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The prediction of the remaining useful life (RUL) is of great significance to ensure the safe operation of industrial equipment and to reduce the cost of regular preventive maintenance. However, the complex operating conditions and various fault modes make it difficult to extract features containing more degradation information with existing prediction methods. We propose a self-supervised learning method based on variational automatic encoder (VAE) to extract features of data's operating conditions and fault modes. Then the clustering algorithm is applied to the extracted features to divide data from different failure modes into different categories and reduce the impact of complex working conditions on the estimation accuracy. In order to verify the effectiveness of the proposed method, we conduct experiments with different network structures on the C-MAPSS dataset, and the results verified that our method can effectively improve the feature extraction capability of the model. In addition, the experimental results further demonstrate the superiority and necessity of using hidden features for clustering rather than raw data.

The global epidemic of Corona Virus Disease 2019 (COVID-19) has been raging since its outbreak in 2019, placing enormous strain on healthcare systems all over the world. The best course of action is to prevent COVID-19 in the future, despite the fact that its virological characteristics and pathogenesis have been largely understood. And all current treatment options probably include two types: one is active immunization and the other is passive immunization. Among them, passive immunity includes supportive therapy as well as neutralizing antibodies, and small molecule drugs and other treatments are used as supportive therapy, which only slows down the symptoms and prevents complications, and then waits for the body's immune system to heal itself. On the other hand, stem cell therapy is one type of active immunity. Typically, doctors use mesenchymal stem cells (MSC) to treat patients with severe or life-threatening disorders because of their potent immunomodulatory, anti-inflammatory, and tissue healing abilities. The remarkable developments in COVID-19 stem cell therapy during the past three years are outlined in this article, along with the current state of affairs. And it was found that although stem cell therapy has been successful in stages, it still has many limitations.

Large language models (LLMs) are increasingly being used in various fields, including labor markets and business cycles. In this paper, we examine the impact potential of LLMs in labor markets and business cycles in China through a qualitative case study. We first provide background information on LLMs and labor markets and business cycles in China, and then review the relevant literature. Next, we describe the methodology of our study, including the data collection process and data analysis techniques. We present the findings of our case study and identify themes and patterns related to the impact potential of LLMs. We then discuss the implications of our findings for the use of LLMs in labor markets and business cycles in China, compare our findings to the literature review, and offer suggestions for future research. Our study contributes to the growing body of literature on LLMs and their impact potential in various fields and provides insights into the potential use of LLMs in labor markets and business cycles in China.

A machine learning method is proposed to aim at the problems of large data processing, complex data, and limited system resources in cloud computing service management. First, multi-department analysis is carried out on cloud service management data, and relevant management data is standardized to form a standardized management data collection; Machine learning is used to classify, mine, and extract service management data, and corresponding management measures are taken promptly to improve the management level of cloud computing services. MATLAB simulation shows that machine learning can improve the level of cloud computing service management, simplify the process of cloud computing service management, shorten the management time of cloud computing services, and meet the actual needs of service management.

Background: In Parkinson’s disease (PD), walking may depend on the activation of the cerebral cortex. Understanding the patterns of interaction between cortical regions during walking tasks is of great importance. Objective: This study investigated differences in the effective connectivity (EC) of the cerebral cortex during walking tasks in individuals with PD and healthy controls. Methods: We evaluated 30 individuals with PD (62.4±7.2 years) and 22 age-matched healthy controls (61.0±6.4 years). A mobile functional near-infrared spectroscopy (fNIRS) was used to record cerebral oxygenation signals in the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left parietal lobe (LPL), and right parietal lobe (RPL) and analyze the EC of the cerebral cortex. A wireless movement monitor was used to measure the gait parameters. Results: Individuals with PD demonstrated a primary coupling direction from LPL to LPFC during walking tasks, whereas healthy controls did not demonstrate any main coupling direction. Compared with healthy controls, individuals with PD showed statistically significantly increased EC coupling strength from LPL to LPFC, from LPL to RPFC, and from LPL to RPL. Individuals with PD showed decreased gait speed and stride length and increased variability in speed and stride length. The EC coupling strength from LPL to RPFC negatively correlated with speed and positively correlated with speed variability in individuals with PD. Conclusion: In individuals with PD, the left prefrontal cortex may be regulated by the left parietal lobe during walking. This may be the result of functional compensation in the left parietal lobe.

