We present a hybrid graphene–silicon waveguide, which consists of a lateral slot waveguide with three layers of graphene flakes inside. Through a theoretical analysis, an effective index variation for about 0.05 is found in the waveguide by applying a voltage on the graphene. We designed a Mach–Zehnder modulator based on this waveguide and demonstrated it can process signals nearly chirp-free. The calculation shows that the driving voltage is only 1 V even if the length of the arm is shortened to be 43.54 μm. An extinction up to 34.7 dB and a minimum chirp parameter of $-0.006$ are obtained. Its insertion loss is roughly $-1.37\mathrm{dB}$. This modulator consumes low power and has a small footprint. It can potentially be ultrafast as well as CMOS compatible.

Emotion recognition is of great importance for artificial intelligence, robots, and medicine etc. Although many techniques have been developed for emotion recognition, with certain successes, they rely heavily on complicated and expensive equipment. Skin potential (SP) has been recognized to be correlated with human emotions for a long time, but has been largely ignored due to the lack of systematic research. In this paper, we propose a single SP-signal-based method for emotion recognition. Firstly, we developed a portable wireless device to measure the SP signal between the middle finger and left wrist. Then, a video induction experiment was designed to stimulate four kinds of typical emotion (happiness, sadness, anger, fear) in 26 subjects. Based on the device and video induction, we obtained a dataset consisting of 397 emotion samples. We extracted 29 features from each of the emotion samples and used eight well-established algorithms to classify the four emotions based on these features. Experimental results show that the gradient-boosting decision tree (GBDT), logistic regression (LR) and random forest (RF) algorithms achieved the highest accuracy of 75%. The obtained accuracy is similar to, or even better than, that of other methods using multiple physiological signals. Our research demonstrates the feasibility of the SP signal’s integration into existing physiological signals for emotion recognition.

Dysphagia is a common and clinically significant complication of ischemic stroke. The prevalence and risk factors for dysphagia may vary at different stages following an ischemic stroke. This study included patients with acute and chronic ischemic stroke who were treated at the Department of Rehabilitation, First Affiliated Hospital, Zhejiang University School of Medicine from 2019 to 2022. Various demographic, clinical, and laboratory parameters were collected, and statistical analyses were performed to investigate their association with dysphagia. Among the 399 ischemic stroke patients included in the study, 165 (41.4 %) experienced dysphagia, with 72 (38.7 %) in the acute phase and 93 (43.7 %) in the chronic phase. Univariate analysis revealed significant associations (p < 0.05) between dysphagia and factors such as pulmonary infection, aphasia, NIHSS score, ADL score, NLR score, lower extremity Brunnstrom's stages, and sit-to-stand balance. Multiple logistic regression analysis, after adjusting for confounding factors, identified the ADL score as an independent predictor of dysphagia. These findings were consistent across three time-windows: the acute phase, the chronic phase, and 180 days after stroke onset. Additionally, the lymphocyte count and pulmonary infection were identified as potential independent indicators. This study investigated the prevalence and risk factors for dysphagia in ischemic stroke patients at different time-windows. A low ADL score (<40) may serve as a valuable and reliable predictor for poststroke dysphagia in clinical settings.

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future.