REST API auto generation: a model-based approach

t. Most of software products, especially mobile applications (apps) rely on a back-end web services to communicate with a shared data repository. Statistics have demonstrated exponential demand on web services, mainly REST, due to the continuous adoption of IoT (Internet of Things) and Cloud Computing. However, the development of back-end REST web services is not a trivial task, and can be intimidating even for seasoned developers. Despite the fact that there are several studies that focus on automatic generation of REST APIs, we argue that those approaches violate the rules of code flexibility and are not appropriate for novice developers. In this study, we present an approach and a framework, named RAAG (REST Api Auto-Generation), that aims to improve productivity by simplifying the development of REST web services. Our RAAG framework abstracts layers, where code generation has been avoided due its limitations. A preliminary evaluation shows that RAAG can significantly improves development productivity and is easy to operate even by novice developers

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Need Analysis for Higher Educational Institutions for using Virtual Reality-TESLA Project: Staff Willingness and Readiness for Using VR in Teaching

Recently, different solutions were proposed in literature to investigate the use of virtual reality in educational context. This is referred to the fact that virtual reality showed some interesting benefits over classical learning materials. This paper shows a study that was carried out in the context of TESLA Erasmus+ project to investigate both willingness and readiness of staff members in Palestinian partners. A questionnaire was distributed, and data were gathered from a sample of 100 staff members from four Palestinian HEIs who are involved in the project. The results are discussed, and some recommendations are given. Among the important results, the instructors’ attitude is positive. However, they need to improve their skills in terms of techno-pedagogical aspects in virtual reality

Illustrative Rendering of Segmented Anatomical Data

Medical illustrations use simple drawing styles to demonstrate shapes and features of organs and organ systems, while omitting irrelevant details. In this contribution, we present a non-photorealistic rendering algorithm for illustrating anatomical data. Unlike other existing methods that rely on available triangulations of the organs to be rendered, our approach relies only on a subset of surface points, together with their normals. The advantage is that surface points and normals can be quite easily extracted from medical data, without an intermediate triangulation.

Live-Wire Revisited

Abstract. The live-wire approach is an interactive, contour-based segmentation technique. Generally, the contour of a targeted object (anatomical structure) is built by interactively selecting control points and finding minimal-cost paths between them. By its very nature, this method is applicable only to 2D images. For the segmentation of 3D datasets (volumes), the interactive generation of live-wire contours has to be applied to each slice of the volume. This process can be quite tedious, due to the sometimes intensive user interaction. In this contribution, we propose adaptive propagation as an alternative to individually processing all image slices or shape-based interpolation of live-wire contours. 1 Introduction and Related Work In the context of medical image segmentation, and due to object characteristics as well as image quality, a fully-automatic segmentation is in most cases not possible. Moreover, the results of automatic segmentation methods often need further correction. For example, the complex anatomical structure of th

Electromagnetic tracking system with reduced distortion using quadratic excitation

PURPOSE: Electromagnetic tracking systems, frequently used in minimally invasive surgery, are affected by conductive distorters. The influence of conductive distorters on electromagnetic tracking system accuracy can be reduced through magnetic field modifications. This approach was developed and tested. METHODS: The voltage induced directly by the emitting coil in the sensing coil without additional influence by the conductive distorter depends on the first derivative of the voltage on the emitting coil. The voltage which is induced indirectly by the emitting coil across the conductive distorter in the sensing coil, however, depends on the second derivative of the voltage on the emitting coil. The electromagnetic tracking system takes advantage of this difference by supplying the emitting coil with a quadratic excitation voltage. The method is adaptive relative to the amount of distortion cause by the conductive distorters. This approach is evaluated with an experimental setup of the electromagnetic tracking system. RESULTS: In vitro testing showed that the maximal error decreased from 10.9 to 3.8 mm when the quadratic voltage was used to excite the emitting coil instead of the sinusoidal voltage. Furthermore, the root mean square error in the proximity of the aluminum disk used as a conductive distorter was reduced from 3.5 to 1.6 mm when the electromagnetic tracking system used the quadratic instead of sinusoidal excitation. CONCLUSIONS: Electromagnetic tracking with quadratic excitation is immune to the effects of a conductive distorter, especially compared with sinusoidal excitation of the emitting coil. Quadratic excitation of electromagnetic tracking for computer-assisted surgery is promising for clinical applications

Clustering-based method for big spatial data partitioning

“Internet of Things” (IoT) is considered one of the main focus areas of research in computer systems and networks. Since IoT devices are installed in static geographic places or on board a moving trackable object, the data generated by the device is mainly characterized as spatial data. The spatial data generated by IoT devices scale up in volume, velocity, and veracity so they tend to be considered “Big Data”. “Big Spatial Data” requires the development of special frameworks that use state-of-the-art technologies in data storage, query, and analysis. These frameworks are mainly characterized by the use of a parallel programming model that partitions the spatial data into smaller chunks that can be handled in parallel. The development of an optimal method for spatial data partitioning is essential in implementing such systems.In this paper, we propose, design, and implement a new method for spatial data partitioning based on K-Means clustering, an unsupervised machine learning algorithm. The design is based on a well-defined conceptual, mathematical, and programming model for a general spatial data partitioning method. The main component of the suggested model is an implementation of K-Means clustering suited for spatial data.The new method is designed, implemented, and tested to prove its ability to achieve partitioning objectives and the efficiency of its performance. The results of the tests are benchmarked against one of the most widely adopted approaches in partitioning spatial data and prove the ability of the novel method to surpass it in some of the evaluation criteria