Modern Applied Science, Issue: Vol.20, No.1

A Machine Learning Framework for Enhancing Scope 3 Emissions Measurement through Integrated Product and Industry Classifications

Fri, 06 Mar 2026 05:39:55 +0000

Scope 3 emissions are major contributor for emissions of many industries. Since these emissions are indirect/complex in nature, data availability is a major challenge in estimating them. Current methods mostly rely on high level economic transactions with less inclusion of product details and have shortcomings in handling lack of data. In this research, we firstly discussed the benefits/shortcomings of existing methodologies for estimating scope 3 emissions and proposed a Machine Learning (ML) based framework to overcome those shortcomings. In our approach we map the Central Product Classification (CPC) system with International Standard Industry Classification (ISIC) and North American Industry Classification System (NAICS) together to gain higher granularity in scope 3 emission estimation. For ML models implementation, we used the supply chain emission factors based on NAICS codes as targeted variable. Our best performing model, Gradient Boosting Decision Trees, achieved an R² score of 0.9108 and MSE of 0.034 while giving balanced importance to CPC and ISIC codes. Results suggest our ML framework, combined with integrated classifications, can enhance the granularity and predictive accuracy for scope 3 emission factors derived from monetary input-output databases.

Research on the Cultivation of Competitiveness of Tourism Management in Universities under the Background of Hainan Free Trade Port

Thu, 16 Apr 2026 08:24:36 +0000

The development of Hainan Free Trade Port has created significant opportunities for the tourism industry while also placing new and higher demands for talent cultivation in tourism management. This paper analyzes emerging trends in tourism development within the free trade port context, identifies key challenges in current training models, and proposes strategies to enhance the competitiveness of tourism management professionals. These strategies focus on reorienting educational objectives, optimizing curriculum systems, strengthening faculty development, deepening industry-education collaboration, and elevating internationalization levels. The findings aim to provide actionable insights for professional reforms in relevant academic institutions. Furthermore, the paper seeks to improve the existing tourism management education system to enhance talent competitiveness and better serve the construction of the free trade port.

A Simultaneous Approach to Process Optimization: Process Simulation, Capital Cost Estimation, and 3D Layout Design

Mon, 27 Apr 2026 14:34:32 +0000

One of the most important items in feasibility reports used in investment decisions of industrial facilities is to make capital cost estimates. The most reliable way of making this cost estimate used in investment decisions directly affects investment decisions and is vital for the future of companies. Therefore, cost estimates are a more strategic step in large-scale facilities. In this study, Advanced System for Process Engineering (ASPEN) software was used to design the process in 3D and for process optimization, examples were given for "Sour Water Stripping" and "Gas Dehydration-Regeneration" and for cost estimation, the classification of "Association for the Advancement of Cost Engineering" was studied. During the 3D design for both processes, the relationships between parameters such as "Water to Saturate Mass Flow" and "Sales Gas Compression Mass Flow" were examined and the results obtained were exemplified.

Evaluation and Prediction of Indoor Hygrothermal Conditions in E-Waste-based Sustainable Buildings Using IoT and Machine Learning

Mon, 27 Apr 2026 08:18:33 +0000

The integration of waste electrical and electronic equipment (WEEE) into construction materials represents a promising circular economy strategy for sustainable building design. However, the indoor hygrothermal performance of such eco-friendly buildings under real climatic conditions remains insufficiently understood. This study investigates the indoor temperature and humidity behavior of buildings incorporating different proportions of WEEE-derived glass materials and develops a predictive framework for indoor environmental conditions. Three experimental buildings were constructed: a control building (C100PV0) and two eco-friendly configurations, C70PV30 (30% glass powder) and GV50Sa50 (50% glass aggregate). Indoor and outdoor data were collected using IoT-based sensors combined with external NASA atmospheric data. An XGBoost model incorporating lagged and rolling features was developed and evaluated using a chronological train–test split to ensure realistic validation. The results show that indoor environments exhibit reduced variability, reflecting the thermal and hygrometric damping effect of construction materials. The C70PV30 building demonstrates the highest thermal stability, while GV50Sa50 exhibits the most stable humidity evolution with minimal fluctuations. From a predictive perspective, the model achieves excellent performance for temperature in both buildings and for humidity in C70PV30 (R² ≈ 0.99), with error levels of RMSE ≤ 0.10 for temperature and ≤ 0.22 for humidity. In contrast, humidity prediction in GV50Sa50 yields a low R² (≈ 0.12) due probably to the near-constant nature of the signal; however, error-based metrics (MedAE ≈ 0.04) confirm that predictions remain accurate in absolute terms. Overall, the findings demonstrate that incorporating recycled glass materials improves indoor hygrothermal stability while maintaining high predictability. The proposed IoT–machine learning framework provides a robust approach for modeling and optimizing indoor environmental conditions in sustainable buildings.

Reviewer Acknowledgements, Vol. 20, No. 1, May 2026

Thu, 30 Apr 2026 11:14:10 +0000

Reviewer Acknowledgements, Vol. 20, No. 1, May 2026