The Influence of Preoperative Hydronephrosis on the Prognosis after Radical Cystectomy among Patients with Different Pathological Stages of Bladder Cancer.

Introduction: Preoperative hydronephrosis is closely associated with the prognosis of patients with bladder cancer. This study assesses the effect of preoperative hydronephrosis on the prognosis after radical cystectomy (RC) among patients with different pathological stages of bladder urothelial carcinoma.

Methods: We retrospectively analyzed the clinical data of 231 patients who underwent RC because of bladder urothelial carcinoma at our institution from January 2013 to December 2017.

A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation.

The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species' continuous and rapid decline, information and intelligent technology have provided innovative knowledge about how functional biological diversity interacts with environmental changes. Especially in complex natural scenes, identifying bird species with a real-time and accurate pattern is vital to protect the ecological environment and maintain biodiversity changes.

Rabbit manure compost as a peat substitute for compound growing media: Proportioning optimization according to physiochemical characteristics and seedling effects.

A large amount of rabbit manure is produced with the development of the rabbit industry, which will cause environmental pollution without proper treatment. Rabbit manure compost may be suitable for seedling cultivation, considering its low moisture, low heavy metal, high lignocellulose, and good fertilizer effect. In this study, a pre-proportioning test of growing media was conducted to optimize the ratio of perlite and vermiculite with peat/rabbit manure compost according to their physicochemical properties.

A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests.

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category.

Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems.

Food quality and safety issues occurred frequently in recent years, which have attracted more and more attention of social and international organizations. Considering the increased quality risk in the food supply chain, many researchers have applied various information technologies to develop real-time risk identification and traceability systems (RITSs) for preferable food safety guarantee. This paper presents an innovative approach by utilizing the deep-stacking network method for hazardous risk identification, which relies on massive multisource data monitored by the Internet of Things timely in the whole food supply chain.