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There has been growing interest in applying the DEM (discrete element method) to study the charging and burden distribution in a BF (blast furnace). In practice, the real particles in a BF are non-spherical. However, spherical particles have mostly been used in previous DEM investigations. Furthermore, various particle models have been developed to describe non-spherical particles. However, the effects of using different particle models on particle behavior in a BF are still unclear. Therefore, a comparative study of how the particle shape model impacts the burden charging in a BF was conducted. Specifically, the DEM using a multi-sphere model, polyhedral model, and super-ellipsoid model was first established. Then, experiments and DEM simulations of the charging and burden distribution of non-spherical quartz sand particles in a lab-scale bell-less top BF were performed. The results indicated that the number of sub-spheres, the principle of creating the particle for multi-spheres, the number of planes for polyhedrons, and the shape indices for super-ellipsoids could all affect the accuracy and efficiency. Moreover, applying the super-ellipsoid model and multi-sphere model could achieve reasonable accuracy and efficiency, with the highest simulation accuracy for the polyhedral model but at the cost of a rather heavy computational burden.

This study focused on the intelligent model for ore pulp density in the hydrometallurgical process. However, owing to the limitations of existing instruments and devices, the feed ore pulp density of thickener, a key hydrometallurgical equipment, cannot be accurately measured online. Therefore, aiming at the problem of accurately measuring the feed ore pulp density, we proposed a new intelligent model based on the long short-term memory (LSTM) and hybrid genetic algorithm (HGA). Specifically, the HGA refers to a novel optimization search algorithm model that can optimize the hyperparameters and improve the modeling performance of the LSTM. Finally, the proposed intelligent model was successfully applied to an actual thickener case in China. The intelligent model prediction results demonstrated that the hybrid model outperformed other models and satisfied the measurement accuracy requirements in the factory well.

Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved.

The grinding process is a key link in mineral processing production and a typical complex, controlled process. The steady-state model is limited by its model structure and thus difficult to applyied in a control system. A hybrid modeling method driven by multi-source compensation is proposed in this paper based on the mechanism model using key equipment in the grinding and classification process, addressing the uncertainties which affect the stability of the control systems. This method combines the relevant multi-source signals with uncertainties by using a priori knowledge, extracts the nonlinear feature vector in the signal through an unsupervised depth network, and constructs a compensation model based on dynamic radial basis function network to realize the integration of mechanism modeling and data-driven compensation modeling. The simulation results show that the model has a high degree of fit to the real physical system; the industrial validation was conducted at a gold concentrator, the grinding product quality was predicted and controlled with the model.

Peri-implant infection is one of the biggest threats to the success of dental implant. Existing coatings on titanium surfaces exhibit rapid decrease in antibacterial efficacy, which is difficult to promisingly prevent peri-implant infection. Herein, we report an N-halamine polymeric coating on titanium surface that simultaneously has long-lasting renewable antibacterial efficacy with good stability and biocompatibility. Our coating is powerfully biocidal against both main pathogenic bacteria of peri-implant infection and complex bacteria from peri-implantitis patients. More importantly, its antibacterial efficacy can persist for a long term (e.g., 12~16 weeks) in vitro, in animal model, and even in human oral cavity, which generally covers the whole formation process of osseointegrated interface. Furthermore, after consumption, it can regain its antibacterial ability by facile rechlorination, highlighting a valuable concept of renewable antibacterial coating in dental implant. These findings indicate an appealing application prospect for prevention and treatment of peri-implant infection. Infection is a major problem for dental implants with current antibacterial coatings losing efficacy quickly. Here, the authors report on the N-halamine polymeric coating of titanium implants to create a long-lasting renewable antibacterial layer and demonstrate application in vivo.

Hydrogenation is an effective way to tune the property of metal oxides. It can conventionally be performed by doping hydrogen into solid materials with noble-metal catalysis, high-temperature/pressure annealing treatment, or high-energy proton implantation in vacuum condition. Acid solution naturally provides a rich proton source, but it should cause corrosion rather than hydrogenation to metal oxides. Here we report a facile approach to hydrogenate monoclinic vanadium dioxide (VO 2 ) in acid solution at ambient condition by placing a small piece of low workfunction metal (Al, Cu, Ag, Zn, or Fe) on VO 2 surface. It is found that the attachment of a tiny metal particle (~1.0 mm) can lead to the complete hydrogenation of an entire wafer-size VO 2 (>2 inch). Moreover, with the right choice of the metal a two-step insulator–metal–insulator phase modulation can even be achieved. An electron–proton co-doping mechanism has been proposed and verified by the first-principles calculations. Hydrogenation is an effective way to tune the property of metal oxides. Here, the authors report a simple approach to hydrogenate VO 2 in acid solution under ambient conditions by placing a small piece of low workfunction metal on VO 2 surface.

