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English literature teaching can improve students’ literary literacy and English proficiency. In this paper, we preprocess the English literature teaching data in colleges and universities from the two modalities of visual information and auditory information, extract the features of auditory modality, text modality, and video modality of the classroom respectively, and represent the features using bidirectional long-time memory neural network. A multilayer attention network mechanism is introduced to complete multimodal fusion in English literature teaching and label multimodal features after the extraction is done. On this basis, a student-centered innovative teaching path for English literature in colleges and universities is constructed, and teaching practice and multimodal discourse analysis are carried out to explore its teaching effect. The results show that the length of time used for facial smiles in inefficient classrooms (780.52/s) is significantly more than that in inefficient classrooms (150.22/s), and the length of time used for eye interactions (832.63/s) is significantly more than that in inefficient classrooms (720.44/s), and the length of time used for animations (450.42/s) is significantly more than that in inefficient classrooms (128.88/s), and efficient classrooms are more emphasized on student interactions. Students’ reading and writing abilities were significantly different from those before the practice (P

Featured Application: The application of this work is fuel cell hybrid electric vehicles. It solves the problem of balancing between the economy of the vehicle and the lifetime of the energy sources during the power distribution process. The energy management strategy plays an essential role in improving the fuel economy and extending the energy source lifetime for fuel cell hybrid electric vehicles (FCHEVs). However, the traditional energy management strategy ignores the lifetime of the energy sources for good fuel economy. In this work, an adaptive equivalent consumption minimization strategy considering performance degradation (DA-ECMS) is proposed by incorporating fuel cell and battery performance degradation models and establishing an optimal covariate predictor based on a long short-term memory (LSTM) neural network. The comparative simulations show that, compared with the adaptive equivalent consumption minimization strategy (A-ECMS), the DA-ECMS reduces the fuel cell stack voltage degradation by 17.1%, 23.2%, and 16.6% for the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), the China Light-Duty Vehicle Test Cycle (CLTC), and the New European Driving Cycle (NEDC), respectively, and the corresponding battery capacity degradation is reduced by 5.1%, 11.1%, and 11.2%. The average relative error between the hardware-in-the-loop (HIL) test and simulation results of the DA-ECMS is 5%. In conclusion, the proposed DA-ECMS can effectively extend the lifetime of the fuel cell and battery compared to the A-ECMS. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions' decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types. [ABSTRACT FROM AUTHOR]

To implement sustainable water resource management, the industries that produce a huge amount of wastewater are aiming to recycle wastewater. Reverse osmosis (RO) is an advanced membrane process that can produce potable water from wastewater. However, the presence of diverse pollutants in the wastewater necessitates effective pretreatment to ensure successful RO implementation. This study evaluated the efficiency of microfiltration (MF) and ultrafiltration (UF) as two pretreatment methods prior to RO, i.e., MF-RO and UF-RO, for recycling poultry slaughterhouse wastewater (PSWW). The direct treatment of PSWW with RO (direct RO) was also considered for comparison. In this study, membrane technology serves as a post treatment for PSWW, which was conventionally treated at Sanderson Farm. The results demonstrated that all of the processes, including MF-RO, UF-RO, and direct RO treatment of PSWW, rejected 100% of total phosphorus (TP), over 91.2% of chemical oxygen demand (COD), and 87% of total solids (TSs). Total nitrogen (TN) levels were reduced to 5 mg/L for MF-RO, 4 mg/L for UF-RO, and 9 mg/L for direct RO. In addition, the pretreatment of PSWW with MF and UF increased RO flux from 46.8 L/m2 h to 51 L/m2 h, an increase of approximately 9%. The product water obtained after MF-RO, UF-RO, and direct RO meets the required potable water quality standards for recycling PSWW in the poultry industry. A cost analysis demonstrated that MF-RO was the most economical option among membrane processes, primarily due to MF operating at a lower pressure and having a high water recovery ratio. In contrast, the cost of using RO without MF and UF pretreatments was approximately 2.6 times higher because of cleaning and maintenance expenses related to fouling. This study concluded that MF-RO is a preferable option for recycling PSWW. This pretreatment method would significantly contribute to environmental sustainability by reusing well-treated PSWW for industrial poultry purposes while maintaining cost efficiency. [ABSTRACT FROM AUTHOR]

Liver cirrhosis arises from liver fibrosis and necroinflammation caused by various mechanisms of hepatic injury. It is a prevalent condition in clinical practice characterized by hepatocellular dysfunction, portal hypertension, and associated complications. Despite its common occurrence, the etiology and pathogenesis of liver cirrhosis remain incompletely understood, posing a significant health threat. Effective prevention of its onset and progression is paramount in medical research. Symptoms often include discomfort in the liver area, while complications such as sarcopenia, hepatic encephalopathy, ascites, upper gastrointestinal bleeding, and infection can arise. While the efficacy of Western medicine in treating liver cirrhosis is uncertain, Chinese medicine offers distinct advantages. This review explores advancements in liver cirrhosis treatment encompassing non-pharmacological and pharmacological modalities. Chinese medicine interventions, including Chinese medicine decoctions, Chinese patent medicines, and acupuncture, exhibit notable efficacy in cirrhosis reversal and offer improved prognoses. Nowadays, the combination of Chinese and Western medicine in the treatment of liver cirrhosis also has considerable advantages, which is worthy of further research and clinical promotion. Standardized treatment protocols based on these findings hold significant clinical implications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Xue, Li, Zao, Li, Liu, Cao, Wang, Qi, Du, Zhang and Ye.)

Peanut root rot , commonly referred to as rat tail or root rot , is caused by a range of Fusarium species. A strain of bacteria (named TG5) was isolated from crop rhizosphere soil in Mount Taishan, Shandong Province, China, through whole genome sequencing that TG5 was identified as Bacillus thuringiensis , which can specifically produce chloramphenicol, bacitracin, clarithromycin, lichen VK 21 A 1 and bacitracin, with good biological control potential. Based on liquid chromatography tandem mass spectrometry metabonomics analysis and transcriptome conjoint analysis, the mechanism of TG5 and carbendazim inducing peanut plants to resist F. oxysporum stress was studied. In general, for peanut root rot caused by F. oxysporum , B. thuringiensis TG5 has greater advantages than carbendazim and is environmentally friendly. These findings provide new insights for peanut crop genetics and breeding, and for microbial pesticides to replace traditional highly toxic and highly polluting chemical pesticides. Based on the current background of agricultural green cycle and sustainable development, it has significant practical significance and broad application prospects., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Du and Li.)