Early warning research on enterprise carbon emission reduction credit risk based on deep learning model under unbalanced data

To enhance the precision of predicting enterprise credit risk related to carbon emission reduction, this study focuses on publicly traded companies. It introduces a risk early warning model grounded in MLP deep learning. Primarily, this research employs the FA-TOPSIS fusion model to comprehensively assess the credit risk associated with carbon emission reduction in enterprises. Subsequently, it employs K-means clustering to compute enterprise similarities, which forms the basis for supervised learning in the MLP model to assign credit risk grade labels. Furthermore, the study tackles the challenge of imbalanced enterprise grade distribution using the ADASYN over-sampling algorithm. Ultimately, the effectiveness of the model proposed herein is confirmed through a series of multi-model comparison experiments. The results show that: First, carbon emission reduction indicators exhibit differing degrees of influence on enterprises at various credit risk levels. Notably, the most influential indicator is carbon emission intensity, while the development capacity indicator exerts the least influence. Second, the adoption of the XGBoost algorithm for screening carbon emission reduction indicators significantly enhances the prediction accuracy of the early warning model by 4.27%. Third, compared to other models, the MLP model achieves an impressive prediction accuracy of 99.48%, representing an average improvement of 15.24%. These results underscore the model’s feasibility and its potential to provide technical support for financial institutions and government entities in conducting credit ratings for enterprise carbon emission reduction

Proceedings of IC-NIDC2010

Abstract In Web 2.0 applications, users always label digital images using textual descriptions, which are also called tags. As a result, a web image usually carries both tag and visual content information. In order to improve the retrieval performance of web images, in this paper, we propose an error-driven fusion co-clustering algorithm, which combines images' tags, visual contents together for analysis. Experimental results demonstrate that our algorithm outperforms other simple clustering methods

General-Purpose Multi-Modal OOD Detection Framework

Out-of-distribution (OOD) detection identifies test samples that differ from the training data, which is critical to ensuring the safety and reliability of machine learning (ML) systems. While a plethora of methods have been developed to detect uni-modal OOD samples, only a few have focused on multi-modal OOD detection. Current contrastive learning-based methods primarily study multi-modal OOD detection in a scenario where both a given image and its corresponding textual description come from a new domain. However, real-world deployments of ML systems may face more anomaly scenarios caused by multiple factors like sensor faults, bad weather, and environmental changes. Hence, the goal of this work is to simultaneously detect from multiple different OOD scenarios in a fine-grained manner. To reach this goal, we propose a general-purpose weakly-supervised OOD detection framework, called WOOD, that combines a binary classifier and a contrastive learning component to reap the benefits of both. In order to better distinguish the latent representations of in-distribution (ID) and OOD samples, we adopt the Hinge loss to constrain their similarity. Furthermore, we develop a new scoring metric to integrate the prediction results from both the binary classifier and contrastive learning for identifying OOD samples. We evaluate the proposed WOOD model on multiple real-world datasets, and the experimental results demonstrate that the WOOD model outperforms the state-of-the-art methods for multi-modal OOD detection. Importantly, our approach is able to achieve high accuracy in OOD detection in three different OOD scenarios simultaneously. The source code will be made publicly available upon publication

Extreme air–sea turbulent fluxes during tropical cyclone Barijat observed by a newly designed drifting buoy

Using in situ observations collected by a drifting air–sea interface buoy (DrIB) in the northern South China Sea from August 30 to September 13, 2018, the extreme air–sea turbulent fluxes that occurred from September 8 to 13 during tropical cyclone (TC) Barijat were investigated. The most striking features were substantial increases in momentum and heat fluxes, with maximum increases of 10.8 m s−1 in the wind speed (WS), 0.73 N m−2 in the wind stress, 68.1 W m−2 in the sensible heat fluxes (SH) and 258.8 W m−2 in the latent heat fluxes (LH). The maximum WS, wind stress, SH and LH values amounted to 15.3 m s−1, 0.8 N m−2, 70.9 W m−2 and 329.9 W m−2, respectively. Using these new DrIB observations, the performance of two state-of-the-art, high-resolution reanalysis products, ERA5 and MERRA2, was assessed. The consistency of the observed values with ERA5 was slightly better than with MERRA2, reflected in higher correlations but both products underestimated the WS during TC conditions. In calm weather conditions, the turbulent heat fluxes were overestimated, because they simulated a too dry and cold atmospheric state, enhancing the air–sea differences in temperature and humidity. Considering that an accurate representation of the air–sea turbulent and momentum fluxes is essential for understanding and predicting ocean and atmospheric variability, our findings indicate that more high-quality temperature and relative humidity observations are required to evaluate and improve existing reanalysis products

