Characterization of severe fever with thrombocytopenia syndrome in rural regions of Zhejiang, China.

Severe fever with thrombocytopenia syndrome virus (SFTSV) infections have recently been found in rural regions of Zhejiang. A severe fever with thrombocytopenia syndrome (SFTS) surveillance and sero-epidemiological investigation was conducted in the districts with outbreaks. During the study period of 2011-2014, a total of 51 SFTSV infection cases were identified and the case fatality rate was 12% (6/51). Ninety two percent of the patients (47/51) were over 50 years of age, and 63% (32/51) of laboratory confirmed cases occurred from May to July. Nine percent (11/120) of the serum samples from local healthy people without symptoms were found to be positive for antibodies to the SFTS virus. SFTSV strains were isolated by culture using Vero, and the whole genomic sequences of two SFTSV strains (01 and Zhao) were sequenced and submitted to the GenBank. Homology analysis showed that the similarity of the target nucleocapsid gene from the SFTSV strains from different geographic areas was 94.2-100%. From the constructed phylogenetic tree, it was found that all the SFTSV strains diverged into two main clusters. Only the SFTSV strains from the Zhejiang (Daishan) region of China and the Yamaguchi, Miyazakj regions of Japan, were clustered into lineage II, consistent with both of these regions being isolated areas with similar geographic features. Two out of eight predicted linear B cell epitopes from the nucleocapsid protein showed mutations between the SFTSV strains of different clusters, but did not contribute to the binding ability of the specific SFTSV antibodies. This study confirmed that SFTSV has been circulating naturally and can cause a seasonal prevalence in Daishan, China. The results also suggest that the molecular characteristics of SFTSV are associated with the geographic region and all SFTSV strains can be divided into two genotypes

Лексико-семантическая группа глаголов межличностных отношений в русском и китайском языках: на материале перевода романа Ф.М. Достоевского «Преступление и наказание»

This study is devoted to the analysis of lexico-semantic group of verbs, which express attitude to someone in Russian and the ways of their translation into Chinese. A group of emotional and evaluative verbs included in the lexico-semantic field of interpersonal relations is analyzed. The choice of the study material is determined by the fact that this group of verbs is one of the most frequent in the use and widely represented in the novel “Crime and punishment” by F.M. Dostoyevsky, occurring 561 times. The significance of this research lies in the absence of a special systematic study of this lexico-semantic group on the material of literature in Russian and Chinese languages, as well as in the need to develop a comprehensive research methodology, methods of comparative and contextual analyses. The study reveals the semantic features of verbs in the Russian and Chinese languages. It is established that the lexico-semantic group under study consists of verbs that are perceived as categorical-lexical semes “relation” and can have both positive and negative semantic meaning. The semes ‘positive attitude’, ‘love’, ‘faith’, ‘respect’, ‘compassion’, ‘pity’ and ‘negative attitude’, ‘suffering’, ‘doubt’, ‘fear’ are subjected to study. These features determine the structure of the group in question in the lexical and semantic system of the Russian and Chinese languages, are expanding the understanding of the content and structure of the group of verbs. The result of the study is that the analysis of interlingual gaps reveals the presence of incomplete lexical correspondence to a foreign language word. The analyzed linguistic material made it possible to identify similarities and differences in the semantics of verbs when translating the text of the novel into Chinese.Актуальность работы заключается в отсутствии специального систематического исследования лексико-семантической группы глаголов, выражающих отношение к кому-либо в русском языке, и способам их перевода на китайский язык, на материале художественной литературы в русском и китайском языках, а также связана с необходимостью разработки комплексной методики исследования, приемов сопоставительного, контекстного анализа. Анализу подвергается группа эмоционально-оценочных глаголов, входящих в лексико-семантическое поле межличностных отношений. Выбор материала исследования определяется тем, что эта группа глаголов является одной из самых частотных в употреблении и широко представленных в романе Ф.М. Достоевского «Преступление и наказание» (встречаются 561 раз). Выявлены семантические особенности глаголов в русском и китайском языках. Установлено, что исследуемую лексико-семантическую группу составляют глаголы, которые воспринимаются как категориально-лексические семы «отношение» и могут иметь как положительное, так и отрицательное семантическое значение. Исследованию подвергаются семы ‘позитивное отношение’, ‘любовь’, ‘вера’, ‘уважение’, ‘сострадание’, ‘жалость’ и ‘негативное отношение’, ‘страдание’, ‘сомнение’, ‘боязнь’. Эти признаки определяют структуру рассматриваемой группы в лексико-семантической системе русского и китайского языков, расширяя представления о содержании и структуре группы рассматриваемых глаголов. Результат исследования заключается в том, что при анализе межъязыковых лакун выявляется наличие неполного лексического соответствия иноязычному слову. Проанализированный языковой материал позволил выявить сходства и различия в семантике глаголов при переводе текста романа на китайский язык

Bearing fault diagnosis in high noise environment using multi-scale processing, channel-attention and feature-enhanced convolutional neural network model

This paper presents a model using deep learning techniques which includes Multi-scale processing, Channel attention, Feature enhancement, and anomaly Classification layers, referred to as MCFCNN, for bearing fault diagnosis in noisy industrial environments. The MCFCNN network combines multi-channel parallel convolution, effectively capturing spatial information, and introduces channel attention mechanisms to adaptively recalibrate channel-level feature responses. Secondary neurons are introduced to enhance the model’s ability to capture complex nonlinear patterns related to bearing faults. The model was tested and compared to other models using a publicly available data set. In a simulated high-noise environment, the proposed model outperforms existing models in fault diagnosis, with accuracy greater than 80% even at high signal-to-noise (SNR) ratio. At SNR = -6, the MCFCNN records higher accuracy (83%), precision (89%), and recall rates (84.5%) as compared to prior models. The proposed model can be integrated into the maintenance management system to enhance bearing health assessment and prediction, improving machine prognostics