Ferroptosis is a necrotic form of iron‐dependent regulatory cell death. Estrogen withdrawal can interfere with iron metabolism, which is responsible for the pathogenesis of postmenopausal osteoporosis (PMOP). Here, it is demonstrated that estrogen withdrawal induces iron accumulation in the skeleton and the ferroptosis of osteocytes, leading to reduced bone mineral density. Furthermore, the facilitatory effect of ferroptosis of osteocytes is verified in the occurrence and development of postmenopausal osteoporosis is associated with over activated osteoclastogenesis using a direct osteocyte/osteoclast coculture system and glutathione peroxidase 4 (GPX4) knockout ovariectomized mice. In addition, the nuclear factor erythroid derived 2‐related factor‐2 (Nrf2) signaling pathway is confirmed to be a crucial factor in the ferroptosis of osteocytic cells. Nrf2 regulates the expression of nuclear factor kappa‐B ligand (RANKL) by regulating the DNA methylation level of the RANKL promoter mediated by DNA methyltransferase 3a (Dnmt3a), which is as an important mechanism in osteocytic ferroptosis‐mediated osteoclastogenesis. Taken together, this data suggests that osteocytic ferroptosis is involved in PMOP and can be targeted to tune bone homeostasis. Ferroptosis of osteocytes is a major factor implicated in the pathogenesis of osteoporosis. Ferroptotic osteocytes play a critical role in modulating bone loss through the regulation of osteoclast‐mediated bone resorption. The transcriptional regulation of Dnmt3a by Nrf2 leads to the methylation of the RANKL promoter, serving as a crucial molecular mechanism by which ferroptotic osteocytes promote osteoclast formation and differentiation.

Nuclear energy cogeneration, which integrates electricity generation with thermal energy utilization, presents a transformative pathway for enhancing energy efficiency and decarbonizing industrial and urban sectors. This comprehensive review synthesizes advancements in technological stratification, economic modeling, and sectoral practices to evaluate the viability of nuclear cogeneration as a cornerstone of low-carbon energy transitions. By categorizing applications based on temperature requirements (low: <250 °C, medium: 250–550 °C, high: >550 °C), the study highlights the adaptability of reactor technologies, including light water reactors (LWRs), high-temperature gas-cooled reactors (HTGRs), and molten salt reactors (MSRs), to sector-specific demands. Key findings reveal that nuclear cogeneration systems achieve thermal efficiencies exceeding 80% in low-temperature applications and reduce CO2 emissions by 1.5–2.5 million tons annually per reactor by displacing fossil fuel-based heat sources. Economic analyses emphasize the critical role of cost allocation methodologies, with exergy-based approaches reducing levelized costs by 18% in high-temperature applications. Policy instruments, such as carbon pricing, value-added tax (VAT) exemptions, and subsidized loans, enhance project viability, elevating net present values by 25–40% for district heating systems. Case studies from Finland, China, and Canada demonstrate operational successes, including 30% emission reductions in oil sands processing and hydrogen production costs as low as USD 3–5/kg via thermochemical cycles. Hybrid nuclear–renewable systems further stabilize energy supply, reducing the levelized cost of heat by 18%. The review underscores the necessity of integrating Generation IV reactors, thermal storage, and policy alignment to unlock nuclear cogeneration’s full potential in achieving global decarbonization and energy security goals.

The ability of a general circulation model (GCM) to capture the variability of El Niño–Southern Oscillation (ENSO) is not only a scientific issue of climate model performance, but also critical for climate change and variability impact studies. Here, we assess 48 CMIP5 GCMs for their skill in simulating ENSO interdecadal variability and its teleconnection with precipitation globally. The results show that (1) only 22 out of 48 GCMs display interdecadal variability that is similar to the observations; (2) the ensemble of the 48 GCMs captures the ENSO–precipitation teleconnection at the global scale; (3) no single GCM can capture the observed ENSO–precipitation teleconnection globally; and (4) a GCM that can realistically simulate ENSO variability does not necessarily capture the ENSO-precipitation teleconnection, and vice versa. The results could also be used by climate change impact studies to select suitable GCMs, especially for regions with a statistically significant teleconnection between ENSO and precipitation, as well as for the comparison of CMIP5 and CMIP6.

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