Data Infrastructure for a Poisoning Registry with Designing Data Elements and a Minimum Data Set

Background: The prevalence of poisoning is on the rise in Iran. A poisoning registry is a key source of information about poisoning patterns used for decision-making and healthcare provision, and a minimum dataset (MDS) is a prerequisite for developing a registry. Objectives: This study aimed to design a MDS for a poisoning registry. Methods: This applied study was conducted in 2021. A poisoning MDS was developed with a four-stage process: (1) conducting a systematic review of the Web of Science, Scopus, PubMed, and EMBASE, (2) examining poisoning-related websites and online forms, (3) classification of data elements in separate meetings with three toxicology specialists, and (4) validating data elements using the two-stage Delphi technique. A researcher-made checklist was employed for this purpose. The content validity of the checklist was examined based on the opinions of five health information management and medical informatics experts with respect to the topic of the study. Its test-retest reliability was also confirmed with the recruitment of 25 experts (r = 0.8). Results: Overall, 368 data elements were identified from the articles and forms, of which 358 were confirmed via the two-stage Delphi technique and classified into administrative (n = 88) and clinical data elements (n = 270). Conclusions: The creation of a poisoning registry requires identifying the information needs of healthcare centers, and an integrated and comprehensive framework should be developed to meet these needs. To this end, a MDS contains the essential data elements that form a framework for integrated and standard data collection.

Power Mean Based Image Segmentation in the Presence of Noise

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state of the art models.

HIV/AIDS-Pneumonia Codynamics Model Analysis with Vaccination and Treatment

In this paper, we proposed and analyzed a realistic compartmental mathematical model on the spread and control of HIV/AIDS-pneumonia coepidemic incorporating pneumonia vaccination and treatment for both infections at each infection stage in a population. The model exhibits six equilibriums: HIV/AIDS only disease-free, pneumonia only disease-free, HIV/AIDS-pneumonia coepidemic disease-free, HIV/AIDS only endemic, pneumonia only endemic, and HIV/AIDS-pneumonia coepidemic endemic equilibriums. The HIV/AIDS only submodel has a globally asymptotically stable disease-free equilibrium if R 1 < 1 . Using center manifold theory, we have verified that both the pneumonia only submodel and the HIV/AIDS-pneumonia coepidemic model undergo backward bifurcations whenever R 2 < 1 and R 3 = max R 1 , R 2 < 1 , respectively. Thus, for pneumonia infection and HIV/AIDS-pneumonia coinfection, the requirement of the basic reproduction numbers to be less than one, even though necessary, may not be sufficient to completely eliminate the disease. Our sensitivity analysis results demonstrate that the pneumonia disease transmission rate β 2 and the HIV/AIDS transmission rate β 1 play an important role to change the qualitative dynamics of HIV/AIDS and pneumonia coinfection. The pneumonia infection transmission rate β 2 gives rises to the possibility of backward bifurcation for HIV/AIDS and pneumonia coinfection if R 3 = max R 1 , R 2 < 1 , and hence, the existence of multiple endemic equilibria some of which are stable and others are unstable. Using standard data from different literatures, our results show that the complete HIV/AIDS and pneumonia coinfection model reproduction number is R 3 = max R 1 , R 2 = max 1.386 , 9.69 = 9.69 at β 1 = 2 and β 2 = 0.2 which shows that the disease spreads throughout the community. Finally, our numerical simulations show that pneumonia vaccination and treatment against disease have the effect of decreasing pneumonia and coepidemic disease expansion and reducing the progression rate of HIV infection to the AIDS stage.

Application of Decision Tree Intelligent Algorithm in Data Analysis of Physical Health Test

With the continuous development of China’s cultural industry, people’s health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained.

Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. We experimented with different missing value imputation techniques and compared machine learning (ML) model performances with different imputation methods. We propose a hybrid model for missing value imputation combining ML and sample-based statistical techniques. Furthermore, we continued with the best missing value inputted dataset, chosen based on ML model performance for feature engineering and hyperparameter tuning. We used k-means clustering and principal component analysis. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, we used K-fold cross-validation.

A Framework for an Artificial-Neural-Network-Based Electronic Nose

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

Improving the Accuracy of Aerial Photography Using Ground Control Points

The authors showed that it is possible to quickly collect up-to-date information on the agricultural land condition using an unmanned aerial vehicle. It was noted that the use of ground control points increases the accuracy of project measurements, helps to compare the project post-processing results with the real measurements. (Research purpose) To compare the results of standard and high-precision post-processing of aerial survey data using ground control points. (Materials and methods) Aerial photography was carried out on a 1.1- hectare breeding field. The authors used DJI Matrice 200 v2 unmanned aerial vehicle with a GNSS L1/L2 receiver and a modified DJI X4S camera, five control points sized 50 × 50 centimeters and an EMLID Reach RS2 multi-frequency GNSS receiver. The results of scientific research into the use of ground control points during aerial photography were studied. (Results and discussion) It was found out that the error of georeferencing images obtained by an unmanned aerial vehicle without control points is significantly higher during the standard data processing compared to the high-precision one. The project error when using five control points is 3.9 times higher during the standard data processing. (Conclusions) It was shown that using ground control points it is possible to improve the project measurement accuracy, as well as compare the project post-processing results with the measurements on the ground. It was detected that the high-precision monitoring enables the use of fewer ground control points. It was found out that in order to obtain data with the accuracy of 2-4 centimeters in plan and height, at least 3 ground control points need to be used during the high-precision post-processing.

The improvement of museum information flow based on paste functional mapping method

As a subsidiary institution of collecting and preserving cultural relics, human cultural heritages and developing information resources, museums are important places to display, disseminate and study excellent national cultures. With the development of the economy and the improvement of people's living standards, people's demand for spiritual culture is getting higher and higher, and museums are getting more and more attention from people. Thanks to the rapid development of computer technology, more and more museums are focusing on informatisation. This article will use the information system's functional mapping method based on the museum's improvement and test analysis, which can make the museum's business unit with the use of the unified system platform, under a unified, homogeneous standard data for the orderly organisation of information resources sharing and efficient, rapid building of information resources of lateral connection, make the administrative, business interaction and interrelated information collection with Word Perfect data integration, and create a museum management platform more conducive to innovative thinking. The innovation system mechanism, to improve the way of ZhanChen for the museum, to speed up the informatisation construction museum, make a museum of play to better spread knowledge, transfer civilisation, materialisation of education function and the important window displaying the world the outstanding civilisation achievement effect, and promote the development of cultural undertakings, where science has a very important realistic and far-reaching historical significance.

Using the Data Quality Dashboard to Improve the EHDEN Network

Federated networks of observational health databases have the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Collaboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Fifteen Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance issues but showed less of an impact on completeness and plausibility checks. This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.

Detailed Study of Cloud Storage Services on Aws Cloud (Amazon Web Services)

This research revolves around understanding the Cloud Storage Services offered by world's most famous Cloud Provider Amazon Web Services (AWS). We will be covering major Cloud Storage Services like EBS, S3 and EFS. But first let’s understand more about AWS. We should use these end-of-life services as a per-project and keep in mind the key benefits of these end-to-end services. Amazon EBS brings the highest end-to-end prices available with block for level of Amazon Elastic Compute Cloud (EC2) instances. Saves data to file system stored after EC2 status closure. Amazon EFS provides portable file storage, also designed for EC2. It can be used as a standard data source for any application or load that works in most cases. Using the EFS file system, you can configure file system installation settings. The main difference between EBS and EFS is that EBS is only accessible from a single EC2 state in your specific AWS region, while EFS allows you to mount a file system in multiple regions and scenarios.