Chemical composition and insecticidal properties of essential oil from aerial parts of Mosla soochowensis against two grain storage insects

Purpose: To determine the insecticidal properties of essential oil from Mosla soochowensis aerial parts against two insect pests, Sitophilus zeamais and Tribolium castaneum.Methods: Hydro-distillation of M. soochowensis was used to extract the essential oil. Gas chromatography/mass spectrometry (GC/MS) analysis was performed, and the contact (topical application) and fumigant toxicity (sealed space) of the essential oil were evaluated.Results: Thirty-nine chemical compounds were identified by GC-MS analysis of M. soochowensis essential oil. The major components are β-caryophyllene (12.82 %), spatulenol (6.34 %), β-eudesmol (6.26 %), carvone (6.12 %), α-thujone (5.12 %), γ-eudesmol (4.86 %), α-cedrol (4.23 %), and α- caryophyllene (4.04 %). The plant essential oil exerted contact toxicity against adults of S. zeamais and T. castaneum (median lethal concentration (LC50), 25.45 and 10.23 μg/adult, respectively). Moreover, the essential oil exhibited pronounced fumigant toxicity towards adults of both species (LC50 12.19 and 10.26 mg/L air, respectively).Conclusion: These results show that M. soochowensis essential oil can be used in development of safer and more natural and effective fumigants/insecticides for stored products.Keywords: Mosla soochowensis, Contact toxicity, Sitophilus zeamais, Fumigant, Insecticide, Essential oil, Tribolium castaneu

Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes further incorporating ultrasound tongue images to improve lip-based AV-SE systems' performance. Knowledge distillation is employed at the training stage to address the challenge of acquiring ultrasound tongue images during inference, enabling an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model. Experimental results demonstrate significant improvements in the quality and intelligibility of the speech enhanced by the proposed method compared to the traditional audio-lip speech enhancement baselines. Further analysis using phone error rates (PER) of automatic speech recognition (ASR) shows that palatal and velar consonants benefit most from the introduction of ultrasound tongue images.Comment: To be published in InterSpeech 202

Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes the incorporation of ultrasound tongue images to improve the performance of lip-based AV-SE systems further. To address the challenge of acquiring ultrasound tongue images during inference, we first propose to employ knowledge distillation during training to investigate the feasibility of leveraging tongue-related information without directly inputting ultrasound tongue images. Specifically, we guide an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model, thus transferring tongue-related knowledge. To better model the alignment between the lip and tongue modalities, we further propose the introduction of a lip-tongue key-value memory network into the AV-SE model. This network enables the retrieval of tongue features based on readily available lip features, thereby assisting the subsequent speech enhancement task. Experimental results demonstrate that both methods significantly improve the quality and intelligibility of the enhanced speech compared to traditional lip-based AV-SE baselines. Moreover, both proposed methods exhibit strong generalization performance on unseen speakers and in the presence of unseen noises. Furthermore, phone error rate (PER) analysis of automatic speech recognition (ASR) reveals that while all phonemes benefit from introducing ultrasound tongue images, palatal and velar consonants benefit most.Comment: Submmited to IEEE/ACM Transactions on Audio, Speech and Language Processing. arXiv admin note: text overlap with arXiv:2305.1493

The Study on In-City Capacity Affected by Pedestrian Crossing

Currently, the urban road traffic congestion is serious and the traffic accident is happening at a high frequency; thus it has not satisfied the travel needs of security and affects the quality of urban trips. In order to effectively relieve the confliction of people and motor vehicle, to make sure of the safety of pedestrians crossing the road, and to improve the capacity of urban roads, this passage focuses on studying the influence of pedestrians crossing the roads on the capacity of urban roads in three pedestrian crossing approaches including freely crossing the street, uncontrolled crossing of the pedestrian crosswalk, and controlled crossing of the pedestrian crosswalk. Firstly, it confirms the general formula of the road capacity when pedestrians are crossing the road based on three preassumptions, combined with the survey data, and then constructs the empirical mathematical model of pedestrian crossing on the capacity impact. Lastly, it takes the step of case calculation and simulation evaluation and calculates errors between them, finding that the error between the model calculation and software simulation is small. The efficiency of the model is validated and improved

New Approaches in Multi-View Clustering

Many real-world datasets can be naturally described by multiple views. Due to this, multi-view learning has drawn much attention from both academia and industry. Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. Clustering has long been serving as a critical technique in data mining and machine learning. Recently, multi-view clustering has achieved great success in various applications. To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning. These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering. Besides, many other multi-view clustering methods can be unified into the frameworks of these five methods. To promote further research and development of multi-view clustering, some popular and open datasets are summarized in two categories. Furthermore, several open issues that deserve more exploration are pointed out in the end

Role of Body Mass Index, Waist-to-Height and Waist-to-Hip Ratio in Prediction of Nonalcoholic Fatty Liver Disease

Objective. To investigate the anthropometric indicators that can effectively predict the nonalcoholic fatty liver disease (NAFLD). Methods. The height, body weight, waist and hip circumference were measured, and body mass index (BMI), waist-to-height (WHtR) and waist-to-hip ratio (WHR) were calculated. M-H chi square test, logistic regression analysis, and receiver-operating characteristic (ROC) curve were employed for the analysis of risk factors. Patients or Materials. 490 patients were recruited, of whom 250 were diagnosed as NAFLD and 240 as non-NAFLD (control group). Results. Compared with the control group, the BMI, WHR, and WHtR were significantly higher in patients with NAFLD. Logistic regression analysis showed that BMI and WHR were effective prognostic factors of NAFLD. In addition, WHR plays a more important role in prediction of NAFLD by the area under curve. Conclusion. WHR is closely related to the occurrence of NAFLD. We assume that WHR is beneficial for the diagnosis NAFLD

Resolving and Tuning Mechanical Anisotropy in Black Phosphorus via Nanomechanical Multimode Resonance Spectromicroscopy

Black phosphorus (P) has emerged as a layered semiconductor with a unique crystal structure featuring corrugated atomic layers and strong in-plane anisotropy in its physical properties. Here, we demonstrate that the crystal orientation and mechanical anisotropy in free-standing black P thin layers can be precisely determined by spatially resolved multimode nanomechanical resonances. This offers a new means for resolving important crystal orientation and anisotropy in black P device platforms in situ beyond conventional optical and electrical calibration techniques. Furthermore, we show that electrostatic-gating-induced straining can continuously tune the mechanical anisotropic effects on multimode resonances in black P electromechanical devices. Combined with finite element modeling (FEM), we also determine the Young's moduli of multilayer black P to be 116.1 and 46.5 GPa in the zigzag and armchair directions, respectively.Comment: Main Text: 13 Pages, 4 Figures; Supplementary Information: 5 Pages, 2 Figures, 2 Table

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of LLMs, function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning. This paper proposes a simple yet controllable target-driven approach called Reverse Chain to empower LLMs with capabilities to use external APIs with only prompts. Given that most open-source LLMs have limited tool-use or tool-plan capabilities, LLMs in Reverse Chain are only employed to implement simple tasks, e.g., API selection and argument completion, and a generic rule is employed to implement a controllable multiple functions calling. In this generic rule, after selecting a final API to handle a given task via LLMs, we first ask LLMs to fill the required arguments from user query and context. Some missing arguments could be further completed by letting LLMs select another API based on API description before asking user. This process continues until a given task is completed. Extensive numerical experiments indicate an impressive capability of Reverse Chain on implementing multiple function calling. Interestingly enough, the experiments also reveal that tool-use capabilities of the existing LLMs, e.g., ChatGPT, can be greatly improved via Reverse Chain