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IntroductionTo explore changes in performance, weaknesses, and utilization of the long-term care (LTC) system for older people with disabilities and dementia (OPWDD) in Zhejiang Province, China, thereby providing a reference for decision-making amid a progressively aging population.MethodsA performance evaluation model of the LTC system for OPWDD was constructed using three dimensions: input, process, and outcome. Performance indicators and trends were calculated based on data collected from statistical yearbooks, documents, and work reports of the Bureau of Statistics and other government departments in Zhejiang Province, China, published in 2015–2021.ResultsSignificant improvements were observed in most LTC performance indicators for OPWDD, such as input, process, and outcome, with notable enhancements in fairness, accessibility, and affordability of LTC services. By 2021, there were 6.20 nursing and rehabilitation beds in medical institutions and 3.77 general practitioners per 1,000 people aged 65 and above, up 144.14% and 13.73%, respectively, from 2015. The rate of health management for older people was 70.91%, representing a 10.33% increase from 2015. The actual reimbursement ratio of hospitalization expenses covered by basic medical insurance for older people rose 7.05%, from 72.76% in 2015 to 77.89% in 2021. Social security satisfaction rose 12.4%, from 71.3% in 2015 to 83.7% in 2021. Certain indicators, however, showed no significant improvement and tended to decline, such as the number of beds at older care institutions and caregivers per 1,000 people aged 65 and over.DiscussionIt is imperative to further balance the allocation of care resources, using a people-centered and integrated LTC system. The proportion of rehabilitation and nursing beds for older people should be consistently increased to effectively alleviate the shortage of care beds. Furthermore, a talent incentive policy should be improved to train caregivers and provide whole-person and whole-life course care based on OPWDD needs.

To conduct an anti-tumor research by using targeted drug-loaded cell-derived microparticles to target the tumor microenvironment and enhance NK cell killing function. In this experiment, we obtained HepG2 tumor cell-derived microparticles by physical extrusion, high speed centrifugation and filtration, modified the hepatocellular carcinoma targeting peptide SP94 on the surface of microparticles and encapsulated the TGF-β inhibitor SB505124. Finally we validated and analyzed whether the new drug delivery system can target to tumor site and enhance the anti-tumor function of NK cells. This type of novel targeted cell-derived microparticles drug delivery system will provide a novel idea for tumor immunotherapy.

Previous studies have demonstrated that promyelocytic leukemia zinc finger protein (PLZF) promotes the expression of gluconeogenic genes and hepatic glucose output, which leads to hyperglycemia. However, the role played by PLZF in regulating lipid metabolism is not known. In this study, we aimed to examine the function of PLZF in regulating hepatic lipid and glucose homeostasis and the underlying mechanisms. The expression of PLZF was determined in different mouse models with regard to non-alcoholic fatty liver disease (NAFLD). In the next step, adenoviruses that express PLZF (Ad-PLZF) or PLZF-specific shRNA (Ad-shPLZF) were utilized to alter PLZF expression in mouse livers and in primary hepatocytes. For the phenotype of the fatty liver, histologic and biochemical analyses of hepatic triglyceride (TG), serum TG and cholesterol levels were carried out. The underlying molecular mechanism for the regulation of lipid metabolism by PLZF was further explored using luciferase reporter gene assay and ChIP analysis. The results demonstrated that PLZF expression was upregulated in livers derived from ob/ob, db/db and diet-induced obesity (DIO) mice. Liver PLZF-overexpressing C57BL/6J mice showed fatty liver phenotype, liver inflammation, impaired glucose tolerance and insulin sensitivity. On the other hand, hepatic PLZF knockdown in db/db and DIO mice alleviated hepatic steatosis. Of note, we found that PLZF activates SREBP-1c gene transcription through binding directly to the promoter fragment of this gene, which would induce a repressor-to-activator conversion depending on its interaction with SIRT1 in the role played by PLZF in the transcription process through deacetylation. Thus, PLZF is identified as an essential regulator of hepatic lipid and glucose metabolism, where the modulation of its liver expression could open up a therapeutic path for treating NAFLD.

