Rapid Application Development: A Rapid Automation Framework

The Framework for Rapid Graphics Application Development: The Multi-scale Problem Visualization

<p>Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons.</p><p>Objectives: The recently emerged automated machine learning (AutoML) has shown promise in making ML more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop the ML models for treatment outcome prediction.</p><p>Methods: The patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Treatment was considered successful if angiographic complete occlusion was achieved at follow-up. A statistical prediction model was developed using multivariate logistic regression. In addition, two ML models were developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC).</p><p>Results: The aneurysm size, stent-assisted coiling (SAC), and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models.</p><p>Conclusions: This study demonstrated the feasibility of using AutoML to develop a high-quality ML model, which may outperform the statistical model and manually derived ML models. AutoML could be a useful tool that makes ML more accessible to the clinical researchers.</p>

Physically unclonable functions (PUFs) are emerging as hardware primitives for key-generation and light-weight authentication. Strong PUFs represent a variant of PUFs which respond to a user challenge with a response determined by its unique manufacturing process variations. Unfortunately many of the Strong PUFs have been shown to be vulnerable to model building attacks when an attacker has access to challenge and response pairs. In mounting a model building attack, typically machine learning is used to build a software model to forge the PUF. Researchers have long been interested in designing Strong PUFs that are resistant to model building attacks. However, with innovations in application of machine learning, nearly all Strong PUFs presented in the literature have been broken. In this paper, first we present results from a set of experiments designed to show that if certain randomness properties can be met, cascaded structure based Strong PUFs can indeed be made machine learning (ML) attack resistant against known ML attacks. Next we conduct machine learning experiments on an abstract PUF model using Support Vector Machines, Logistic Regression, Bagging, Boosting and Evolutionary techniques to establish criteria for machine learning resistant Strong PUF design. This paper does not suggest how to harvest the process variation, which remains within the purview of a circuit designer; rather it suggests what properties of the building blocks to aim for towards building a machine learning resistant Strong PUF - thus paving the path for a systematic design approach.

Rapid Application Development (RAD) has been proposed by some as the latest ‘silver bullet‘ to address the software crisis, while others have likened it to ‘old wine in new bottles’, suggesting that it doesn’t really provide anything new in terms of tools or techniques. Researchers have noted that while much has been written on the subject, there is a paucity of empirical research on the concept in practice. This paper discusses the rationale behind the emergence of RAD and the fundamental principles underpinning it. The findings of a preliminary study which investigated the RAD phenomenon in practice are presented. Briefly, the study findings focus on the profile of RAD usage, suitability criteria for using RAD on development projects, and how RAD is used to deliver its mission of faster development, within budget and without sacrificing system quality.

Several difficulties commonly arise during the software development process. Among them are the lengthy technical process of developing a system, the limited number and technical capabilities of human resources, the possibility of bugs and errors during the testing and implementation phase, dynamic and frequently changing user requirements, and the need for a system that supports multi-platforms. Rapid application development (RAD) is the software development life cycle (SDLC) that emphasizes the production of a prototype in a short amount of time (30-90 days). This study discovered that implementing a model-driven architecture (MDA) approach into the RAD method can accelerate the model design and prototyping stages. The goal is to accelerate the SDLC process. It took roughly five weeks to construct the system by applying all of the RAD stages. This time frame does not include iteration and the cutover procedure. During the prototype test, there were no errors with the create, read, update, and delete (CRUD) procedure. It was demonstrated that automatic transformation in MDA can shorten the RAD phases for designing the model and developing an early prototype, reduce code errors in standard processes like CRUD, and construct a system that supports multi-platform.

Mobile app development within mental health is often time- and resource-consuming, challenging the development of mobile apps for psychiatry. There is a continuum of software development methods ranging from linear (waterfall model) to continuous adaption (Scrum). Rapid application development (RAD) is a model that so far has not been applied to psychiatric settings and may have some advantages over other models. This study aims to explore the utility of the RAD model in developing a mobile app for patients with borderline personality disorder (BPD) in a psychiatric outpatient setting. The 4 phases of the RAD model: (1) requirements planning, (2) user design, (3) construction, and (4) cutover, were applied to develop a mobile app within psychiatric outpatient services for patients diagnosed with BPD. For the requirements planning phase, a short time frame was selected to minimize the time between product conceptualization and access within a clinical setting. Evidenced-based interactive content already developed was provided by current staff to enhance usability and trustworthiness. For the user design phase, activity with video themes and a discrete number of functions were used to improve the app functionality and graphical user interface. For the construction phase, close collaboration between clinicians, researchers, and software developers yielded a fully functional, in-house-developed app ready to be tested in clinical practice. For the cutover phase, the mobile app was tested successfully with a small number (n=5) of patients with a BPD. The RAD model could be meaningfully applied in a psychiatric setting to develop an app for BPD within a relatively short time period from conceptualization to implementation in the clinic. Short time frames and identifying a limited number of stakeholders with relevant skills in-house facilitated the use of this model. Despite some limitations, RAD could be a useful model in the development of apps for clinical populations to enable development and access to evidence-based technology.

