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We studied China’s Common Prosperity process by assessing and comparing the level of Common Prosperity in different regions of China and made some beneficial recommendations to government departments. The research data comes from the China Statistical Yearbook, which includes data from 31 provinces and cities from 2015 to 2020. According to the relevant research, eleven evaluation indicators were selected. We combined GRA with the TOPSIS method for scoring and the K-means clustering algorithm for dividing the GRA-TOPSIS scoring results into three evaluation levels. Then, the convolutional neural network model was used to predict and simulate the level of common prosperity. Taking 2020 as an example, the results show: (1) From 2015 to 2020, China’s Common Prosperity level reached its highest point in 2020. Due to the impact of COVID-19 in 2019, the scores of 31 regions are generally lower than in the previous four years. The situation changed in 2020; (2) In terms of regional distribution, the economic development of Beijing, Shanghai, and other eastern regions is relatively good, with a higher degree of Common Prosperity than that of other regions; (3) The average prediction accuracy is high in our model. It can be close to 100%, indicating that the model has a good prediction effect. In addition, we made recommendations based on the research results, which have good references for actively promoting common prosperity.

Most methods for evaluating a company’s financial performance currently focus on scoring, when there is a large amount of data, it is difficult to distinguish the company’s financial status. To cluster and predict the financial performance of companies, a hybrid model based on the fuzzy C-means clustering algorithm (FCM) and convolutional neural network (CNN) is proposed in this paper. Pearson correlation analysis was first performed on the indicators to ensure that they are not correlated with each other and to avoid indicator redundancy. The entropy method determined the weight of each index and ensured the high validity of the selected indicators. Then, FCM clustering was carried out, and the performance of each company was clustered according to the indexes after data preprocessing with clustering labels. The processed data and labels were introduced into CNN to predict the level. The empirical study showed that the FCM-CNN model was superior to other machine learning models, which proved that this model has better clustering and forecasting ability, and could be applied to the prediction of corporate financial performance.

Purpose This paper aims to evaluate innovation capability of companies based on comprehensive methods. Design/methodology/approach This paper used principal component analysis (PCA), kernel principal component analysis (KPCA) and principal component cluster (PCC) analysis to analyze the listed companies’ innovation capability. On these bases, mean method, Borda method, Copeland method, alienation coefficient method and fuzzy Borda method were used separately for the comprehensive evaluation. Findings The results show that the comprehensive evaluation can overcome the shortage of the single method and improve the reliability of the innovation ability evaluation. In addition, the method also reveals that the innovation ability of the listed companies is closely related to the innovation investment and their industry and further regional economic development level of each province (city and area). Originality/value This paper uses PCA, KPCA and PCC to evaluate and study their innovation ability. On the basis of these, five methods (mean method, Copeland method, Borda method, divorced coefficient method and fuzzy Borda method) are applied respectively to combine the sort results of PCA, KPCA and PCC. The results show that combination methods have better theoretical and practical significance for innovation ability.

Gibberellins (GAs) are important phytohormones that regulate many developmental processes in plants. Clove, as the reproductive organ of garlic, dramatically affected garlic bulb development. Considering the potential of gibberellins in plant development and our previous studies, we investigated the effect of soaking two types of seed cloves (seed clove-I: without root/shoot sprouting; seed clove-II: with root/shoot sprouting) in GA3 solution on axillary bud development and examined the effect of soaking seed cloves in GA3 solution on bulb development, phytohormone level and sugar content in this study. Results indicated seed clove types, soaking liquids and their interaction significantly affected the number of cloves per bulb and the rate of single-clove bulb. Moreover, soaking seed cloves in 1 mmol L−1 GA3 solution for 24 h not only promoted axillary bud formation and secondary plant growth (equal to tillering or branching), but also slightly increased the number of cloves per bulb and changed bulb structure with a low yield and marketable quality. On the 40th day after GA3 treatment (at axillary bud outgrowth stage), zeatin riboside (ZR) and soluble protein in stem were sharply increased with the increase of GA3, sucrose, fructose and soluble protein in leaf. However, GA3, indole-3-acetic acid (IAA), soluble sugar and sucrose in stem (3.52 ng g−1 fresh weight (FW), 19.88 ng g−1 FW, 237.3 mg g−1 FW, and 8.24 mg g−1 FW, respectively) were significantly decreased on the 40th day after GA3 treatment, compared to the control of water treatment (5.56 ng g−1 FW, 32.96 ng g−1 FW, 263.6 mg g−1 FW, and 10.37 mg g−1 FW, respectively). To our knowledge, these novel results indicate seed cloves soaked in GA3 solution promotes axillary bud formation and outgrowth that caused the changes in plant architecture and bulb structure. Meanwhile, our findings suggest that the level of endogenous plant hormone (GA3, IAA and ZR) cooperates with the content of sugar (sucrose and fructose) in leaf and stem to regulate axillary bud outgrowth in garlic.