Scientific evaluation of the competitiveness of China’s dairy industry is of great significance to the development of regional dairy industry. In view of the fact that the existing evaluation model cannot solve the dynamic nonlinear optimization problem, this paper established a projection pursuit model based on accelerating genetic algorithm and carried on the comprehensive evaluation and dynamic analysis on the competitiveness of dairy industry in China’s 31 provinces and cities (not including Hong Kong, Macao, and Taiwan) from five dimensions, namely, basic factors of production, the industry demand conditions, the enterprise competitiveness and growth force, related and supporting industries, and the natural environmental elements. It was found that firstly, from the perspective of time, the development trend of China’s dairy industry basically shows a positive trend. With the improvement of national living standards, the competitiveness of the dairy industry is also constantly improved. Secondly, from the perspective of space, regional differences in the competitiveness of the dairy industry are significant. Inner Mongolia, Heilongjiang, Shandong, and Hebei are at the forefront of the development of the industry, while the development of the dairy industry in the rest of the region has a big gap with those areas. From the perspective of regional layout, the advantageous region of China’s dairy industry has been preliminarily formed, and the competitive advantage is mainly concentrated in the northern region. Accordingly, the countermeasures and suggestions to enhance the competitiveness of China’s dairy industry were put forward.

Supplementary materials for the manuscript including Fig. S1 to Fig. S13 and Table S1 to Table S11.

The supplementary materials including Fig. S1 to Fig. S13 and Table S1 to Table S11 used in the manuscript.

Supplemental Document

Multi-focus image fusion technologies compress different focus depth images into an image in which most objects are in focus. However, although existing image fusion techniques, including traditional algorithms and deep learning-based algorithms, can generate high-quality fused images, they need multiple images with different focus depths in the same field of view. This criterion may not be met in some cases where time efficiency is required or the hardware is insufficient. The problem is especially prominent in large-size whole slide images. This paper focused on the multi-focus image fusion of cytopathological digital slide images, and proposed a novel method for generating fused images from single-focus or few-focus images based on conditional generative adversarial network (GAN). Through the adversarial learning of the generator and discriminator, the method is capable of generating fused images with clear textures and large depth of field. Combined with the characteristics of cytopathological images, this paper designs a new generator architecture combining U-Net and DenseBlock, which can effectively improve the network's receptive field and comprehensively encode image features. Meanwhile, this paper develops a semantic segmentation network that identifies the blurred regions in cytopathological images. By integrating the network into the generative model, the quality of the generated fused images is effectively improved. Our method can generate fused images from only single-focus or few-focus images, thereby avoiding the problem of collecting multiple images of different focus depths with increased time and hardware costs. Furthermore, our model is designed to learn the direct mapping of input source images to fused images without the need to manually design complex activity level measurements and fusion rules as in traditional methods.

High-resolution 3D medical images are important for analysis and diagnosis, but axial scanning to acquire them is very time-consuming. In this paper, we propose a fast end-to-end multi-focal plane imaging network (MFPINet) to reconstruct high-resolution multi-focal plane images from a single 2D low-resolution wild filed image without relying on scanning. To acquire realistic MFP images fast, the proposed MFPINet adopts generative adversarial network framework and the strategies of post-sampling and refocusing all focal planes at one time. We conduct a series experiments on cytology microscopy images and demonstrate that MFPINet performs well on both axial refocusing and horizontal super resolution. Furthermore, MFPINet is approximately 24 times faster than current refocusing methods for reconstructing the same volume images. The proposed method has the potential to greatly increase the speed of high-resolution 3D imaging and expand the application of low-resolution wide-field images., Comment: 9 pages, 4 figures,3 Tables

Nowadays, the internal structure of the gearbox tends to be complicated and the working environment is subject to more interference factors, so that the collected vibration signal is rich in more interference items, which makes the fault diagnosis of the gearbox more difficult. In order to find a new method to improve the efficiency and accuracy of fault diagnosis of various components in the gearbox, this paper proposes to combine the powerful image recognition capability of convolutional neural network with short-time Fourier transform to apply to gearbox diagnosis. The method transforms the one-dimensional vibration signal into a two-dimensional spectrogram by short-time Fourier transform, and performs normalization preprocessing on the image, and inputs it into the convolutional neural network through Shuffle operation to perform feature extraction to train the model. Using the operations such as Dropout makes the model training faster, and finally uses the trained model to diagnose the fault. The experimental results show that this method can effectively complete a variety of gearbox fault diagnosis and provide a possibility of a diagnostic method connected with “big data”. Compared with the traditional neural network method, the method has a higher efficiency and accuracy of about 5 percent.