The rapid adoption of machine learning (ML) has increased the computational and storage demands on high-performance computing (HPC) and cloud datacenters. Unfortunately, deploying ML models on HPC and cloud platforms is challenging because it is often difficult to manage, financially costly, and poses substantial environmental concerns. This dissertation attempts to answer a simple but ambitious question: can we deploy ML models to meet the Quality of Service (QoS) requirements while remaining cost-effective and environmentally sustainable? The dissertation demonstrates that achieving this balance is indeed possible, albeit through employing non-intuitive strategies and user engagements. We show that a combination of fast and slow hardware can outperform the common strategy of exclusively employing the latest and fastest hardware when co-optimizing for both performance and cost. Realizing this potential, however, requires a strategy to create an effective hardware mix, a process rigorously developed within this dissertation. This dissertation further reveals that the carbon footprint of ML models can be managed by opportunistically trading off answer quality transiently and re-configuring/partitioning the GPU hardware resources, depending on the carbon intensity of available energy sources in the datacenter's region. Finally, this dissertation discovers that ML deployments can be made more environmentally sustainable by strategically engaging users and tuning response brevity, especially in the context of large language models (LLMs). The scientific advancements made in this dissertation aim to relieve the community from the cost constraints, hardware inefficiencies, and potential negative environmental impacts of large-scale deployment of ML models on HPC and cloud platforms. We are hopeful that its open-source contributions will promote further innovation and broader engagement toward sustainable ML deployment.--Author's abstract

Computer technology that is increasingly developing has created a situation that demands everything to be computerized. Science from technology has also changed the way of life of most Indonesians. The presence of information and communication technology affects the technology applied in information systems, especially the input and output mechanisms. One of them is at the Clinic which requires computer technology in its operations. The design of this website also uses 3 stages of Rapid Application Development (RAD) system design, the result of which is a website for a medical record information system. In order for clinical activities to run smoothly at the Clinic, an information system using the Rapid Application Development (RAD) method is needed because the software development process model is classified as an incremental technique and emphasizes short, short, and fast development cycles. Medical record system testing will be tested using Blackbox Testing and User Acceptance Test (UAT) testing.

The development of the internet of things has penetrated in several lines, including in agriculture. To respond to the development of this IoT requires rapid technological adaptation as well. To build a system quickly, one can utilize the Rapid Application Development (RAD) method. RAD is a model that allows non-experts to benefit from high-performance computing, while allowing expert programmers to take full advantage of the underlying hardware. This enables rapid prototyping, retargeting, and reuse of existing software, while allowing hardware-specific optimization if needed. The RAD system emphasizes the fast development cycle that is designed and high-quality results from other methods such as waterfall, agile, scrum and others. The system was built using the minimum NodeMCU system with the help of a soil moisture sensor. The use of the RAD method in building an IoT-based agricultural irrigation system gives good results in testing all functions and equipment controls. The average delay level on testing IoT devices is around 4.6 seconds so that the RAD method can be used as a reference in the construction of an IoT-based system.

Layanan warga di tingkat kelurahan memiliki peran vital dalam meningkatkan kualitas hidup masyarakat. Desa Harjatani, seperti banyak kelurahan lainnya, menghadapi tantangan keterbatasan sumber daya manusia, prosedur birokrasi yang kompleks, dan kurangnya transparansi. Penelitian ini bertujuan mengatasi permasalahan tersebut melalui pengembangan sistem informasi layanan masyarakat berbasis web dengan model Rapid Application Development (RAD). Dengan pendekatan RAD yang iteratif dan berorientasi pengguna, waktu pengembangan sistem informasi dapat dipersingkat. Tinjauan pustaka membahas konsep pelayanan publik, metode pengembangan perangkat lunak, dan model RAD. Metode penelitian melibatkan tahapan dari Requirements Planning hingga Construction, dengan penekanan pada interaksi intensif dengan pengguna dan adaptasi fleksibel terhadap perubahan. Penelitian ini berpotensi menjadi panduan bagi pengembangan website sistem informasi layanan masyarakat di Desa Harjatani, dengan tujuan meningkatkan efisiensi pelayanan dan kepuasan masyarakat.Kata kunci: Sistem Informasi, Layanan Masyarakat, RAD, Web

AIS research opportunities utilizing Machine Learning: From a Meta-Theory of accounting literature

