Model Design and Implementation of Enterprise Credit Information Based On Data Mining

Smart city construction emphasizes building an effective whole social credit system, promoting the construction of government integrity, business integrity, social integrity and public confidence in the judiciary. For credit risk has become an important assessment. Meanwhile, administration credit system is one of three major data. It proposes unified credit discipline and warning regulatory purposes, led by the government and its main functions, taken governmental data as the main basis. Accordingly, the paper constructs Corporate Dishonest Credit Executed (CDCE) Risk Assessment Model, based on governmental data. The model uses a set of urban enterprise data, selecting the explanatory variables from five aspects, administrative punishment, innovation, credit information, credit situation, and social responsibility, to screen CDCE Logit regression, to filter out and find out those variables which are significantly predicted effects for CDCE risk. And then construct a Logit regression model with the above selected variables. The experimental results and comparison of practical applications in China, we found that the model promises to higher business risk identification accuracy for CDCE. The model has a higher applied value and developmental prospects

Spore Powder of Ganoderma lucidum Improves Cancer-Related Fatigue in Breast Cancer Patients Undergoing Endocrine Therapy: A Pilot Clinical Trial

The fatigue prevalence in breast cancer survivors is high during the endocrine treatment. However, there are few evidence-based interventions to manage this symptom. The aim of this study was to investigate the effectiveness of spore powder of Ganoderma lucidum for cancer-related fatigue in breast cancer patients undergoing endocrine therapy. Spore powder of Ganoderma lucidum is a kind of Basidiomycete which is a widely used traditional medicine in China. 48 breast cancer patients with cancer-related fatigue undergoing endocrine therapy were randomized into the experimental or control group. FACT-F, HADS, and EORTC QLQ-C30 questionnaires data were collected at baseline and 4 weeks after treatment. The concentrations of TNF-α, IL-6, and liver-kidney functions were measured before and after intervention. The experimental group showed statistically significant improvements in the domains of physical well-being and fatigue subscale after intervention. These patients also reported less anxiety and depression and better quality of life. Immune markers of CRF were significantly lower and no serious adverse effects occurred during the study. This pilot study suggests that spore powder of Ganoderma lucidum may have beneficial effects on cancer-related fatigue and quality of life in breast cancer patients undergoing endocrine therapy without any significant adverse effect

Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Recent research has explored the utilization of pre-trained text-image discriminative models, such as CLIP, to tackle the challenges associated with open-vocabulary semantic segmentation. However, it is worth noting that the alignment process based on contrastive learning employed by these models may unintentionally result in the loss of crucial localization information and object completeness, which are essential for achieving accurate semantic segmentation. More recently, there has been an emerging interest in extending the application of diffusion models beyond text-to-image generation tasks, particularly in the domain of semantic segmentation. These approaches utilize diffusion models either for generating annotated data or for extracting features to facilitate semantic segmentation. This typically involves training segmentation models by generating a considerable amount of synthetic data or incorporating additional mask annotations. To this end, we uncover the potential of generative text-to-image conditional diffusion models as highly efficient open-vocabulary semantic segmenters, and introduce a novel training-free approach named DiffSegmenter. Specifically, by feeding an input image and candidate classes into an off-the-shelf pre-trained conditional latent diffusion model, the cross-attention maps produced by the denoising U-Net are directly used as segmentation scores, which are further refined and completed by the followed self-attention maps. Additionally, we carefully design effective textual prompts and a category filtering mechanism to further enhance the segmentation results. Extensive experiments on three benchmark datasets show that the proposed DiffSegmenter achieves impressive results for open-vocabulary semantic segmentation