Dongba culture is a treasure in the world’s historical civilization. In the traditional method of classification and preservation of Dongba ancient books, it is mainly through the translation of the scriptures of Dongba priests, and the artificial identification of damaged books is carried out, and then a large amount of manpower is spent on each The book is compiled and compiled, and finally classified. This paper proposes a Dongba classic ancient book intelligent image classification method based on ReN-Softplus convolution residual neural network: in the structural design of convolutional neural network oriented to Dongba image, by using the principle of residual network, and using 1*1, 3*3 and 5*5, which are relatively small convolution kernels, construct a network suitable for Dongba image recognition and classification, and then form a final network structure by repeated superposition. In addition, combining the smoothing characteristics of the Softplus function with the advantages of the better sparse representation of the ReLUs function, a ReN-Softplus activation function is proposed to improve the performance and classification accuracy. Applying this method to the classification identification of Dongba classic ancient books, the test results show that the accuracy of the AlexNet method is increased by 0.7%, and the time of the VGGNet method is reduced by 18s.

Through a multicenter study, we collected seven cases of gastric plexiform fibromyxoma including four females and three males, 21 to 79 y old (46.1±10.1). All cases showed a unilocular lesion measuring 0.3 to 17 cm (5.3±2.4), arising from antrum (5/7) or body (2/7). Six of the seven cases had intraoperative frozen sections and/or endoscopic ultrasound fine needle aspiration (EUS-FNA), and all of them were preoperatively or intraoperatively diagnosed as gastrointestinal stromal tumor (GIST). EUS-FNA material showed markedly elongated spindle cells with streaming oval to elongated nuclei with rounded ends. Histologically, the tumors exhibited a plexiform growth pattern and were composed of a rich myxoid stroma and cytologically bland uniform spindle cells without mitotic figures, with the exception of one case which displayed nuclear pleomorphism and increased mitosis. Immunostains showed the tumor cells to be focally positive for SMA (6/6), focally and weakly positive for desmin (3/6) and caldesmon (2/3), negative for CD117 (0/7), CD34 (0/7), DOG1 (0/4), and S100 (0/5). No mutations were identified on Next-Generation Sequencing test, and no loss of SDHB immunoreactivity was identified in the tumor with nuclear pleomorphism. One case was treated with Gleevec because of the initial diagnosis of GIST. All patients had a follow-up for up to 11y, with no tumor recurrence or metastasis reported. Our results suggest that gastric plexiform fibromyxoma is rare and may be underrecognized and misinterpreted as GIST during intraoperative frozen section or preoperative EUS-FNA diagnosis without immunostains leading to inappropriate treatment.

Concerning the problem of large rotating machinery with non-stationary state like wind turbine, this research mainly makes an emphasis on the method of state deterioration recognition based on bi-spectrum entropy and HMM (Hidden Markov Model). Firstly, the true signal such as low-speed start vibration signals of rotor test rig in the normal state and a plurality of imbalance deterioration degrees are collected. Bi-spectrum is applied to obtain the fault feature from the vibration signals mixed with a complex background noise. On the basis of bi-spectrum analysis, a bi-spectrum entropy algorithm is derived under the condition of subspace distribution probability, and the HMM for the fault pattern recognition is established by using the bi-spectrum entropy feature as input. This method is verified by successfully recognizing four state deterioration degrees. Finally, the method is applied to recognize the imbalance deterioration degree of wind turbine with the type of SL1500/82 and equipment actual working condition verified the effectiveness of the proposed method.

A selective procedure for the synthesis of 5-substituted N-aryloxazolidinones by the coupling of epoxides with arylcarbamates catalyzed by ionic liquids has been developed. The effects of reaction time, reactant molar ratio, amount of catalyst, and temperature were investigated. Under the optimal reaction conditions, BmimOAc exhibited efficient catalytic activity compared with other ionic liquids leading to the formation of 5-substituted N-aryloxazolidinones in excellent yields. In addition, chiral 5-substituted oxazolidinones were synthesized by this procedure in good-to-excellent yields with enantiomeric excesses in excess of 99.9 % starting from chiral terminal epoxides. A possible reaction mechanism is discussed in accord with the results obtained by 1H NMR spectroscopy and DFT calculations, which indicate the cooperative activation by BmimOAc through the formation of hydrogen bonds with the substrates.

Homogeneous catalysts generally show higher catalytic activities, while heterogeneous catalysts are more easily separated from products. To combine the advantages of heterogeneous and homogeneous catalysts has been of great interest for many years. Here, we report a kind of facilely prepared cross-linked poly(ionic liquid)s (PILs) with swelling property to increase catalytic activities of heterogeneous catalysts. The swelling ability of PILs was greatly affected by cross-linking density and chain length of substituents on imidazolium, and the unique swelling property prompted the nonporous PILs to contact with substrates sufficiently, enhancing their catalytic activities similar to homogeneous ionic liquid monomers.

