An efficiency improvement method for high-voltage nanosecond pulse spiral generator based on optimized voltage wave propagation process.

The spiral generator, based on the principle of the electric field vector inversion, is capable of delivering repetitive high-voltage nanosecond pulses in the commercial portable pulsed x-ray source and gas switch trigger source. However, the spiral generator suffers from extremely low output efficiency, which significantly affects the compactness and accelerates the insulation film breakdown at electrode foil edges since the high charging voltage is required. A novel output efficiency improvement method for the spiral generator was proposed, implementing the permalloy film inside the passive layer to optimize internal voltage wave propagation processes during the pulser erection.

Conference 2024: Building an innovative scientific research ecosystem for microbiome and One Health.

The Conference 2024 provides a platform to promote the development of an innovative scientific research ecosystem for microbiome and One Health. The four key components - Technology, Research (Biology), Academic journals, and Social media - form a synergistic ecosystem. Advanced technologies drive biological research, which generates novel insights that are disseminated through academic journals.

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China.

Systemic immune inflammation index as a predictor of disease severity in tetanus patients: A retrospective observational study.

Objective: This study aimed to analyze the predictive value of the systemic immune inflammation index (SII) for the severity of disease in tetanus patients.

Methods: Clinical data of 34 tetanus patients admitted to the Second People's Hospital of Fuyang from January 1, 2019 to December 31, 2022 were analyzed. Based on whether patients received intensive care unit (ICU) treatment after admission, the patients were divided into ICU and non-ICU groups.