Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients\' short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of life-threatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code\'s tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.

BACKGROUND: Narrative Medicine (NM), a contemporary medical concept proposed in the 21st century, emphasizes the use of narrative as a literary form in medicine. This study aims to explore the understanding about NM and willingness to learn NM among medical students in our hospital.
METHODS: A questionnaire survey was conducted among 130 students at Xiangya Medical College of Central South University.
RESULTS: The findings revealed that a small percentage of students (3.1%) were familiar with narrative medicine and its training methods. Knowledge about the treatment skills (77.7%) and core content (55.4%) of narrative medicine was limited among the students. Despite this, a majority (63.1%) expressed a lack of interest in further understanding and learning about narrative medicine. Surprisingly, the survey indicated that students possessed a high level of narrative literacy, even without formal training in narrative medicine. Additionally, over half of the surveyed students (61.5%) believed that narrative medicine could benefit their clinical practice.
CONCLUSIONS: This study serves as a preliminary basis for the future development of narrative medicine education in China. It highlights the need to prioritize medical humanities education and provide medical students with more opportunities to access information on narrative medicine. By doing so, we can strive to enhance the visibility and promote the integration of narrative medicine into medical humanities education in China.

Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.

The pavement is vulnerable to damage from natural disasters, accidents and other human factors, resulting in the formation of cracks. Periodic pavement monitoring can facilitate prompt detection and repair the pavement diseases, thereby minimizing casualties and property losses. Due to the presence of numerous interferences, recognizing highway pavement cracks in complex environments poses a significant challenge. Nevertheless, several computer vision approaches have demonstrated notable success in tackling this issue. We have employed a novel approach for crack recognition utilizing the ResNet34 model with a convolutional block attention module (CBAM), which not only saves parameters and computing power but also ensures seamless integration of the module as a plug-in. Initially, ResNet18, ResNet34, and ResNet50 models were trained by employing transfer learning techniques, with the ResNet34 network being selected as a fundamental model. Subsequently, CBAM was integrated into ResBlock and further training was conducted. Finally, we calculated the precision, average recall on the test set, and the recall of each class. The results demonstrate that by integrating CBAM into the ResNet34 network, the model exhibited improved test accuracy and average recall compared to its previous state. Moreover, our proposed model outperformed all other models in terms of performance. The recall rates for transverse crack, longitudinal crack, map crack, repairing, and pavement marking were 88.8%, 86.8%, 88.5%, 98.3%, and 99.9%, respectively. Our model achieves the highest precision of 92.9% and the highest average recall of 92.5%. However, the effectiveness in detecting mesh cracks was found to be unsatisfactory, despite their significant prevalence. In summary, the proposed model exhibits great potential for crack identification and serves as a crucial foundation for highway maintenance.

Attention and memory are fundamental cognitive processes that closely interact. In the attentional boost effect (ABE), the stimuli that co-occur with targets are remembered better than those that co-occur with distractors in target detection tasks performed during memory encoding. In target detection tasks performed during retrieval, the stimuli that co-occur with targets are recognized as \'old\' more easily than the stimuli that co-occur with distractors. This study mainly explored the internal mechanism of the effect of target detection on recognition. In Experiment 1, the full attention (FA; where participants performed only the memory task) condition was used to compare with divided attention (DA; where participants performed target detection while performing memory retrieval) condition to explore the impact of target detection and distraction inhibition on recognition. In Experiment 2, the proportion of old and new words in the retrieval stage was adjusted to 1:1 to eliminate the possible reaction tendency caused by the high proportion of old words. In Experiment 3, the presentation time of words was extended to 1.5 s and 3 s to eliminate the possible impact of rapid processing. The results indicated that the effect of target detection on recognition was attributed to both target detection and distraction rejection and is not affected by the ratio of old and new words and the word presentation time. The effect of target detection on recognition may be owing to temporal yoking of the dual tasks, which is different from the effect of target detection on memory encoding.

BACKGROUND: There is a mutual influence between emotions and diseases. Thus, the subject of emotions has gained increasing attention.
OBJECTIVE: The primary objective of this study was to conduct a comprehensive review of the developments in emotion recognition technology over the past decade. This review aimed to gain insights into the trends and real-world effects of emotion recognition technology by examining its practical applications in different settings, including hospitals and home environments.
METHODS: This study followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines and included a search of 4 electronic databases, namely, PubMed, Web of Science, Google Scholar and IEEE Xplore, to identify eligible studies published between 2013 and 2023. The quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) criteria. The key information from the studies, including the study populations, application scenarios, and technological methods employed, was summarized and analyzed.
RESULTS: In a systematic literature review of the 44 studies that we analyzed the development and impact of emotion recognition technology in the field of medicine from three distinct perspectives: \"application scenarios,\" \"techniques of multiple modalities,\" and \"clinical applications.\" The following three impacts were identified: (i) The advancement of emotion recognition technology has facilitated remote emotion recognition and treatment in hospital and home environments by healthcare professionals. (ii) There has been a shift from traditional subjective emotion assessment methods to multimodal emotion recognition methods that are grounded in objective physiological signals. This technological progress is expected to enhance the accuracy of medical diagnosis. (iii) The evolving relationship between emotions and disease throughout diagnosis, intervention, and treatment processes holds clinical significance for real-time emotion monitoring.
CONCLUSIONS: These findings indicate that the integration of emotion recognition technology with intelligent devices has led to the development of application systems and models, which provide technological support for the recognition of and interventions for emotions. However, the continuous recognition of emotional changes in dynamic or complex environments will be a focal point of future research.

Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.

BACKGROUND: Crop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method.
RESULTS: To balance the accuracy and reliability of rice plant recognition, a combined visual-tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants.
CONCLUSIONS: The contact speed between the recognition perceptron and rice-weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.