Rotating machinery is the most widely used mechanical equipment in production, and wind turbines are typical large rotating machinery, whether the equipment running in safe and stable state has an important impact on the economy and society. The wind turbine gear running in variable conditions and variable speed state, so traditional method which studying fault mechanism through fault feature frequency under certain speed is no longer applicable. This paper describes the evolutionary trajectory of spalling fault of wind turbine gearbox transmission system. Deterioration evolutionary matrix has been proposed based on higher-order cumulant method, and the mapping mechanism is established between the fault evolution and fault state migration. Fault evolution parameter such as mean value and variance of normal distribution has been extracted and used to depict fault evolutionary track. Finally, the graphical representation of fault evolution process is explored. Concerning the fault of gearbox transmission system, this research mainly emphasizes on fault evolution. It will provide scientific means to implement predictive maintenance, and avoid occurrence of fatal accidents.

With the development of wind power, the fault of wind turbine is increasing year by year. The gearbox is the fault multiple component of the wind turbine. The monitoring system gets massive data, and the health monitoring of the fan has also entered the era of “big data”. The new problem in the field of health monitoring of mechanical equipment is to excavate information from these large data and to identify the health status of equipment efficiently and accurately. Deep learning is an effective tool for large data processing. Based on the strong perception and self-learning ability of deep learning theory, a fault diagnosis method of wind turbine gearbox based on deep belief network and vibration signal is proposed in this paper. The original fault sample vibration signal is input to the deep belief network to get the deep learning sample model. At the same time, Batch Normalization is added to reduce the overfitting probability and improve the convergence speed of the network. Then the trained deep neural network model is applied to the fault diagnosis of the planetary gear box of the wind turbine gearbox, and the results show that the method is more accurate than the DBN, the BPNN algorithm and the traditional fault diagnosis method.

The development of wind power industry has put forward higher requirements for the remote monitoring method and system of wind turbines. To effectively guarantee the normal work of the equipment, the processing quality and fault forecast, remote on-line detection and intelligent fault diagnosis system platform is built. Firstly, the theory and method of remote monitoring and maintenance for wind turbines are put forward: dynamic characteristics degradation development tendency weak information signal processing and pre-processing, extraction of dynamic characteristics degradation trend characteristics in long-duration and variable-load state, establishment of trend prediction model and system based on combination of measured big data trend forecast and mechanical dynamic property trend prediction. Then, a remote monitoring system for wind turbines is constructed integrating with sensor technology, dynamic measurement and control technology, signal processing technology, fault pattern recognition technology and artificial intelligence technology. Field application shows that the system can effectively monitor the running status of the fan, achieve the real time alarm and running trend prediction effectively.

The prediction theories and approaches of stability deterioration trend were proposed through intensive studies against the operating stability and safety of complicated electromechanical systems in new energy equipment represented by conventional wind turbines, which provides the research foundation for improving the technical support level and scientific maintenance level of safety and stability of complicated electromechanical systems. In this research, the development rule of stability deterioration of electromechanical systems was revealed; the signal processing techniques for weak information of development trend of early failure were proposed; the feature extraction method for development trend of operating status of long-course load-variant electromechanical systems was proposed; and the prediction model and system for deterioration trend of operating stability of systems was constructed. The research achievement can be applied to the safety monitoring and fault warning of new energy equipment represented by wind turbines, which not only enhances the R&D level of related safety monitoring instruments, but also ensures safe and reliable operation of equipment and achieves scientific maintenance, improves the operation and management level of equipment, reduces the economic losses caused by failure, and promotes the utilization and development of green energy.

In an attempt to produce effective and lower price gaseous Hg0adsorbents, two methods of HCl and KMnO4/heat treatment were used respectively for the surface modification of liginite semicoke from inner Mongolia. The different effects of modification process on the surface physical and chemical properties were analyzed. The characteristics and stability of mercury vapor adsorption over two kinds of modified semicoke were investigated. The results indicated that modification process caused lower micropore quantity and volume capacity of semicoke; the C-Cl functional groups, C=O bond and delocalized electronπon the surface of Cl-SC, the amorphous higher valencyMnx+, and O=C–OH functional groups on the surface of Mn-H-SC were the active sites for oxidation and adsorption of gaseous Hg0. Modification process led to higher mercury removal efficiency of semicoke at 140°C and reduced the stability of adsorbed mercury of semicoke in simulated water circumstance simultaneously.

