Tongling Nonferrous Metals Group Holding (China)

Abstract In this study, ZnFe 2 O 4 was synthesized at 200°C via a solvothermal pathway using acetilacetonate salts of zinc and iron as raw materials in self-developed magnetic fields of varying intensities. The following phenomena were observed. During the synthesis process, applying a magnetic field causes spherical assemblies with micrometer-scale diameters to transform into coarse chain-like aggregates exhibiting a length-to-diameter ratio of approximately 4.8. As the strength of the magnetic field increases, the ZnFe 2 O 4 particle size gradually decreases. The synthetic magnetic field causes the magnetization value of ZnFe 2 O 4 to increase or decrease. This is due to the interaction between surface spins and bulk spins, and this interaction is regulated by the particle size. The zero-field-cooling (ZFC) curves measured under a 100-Oe magnetic field obey the Curie-Weiss law in the high-temperature region. The effective magnetic moments of the superparamagnetic particles obtained through fitting are μ sp = 2.53×10 4 μ B (ZnFe 2 O 4 prepared under the condition without magnetic field), μ sp = 1.69×10 4 μ B (ZnFe 2 O 4 prepared under the condition of two magnets), and μ sp = 1.85×10 4 μ B (ZnFe 2 O 4 prepared under the condition of four magnets). The estimated magnetic particle sizes are 11.4, 10.6, and 9.9 nm, respectively, which are larger than the corresponding sizes (8.6, 7.2, and 6.2 nm) obtained from electron microscopy, indicating that the magnetic moments of adjacent particles tend to be parallel. This work reports a new approach that enables the preparation of magnetic particles with clean surfaces as well as tunable sizes, morphologies, and properties simply by adjusting the magnetic field strength without the need for any additives or templates, thus broadening their potential for various applications.

Abstract In copper anode slime hydrometallurgical plant, Au and Ag are recovered by perchloric leaching and ammonia leaching, stepwisely and respectively. However, their recovery is incomplete with a significant amount of refractory Au/Ag remaining in ammonia leaching residue, encapsulated by PbSO 4 and BaSO 4 . These gold and silver can only be released by the removal of PbSO 4 /BaSO 4 , which can be realized by Na 2 CO 3 carbonation and the subsequent HCl leaching. In this work, a process flowsheet has been designed to recover Au/Ag in ammonia leaching residue and individual PbSO 4 and BaSO 4 products were also obtained with extra value. The cost of this process can be greatly reduced by Na 2 CO 3 recycling between the two-stage Ba carbonation and one-stage Pb carbonation with freezing crystallization of Na 2 CO 3 .

The proliferation of smart devices and the Internet of Things (IoT) has led to massive data generation, particularly in complex domains such as aerospace. Cloud computing provides essential scalability and advanced analytics for processing these vast datasets. However, relying solely on the cloud introduces significant challenges, including high latency, network congestion, and substantial bandwidth costs, which are critical for real-time on-orbit spacecraft services. Cloud-edge Internet of Things (cloud-edge IoT) computing emerges as a promising architecture to mitigate these issues by pushing computation closer to the data source. This paper proposes an improved YOLOV8-based model specifically designed for edge computing scenarios within a cloud-edge IoT framework. By integrating the Cross Stage Partial Spatial Pyramid Pooling Fast (CSPPF) module and the WDIOU loss function, the model achieves enhanced feature extraction and localization accuracy without significantly increasing computational cost, making it suitable for deployment on resource-constrained edge devices. Meanwhile, by processing image data locally at the edge and transmitting only the compact segmentation results to the cloud, the system effectively reduces bandwidth usage and supports efficient cloud-edge collaboration in IoT-based spacecraft monitoring systems. Experimental results show that, compared to the original YOLOV8 and other mainstream models, the proposed model demonstrates superior accuracy and instance segmentation performance at the edge, validating its practicality in cloud-edge IoT environments.

