Measurement Guidance in Diffusion Models: Insight from Medical Image Synthesis.

In the field of healthcare, the acquisition of sample is usually restricted by multiple considerations, including cost, labor- intensive annotation, privacy concerns, and radiation hazards, therefore, synthesizing images-of-interest is an important tool to data augmentation. Diffusion models have recently attained state-of-the-art results in various synthesis tasks, and embedding energy functions has been proved that can effectively guide the pre-trained model to synthesize target samples. However, we notice that current method development and validation are still limited to improving indicators, such as Fréchet Inception Distance score (FID) and Inception Score (IS), and have not provided deeper investigations on downstream tasks, like disease grading and diagnosis. Moreover, existing classifier guidance which can be regarded as a special case of energy function can only has a singular effect on altering the distribution of the synthetic dataset. This may contribute to in-distribution synthetic sample that has limited help to downstream model optimization. All these limitations remind that we still have a long way to go to achieve controllable generation. In this work, we first conducted an analysis on previous guidance as well as its contributions on further applications from the perspective of data distribution. To synthesize samples which can help downstream applications, we then introduce uncertainty guidance in each sampling step and design an uncertainty-guided diffusion models. Extensive experiments on four medical datasets, with ten classic networks trained on the augmented sample sets provided a comprehensive evaluation on the practical contributions of our methodology. Furthermore, we provide a theoretical guarantee for general gradient guidance in diffusion models, which would benefit future research on investigating other forms of measurement guidance for specific generative tasks. Codes and models are available at: https://github.com/yangqy1110/MGDM.

Progressive Insights into Metal-Organic Frameworks and Metal-Organic Framework-Membrane Composite Systems for Wastewater Management.

The sustainable management of wastewater through recycling and utilization stands as a pressing concern in the trajectory of societal advancement. Prioritizing the elimination of diverse organic contaminants is paramount in wastewater treatment, garnering significant attention from researchers worldwide. Emerging metal-organic framework materials (MOFs), bridging organic and inorganic attributes, have surfaced as novel adsorbents, showcasing pivotal potential in wastewater remediation. Nevertheless, challenges like limited water stability, elevated dissolution rates, and inadequate hydrophobicity persist in the context of wastewater treatment. To enhance the performance of MOFs, they can be modified through chemical or physical methods, and combined with membrane materials as additives to create membrane composite materials. These membrane composites, derived from MOFs, exhibit remarkable characteristics including enhanced porosity, adjustable pore dimensions, superior permeability, optimal conductivity, and robust water stability. Their ability to effectively sequester organic compounds has spurred significant research in this field. This paper introduces methods for enhancing the performance of MOFs and explores their potential applications in water treatment. It delves into the detailed design, synthesis strategies, and fabrication of composite membranes using MOFs. Furthermore, it focuses on the application prospects, challenges, and opportunities associated with MOF composite membranes in water treatment.

[Study on fault diagnosis of power-shift steering transmission based on spectrometric analysis and SVM].

Spectrometric oil analysis is an important method to study the running state of Power-Shift Steering Transmission (PSST). A method of multiple out least squares support vector regression was developed using spectrometric oil analysis data and SVM (Support Vector Machine). The spectrometric oil analysis data were studied using multiple out least squares support vector regression. It has been proved that the regression data are good in approximation effect for No. 1 PSST. And the predictive values for No. 2 PSST are highly veracious with the test data. The fault information was found and the fault position was determined through compar4tive analysis. This method has been proved to have practice significance for finding fault-hidden dangers and judging fault positions.

[Health state evaluation of power-shift steering transmission using PCA-AHP methodology based on spectrometric oil analysis].

Spectrometric oil analysis is an important method to study the running state of power-shift steering transmission (PSST). An evaluation model of PSST health state was developed on the basis of the theories of principal component analysis (PCA) and analytic hierarchy process (AHP) using spectrometric oil analysis data. Considering the concept of mechanical equipment and wear elements in spectrometric oil analysis data, the health value was employed to quantitatively describe the running state degree of PSST, and the grades of health state were classified based on the health values. The oil analysis data were studied during the process of choosing principal components. The weight vectors of principal components were obtained by using the AHP method. In the course, the conformation of judgement matrix and the consistency check were also studied. The evaluation model was developed by combining the PCA and AHP methods. This model has been proved to have better accuracy in evaluating the running state of PSST. This work is important for developing state evaluation of PSST.