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In speech recognition and natural language processing of doctor-patient voice communication, it is critical to distinguish what comes from the healthcare worker and what comes from the patient. In addition, speech contains acoustic and linguistic features that can be identified by machine learning models to measure the speaker's behavioral health. At the same time, it is relatively simple and attractive for patients to use voice data acquisition, as well as relatively cheap and convenient, requiring only a microphone, a quiet place and a device to collect audio samples. Thus, voice-based biomarkers can prescreen for disease, monitor disease progression and response to treatment, and be useful alternative markers for clinical studies with informed consent, but the premise of this process again requires us to distinguish between doctors and patients when taking audio samples. For audio samples of patient voices, in practice, most of the doctor's and patient's voices are not taken separately, but are mixed together. Several speaker recording methods have been used to isolate sound in the time domain; However, these studies do not address how to obtain timelabel-based speech samples, nor how to identify speakers. In this paper, a speech separation method is proposed for the audio separation situation between a doctor and several patients. The method mainly includes three parts: voiceprint segmentation clustering, cutting and splicing, speech identity determination. Doctor and patient audio can be separated while respecting the privacy of the conversation content, and can be stored separately based on the identity of the voice.

Amidst the accelerating growth of intelligent power systems, the integrity of vast and complex datasets has become essential to promoting sustainable energy management, ensuring energy security, and supporting green living initiatives. This study introduces a novel hybrid machine learning model to address the critical issue of missing power load data—a problem that, if not managed effectively, can compromise the stability and sustainability of power grids. By integrating meteorological and temporal characteristics, the model enhances the precision of data imputation by combining random forest (RF), Spearman weighted k-nearest neighbors (SW-KNN), and Levenberg–Marquardt backpropagation (LM-BP) techniques. Additionally, a variance–covariance weighted method is used to dynamically adjust the model's parameters to improve predictive accuracy. Tests on five metrics demonstrate that considering various correlated factors reduces errors by approximately 8–38%, and the hybrid modeling approach reduces predictive errors by 12–24% compared to single-model approaches. The proposed model not only ensures the resilience of power grid operations but also contributes to the broader goals of energy efficiency and environmental sustainability. [ABSTRACT FROM AUTHOR]

Due to various reasons, such as limitations in data collection and interruptions in network transmission, gathered data often contain missing values. Existing state-of-the-art generative adversarial imputation methods face three main issues: limited applicability, neglect of latent categorical information that could reflect relationships among samples, and an inability to balance local and global information. We propose a novel generative adversarial model named DTAE-CGAN that incorporates detracking autoencoding and conditional labels to address these issues. This enhances the network's ability to learn inter-sample correlations and makes full use of all data information in incomplete datasets, rather than learning random noise. We conducted experiments on six real datasets of varying sizes, comparing our method with four classic imputation baselines. The results demonstrate that our proposed model consistently exhibited superior imputation accuracy. [ABSTRACT FROM AUTHOR]

Laser-assisted simulation technique has played a crucial role in the investigation of dose rate effects of silicon-based devices and integrated circuits, due to its exceptional advantages in terms of flexibility, safety, convenience, and precision. In recent years, wide band gap materials, known for their strong bonding and high ionization energy, have gained increasing attention from researchers and hold significant promise for extensive applications in specialized environments. Consequently, there is a growing need for comprehensive research on the dose rate effects of wide band gap materials. In response to this need, the use of laser-assisted simulation technology has emerged as a promising approach, offering an effective means to assess the efficacy of investigating these materials and devices. This paper focused on investigating the feasibility of laser-assisted simulation to study the dose rate effects of wide band gap semiconductor devices. Theoretical conversion factors for laser-assisted simulation of dose rate effects of GaN-based and SiC-based devices were been provided. Moreover, to validate the accuracy of the conversion factors, pulsed laser and dose rate experiments were conducted on GaN-based and SiC-based PIN diodes. The results demonstrate that pulsed laser radiation and γ-ray radiation can produce highly similar photocurrent responses in GaN-based and SiC-based PIN diodes, with correlation coefficients of 0.98 and 0.974, respectively. This finding reaffirms the effectiveness of laser-assisted simulation technology, making it a valuable complement in studying the dose rate effects of wide band gap semiconductor devices. [ABSTRACT FROM AUTHOR]

Nitrogen is one of the most important elements for improving potato yield. However, excessive application of nitrogenous fertilizer not only produces river and other environmental pollution but also increases agricultural production costs. In recent years, to explain the molecular mechanisms of nitrogen metabolites, some vital genes involved have been reported; however, only limited success has been achieved in potato. Here, we report that the expression of cytosolic glyceraldehyde-3-phosphate dehydrogenase 1 (StGAPC1) is increased under low-nitrogen stress. StGAPC1-overexpressing potato seedlings had more biomass and a significant increase in total nitrogen content and root nitrate influx rate compared to the wild type. The overexpression of StGAPC1 also increased the expression of nitrate transporters and increased ROS system activity to reduce hydrogen peroxide content under low-nitrogen stress. Our results provide a foundation for further research on StGAPC1 function in nitrogen absorption and utilization mechanisms in potato. [ABSTRACT FROM AUTHOR]