reference for the establishment of accident database and the management for the pipeline operation enterprise. [ABSTRACT FROM AUTHOR]

With the continuous economic development, the total amount of data in banks is growing bigger and bigger and it is urgent and necessary to manage these data effectively. This paper studied Hadoop-based bank historical data management from the perspective of data migration, and designed and realized a data migration module. Map Reduce data migration was used in the process of structured data migration. An IO load-based scheduling algorithm was also designed. When scheduling, considering the consumption of resources, it is avoided to assign tasks to heavy-loaded IO nodes. An unstructured data migration tool was developed by smartly using FTP to concurrently migrate log files of online service platforms and other data to HDFS specified directories. The final system test results show that the system in this paper can work normally and meet the design requirements. [ABSTRACT FROM AUTHOR]

Approaches to access control (AC) policy languages, such as eXtensible access control markup language, do not provide a formal representation for specifying rule- and policy-combining algorithms or for verifying properties of AC policies. Some authors propose formal representations for these combining algorithms. However, the proposed models are not expressive enough to represent formally history-based classes of these algorithms, such as ordered-permit-overrides. In addition, some other authors propose a formal representation but do not present automated support for formal verification of properties of AC policies that use these algorithms. This paper demonstrates a new representation that can express all existing AC rule and policy combinations of which the authors are aware. This representation can also be used to automate the formal verification of properties of AC policies related to these algorithms. A new modeling representation for rule- and policy-combining algorithms based on state machines is used to specify rule- and policy-combining algorithms. Examples of these algorithms are programmed in the language of the SPIN model checker, and the programs are then used to support the automated formal verification of properties of AC policies. We present our approach and then use the AC policies and properties of CONTINUE, a conference management system, to compare it with prior work. Our first contribution is a new modeling representation for combining algorithms based on state machines. The second contribution is the formal verification of AC properties under certain combining algorithms that are beyond the capability of other approaches. [ABSTRACT FROM AUTHOR]

Exploitation of the ever-increasing volume of observational data has become a major challenge for real-world evidence (RWE). Causal inference can be viewed as emulations of clinical trials (ECTs) from real-world data (RWD). This requires in-depth knowledge of data management and modern statistical models, which evolve considerably. We have developed Plug-Stat® to facilitate these analyses. Plug-Stat® is a user-friendly graphical interface of R software implemented with JavaScript and VB.net. The implementation of Plug-Stat® for a specific database consists of two main tasks. The first concerns the automatic importation of data to link the software with the data and to avoid the repetition of data management activities for each study. The second is to define the outcomes and the associated possible confounders. To perform an ECT, we proposed the use of inverse probability weighting, flexible parametric estimation of the propensity score model with B-splines, and robust estimation of the variance. The statistical modeling steps are automatized to direct the user toward the correct methodology without data management. We illustrated the usefulness of Plug-Stat® with several recently published ECTs. In this paper, we presented the outcome comparison of preemptive deceased-donor kidney transplantations versus transplantations after dialysis. The interactive interfaces assist the user in conducting causal research based on ECT, and tutorials are available, offering self-training opportunities. Plug-Stat® reduces the time and, consequently, the cost necessary for different studies from the same cohort. [ABSTRACT FROM AUTHOR]

Purpose: Trauma registries are essential tools for trauma systems and underpin any quality improvement activities. This paper describes the history, function, challenges, and future goals of the New Zealand National Trauma Registry (NZTR). Methods: Using the available publications and knowledge of the authors, the development, governance, oversight, and usage of the registry is outlined. Results: The New Zealand Trauma Network has run a national trauma registry since 2015 and this now contains over fifteen thousand major trauma patient records. Annual reports and a range of research outputs have been published. Key quality improvement initiatives have been undertaken and are described. Vulnerabilities include lack of longterm funding and a small workforce. Conclusions: The NZTR has proven to be a critical component of trauma quality improvement in New Zealand. A user-friendly portal and a simple minimum dataset have been keys to successes but maintenance of an effective structure in a constrained healthcare system is a challenge. [ABSTRACT FROM AUTHOR]