Minling Teng, Jian Suo, Haojie Zhong, Ningbo Kou, Bingwen Song, Guopeng Li School of Architecture and Fine Art, Dalian University of Technology, Dalian, 116000, People’s Republic of ChinaCorrespondence: Jian Suo, School of Architecture and Fine Art, Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China, Tel +86-138-4090-7234, Fax +86 411-84707504, Email suojian727@dlut.edu.cnBackground: With the expansion of comprehensive renovation projects in China’s old residential areas, residents’ actual sense of access and well-being has become a source of concern. As the population in old residential areas ages, the results of old renovation will have a significant impact on the older adults quality of life and subjective well-being.Purpose: To date, most relevant studies have focused on urban and community scales, with little discussion of the impact of neighborhood-level renewal outcomes on the well-being of older adults. As a result, the purpose of this research is to optimize the quality renewal elements of existing old residential areas based on the results of their renovation in order to create a more livable living environment for older adults in existing old residential areas.Methods: Based on a survey of old neighborhoods in Dalian, the study examines the mediating effect of psychological resilience between multi-quality renewal elements and the subjective well-being of the older adults, and it discusses the influence of multi-quality renewal elements on the subjective well-being of the older adults in different residential types.Results: According to the findings, multi-quality renewal elements of residence positively predicted the subjective well-being of the older adults; there was a significant mediating effect of psychological resilience between the renewal elements and subjective well-being of the older adults. The study confirms that multiple quality renewal elements can contribute to older people’s subjective well-being, and that psychological resilience plays an important role in the selection of multiple quality renewal elements and older people’s subjective well-being.Conclusion: The research provides guidance for the establishment of health-oriented approaches to environmental restoration in residential areas for the sustainable development of communities and cities.Graphical Abstract: Keywords: existing old residential areas, multi-quality renewal elements, older adults, subjective well-being, impact

Automatic source code summarization system aims to generate a valuable natural language description for a program, which can facilitate software development and maintenance, code categorization, and retrieval. However, previous sequence-based research did not consider the long-distance dependence and highly structured characteristics of source code simultaneously. In this article, we present a Transformer-based Graph-Augmented Source Code Summarization (GA-SCS), which can effectively incorporate inherent structural and textual features of source code to generate an effective code description. Specifically, we develop a graph-based structure feature extraction scheme leveraging abstract syntax tree and graph attention networks to mine global syntactic information. And then, to take full advantage of the lexical and syntactic information of code snippets, we extend the original attention to a syntax-informed self-attention mechanism in our encoder. In the training process, we also adopt a reinforcement learning strategy to enhance the readability and informativity of generated code summaries. We utilize the Java dataset and Python dataset to evaluate the performance of different models. Experimental results demonstrate that our GA-SCS model outperforms all competitive methods on BLEU, METEOR, ROUGE, and human evaluations.

A high-sucrose diet causes microbiota composition shift, and promotes the susceptibility of mice to Salmonella Typhimurium infection.

The polysaccharides from seeds of Gleditsia sinensis Lam. alleviate DSS-induced colitis in mice.

A possible mechanism by which cherry juice improves obesity.

A concise and efficient ring-opening difluorination strategy was developed for the synthesis of highly functionalized hydroxy-containing α,α-difluoro-β-ketoamides from the one-pot multicomponent reaction of 4-aminocoumarins, NFSI, and water in dimethyl carbonate (DMC) as a green solvent. The reactions were smoothly achieved under visible light irradiation in air at room temperature without the addition of any other external photocatalysts. With this protocol, various α,α-difluoro-β-ketoamides were successfully synthesized under mild conditions (25 examples, 73-91% yields). This transition-metal-free synthetic procedure shows good functional group compatibility and attractive practical potential for large-scale synthesis.

Spinel oxides are mounted on nickel foam to form chrysanthemum-like crystal structures assembled from nanowires. The as-synthesized CuCo2O4–Co3O4/NF shows a high surface area, a large number of mesoporous structures and excellent charge transfer channels.

