SDI Engineering (United States)

Addressing the underexplored catalytic potential of natural polymetallic sulfide minerals and the unclear structure–activity relationship between symbiotic structures and catalytic performance, this study explores the use of mechanically activated natural copper sulfide ore to construct a bimetallic synergistic Fenton-like system for tetracycline degradation, with a focus on the synergistic role of Cu/Fe/S multi-active sites. By optimizing ball milling parameters (ball-to-powder ratio of 3 : 1, duration of 24 h), the catalyst achieved a remarkable 90.11% tetracycline degradation within 10 minutes. Mechanistic investigations revealed that mechanical activation refined particle size, increased specific surface area, and exposed more Cu/Fe/S active sites, establishing a “homogeneous (66.36%) – heterogeneous (33.64%)” synergistic catalytic mechanism. In the homogeneous phase, dissolved Cu2+/Fe2+ accelerated H2O2 decomposition. In the heterogeneous phase, the Cu+/Fe3+ redox couple (0.16 V/0.77 V) created an energy level difference. Coupled with reductive sulfur species (S2−, S22−)-mediated electron transfer, this facilitated the Fe3+ → Fe2+ and Cu2+ → Cu+ cycles, thereby enhancing radical generation efficiency. Two distinct degradation pathways for tetracycline by the copper sulfide concentrate were identified, with intermediates undergoing deep oxidation and ring-opening reactions to mineralize into H2O, CO2, and NO3−. This study overcomes the limitations of traditional single iron-based sulfide catalysts, revealing the catalytic enhancement mechanism of natural mineral symbiotic structures under mechanical activation. It offers a cost-effective and efficient heterogeneous Fenton-like solution for antibiotic wastewater treatment.

Abstract Earthwork Allocation and Transportation (EAT) significantly impacts construction cost in Railway Alignment (RA) optimization, but slight research has been devoted to this problem. To this end, an RA-EAT bi-level optimization framework is developed. A concurrent RA-EAT design model is formulated at the upper-level. At the lower level, the EAT system is modelled by incorporating earthwork allocation sections partition, soil waste/borrow pits selection and access roads generation. To solve this bi-level model, a tailored solution strategy is proposed. Specifically, a candidate pool for borrow/waste pits is established via a moving window method. Then, RA alternatives are generated using a Particle Swarm Optimization (PSO). Afterward, the EAT model is solved with a hierarchical approach, including determining EAT sections along an RA with a divide-and-conquer method and configuring access roads with a modified Dijkstra's algorithm. Thus, the total RA-EAT solution can be iteratively evolved through the PSO rationale. Finally, the proposed method is verified and analyzed in a real-world case via a sensitivity analysis and a comparative experiment.

As urbanization accelerates and the number of buildings continues to increase, the maintenance and upkeep of structures have become particularly critical; traditional maintenance methods, which predominantly rely on manual labor, are not only costly but also inefficient. In recent years, the rapid development of robotics has led to its growing application in the field of building maintenance, where automation and intelligent techniques have significantly enhanced operational efficiency and safety. In the realm of architectural reverse modeling, achieving intelligent perception of both indoor and outdoor environments is essential for advancing the field; however, reliable scene understanding remains a formidable challenge due to complex structural configurations, dynamic targets, variations in lighting conditions, and occlusions within building environments. To address these issues, this paper proposes a novel scene segmentation approach based on multimodal perception that integrates RGB imagery with LiDAR point cloud data, thereby fully leveraging the complementary strengths of semantic information and spatial geometric details inherent in the two modalities. By employing advanced deep learning techniques, the proposed method markedly improves segmentation accuracy and robustness. Extensive experimental results demonstrate that our approach outperforms traditional single-sensor solutions in fine-grained segmentation tasks across both indoor and outdoor scenarios, maintaining high performance even under complex conditions.

This paper presents a calculation model for tuyere blast volume distribution in a blast furnace, integrating mechanistic simulation with data-driven approaches. By employing computational fluid dynamics (CFD), a detailed model of the blast furnace air supply system is developed, enabling precise determination of resistance coefficients and blast density under varying conditions. Building on the mechanistic foundation, a data-driven model is formulated to rapidly calculate blast volume distribution, raceway depth, and kinetic energy, achieving significant improvements in both prediction accuracy and computational speed. The model also incorporates multiple factors, including tuyere tip diameter, length, wear, coal injection rate, and oxygen enrichment rate. It reveals critical insights: increasing tuyere tip diameter boosts blast flow rate and raceway depth, while changes in tuyere length have minimal impact. Deeper tuyere insertion reduces the central dead zone but enlarges the peripheral one. Furthermore, the model highlights the trade-offs between enhanced reaction efficiency through increased coal injection and oxygen enrichment, and the resulting increase in resistance that decreases volume flow. Tuyere blockage leads to uneven blast volume distribution, with unblocked tuyeres experiencing higher blast volume, kinetic energy, and raceway depth. The key innovation of this model lies in its hybrid approach, seamlessly integrating mechanistic and data-driven techniques to offer a more accurate, real-time, and comprehensive understanding of blast furnace operations.

This paper examines the role of international non-governmental organizations (INGOs) in promoting and protecting international humanitarian law (IHL) and human rights in noninternational armed conflicts (NIACs).INGOs play a critical role in monitoring compliance with IHL, advocating for the enforcement of human rights standards, and providing humanitarian assistance.The study explores how INGOs engage with regional human rights mechanisms, including the European, American, and African human rights systems, to address violations and hold perpetrators accountable.Additionally, the research highlights INGOs' contributions to legal and institutional reforms by providing expertise, publishing shadow reports, and supporting national and international initiatives to strengthen IHL.Beyond legal advocacy, INGOs also play a key role in direct humanitarian intervention.They facilitate prisoner exchanges, medical aid, and civilian protection efforts, helping mitigate the impact of armed conflicts.The paper further discusses INGOs' efforts in spreading awareness of IHL among military personnel and civilians, emphasizing the importance of legal education and training.Despite these contributions, INGOs face significant challenges, including state resistance, funding limitations, and political constraints that hinder their effectiveness.The findings underscore the necessity of strengthening cooperation between INGOs, national governments, and international institutions to enhance the implementation of IHL and human rights protections.Greater emphasis on education, policy reform, and enforcement mechanisms is essential to ensure broader compliance with legal standards in conflict settings.

Penelitian ini menginvestigasi dampak profitabilitas dan free cash flow terhadap kebijakan dividen, dengan investment opportunity set yang menjadi variabel mediasi. Kajian ini melibatkan perusahaan yang terindeks SRI-KEHATI sebagai subjek penelitian. Metode yang digunakan adalah purposive sampling dengan alat analisis menggunakan path analysis, dengan 86 pengamatan. Hasil penelitian menunjukkan bahwa profitabilitas tidak memiliki pengaruh signifikan pada investment opportunity set. Sementara itu, free cash flow memiliki pengaruh negatif yang signifikan pada investment opportunity set. Profitabilitas juga tidak memiliki pengaruh signifikan pada kebijakan dividen, sedangkan free cash flow memiliki pengaruh positif yang signifikan pada kebijakan dividen. Investment opportunity set tidak memiliki pengaruh signifikan pada kebijakan dividen. Namun, hasil penelitian menunjukkan bahwa investment opportunity set berperan sebagai variabel mediasi dalam pengaruh profitabilitas dan free cash flow terhadap kebijakan dividen.