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BACKGROUND: Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain's ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients. OBJECTIVE: The study goal is to develop an effective method to detect and segment brain tumor with high accurancy. METHODS: This paper proposes a novel U-Net + ⁣ + network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net + ⁣ + model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap. RESULTS: The proposed segmentation model is trained and tested on Kaggle's LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180. CONCLUSION: This paper conducts research on brain tumor segmentation, using the U-Net + ⁣ + network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net + ⁣ + model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180. [ABSTRACT FROM AUTHOR]

Summary: High penetration of renewable energy is the development trend of the future power system. As one of the clean energy sources, wind power generation has an increasing share in the energy market. However, due to the harsh working environment, the high fault rate and poor accessibility of the wind farms, resulting in the difficult maintenance process and high cost. This article proposes a fault diagnosis (FD) method based on long short‐term memory (LSTM) and feature optimization strategies for wind turbines (WTs), thus reducing the operation and maintenance costs of WTs. First, Pearson correlation coefficient analysis is performed on the collected data features to remove redundant features, and wavelet transform is adopted to remove the redundant data, so as to optimize the fault features and fault data. Then the selected features samples are used to train LSTM‐based FD model. Finally, the actual production data is adopted to verify the proposed method. The proposed method can effectively locate the faults, and provide data support for wind farms, thus improving the reliability, safety, and economic benefits of wind farms. [ABSTRACT FROM AUTHOR]

Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST) are the most commonly reported serotypes detected for the occurrence of Salmonellosis through foodborne transmission in recent years. In this study, a phagomagnetic separation in combination with qPCR (PhMS‐qPCR) based on phage T102 as a recognition element was developed for rapid enrichment and detection of Salmonella in dairy products. Phage T102 was coated onto magnetic beads for capturing Salmonella from samples through magnetic separation, with a capture efficiency of ~90% demonstrated. During the subsequent qPCR process, captured Salmonella was identified by detecting the OmpC gene, and specific sdf and STM4495 genes to further distinguish the serotypes of Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), respectively. The whole procedure can be performed in 2.5 h with a low detection limit of 10 CFU/mL in PBS. Subsequently, this detection strategy was successfully applied for the detection of Salmonella and serotype identification in spiked milk samples. This cell separation and detection system offers a promising alternative for rapid and accurate detection of Salmonella or Salmonella spp. [ABSTRACT FROM AUTHOR]

COVID-19 has had a profound effect on frontline social workers as well as their clients, and mitigating the risk of recurrence warrants an investigation of the individual and contextual factors associated with social workers' experience of helper-related trauma. In this study authors explore the effects of government support and professional support on social workers' vicarious trauma and the mediating roles of compassion fatigue and professional identity during the COVID-19 pandemic. Surveying 388 social workers from Wuhan, Hubei Province, China, during the pandemic, indicated that professional support was indirectly related to vicarious trauma while compassion fatigue played a fully mediating role. Government support had a negative direct effect on vicarious trauma, and professional identity played a partially mediating role. However, contrary to authors' expectations, professional identity was positively rather than negatively related to vicarious trauma. These findings suggest, first, that coping strategies for compassion fatigue should be included in professional supervision and, second, that government should provide more substantive support to effectively reduce vicarious trauma among social workers during the pandemic. [ABSTRACT FROM AUTHOR]