Энэхүү судалгаа нь Ойрадын Зая бандида Огторгуйндалайн орчуулсан буддын шашны бүтээлүүдэд хэрэглэгдсэн санскрит үгсийг авч үзэн, тэдгээрийн тод үсгийн бичлэгийн онцлогийг шинжилнэ. Санскрит хэлний ормол үгс нь шашин, соёлын харилцаагаар дамжин монгол хэлэнд эрт цагт нэвтэрч байсан бөгөөд Зая бандидын орчуулгын бүтээлүүд монгол хэлний үгийн санд тодорхой хэмжээний хувь нэмэр оруулжээ. Үүнээс гадна тэрээр санскрит хэлний үгийг тод үсгээр хэрхэн зөв тэмдэглэж, зөв унших аргыг тайлбарласан гарын авлагууд боловсруулж, тэр нь хойшид баримтлах зарчим болон тогтжээ. Түүний орчуулсан бүтээлүүдийн санскрит гаралтай үгсийг түүвэрлэн утга зүйн ангиллаар ангилж, тэдгээрийн тод, төвд, монгол, уйгур, согд бичгийн дурсгалуудын үгийн сан дахь санскрит үгсийн бичлэгүүдээс ялгарах онцлогийг харьцуулан судалсан болно. Судалгааны үр дүнгээс харахад Зая бандида нь өмнөх монгол галигийн уламжлалыг дагасан төдийгүй санскрит, төвд хэлэнд зориулсан тусгай үсэг тэмдэг болон авиазүйн дүрэм зохиосноор түүнийг улам боловсронгуй болгожээ. Түүний орчуулгын арга зүй нь монгол уламжлалт орчуулгын зохиолд түгээмэл тохиолддог санскрит хэлний зарим үгийг тэр хэвээр хэрэглэсэн, зарим санскрит үгийг шууд зээлдэн авах болон монгол хэлээр утгачлан орчуулах аргыг зохистой хослуулсан бөгөөд энэ нь монгол хэлний үгийн сангийн хөгжил болон тод үсгийн зөв бичих зүй тогтолд чухал хувь нэмэр оруулсан байна.

The main barrier between eco-driving research and practice on hydrogen fuel-cell trains is the lack of real-time deployability. To close this gap, we design a runtime-aware genetic optimisation framework that guarantees feasibility within a fixed compute budget. A physics-based plant is integrated with an evolutionary loop that incorporates constraint repair, rolling reoptimisation, parallel evaluation, and delay compensation, ensuring feasible advice within strict real-time limits. Feasibility is ensured not only by penalties but also by trajectory repairs that clip overspeed, re-shape terminal braking and preserve coasting windows under State of Charge (SOC) limits. To maintain drivability, advice is published as a minimal distance-triggered command set. We evaluate deployability rather than energy, reporting zero-violation compliance, stopping accuracy, replanning triggers and time-to-advice distributions. A case study on an inter-urban section shows smooth, constraint-compliant behaviour and stable time-to-feasible under a fixed compute budget, indicating suitability for on-board advisory and Automatic Train Operation (ATO) interfaces. Numerical results confirm consistent feasibility under strict timing limits.

The control of slurry pH in the absorber tower of the wet flue gas desulfurization (WFGD) system is characterized by significant time delays and strong nonlinearity. To maintain stable operation under these conditions, traditional controllers must adopt conservative parameter settings, which lead to slower response speeds and diminished control accuracy. To address this limitation, this paper proposes an improved Smith predictor based on a deep neural network (referred to as DNN-Smith) to enhance the performance of traditional controllers in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control. First, based on the existing neural network-based Smith predictor structure, the two internal neural network models are consolidated into a single one. Second, the dynamic characteristics of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> in the WFGD system are modeled using a deep neural network. Finally, the improved DNN-Smith is implemented for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control in the WFGD system. The results demonstrate that the deep neural network-based pH prediction model reduces key prediction errors (RMSE and MaxAE) by 84.54 % and 87.04 %, respectively, compared to the traditional ARX model. The proposed DNN-Smith-PI controller outperforms the conventional PI controller, reducing the RMSE and IAE in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{8 7. 0 \%}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{8 6. 4 \%}$</tex>, respectively. Furthermore, the improved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{p H}$</tex> control performance contributes to more stable control of the clean flue gas <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SO}_{2}$</tex> concentration, achieving an approximately 22 % reduction in RMSE compared to traditional PI control. Therefore, the DNN-Smith-PI controller presented in this paper provides an effective solution.