The Shiyang River Basin in Gansu Province is located in a semiarid area of northwestern China.In the context of global warming,the intensity and frequency of the drought in the area have experienced significant changes,which brings serious influences to agriculture in the area.The area of the drought in Shiyang River Basin was expanding in the past 50 years.So the drought has become one of the key and hot topics in climate change research,especially in a semiarid area of northwestern China in recent year.,and it is necessary to choose a more effective method to analyze temporal and spatial distribution of drought.Based on the observed data of 5 meteorological stations from 1962 to 2010,the method of a series of mathematical statistic theory,GIS spatial analysis techniques and a composite index(CI) of the meteorological drought was applied to analyze the temporal and spatial characteristics of the drought in the area.According to the national standard "Classification of Meteorological Drought"(GB/T20481—2006),CI was calculated by using the standard precipitation index of the last 30 days(equivalent to monthly scale),the last 90 days(equivalent to seasonal scale) and the relative moisture index of the last 30 days.According to the day-to-day CI index,the drought process of the stations and the drought events of different periods during the recent 50 years were calculated,and the occurrence frequency,coverage area intensity,and annual mean days of the drought in each station were analyzed to reveal the temporal and spatial pattern of meteorological droughts in the Shiyang River Basin.The results are as follows:(1)The seasonal occurrence frequencies of the meteorological drought in the area generally decreased from north to south but it was also different.The highest occurrence frequency was in summer,and the lowest was in winter;The minimum frequency in four seasons all occurred in the Wushaoling Station.However,the maximum in spring,summer and winter was observed in the Minqin Station with exception of the Wuwei Station in autumn.(2) The large-area drought in the Shiyang River Basin occurred mostly in summer and autumn but the least one in winter during 1962—2010.Meanwhile,there are 22 years and 11years of drought in summer and autumn,respectively,whereas there is 3 years in winter.(3) The drought days of different grades which was negatively correlative with precipitation distribution reduced from north to south in general,and were higher in summer and lower in winter in the Shiyang River Basin.(4) In the Shiyang River Basin,drought is mainly controlled by the Eastern Asian Monsoon and Southwest Monsoon,while the runoff factors should also be considered.This study can reflecet the fact and provide a reference for developing appropriate measures to reduce droughts in the Shiyang River Basin.

A selective procedure for the synthesis of 5-substituted N-aryloxazolidinones by the coupling of epoxides with arylcarbamates catalyzed by ionic liquids has been developed. The effects of reaction time, reactant molar ratio, amount of catalyst, and temperature were investigated. Under the optimal reaction conditions, BmimOAc exhibited efficient catalytic activity compared with other ionic liquids leading to the formation of 5-substituted N-aryloxazolidinones in excellent yields. In addition, chiral 5-substituted oxazolidinones were synthesized by this procedure in good-to-excellent yields with enantiomeric excesses in excess of 99.9 % starting from chiral terminal epoxides. A possible reaction mechanism is discussed in accord with the results obtained by 1H NMR spectroscopy and DFT calculations, which indicate the cooperative activation by BmimOAc through the formation of hydrogen bonds with the substrates.

With the rapid development of economy in China, the issue is increas ingly serious involving the degradation of environment, the shortage of resource and the absence of environmental protection. To keep sustainable development of economy, this paper illustrates stimulating function of tax policy involving in environmental prot ection, energysaving and emission-reducing from aspect of encouraging technology innovation. Furthermore, it put s forward policy orientation including further improving tax to support energy -saving and emission-reducing and independent innovation with the analysis of existing tax policy on environmental protection, energy-saving and emission-reducing.

A patient presenting with concomitant epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) translocation is very rare. We report a non-small cell lung cancer (NSCLC) patient with concomitant EGFR (exon 19-del) mutation and ALK rearrangement. The positron emission tomography-computed tomography (PET-CT) scan revealed a highly metabolic mass lesion in the left lower lobe, measured 5.0 cm in the largest dimension in the S6 segment. Transbronchial lung biopsy (TBLB) showed the pathological diagnosis of invasive adenocarcinoma. Thus, the patient underwent left lower lobectomy and hilar-mediastina lymph node dissection (pT2N0M0). The tumor harbor an ALK (D5F3 +) rearrangement and EGFR (exon 19-del) mutation. The patient initially received four cycles of chemotherapy (pemetrexed and carboplatin), and achieved partial response (PR).