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Dealing with huge amounts of information in traditional ways has become difficult in a particular place, even if the complexity and overlapping of interests between the different institutions between them, as well as between these institutions and the members of the community that they serve, has necessitated dealing with more than one customer at the same time and with the same documents sometimes and this is not provided by the work environment Paper, the dealing in the paper environment does not exceed two at one time as it is prone to loss or damage, and from here emerged to us another different environment which is the digital environment for dealing with official records and files and archiving them electronically to achieve more effectiveness in dealing with them. This research aims to shed light on the importance of the digital transformation in institutional work, the procedures followed and the various stages of completing the transformation process from paper to digital.

The electrocardiogram (ECG) signal describes the heart’s electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient’s clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists.

Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value

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The electrocardiogram (ECG) signal describes the heart's electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient's clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists. [ABSTRACT FROM AUTHOR]

Government digital agendas worldwide want to help develop the digital transformation in businesses and public administra-tions, while acknowledging the digital changes taking place in society and the need to integrate information security (IS). Although information communication technology (ICT) shapes our lives, we tend to have an insufficient knowledge of the risks involved, of information security (IS), and of the General Data Protection Regulation (GDPR); this is compounded by carelessness in handling data and insufficient IS awareness (ISA). Backed by a clear conceptual approach, information security awareness trainings (ISAT) are also essential for everyone. However, classical trainings are not currently working. Psychologically based research shows that a systemic approach might be helpful. This is where analogue game-based learning (GBL) comes into play.

With paper map use in decline, one of the strategies that libraries and archives can adopt to make the information contained within them more accessible and usable is to extract features of interest from their scanned raster maps and convert those to geospatial vector data. This process adds valuable unique data to library geospatial collections and enables those previously map-bound features to be used separately in geographic information systems (GIS) software for custom mapping and analysis. Advances in partially automating most of the process have made this a much more viable option for libraries and archives. Although there is no one-size-fits-all automated solution for all maps and map features, this paper provides a complete description of the entire process incorporating examples of the various techniques and software used in selected studies that would be applicable in the library and archive environment.

In the future, learning will be essentially characterized by the ability to regulate the learning process and monitor success independently from a teacher. The technical possibilities offer better access to learning contents, precise and more individualized feedback, and learning phases adapted precisely to the needs of the learner in terms of scope and pace. In this study, we investigate an important aspect of the digitization of teaching/learning processes using the example of laboratory scripts for chemistry students at university. The focus is on looking up terms and concepts in preparation for the lab internships, firstly in a paper-based glossary and secondly in a digital glossary. During a two-day study, a total of 16 students prepared for experiments on two topics with completely identical materials. We then studied the influence of content knowledge, motivation, and cognitive load. While all students show significant learning achievements, there are no significant differences between the groups. Furthermore, results show that pure digitization of information has no effect, despite the theoretically assumed advantages.In the future, learning will be essentially characterized by the ability to regulate the learning process and monitor success independently from a teacher. The technical possibilities offer better access to learning contents, precise and more individualized feedback, and learning phases adapted precisely to the needs of the learner in terms of scope and pace. In this study, we investigate an important aspect of the digitization of teaching/learning processes using the example of laboratory scripts for chemistry students at university. The focus is on looking up terms and concepts in preparation for the lab internships, firstly in a paper-based glossary and secondly in a digital glossary. During a two-day study, a total of 16 students prepared for experiments on two topics with completely identical materials. We then studied the influence of content knowledge, motivation, and cognitive load. While all students show significant learning achievements, there are no significant differences between the groups. Furthermore, results show that pure digitization of information has no effect, despite the theoretically assumed advantages. [ABSTRACT FROM AUTHOR]