Classification of Bearing Degradation Stage Based on Automatic Label Assignment and Multi-scale Channel-attention Network

Predicting bearing degradation is crucial for precise maintenance. However, accurately predicting the degradation stages of bearings to achieve appropriate maintenance has always been challenging. To address this problem, we propose a network architecture based on automatic label assignment called FAEK and a multi-scale channel-attention classification (MCC) prediction model to predict the degradation stage of bearings at a given time. Our method achieved outstanding performance on the FEMTO dataset with an accuracy of 0.9665. This approach provides an efficient and reliable solution for the predictive maintenance of bearings

Learning Accurate Entropy Model with Global Reference for Image Compression

In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate. The proposed method scans decoded latents and then finds the most relevant latent to assist the distribution estimating of the current latent. A by-product of this work is the innovation of a mean-shifting GDN module that further improves the performance. Experimental results demonstrate that the proposed model outperforms the rate-distortion performance of most of the state-of-the-art methods in the industry

Vibration characteristics of a compression ignition engine fuelled with different biodiesel-diesel blends

Biodiesel has wide application prospects due to its good power performance, fuel economy and emission reduction. Experimental studies have found that the measured engine vibration presents an N-shaped nonlinear trend with the increase of the biodiesel proportion in blends, which cannot be explained solely based on the combustion characteristics of blended fuels. To study the mechanisms for this nonlinear trend of engine vibration, a two-degree-of-freedom nonlinear model of piston–cylinder system was established and verified to analyse the correspondence between in-cylinder combustion behaviour and engine dynamic responses. By correlating simulation results with measured signals, it is found that the root cause of the nonlinear vibration trend is the coupling effect of in-cylinder pressure and piston inertial force. The time integral of piston lateral force in the interval from combustion top dead centre (TDC) to the subsequent piston slap ultimately determines the trend of liner vibrations. These key findings pave the fundamentals for the vibration analysis of engines fuelled with other alternative fuels, which is important for improve engine operation performances including reliability assessment and NVH control.</p

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective, efficient and economic LLM-based Text-to-SQL solutions. To address this challenge, in this paper, we first conduct a systematical and extensive comparison over existing prompt engineering methods, including question representation, example selection and example organization, and with these experimental results, we elaborate their pros and cons. Based on these findings, we propose a new integrated solution, named DAIL-SQL, which refreshes the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To explore the potential of open-source LLM, we investigate them in various scenarios, and further enhance their performance with supervised fine-tuning. Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning. Additionally, towards an efficient and economic LLM-based Text-to-SQL solution, we emphasize the token efficiency in prompt engineering and compare the prior studies under this metric. We hope that our work provides a deeper understanding of Text-to-SQL with LLMs, and inspires further investigations and broad applications.Comment: We have released code on https://github.com/BeachWang/DAIL-SQ

Functional Features of Verbs of Interpersonal Relations in Russian and Chinese Languages

The article studies functional features of verbs involved in the expression of interpersonal relations in the Russian and Chinese languages. Considering this group of verbs as a subfield of the general lexical-semantic field of attitude verbs is primarily conditioned by the fact that it expresses relations arising among people in the form of feelings, judgments and appeals to one another in course of everyday life. The group of verbs with a variety of both direct and figurative meanings, which are used in different spheres of human communication, is analyzed. The analysis of the studied verbs allows us find out the ways of their realization and appropriate the means to describe human relations in the Russian and Chinese languages. In accordance with the aim and objectives of the study, the article considers cases of expressing relations using adverbs in some constructions of fiction texts, which allows us reveal such features of relations as multiple repetition, duration and length. The analysis of this group of verbs of interpersonal relations helps us understand the semantic structure of the verb that plays a leading role in the context and show the characteristics of the author’s individual expression of interpersonal relations. It was revealed that in isolated cases the verb in Chinese can fulfill the function of a subject, which is explained by the syntactic peculiarities of the structure of this language. The results of the study confirm the hypothesis of incomplete correspondence between the functional features of verbs of interpersonal relations in the two languages under comparison

Q-Diffusion: Quantizing Diffusion Models

Diffusion models have achieved great success in image synthesis through iterative noise estimation using deep neural networks. However, the slow inference, high memory consumption, and computation intensity of the noise estimation model hinder the efficient adoption of diffusion models. Although post-training quantization (PTQ) is considered a go-to compression method for other tasks, it does not work out-of-the-box on diffusion models. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture of the diffusion models, which compresses the noise estimation network to accelerate the generation process. We identify the key difficulty of diffusion model quantization as the changing output distributions of noise estimation networks over multiple time steps and the bimodal activation distribution of the shortcut layers within the noise estimation network. We tackle these challenges with timestep-aware calibration and split shortcut quantization in this work. Experimental results show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance (small FID change of at most 2.34 compared to >100 for traditional PTQ) in a training-free manner. Our approach can also be applied to text-guided image generation, where we can run stable diffusion in 4-bit weights with high generation quality for the first time.Comment: The code is available at https://github.com/Xiuyu-Li/q-diffusio