Robotic-Assisted Bladder Diverticulectomy: Indications, Technique, and Outcomes-A Case Series.

This case series describes the experiences and outcomes of multiple Australian surgeons performing robotic-assisted bladder diverticulectomy (RABD), highlighting the procedural effectiveness and safety, for both benign and malignant indications for diverticulectomy. Outcomes were analyzed from 13 experienced Australian urologists who performed RABD between 2016 and 2023. Retrospective analysis was performed on prospectively collected data, which included patient demographics, diverticulum characteristics, surgical approaches, and post-operative outcomes.

Incidence and Mortality of COVID-19-Associated Invasive Fungal Infections Among Critically Ill Intubated Patients: A Multicenter Retrospective Cohort Analysis.

Background: An association between coronavirus disease 2019 (COVID-19)-associated invasive fungal infections (CAIFIs) and high mortality among intubated patients has been suggested in previous research. However, some of the current evidence was derived from small case series and multicenter studies conducted during different waves of the COVID-19 pandemic. We examined the incidence of CAIFIs and their associated mortality using a large, multicenter COVID-19 database built throughout the pandemic.

Variables' classification via equivalence relations for the trophic state of a Mediterranean ecosystem.

The trophic state of an aquatic body is influenced by many biotic and abiotic factors. When lots of parameters affect a phenomenon, such as eutrophication, it is difficult to distinguish which are the ones that affect the ecosystem the most. In this paper, we propose an alternative way for data analysis, in order to avoid complex systems with many variables.

Fuzzy Surrogate Safety Metrics for real-time assessment of rear-end collision risk. A study based on empirical observations.

The present paper discusses two fuzzy Surrogate Safety Metrics (SSMs) for rear-end collision, the Proactive Fuzzy SSM (PFS) and Critical Fuzzy SSM (CFS). The objective is to investigate their applicability for evaluating the real-time rear-end risk of collision of vehicles to support the operations of advanced driver assistance and automated vehicle functionalities (from driving assistance systems to fully automated vehicles). The proposed Fuzzy SSMs are evaluated and compared to other traditional metrics on the basis of empirical observations.

Neuro-Fuzzy Networks and Their Applications in Medical Fields.

The fusion of artificial neural networks and fuzzy logic systems allows researchers to model real-world problems through the development of intelligent and adaptive systems. Artificial neural networks are able to adapt and learn by adjusting the interconnections between layers, while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as "neuro-fuzzy" systems.