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.

A GIS and Space Syntax-Based Walkability Development Plan for The Heritage District of Taal, Batangas

2026-02-04T22:48:45+02:00

This study developed a GIS- and Space Syntax–based Walkability Development Plan (WDP) for the Población Heritage District of Taal, Batangas, a heritage town where walking supports everyday mobility and tourism activity but is constrained by narrow streets, discontinuous sidewalks, and encroachments. Guided by local planning intentions that prioritize pedestrian connectivity while preserving heritage values, the research examined how spatial configuration relates to pedestrian movement and on-ground walkability conditions. A mixed-method convergent design integrated GIS-based spatial analysis, segment-based Space Syntax modeling, walkability audits, pedestrian counts, and perception surveys to assess pedestrian accessibility, safety, comfort, and infrastructure adequacy. Results show that pedestrian movement patterns align closely with spatial configuration: corridors with high global integration (Rn), high local integration (R3), and high choice (betweenness) correspond with observed pedestrian concentrations near major religious, commercial, and heritage nodes. However, audit and survey results indicate that many movement-critical streets exhibit poor walkability due to discontinuous or narrow sidewalks, obstructions, limited accessibility for persons with disabilities, and inadequate supporting amenities. The composite walkability index further indicates that very low walkability conditions occur in high-demand areas, clarifying the need for targeted and tiered interventions. Based on these findings, the study proposes a WDP that prioritizes corridors by urgency, classifies streets by functional typology, and outlines heritage-sensitive strategies for pedestrian space reallocation, traffic management, vendor regulation, inclusive accessibility, and monitoring mechanisms.

Bit-Width Quantization and Prompt Optimization: Achieving 90% Energy Savings in Large Language Models

2026-02-04T22:54:20+02:00

For the rapidly evolving field of Large Language Models (LLMs, the rapid scaling has posed significant challenges. These problems include exorbitant energy consumption, prohibitively expensive deployment, and a significant impact on environmental sustainability. A major contributor to this problem is LLMs' colossal size. Typically, there are billions of parameters, and the need for them to be run in resource-scarce or edge environments. Our research delves into a functional and immediately applicable solution to kickstart the energy efficiency of LLMs by merging low-bit-width quantization and streamlined prompt techniques.

We have tested this approach with Llama-based models ranging from hundreds of millions to over one billion parameters and applied 4-bit post-training compression combined with structured prompt and query optimization to this spectrum of models. Utilizing a well-controlled A/B testing framework, we evaluated the task accuracy, delay, and power consumption between our baseline and optimized configurations. Since we can measure the actual power usage of our hardware, we could use the formula accuracy-per-watt to sum up the performance of both configurations. Our results show that 4-bit compression all by itself knocks out a significant portion of memory usage and electricity consumption, and then, our fine-tuning of the prompts cuts down the cost of token-level inference. When used in tandem, these two techniques have led to a 90% reduction in energy consumption with virtually no or statistically insignificant losses in accuracy on the tests we ran.

We also verified the effectiveness of this strategy for real-world use, demonstrating that it delivers consistent efficiency benefits when running on severely constrained hardware. The scalability analysis showed that this method still delivers a lot of bang for the buck even for models that have over a billion parameters.

Molecular Dynamics Simulation of Aluminum Nitride Deposition: Temperature Effects and Energy Analysis

2026-02-20T22:42:37+02:00

At optimal substrate temperatures of 1400–1600 K, aluminum nitride (AlN) thin films exhibited up to a 35% reduction in defect density and retained over 90% of injected atoms compared to films grown outside this range, promising significant improvements in device performance by enhancing crystalline quality and reducing failure risk. This clear quantitative outcome highlights a precise processing window for device engineers seeking to maximize reliability and efficiency in AlN-based thin-film components. This study makes several unique contributions to the field. First, unlike prior molecular dynamics or experimental studies which have largely provided qualitative insights or reported only isolated retention or energy data our work delivers the first set of quantitative benchmarks that directly correlate atom retention rates with the energy evolution of the system across a continuous series of deposition temperatures. Notably, previous works such as Zhang et al. (2018) and Chen et al. (2016) have discussed general temperature effects on crystallinity and defect formation, but have not systematically provided explicit, temperature-dependent retention-energy relationships or defined actionable processing windows. Here, we introduce continuous, stepwise analysis that tracks both retained atom fraction and corresponding energy changes at each deposition interval, mapped for every temperature in the deposition range. By rigorously mapping the interplay between temperature, atom retention, and film defect density, and providing new retention-versus-energy performance curves, our study establishes a practical temperature window and a quantitative framework that enables direct comparison with both simulations and experimental results. This previously unreported set of benchmarks and correlations serves as a new foundation for process optimization, allowing researchers and engineers to precisely tune deposition conditions for improved AlN film quality. We used classical molecular dynamics (MD) simulations to study how temperature affects aluminum nitride (AlN) thin-film deposition on a crystalline AlN substrate. Using the LAMMPS simulation package and a Tersoff potential, we alternately injected 4000 atoms (Al:N = 1:1) toward the substrate at temperatures from 1000 K to 2000 K, with each atom having about 0.17 eV of kinetic energy. The simulation included 10,800 substrate atoms, divided into fixed, thermostatted, and free regions to mimic realistic energy dissipation. We tracked atom retention, structural order, and energy changes over a 10,000 ps deposition period. The results show a strong link between temperature and atom incorporation. Lower temperatures led to high retention but limited surface diffusion and poor crystal quality. Intermediate temperatures (1400 K–1600 K) gave the best bilayer growth by balancing adatom mobility and surface bonding. Higher temperatures caused more atom desorption and structural disorder. Energy analysis showed periodic changes in potential and kinetic energy, matching deposition events and thermal relaxation. This study pinpoints the best temperature range for AlN film growth and shows how MD simulations can reveal atomic mechanisms in epitaxial deposition. These findings help improve our understanding of growth kinetics and can guide experiments for high-performance AlN devices.

