European Journal of Science, Innovation and Technology

Architectural Patterns for Integrating Large Language Models (LLMs) into Node.js Server Applications

2026-02-01T15:33:40+02:00

This article examines the evolution and systematization of architectural patterns for integrating large language models (LLMs) into server applications built on the Node.js platform, against the backdrop of the rapid diffusion of generative technologies in industrial software development and the expanding market for Retrieval-Augmented Generation (RAG) solutions. The relevance stems from the fact that by 2025, LLMs will have become an indispensable component of digital products, while server architectures must embed computational speech into existing infrastructures under constraints of token budgets, call costs, and network latency. The objective is to identify and analytically describe stable architectural patterns that enable efficient, predictable LLM integration in Node.js backends. Methodologically, the work combines systemic architectural analysis, modeling of interactions with LLM APIs, and content analysis of industrial practices, enabling the author to construct an engineering-economic efficiency model for each configuration. The article’s novelty lies in formulating the concept of a balanced LLM-integration architecture in which throughput, token price, and service-layer observability are treated as interdependent architectural variables. An evolutionary pathway is proposed for transitioning from monolithic model calls to microservice and serverless patterns, informed by market growth dynamics and the scaling of compute resources. The article will benefit researchers and engineers engaged in server-application architectural design, cloud-service developers, and AI-engineering specialists aiming for resilient and cost-balanced deployment of LLM technologies in production environments.

Machine-Learning-Based Mapping and Ranking of Energy Materials in African Economies

2026-02-01T15:36:16+02:00

The global transition toward clean energy and advanced technologies has led to a rapid increase in demand for critical energy materials, including cobalt, lithium, rare earth elements, and platinum group metals. Although Africa possesses a significant share of these strategic minerals, the continent remains underrepresented in structured, data-driven mineral mapping initiatives. This research introduces a machine-learning framework based on artificial neural networks (ANNs) to predict and prioritize the likelihood of energy material occurrences across African nations. As demonstrated in a 2023 Nature Communications article, machine learning frameworks can map infrastructure such as distribution grids using publicly available multi-modal data, including street view images, road networks, and building maps. The results of this study confirm established mineral hubs, such as the Democratic Republic of Congo and South Africa, while also highlighting underexplored regions with substantial hidden potential. By addressing a critical data and strategy gap, this work provides a reproducible and scalable approach to resource intelligence, offering practical benefits for investors, policymakers, and researchers aiming to align African mineral development with the global energy transition.

A Proposed Master Development Plan for Conservation-Based Ecotourism at Tikub Lake, Tiaong, Quezon

2026-02-01T15:38:18+02:00

Rapid tourism growth, expanding settlements, and resource-based livelihoods are exerting increasing pressure on many small freshwater landscapes in the Philippines. In sensitive crater-lake environments, even modest disturbances can trigger water-quality decline, shoreline erosion, habitat loss, and long-term ecological imbalance. These conditions underscore the need for planning approaches that safeguard natural systems while supporting local economic opportunities. This study responds to that challenge by examining the environmental, spatial, and governance conditions affecting Tikub Lake and formulating a conservation-based Master Development Plan (MDP) that aligns ecotourism development with ecological protection. The research evaluates the lake’s physical characteristics, land use patterns, stakeholder perspectives, and management issues to determine the requirements for a sustainable development framework. It also assesses existing zoning provisions, aquaculture practices, visitor behavior, and infrastructure gaps that influence the lake’s carrying capacity. The proposed MDP consolidates these findings into a structured set of planning strategies that include: (1) a spatial framework composed of a conservation core, protected shoreline strip, and ecotourism support zones; (2) refined land use and zoning designations to regulate development intensity; (3) access and circulation systems that minimize slope disturbance and manage visitor flows; and (4) facility planning standards for sanitation, waste management, trails, viewing areas, community markets, and low-impact eco-lodges. Together, these components create an integrated approach that strengthens environmental safeguards while enabling small-scale, community-driven ecotourism that is both economically viable and ecologically responsible.