Journal of Progress in Engineering and Physical Science

Downslope Gradient of Metal Concentration in Soils Along the Mayon Fluvial System

Milagros H. Sipalay, Benhur L. Catubig — Tue, 29 Apr 2025 00:00:00 +0000

This study investigates the spatial distribution and environmental implications of heavy metal concentrations in soils along a downslope gradient in the Mayon Volcano fluvial system, Philippines. Fifteen georeferenced sampling plots were established across three slope zones—upper (800–1000 m), middle (400–600 m), and lower (50–200 m)—to evaluate the concentrations of six metals: Fe, Mn, Zn, Cu, Pb, and Cr. Soil samples were analyzed using microwave-assisted acid digestion followed by AAS quantification. Results revealed statistically significant increases in all target metals with decreasing elevation, with the highest concentrations consistently observed in the agriculturally active lower slopes. ANOVA and Tukey’s HSD tests confirmed that Fe, Zn, and Cu displayed the strongest elevation-based variance (p < 0.01), while pollution indices such as the Geoaccumulation Index (I_geo) and Contamination Factor (CF) indicated moderate contamination by Pb and Cr in depositional floodplain zones. These patterns were attributed to lahar-mediated sediment transport, grain-size sorting, and organic matter-metal interactions in lowland soils. The environmental implications are substantial: elevated bioavailable metals pose risks to food safety, human health, and long-term soil productivity. Findings highlight the need for integrated land use planning, soil remediation practices, and community-based monitoring in volcanic agroecosystems.

Noospheric Technology of Interpersonal Communication Using AI

Evgeniy Bryndin — Tue, 29 Apr 2025 00:00:00 +0000

Constant changes in nature are carried out under the influence of living information from the Creator. Each person reflects and perceives the real world along an individual semantic information trajectory. The informational semantic paths of man in the noosphere represent complex networks of interactions that contribute to the development of knowledge, cultures and technologies necessary for a sustainable future. The noosphere is a natural information space that reflects the outside world. It serves as a source of information and knowledge for humans. It exists independently of humans and contains descriptions of the surrounding world. However, the knowledge of this space is carried out on the basis of the tools that humans possess. As science and technology develop, the tools are improved. This expands the natural information space as a source of knowledge of the surrounding world and communications.

Intelligent Production in the Silicone Rubber Processing Industry: Applications and Challenges

Min Yang — Tue, 29 Apr 2025 00:00:00 +0000

This paper explores the current applications, challenges, and future development trends of intelligent production technology in the silicone rubber processing industry. By analyzing the practical applications of intelligent production technology in silicone rubber processing, such as the application status of automated production lines and intelligent inspection systems, this paper discusses the technical difficulties encountered in implementing intelligent production, such as equipment compatibility and data integration issues. It also analyzes how intelligent production can improve production efficiency, product quality, and market competitiveness in the silicone rubber industry and proposes strategies and suggestions for promoting intelligent production in this field.

Carbon Emission Assessment of Prefabricated Residential Buildings Based on Integrated BIM and LCA: A Case Study of Nanjing

Yuxin Chen — Tue, 29 Apr 2025 00:00:00 +0000

As China accelerates its urban development and decarbonization agendas, prefabricated construction has emerged as a promising strategy for delivering low-carbon housing. However, the true carbon performance of prefabricated systems remains understudied, particularly across full building life cycles. This study evaluates the life cycle carbon emissions of a mid-rise prefabricated residential building in Nanjing by integrating Building Information Modeling (BIM) with Life Cycle Assessment (LCA). Using a cradle-to-grave framework, the research identifies material-specific emission hotspots, quantifies embodied and operational carbon contributions, and conducts scenario testing to assess the sensitivity of design variations. Results show that the total carbon footprint of the building is 419 kgCO₂e/m², with embodied carbon accounting for 71% of life cycle emissions. Major contributors include precast concrete, steel reinforcement, and insulation materials. Scenario analysis reveals that substituting high-carbon materials and improving logistics can reduce emissions by up to 18%. The study concludes with policy recommendations for integrating BIM-LCA tools into municipal design regulation and national prefabrication strategy. These findings offer both methodological and practical insights for advancing carbon-conscious construction in China’s rapidly urbanizing regions.

A Study on Multi-Target Dairy Cow Feeding Behavior Recognition Based on Improved YOLOv7

Ruilong Kui, Weiping Luo, Yapeng Zhang — Tue, 29 Apr 2025 00:00:00 +0000

To make the research on multi-target dairy cow feeding behavior recognition in pastures more lightweight and improve the detection accuracy and inference speed of the model, this paper proposes a lightweight and improved algorithm YOLOv7-CDD based on the YOLOv7 object detection model. Firstly, the algorithm adds the CA attention mechanism module to the last layer of all backbone extraction networks to replace the original output layer, resulting in better detection performance and higher accuracy without the need for manual threshold adjustment. Secondly, DSConv is introduced to replace some conventional convolutions (3×3 convolutions) in the back-bone network and in the multi-branch stacking module (Multi_Concat_Block), further reducing the number of model parameters without compromising detection accuracy. Finally, the dynamic detection head Dynamic Head is added, enhancing the expression capability of the target detection head and further improving detection accuracy without increasing computational complexity. Experimental results show that the YOLOv7-CDD model achieves an accuracy of 98.4%, a recall rate of 98.3%, and an mAP@0.5 of 99.3%, representing improvements of 2.8%, 2.6%, and 3.1%, respectively, compared to the YOLOv7 model, while significantly reducing model parameters and GFLOPs, demonstrating that YOLOv7-CDD meets the application requirements in pastures.

MPPT Techniques in Wind-Solar Hybrid Systems: A Review of Algorithms and Implementation

Wei Liang — Tue, 29 Apr 2025 00:00:00 +0000

The increasing global demand for sustainable and decentralized energy solutions has accelerated the adoption of wind-solar hybrid renewable energy systems. These systems offer improved reliability and energy availability by leveraging the complementary nature of solar and wind resources. However, the inherent variability and nonlinear characteristics of these sources necessitate the use of Maximum Power Point Tracking techniques to ensure optimal power extraction under changing environmental conditions. This paper presents a comprehensive review of both classical and intelligent MPPT algorithms, including Perturb and Observe, Incremental Conductance, Fuzzy Logic Control, Artificial Neural Networks, Particle Swarm Optimization, and hybrid approaches. The paper critically examines the principles, implementation strategies, strengths, and limitations of each method, with a focus on their application in wind-solar hybrid systems. Particular attention is given to the integration challenges associated with real-time deployment, control coordination between energy sources, convergence stability, and computational overhead. Emerging trends such as IoT-enabled control, machine learning integration, and predictive optimization are also discussed. This review aims to guide researchers and system designers in selecting and developing MPPT strategies that balance efficiency, adaptability, and system complexity for future-ready hybrid renewable energy applications.