A light-fueled dissipative aggregation-induced emission (LDAIE) system with reversible AIE fluorescence on/off switching is successfully fabricated, which can show potential in time-dependent information encryption with self-erasing ability.

By virtue of its unique core–shell open tubular structure, NiMoP@Cu3P/CF shows large specific surface area and short diffusion paths, which facilitates mass transport and easy accessibility of water, resulting in a superior HER activity in 1 M KOH.

The greenhouse’s energy consumption is a major limiting factor for output and development. To address this, it is necessary to adopt green and low-carbon heating technologies to replace traditional fuels. This will not only help conserve energy but will also reduce emissions, thereby improving the thermal environmental conditions for agriculture. This paper aims to research and develop a vertical heat exchange tube array device specifically designed for greenhouses. The focus is on enhancing the passive heat absorption and heat storage efficiency of the device and its influence on the thermal environment of the greenhouse. In order to improve the heat absorption and storage efficiency of the heat exchanger device and its impact on the greenhouse thermal environment, experimental comparative analysis was conducted using air, water, and phase-change materials as working fluids inside the pipes. Through a combination of experiments and simulations, it was verified that the heat exchanger device is capable of actively regulating the greenhouse thermal environment. The results show that heat exchangers of all three types of working fluids can effectively improve the stability of soil temperature and play a “shifting the peak and filling the valley” role in regulating the indoor air temperature while positively regulating the relative humidity of the air. Notably, when the working fluid is a phase-change material, it has the most significant impact on the thermal environment of the greenhouse.

A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone are required to understand the limits of anthropogenic emissions controlling ozone reduction. Long-term estimates of background ozone assist in characterising changes in atmospheric composition and can help quantify the influence of human activity on the atmosphere. Background tropospheric ozone measurements representative of continental air masses are scarce in Australia. Here, we use k-means clustering to identify a cluster of measurements from the long-term air quality monitoring station at Oakdale, NSW, which are likely to be representative of background air. The cluster is associated with NOx-limited air masses of continental origin. From this analysis, we estimate background ozone representative of Eastern Australia. We find recent (2017–2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α = 0.05) trend in the mean of +1.8 (1.0–2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring.

Heterogeneous carbon submicron fibers loaded with Co3O4/Co and elemental cobalt were prepared by electrospinning and annealing processes. The phase transformation mechanism of Co3O4 and elemental cobalt in heterogeneous submicron fibers at different annealing temperatures was studied. The electrochemical properties of carbon submicron fiber self-supported electrode materials loaded with Co3O4/Co and elemental cobalt were investigated. The experimental results show that the submicron fibers loaded with Co3O4/Co have better electrochemical performance because Co3O4 has large capacitance, the improved electro conductibility of cobalt metal, and the cellular structure of carbon submicron fibers, which provide sufficient ion transport channels while alleviating the changes in the structure and volume of Co3O4 during the cycle.

Biomass gasification is a promising technology to produce renewable syngas used for energy and chemical applications. However, biomass gasification has challenges of low process energy efficiency, low syngas production with low H2/CO ratio and the sintering of biomass ash which limit the deployment of the technology. This work investigated the influence of in-situ generated heat from CaO–CO2 on cellulose CO2 gasification using a fixed bed reactor, thermogravimetric analysis-Fourier transform infrared spectroscopy (TGA-FTIR) and differential scanning calorimetry (DSC). Experimental results indicate an approximate 20 °C temperature difference in the fix-bed reactor between cellulose CO2 gasification with the energy compensation of CaO carbonation (denoted auto-thermal biomass gasification) and conventional CO2 gasification of cellulose after the power of external furnaces were turned off. Around 5 times H2/CO molar ratio is obtained after switching off the power in the auto-thermal biomass gasification compared with conventional gasification. The gas yield enhances significantly from 0.29 g g−1 cellulose to 0.56 g g−1 cellulose when CaO/cellulose mass ratio increases from 0 to 5. Furthermore, the TGA-FTIR results demonstrate the feasibility of adopting energy compensation of CaO carbonation to reduce the gasification temperature. DSC analysis also proves that the released heat from the CaO–CO2 reaction reduces the required energy for cellulose degradation.