A Comparative Analysis of Next.js, SvelteKit, and Astro for E-commerce Web Development

Comparative Analysis of React.js and Vue.js Technologies

The selection of JavaScript-based libraries and frameworks for modern web application development has a direct impact on software performance, scalability, and long-term maintainability. This paper presents a comparative analysis of React.js and Vue.js, two widely used technologies in front-end development. The aim of the study is to evaluate these two approaches not only in terms of syntax and popularity but also with respect to architectural principles, component model, reactivity mechanisms, Virtual DOM implementation, performance, server-side rendering (SSR), ecosystem maturity, tooling support, and long-term maintainability. The research is based on a comparative analysis of the official technical documentation for React.js and Vue.js, as well as scholarly publications indexed in the Scopus and Web of Science databases. The findings indicate that React.js stands out due to its extensive ecosystem, architectural flexibility, and suitability for complex, largescale applications. In contrast, Vue.js offers an integrated reactivity system, a gentler learning curve, comprehensive official tooling, and lower initial complexity, making it an effective solution for smalland medium-sized projects. The study concludes that the choice between React.js and Vue.js should be determined by the project's scale, functional requirements, the development team's technical expertise, and the product's long-term development strategy.

An Empirical Study of WebAssembly Usage in Node.js

VulnScanner Pro: A Rule-Based Automated Web Application Vulnerability Assessment and Risk-Scoring System

VulnScanner Pro is a self-hosted assessment tool that tries to sit in the gap between the two extremes of manual penetration testing and automated security assessment, and recommends four recommendations for fixing the six missing HTTP security headers.

React JS: A Comprehensive Study of an Open-Source JavaScript Library for Building Modern User Interfaces

ReactJS, also known as React or React.js, is an open-source JavaScript library maintained by Meta (formerly Facebook) and a large community of individual developers and companies, used for building fast and interactive user interfaces, especially for single-page applications. It follows a component-based, declarative programming model that makes it possible to build encapsulated components that manage their own state and compose them to create complex user interfaces. React introduced the concept of the virtual DOM, JSX syntax, unidirectional data flow, and, more recently, Hooks, which together provide a productive and predictable way of developing scalable web and mobile applications. This paper extends earlier work on ReactJS by presenting an in-depth review of its architecture, lifecycle methods, Hooks, state-management ecosystem (Redux, Context API, Recoil, Zustand), React Native for cross-platform mobile development, and a comparative performance and adoption analysis against competing frameworks such as Angular and Vue.js. Original charts, a component-hierarchy diagram, and comparison tables are included to support the discussion, and forty peer-reviewed and industry references are cited. The paper concludes that React's flexibility, large ecosystem, and virtual-DOM-driven performance continue to make it one of the most widely adopted front-end technologies in the industry.

A Comparative Evaluation of Large Language Models for Enterprise Deployment: Performance, Safety, Cost, and Scalability Using a Multi-Criteria Decision Framework

The proliferation of large language models (LLMs) across enterprise, research, and public-sector applications has created an urgent need for rigorous, multi-dimensional evaluation frameworks. This paper presents a comprehensive comparative analysis of seven state-of-the-art LLMs — GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro and Flash, LLaMA 3 70B, Mistral Large, and Claude 3 Haiku — across eight evaluation dimensions: benchmark accuracy, safety alignment, cost efficiency, inference latency, context handling, deployment flexibility, multilingual capability, and scalability. A weighted Multi-Criteria Decision Analysis (MCDA) framework is applied to produce transparent composite rankings from empirical benchmark data using five standardized benchmarks (MMLU, HumanEval, HellaSwag, GSM8K, MATH). Results indicate that Claude 3.5 Sonnet achieves the highest MCDA composite score (0.801), driven by accuracy (90.4% MMLU, 92.0% HumanEval) and safety alignment (4.9/5). Gemini 1.5 Flash emerges as optimal for cost-sensitive deployments ($0.075/1M tokens; 210 tok/s). The paper analyzes architectural trade-offs between dense transformers and Mixture-of-Experts designs, provides a deployment recommendation matrix, and contributes an extensible, evidence-based decision framework for enterprise AI practitioners.

AI: Claude Code Model Comparative Study in MAPI

This research would help developers to opt for the right model with the default option suggested by Cloud Code, in terms of effectiveness, correctness, time effectiveness, and LOC (lines of code).