Adsorption characteristics of Ag+ on sphalerite surface: a combined experimental and first-principle study.

The rapid development of industrial society is also accompanied by the generation of a large amount of heavy metal wastewater, which has caused serious harm to the ecological environment and human society. Natural sphalerite has an important value in the environmental field due to its own semiconducting properties. In order to effectively remove Ag+ from wastewater containing silver, this study develops a natural mineral-based Ag+ adsorbent material (sphalerite) based on elemental affinity qualities and mineralization principles. The results of batch experiments showed that the initial Ag+ concentration of 50 mg/L reduced to 0.094 mg/L with a reaction duration of 15 min, a sphalerite dose of 5 g/L, an initial particle size of -400 mesh (38 µm), a reaction temperature of 25 °C, and a pH of 5. The highest adsorption capacity is 19.77 mg/g, and the adsorption behavior is consistent with the Freundlich isotherm model and pseudo-second-order adsorption kinetics. The results of solution chemical analysis indicate that the presence of Ag+ is primarily influenced by the presence of S2-. Further analysis using SEM-EDS, FTIR, and XPS techniques reveals that Ag+ is chemically adsorb onto the mineral surface, resulting in the formation of Ag2S. DFT calculations further confirm the overlap between the Ag 4d orbitals and the S 3p orbitals on the surface of sphalerite, further confirming its chemical adsorption. Mulliken populations suggest that charge transfer occurs between Ag+ and S atoms in the sphalerite surface. This research systematically reveals the Ag+ adsorption mechanism on sphalerite surface and expands research ideas for treating heavy metal wastewater.

Blockchain-Based Secure Storage and Access Control Scheme for Supply Chain Ecological Business Data: A Case Study of the Automotive Industry.

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.

A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7.

Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in real-world scenes remains a challenging problem. In order to improve the recognition of road traffic signs, this paper proposes a small object detection algorithm for traffic signs based on the improved YOLOv7. First, the small target detection layer in the neck region was added to augment the detection capability for small traffic sign targets. Simultaneously, the integration of self-attention and convolutional mix modules (ACmix) was applied to the newly added small target detection layer, enabling the capture of additional feature information through the convolutional and self-attention channels within ACmix. Furthermore, the feature extraction capability of the convolution modules was enhanced by replacing the regular convolution modules in the neck layer with omni-dimensional dynamic convolution (ODConv). To further enhance the accuracy of small target detection, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to mitigate the sensitivity to minor positional deviations of small objects. The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88.7% mAP@0.5, outperforming the baseline model YOLOv7 by 5.3%.

Neighbor2Neighbor: A Self-Supervised Framework for Deep Image Denoising.

In recent years, image denoising has benefited a lot from deep neural networks. However, these models need large amounts of noisy-clean image pairs for supervision. Although there have been attempts in training denoising networks with only noisy images, existing self-supervised algorithms suffer from inefficient network training, heavy computational burden, or dependence on noise modeling. In this paper, we proposed a self-supervised framework named Neighbor2Neighbor for deep image denoising. We develop a theoretical motivation and prove that by designing specific samplers for training image pairs generation from only noisy images, we can train a self-supervised denoising network similar to the network trained with clean images supervision. Besides, we propose a regularizer in the perspective of optimization to narrow the optimization gap between the self-supervised denoiser and the supervised denoiser. We present a very simple yet effective self-supervised training scheme based on the theoretical understandings: training image pairs are generated by random neighbor sub-samplers, and denoising networks are trained with a regularized loss. Moreover, we propose a training strategy named BayerEnsemble to adapt the Neighbor2Neighbor framework in raw image denoising. The proposed Neighbor2Neighbor framework can enjoy the progress of state-of-the-art supervised denoising networks in network architecture design. It also avoids heavy dependence on the assumption of the noise distribution. We evaluate the Neighbor2Neighbor framework through extensive experiments, including synthetic experiments with different noise distributions and real-world experiments under various scenarios. The code is available online: https://github.com/TaoHuang2018/Neighbor2Neighbor.

Flue gas torrefaction of municipal solid waste: Fuel properties, combustion characterizations, and nitrogen /sulfur emissions.

Flue gas torrefaction (FGT) was proposed as the pretreatment of the municipal solid waste (MSW) combustion process to improve the fuel properties of MSW and achieve better combustion performance. The optimal FGT parameters were obtained at 300 ℃ and 30 min, with the energy-mass co-benefit index (EMCI) reaching the maximum of 23.38. FGT could significantly increase the heating value and energy density of MSW while reducing the H/C and O/C ratio. Then, the pyrolysis and combustion experiments were performed by tube furnace and TG-MS. The results proved the chemical compositions of MSW were altered, and the heat transfer was enhanced. With FGT, NOx and SO2 emissions could be reduced by 25.7 % and 52.4 %, respectively. This study provides an in-depth understanding of the mechanism of FGT and paves the way for the clean treatment and energy utilization of MSW.

Relapse and Prognosis of Non-Muscle Invasive Bladder Cancer After Combined HOLRBT with Sunitinib and TGC Perfusion Chemotherapy.

Objective: To prevent postoperative relapse after HOLRBT, we compared postoperative adjuvant therapies. Methods: One hundred fifty patients with non-muscle invasive bladder cancer (NMIBC) were meanly divided into three groups: A, B, and C. Group A patients only took sunitinib, group B patients underwent TGC perfusion chemotherapy, and group C patients took sunitinib and underwent TGC chemotherapy. Results: It was discovered that TGC perfusion chemotherapy combined with taking sunitinib can significantly reduce the relapse rate. Most of the tumor relapse period was assembled at 9 months after the operation. No-tumor relapse survival rate and no-fluorescence in situ hybridization positive survival rate in Group C were significantly higher than those of Group A and Group B. Conclusion: Therefore, the combined application of taking sunitinib drug and going through TGC perfusion chemotherapy after secondary HOLRBT will evidently improve the prognosis of patients with a glorious applicated prospect.