In recent years, the number and scope of events in the Asia and Middle East / North Africa region has increased significantly. Events are one of the things that attracts the attention of foreign tourists to come to visit a country as a tourist destination. Refers toMinister of Tourism Regulation Number 2 Year 2017on Guidelines for Venues for Meetings, Incentive Trips, Conventions and Exhibitions, explains that criteria and indicators are needed to increase the competitiveness of venues for meetings, incentive trips, conventions and exhibitions at the regional and global levels. This is done to increase the number of visits, length of stay, and expenditures for domestic and foreign tourists. Therefore, visual indicators or data are needed that can present the location of the event and a list of events in DKI Jakarta. From these problems, this study aims to produce a web-based spatial information system for the distribution of tourism industry events to map event locations in DKI Jakarta. The methodology used in collecting this data is the method of observation, interviews, and literature studies. While the system development methodology uses PHP and MySQL with the Rapid Application Development (RAD) approach and uses the Unified Modeling Language (UML) which is a tool for system description and design, as well as the Google Maps API for digital maps. The results of this study are in the form of a spatial information system that can display a map of the distribution of event locations in DKI Jakarta based on event data and event categories at the DKI Jakarta Tourism and Creative Economy Department. While the system development methodology uses PHP and MySQL with the Rapid Application Development (RAD) approach and uses the Unified Modeling Language (UML) which is a tool for describing and designing systems, as well as the Google Maps API for digital maps. The results of this study are in the form of a spatial information system that can display a map of the distribution of event locations in DKI Jakarta based on event data and event categories at the DKI Jakarta Tourism and Creative Economy Department. While the system development methodology uses PHP and MySQL with the Rapid Application Development (RAD) approach and uses the Unified Modeling Language (UML) which is a tool for describing and designing systems, as well as the Google Maps API for digital maps. The results of this study are in the form of a spatial information system that can display a map of the distribution of event locations in DKI Jakarta based on event data and event categories in the DKI Jakarta Tourism and Creative Economy Department.

The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature, and catalytic function is crucial for unlocking a multitude of enzyme applications. Machine learning has recently emerged as a tool for quantitative prediction of enzyme activity from protein sequences. Unfortunately, ML models designed to predict the comprehensive enzyme activity parameter, kcat/Km, from protein sequences are rare compared to those predicting kcat or Km alone. Combining both protein sequence and temperature as input features further challenges predictions; no current ML models capture the nonlinear relationship between kcat/Km and temperature for a protein sequence of interest. In this study, we developed a unique three-module ML framework that predicts β-glucosidase kcat/Km values based on protein sequence and temperature. Each module was designed to capture a distinct aspect of the interplay among protein sequence, temperature, and kcat/Km for β-glucosidase activity; when integrated, they formed an ML framework that maps the sequence and temperature spaces associated with β-glucosidase kcat/Km. This modular approach allowed for optimizations of ML models within each module, collectively achieving notable generalization performance when predicting temperature-dependent kcat/Km values for protein sequences not encountered during training. Our findings underscore the advantages of the three-module framework over traditional single-module methods, particularly by reducing prediction variability due to data splitting and mitigating overfitting. We anticipate that our multimodule ML framework will be directly applicable to other complex systems, enabling quantitative exploration of their property domains.

BackgroundAlterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts.ResultsWe develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves strong performance across functional annotation tasks and several cell line and primary tumor cancer samples. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to mGSH metabolism in cancers. SLC25A10, SLC25A50, and orphan SLC25A24, SLC25A43 are predicted to be associated with mGSH metabolism in multiple biological contexts and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39.ConclusionThese findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers.

The effectiveness of system analysis and design methodologies plays a pivotal role in the success of software development projects. This research conducts a comparative analysis of various methodologies, including the Waterfall Model, Spiral Model, Prototyping Model, Iterative Model, Unified Process, Object-Oriented Analysis and Design, Joint Application Design, Computer Aided Software Engineering Tools, Rapid Application Development / Rapid Systems Development, Feature- Driven Development, Extreme Programming, Agile Method, and DevOps, to explore their respective strengths and considerations. The study examines key aspects such as flexibility, documentation, risk management, complexity, and suitability across these methodologies. Findings reveal that each methodology offers unique advantages and challenges, influencing their applicability in different project contexts. The research underscores the importance of aligning methodology choices with project-specific requirements, organizational culture, and environmental factors to optimize software development outcomes. Moreover, it suggests that hybrid approaches, combining elements from multiple methodologies, may offer a balanced approach to address diverse project needs and maximize effectiveness. By providing insights into the comparative effectiveness of system analysis and design methodologies, this research contributes to informed decision- making and improved project success in software development endeavors.