Chronology and geochemical composition of cassiterite and zircon from the Maodeng Sn-Cu deposit, Northeastern China: Implications for magmatic-hydrothermal evolution and ore-forming process

Primary tin deposits usually contain little or no copper due to the distinct geochemistry of Sn and Cu. However, examples of copper-rich tin deposits in many tin provinces around the world are also well known. The genesis of copper-rich tin deposits remains controversial. The Maodeng Sn-Cu polymetallic, a typical Cu-rich Sn deposit in the southern Great Xing'an Range (SGXR) Northeast China, offers an excellent opportunity to reveal the genesis of coupled copper-tin deposits. The ore mineralization is associated with the granite porphyry, complement phase of the Alubaogeshan complex, which emplaced into the volcanic rocks of the Lower Permian Dashizhai Formation. Herein, we report new zircon and cassiterite U-Pb ages and their trace elements compositions, with the aim of constraining the metallogenic chronology framework, and clarifying the indicative effects of the ore-forming fluids on mineralization in different ore-forming stages, and thus establishing the genetic model for the Sn-Cu deposit. LA-ICP-MS U-Pb dating of zircon from the granite porphyry yields a weighted mean U-Pb age of 134.6 ± 0.4 Ma, which consistent to the zircon U-Pb age (ca. 138 Ma) of porphyry monzogranite (main phase of the Alubaogeshan complex) and cassiterite U-Pb ages (137–140 Ma), suggesting an Early Cretaceous Sn-Cu mineralization under the Paleo-Pacific plate slab roll-back setting. Granite porphyry displays more evolved characterisrics, with higher SiO2 contents (71.5 ∼ 77.4 wt%), Rb/Sr ratios (3.39 ∼ 10.33) and lower Nb/Ta ratios (10.86 ∼ 13.06) compared to those of porphyry monzogranite (SiO2 contents of 70.4 ∼ 72.1 wt%; Rb/Sr and Nb/Ta ratios of 1.03 ∼ 1.72 and 13.34 ∼ 15.83, respectively). This is further supported by the trace elements contents of zircons from granite porphyry and porphyry monzogranite, because the former has a stronger Eu anomaly, higher Hf concentrations, lower Zr/Hf and Th/U ratios than the latter. Our new data, integrated with previously published geochemical data, suggest that the granites associated with Cu-rich Sn deposits are characterized by higher oxygen fugacity, lower differential degree and aluminum saturation index (ASI) than those of Sn-W deposits. This could also use to address the Sn-Cu deposits usually have relatively small Sn mineralization potential. The trace elements of the cassiterites are characterized by high Fe (up to 3358 ppm), Ti (up to 1894 ppm) and abnormal high In (∼2500 ppm) concentrations, but low Nb, Ta contents. From early to late stage, the W and U contents and Nb/Ta and Zr/Hf ratios are increased, reveal that the ore-forming process experienced the cooling, increasing volatile contents and fluid-rock reaction. Finally, a new metallogenic model was established, which highlight oxidized Cu-rich fluids exsolved from the upwelling mantle magma would add to the reduced, Sn-rich magma chambers. This contribution indicate that, the Cu and Sn metals come from the mantle magma and crustal granitic magma, respectively, and that Cu-rich Sn deposits are more likely a product of spatial coupling

Evaluation of Individual Contribution in Blended Collaborative Learning

With the deepening of classroom teaching reform, blended collaborative learning has become a common collaborative learning method, and its significance and value has been verified by many parties. However, there is still a lack of quantitative analysis and detailed insight into the internal interaction dynamics of the group at the individual level. There are limitations in the evaluation dimensions and methods of individual contribution in collaborative learning in previous studies, so it is difficult to obtain a comprehensive evaluation of individual contribution. The purpose of this study is to build an effective evaluation model of individual contribution in blended collaborative learning. Discussion recordings and text data in collaboration were collected in a non-invasive way to validate the model. Based on evaluation model, the characteristics and rules behind the data deeply were explored, the collaborative process of the blended collaborative learning was analyzed and mined, and the characteristics of learners\u27 contribution were summarized to support the development of blended collaborative learning

Training-Free Semantic Video Composition via Pre-trained Diffusion Model

The video composition task aims to integrate specified foregrounds and backgrounds from different videos into a harmonious composite. Current approaches, predominantly trained on videos with adjusted foreground color and lighting, struggle to address deep semantic disparities beyond superficial adjustments, such as domain gaps. Therefore, we propose a training-free pipeline employing a pre-trained diffusion model imbued with semantic prior knowledge, which can process composite videos with broader semantic disparities. Specifically, we process the video frames in a cascading manner and handle each frame in two processes with the diffusion model. In the inversion process, we propose Balanced Partial Inversion to obtain generation initial points that balance reversibility and modifiability. Then, in the generation process, we further propose Inter-Frame Augmented attention to augment foreground continuity across frames. Experimental results reveal that our pipeline successfully ensures the visual harmony and inter-frame coherence of the outputs, demonstrating efficacy in managing broader semantic disparities

Strontium chloride improves bone mass by affecting the gut microbiota in young male rats

IntroductionBone mass accumulated in early adulthood is an important determinant of bone mass throughout the lifespan, and inadequate bone deposition may lead to associated skeletal diseases. Recent studies suggest that gut bacteria may be potential factors in boosting bone mass. Strontium (Sr) as a key bioactive element has been shown to improve bone quality, but the precise way that maintains the equilibrium of the gut microbiome and bone health is still not well understood.MethodsWe explored the capacity of SrCl2 solutions of varying concentrations (0, 100, 200 and 400 mg/kg BW) on bone quality in 7-week-old male Wistar rats and attempted to elucidate the mechanism through gut microbes.ResultsThe results showed that in a Wistar rat model under normal growth conditions, serum Ca levels increased after Sr-treatment and showed a dose-dependent increase with Sr concentration. Three-point mechanics and Micro-CT results showed that Sr exposure enhanced bone biomechanical properties and improved bone microarchitecture. In addition, the osteoblast gene markers BMP, BGP, RUNX2, OPG and ALP mRNA levels were significantly increased to varying degrees after Sr treatment, and the osteoclast markers RANKL and TRAP were accompanied by varying degrees of reduction. These experimental results show that Sr improves bones from multiple angles. Further investigation of the microbial population revealed that the composition of the gut microbiome was changed due to Sr, with the abundance of 6 of the bacteria showing a different dose dependence with Sr concentration than the control group. To investigate whether alterations in bacterial flora were responsible for the effects of Sr on bone remodeling, a further pearson correlation analysis was done, 4 types of bacteria (Ruminococcaceae_UCG-014, Lachnospiraceae_NK4A136_group, Alistipes and Weissella) were deduced to be the primary contributors to Sr-relieved bone loss. Of these, we focused our analysis on the most firmly associated Ruminococcaceae_UCG-014.DiscussionTo summarize, our current research explores changes in bone mass following Sr intervention in young individuals, and the connection between Sr-altered intestinal flora and potentially beneficial bacteria in the attenuation of bone loss. These discoveries underscore the importance of the “gut-bone” axis, contributing to an understanding of how Sr affects bone quality, and providing a fresh idea for bone mass accumulation in young individuals and thereby preventing disease due to acquired bone mass deficiency

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure