Programming Language Of Choice Research Articles

DESIGN AND DEVELOPMENT OF A HOSPITAL BIOMETRIC DATA MANAGEMENT SYSTEM

The need to drastically reduce the delay faced by patients before they receive treatment in the University Clinic and the need to reduce the occurrence of medical mistakes due to patient’s misidentification led to this work which aimed at developing a hospital biometric data management system.Java was the programming language of choice and Netbeans was the Integrated Development Environment (IDE) used to design the Graphical User Interphase (GUI) and write codes. For the backend database, MySQL was used for demonstration purposes. The analytical diagrams were drawn using VioletUMLEdit. The Griaule Fingerprint Software Development Kit (SDK) was used to implement the fingerprint capture, enrollment, identification and verification features of the software.The tests and results obtained showed that with this hospital management system, the time taken to retrieve patient’s record has been reduced from several minutes to less than five seconds thereby eliminating the delay experienced by patients during search of their records. The problem of medical mistakes due to patient’s misidentification was also addressed by the biometric feature of the management system.

PIKAChU: a Python-based informatics kit for analysing chemical units

As efforts to computationally describe and simulate the biochemical world become more commonplace, computer programs that are capable of in silico chemistry play an increasingly important role in biochemical research. While such programs exist, they are often dependency-heavy, difficult to navigate, or not written in Python, the programming language of choice for bioinformaticians. Here, we introduce PIKAChU (Python-based Informatics Kit for Analysing CHemical Units): a cheminformatics toolbox with few dependencies implemented in Python. PIKAChU builds comprehensive molecular graphs from SMILES strings, which allow for easy downstream analysis and visualisation of molecules. While the molecular graphs PIKAChU generates are extensive, storing and inferring information on aromaticity, chirality, charge, hybridisation and electron orbitals, PIKAChU limits itself to applications that will be sufficient for most casual users and downstream Python-based tools and databases, such as Morgan fingerprinting, similarity scoring, substructure matching and customisable visualisation. In addition, it comes with a set of functions that assists in the easy implementation of reaction mechanisms. Its minimalistic design makes PIKAChU straightforward to use and install, in stark contrast to many existing toolkits, which are more difficult to navigate and come with a plethora of dependencies that may cause compatibility issues with downstream tools. As such, PIKAChU provides an alternative for researchers for whom basic cheminformatic processing suffices, and can be easily integrated into downstream bioinformatics and cheminformatics tools. PIKAChU is available at https://github.com/BTheDragonMaster/pikachu.Graphical

Netherite

Serverless is a popular choice for cloud service architects because it can provide scalability and load-based billing with minimal developer effort. Functions-as-a-service (FaaS) are originally stateless, but emerging frameworks add stateful abstractions. For instance, the widely used Durable Functions (DF) allow developers to write advanced serverless applications, including reliable workflows and actors, in a programming language of choice. DF implicitly and continuosly persists the state and progress of applications, which greatly simplifies development, but can create an IOps bottleneck. To improve efficiency, we introduce Netherite, a novel architecture for executing serverless workflows on an elastic cluster. Netherite groups the numerous application objects into a smaller number of partitions, and pipelines the state persistence of each partition. This improves latency and throughput, as it enables workflow steps to group commit, even if causally dependent. Moreover, Netherite leverages FASTER's hybrid log approach to support larger-than-memory application state, and to enable efficient partition movement between compute hosts. Our evaluation shows that (a) Netherite achieves lower latency and higher throughput than the original DF engine, by more than an order of magnitude in some cases, and (b) that Netherite has lower latency than some commonly used alternatives, like AWS Step Functions or cloud storage triggers.

Productively accelerating positron emission tomography image reconstruction on graphics processing units with Julia

Research in medical imaging is hampered by a lack of programming languages that support productive, flexible programming as well as high performance. In search for higher quality imaging, researchers can ideally experiment with novel algorithms using rapid-prototyping languages such as Python. However, to speed up image reconstruction, computational resources such as those of graphics processing units (GPUs) need to be used efficiently. Doing so requires re-programming the algorithms in lower-level programming languages such as CUDA C/C++ or rephrasing them in terms of existing implementations of established algorithms in libraries. The former has a detrimental impact on research productivity and requires system-level programming expertise, and the latter puts severe constraints on the flexibility to research novel algorithms. Here, we investigate the use of the Julia scientific programming language in the domain of PET image reconstruction as a means to obtain both high performance (portability) on GPUs and high programmer productivity and flexibility, all at once, without requiring expert GPU programming knowledge. Using rapid-prototyping features of Julia, we developed basic and performance-optimized GPU implementations of baseline maximum likelihood expectation maximization (MLEM) positron emission tomography (PET) image reconstruction algorithms, as well as multiple existing algorithmic extensions. Thus, we mimic the effort that researchers would have to invest to evaluate the quality and performance potential of algorithms. We evaluate the obtained performance and compare it to state-of-the-art existing implementations. We also analyse and compare the required programming effort. With the Julia implementations, performance in line with existing GPU implementations written in the low-level, unproductive programming language CUDA C is achieved, while requiring much less programming effort, even less than what is needed for much less performant CPU implementations in C++. Switching to Julia as the programming language of choice can therefore boost the productivity of research into medical imaging and deliver excellent performance at a low cost in terms of programming effort.

Classification of galaxy type from images using Microsoft R Server

AbstractMany astronomers working in the field of AstroInformatics write code as part of their work. Although the programming language of choice is Python, a small number (8%) use R. R has its specific strengths in the domain of statistics, and is often viewed as limited in the size of data it can handle. However, Microsoft R Server is a product that removes these limitations by being able to process much larger amounts of data. I present some highlights of R Server, by illustrating how to fit a convolutional neural network using R. The specific task is to classify galaxies, using only images extracted from the Sloan Digital Skyserver.

PyParse: A semiautomated system for scoring spoken recall data

Studies of human memory often generate data on the sequence and timing of recalled items, but scoring such data using conventional methods is difficult or impossible. We describe a Python-based semiautomated system that greatly simplifies this task. This software, called PyParse, can easily be used in conjunction with many common experiment authoring systems. Scored data is output in a simple ASCII format and can be accessed with the programming language of choice, allowing for the identification of features such as correct responses, prior-list intrusions, extra-list intrusions, and repetitions.

Sharing with Python

Sharing with Python

Visual Basic and the future of Web-application development

For the past decade, Microsoft's Visual Basic has been the programming language of choice for Windows-application developers. And recently, Microsoft has made the popular language available for Web-based applications. However, as a growing amount of application development targets the Web, other languages, such as Sun Microsystems' Java, appear to be challenging VB's dominance. VB has remained popular despite prior changes to the language. However, there may not have been as many viable alternatives as there are now today, other languages such as Java and C++ have become Web-development tools of choice. Developers may decide to use these languages because they are more mature and because VB.Net will be more complex to learn and use.

Lisp: An AI programming language

Lisp has been the programming language of choice of most artificial intelligence (AI) researchers since the very early days of AI research. There are many reasons why this is so, some of which will be examined later in this paper, but perhaps the simplest reason is functional crispness. Every language has arbitrary features, and most languages have been overloaded with them in order to support diverse constituencies. Lisp is built around a kernel of mathematical principles which makes it far more flexible than most other (perhaps all) computer programming languages.