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The building blocks of research are developing at an unprecedented pace. Data collection, analysis, interpretation, presentation, review, and publication take place completely on computers. The final product often is still a static document with only limited links to the underlying digital material, making transparency and reproducibility a challenge. In this work we apply the mechanism of badges to provide prominent connections to underlying analyses environments and important (meta-)data to readers of scholarly publications in geospatial data science. An API specification and implementation for a badge server provide extended and regular badges. The badges leverage recognition value for executability, licensing, spatial extent, and peer-review metadata – base information which either is or should be made available. We show a client-side integration method across many third-party platforms that allows evaluation of badges in realistic scenarios. The server and client enable an independent spreading of badges. The open source publication of all used software enables reproducibility and extensibility. The badges show potential to enhance interaction with scholarly works and should be evaluated with academic users in the future.

With the vast amount of data and information difficult to deal with, especially in the health system, machine learning algorithms and data mining techniques have an important role in dealing with data. In our study, we used machine learning algorithms with thyroid disease. The goal of this study is to categorize thyroid disease into three categories: hyperthyroidism, hypothyroidism, and normal, so we worked on this study using data from Iraqi people, some of whom have an overactive thyroid gland and others who have hypothyroidism, so we used all of the algorithms. Support vector machines, random forest, decision tree, naïve bayes, logistic regression, k-nearest neighbors, multi-layer perceptron (MLP), linear discriminant analysis. To classification of thyroid disease.

Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.