Neurotrophic Effect of Adipose Tissue-Derived Stem Cells on Erectile Function Recovery by Pigment Epithelium-Derived Factor Secretion in a Rat Model of Cavernous Nerve Injury

The paracrine effect is the major mechanism of stem cell therapy. However, the details of the effect’s mechanism remain unknown. The aim of this study is to investigate whether adipose tissue-derived stem cells (ADSCs) can ameliorate cavernous nerve injury-induced erectile dysfunction (CNIED) rats and to determine its mechanism. Twenty-eight days after intracavernous injection of 5-ethynyl-2-deoxyuridine- (EdU-) labeled ADSCs, the erectile function of all the rats was evaluated by intracavernosal pressure (ICP). The ADSCs steadily secreted detectable pigment epithelium-derived factor (PEDF) in vitro. The expression of PEDF increased in the penis of the bilateral cavernous nerve injury (BCNI) group for 14 days and then gradually decreased. On day 28 after the intracavernous injection, the ADSCs group exhibited a significantly increased ICP compared with the phosphate buffered saline- (PBS-) treated group. Moreover, the neuronal nitric oxide synthase (nNOS) and S100 expression in penile dorsal nerves and the smooth muscle content to collagen ratio in penile tissues significantly increased. Furthermore, elevated PEDF, p-Akt, and p-eNOS were identified in the ADSCs group. This study demonstrated that intracavernous injection of ADSCs improved erectile function, repaired the nerve, and corrected penile fibrosis. One potential mechanism is the PEDF secretion of ADSCs and subsequent PI3K/Akt pathway activation

Precision radiotherapy for nasopharyngeal carcinoma

Abstract Nasopharyngeal carcinoma(NPC) occurs frequently in Southern China, and radiotherapy is the main treatment method. At present, intensity‐modulated radiotherapy is widely used, which has improved efficacy in patients with NPC and reduced toxicity and side effects. Recently, helical tomography radiotherapy, proton radiotherapy, carbon particle radiotherapy, and other radiotherapy techniques have been used for the clinical treatment of NPC. Individualized nasopharyngeal cancer targets have also been explored. This paper reviews the research progress in radiotherapy techniques and target volume for NP

Free testosterone value before radical prostatectomy is related to oncologic outcomes and post-operative erectile function

Abstract Purpose To investigate whether free testosterone (FT) prior to radical prostatectomy was related to post-operative oncologic outcomes, erectile function and continence. Methods The data of 586 patients with available information underwent treatment in our center was retrospectively reviewed. Total testosterone (TT) was tested by chemiluminescence immunoassay, and FT value was calculated using Vermeulen’s formula. Post-operative continence and erectile function were evaluated by the requirement of pad and the IIEF-5 score at 12 months. Results The median TT and FT value was 344 ng/dL (interquartile, IQR 314–374) and 6.9 ng/dL (IQR 6.4–7.3), and 106 patients (18.1%) and 152 patients (25.9%) were evaluated as having low TT and low FT based on current guidelines. Low TT and FT value were both related to older age (both p < 0.001), concomitant diabetes (p = 0.018 & 0.049), higher possibility of pre-operative erectile dysfunction (ED, both p < 0.001), higher pre-operative PSA value (both p < 0.001), higher clinical stage (both p < 0.001) and higher Gleason score in biopsy (both p < 0.001). Low FT was related to higher risk for pT3 (p = 0.020) and high Gleason score (p = 0.011) in logistic regression. The median follow-up duration was 52 moths (IQR 29–67) and FT was found to be an independent risk factor for biochemical recurrence (p = 0.005). In logistic regression TT was related to pre-operative ED (p = 0.010) and FT was related to post-operative ED (p = 0.001). Conclusion Low FT value before radical prostatectomy was related to adverse pathological outcomes, biochemical recurrence and post-operative ED