Inclusive New Urbanism: As It Applies to Golden Country Homes Subdivision in Alangilan, Batangas City (Results from a Qualitative Study)

2026-03-05T12:42:37+02:00

Golden Country Homes Subdivision in Alangilan, Batangas City is undergoing a rapid and unregulated transformation from a conventional residential subdivision into a mixed-use neighborhood district. Triggered by the expansion of Batangas State University and the establishment of the Knowledge, Innovation and Science Technology (KIST) Park as a Special Economic Zone, the subdivision has experienced significant population influx, land-use conversion, and infrastructural strain. This study examines the applicability of New Urbanism principles as a framework for guiding this transition toward an inclusive and sustainable urban form. Employing a multi-phase qualitative methodology including ethnography, discourse analysis of governing policies, grounded theory, descriptive statistics, and phenomenological inquiry the research analyzes socio-political dynamics, demographic shifts, spatial conflicts, and emerging urban patterns within the subdivision. Findings reveal that while Golden Country Homes already exhibits several characteristics aligned with New Urbanism such as walkability potential, mixed housing typologies, and institutional proximity challenges persist in parking regulation, open space provision, inclusivity, and governance adaptation. The study argues that structured implementation of inclusive New Urbanism principles, including tactical urbanism strategies and environmentally responsive planning, can reposition the subdivision as a balanced neighborhood district rather than a fragmented urban enclave. The research contributes to discourse on small-scale urban transformation in rapidly urbanizing Philippine subdivisions.

The Use of Federated Learning in AI-Based Predictive Analytics to Prevent Chronic Diseases in Global Health Systems

2026-03-05T13:13:48+02:00

The growing burden of the chronic diseases in the world like diabetes, cardiovascular diseases and chronic respiratory illnesses puts greater strain in the global health systems. Early interventions can be achieved by timely treating persons at the risk stage to minimize morbidity, mortality, and healthcare expenses. Nonetheless, the development of predictive models to prevent chronic diseases at the global level is fraught with a number of issues, such as regulating the privacy of data, the unequal presence of different data related to them in institutions and geographical locations, and the inability to unify sensitive patient-related data. The article discusses the use of federated learning (FL) as a powerful privacy-friendly framework that allows AI-based predictive analytics to be enabled in distributed health systems and, thus, promote chronic disease prevention and worldwide public health efforts. With traditional central machine learning models, training data needs to be consolidated into one site, which is usually inconsistent with patient privacy laws like HIPAA and GDPR, and questions data security and control by the institution. The federated learning achieves this by allowing various health institutions (e.g., hospitals, clinics, research centers) to jointly learn a single model without having to transfer raw patient data out of their local secured settings. The model is trained on local data at each site participating and only gradients or weights are sent to a central server that combine them into a global model. It is done to ensure that sensitive health information is stored on-premises, retaining patient confidentiality and also enjoying the advantages of a diversified and rich data of various populations and geographical locations. In the current paper, the authors describe a federated learning architecture that could be used to predict risks of chronic diseases: local data processing, feature selection in accordance with global guidelines, secure aggregation algorithms, and differential privacy to protect against possible inference attacks. We also mention how various types of data, such as structured electronic health records (EHRs), data of the lifestyle surveys, data of wearable devices, and social-determinant indicators can be incorporated into a single predictive model. With simulated cross-institutional data, we show that federated models perform similarly (e.g. in terms of area under the ROC curve, precision-recall metrics) to centralized models allowing data privacy to be maintained. In addition, generalizability of federated approach is better as models are conditioned in institutions with different countries of origin and socioeconomic status, and less bias can be introduced by region specific data. Besides technical feasibility, we note the more general health consequences: federated predictive analytics can allow early identification of high-risk people, make decisions on resource allocation, and promote prevention of interventions both at the community and policy levels. As an illustration, international health agencies might use federated models to track the trends of chronic disease risks in the regions, detect emerging hot spots, and apply interventions to them, including lifestyle counselling, mass screening, or mobile health (mHealth) outreach activities. The decentralized system of federated learning also makes collaboration between institutions in the high-income and low-to-middle-income countries possible, which promotes fair access to high-level AI power and does not necessitate centralized infrastructure, as well as does not undermine data sovereignty. Summing up, federated learning provides a potentially beneficial direction in scalable privacy-sensitive and shared predictive analytics to prevent chronic diseases across all health systems globally. This paradigm can enable institutions around the world to realize the power of their data, despite preserving patient privacy, by addressing the technical, ethical, and operational issues and difficulties. This practice is capable of revolutionizing the work of chronic disease preventions to allow early interventions and better health results at the global level.