Garlic (Allium sativum L.) is an important bulb vegetable with high culinary and medicinal values. It is cultivated by exclusively vegetative propagation which leads to complex virus infection, biodiversity reduction, inhibition of multiple breeding strategies. An efficient and widely applicable shoot regeneration system was established from garlic inflorescence as explant. By optimizing the basal medium type, pH value and explant size, a mean shoot regeneration rate of 97% and mean shoot number of 23.4 per explant were achieved in 14 commercial cultivars with various characteristics and origins. Histological observation revealed that shoots were regenerated through direct organogenesis when meristemoid initiated from subepidermal cells without callus formation followed by periclinal and anticlinal division of epidermal and subepidermal cells. No polymorphic bands were detected by simple sequence repeat (SSR) analysis between regenerants and donor plants. Furthermore, flow cytometry analysis indicated that there was no significant variability of genome size, and all plants maintained their ploidy. These results confirmed the clonal fidelity of regenerants. In conclusion, the present study provides a shoot regeneration system with great potential in micropropagation, germplasm preservation, genetic transformation and ploidy manipulation of garlic. Our study developed a widely applicable procedure for direct shoot regeneration via inflorescence of garlic and ascertained the clonal fidelity of regenerants by using simple sequence repeat (SSR) and flow cytometry.

The financial status of an enterprise is related to its healthy and long-term development, and whether the interests of investors and bank loans can be guaranteed. To improve the prediction accuracy of corporate financial risk, this paper proposes a prediction model for corporate financial risk that integrates GRA-TOPSIS and SMOTE-CNN. First, using GRA-TOPSIS to make a comprehensive evaluation of the financial situation of listed companies. Second, the evaluation results are clustered to obtain the scientific level and interval of financial risk, which lays the foundation for the supervised learning of the convolutional neural network. Then, the SMOTE algorithm is introduced to solve the problem of data imbalance of enterprises at all levels, and the focal loss function is used instead of the cross-entropy loss function to further balance the data. Finally, the listed companies in A shares are randomly selected, and experiments were designed to verify the performance of the model built in this paper. The results show that the prediction accuracy of the financial risk prediction model based on GRA-TOPSIS and SMOTE-CNN is 98.57%, which indicates that the model is feasible and has certain reference value.

Surface urban heat islands (SUHIs) are a global concern. Although their spatial pattern and the cooling effect of blue-green landscapes have been documented, exploring more accurate and quantitative results is still necessary. For Hangzhou, we combined nighttime light (NTL) data with LST images to investigate the spatial morphology of SUHIs and analyze the cooling effect of blue-green landscapes. The radiative transfer equation (RTE) method was used to derive the land surface temperature (LST). Then, based on the unique feature of Luojia1-01 NTL data, the concentric zone model (CZM) was proposed to depict the urban spatial structure. The CZM was applied to construct a number of equal-area concentric belts along the urban-rural gradient to determine the SUHI range and the corresponding blue-green landscape cooling effects. Finally, local Moran's I indices were adopted to identify the cold-hot spots of the SUHI and the relationship with land use. The minimum, average and maximum LSTs were 21.81 °C, 32.79 °C and 44.79 °C, respectively. Additionally, 59.16 % of the study area was affected by the SUHI, and the mean LST inside the SUHI was 36.4 °C, clearly higher than that of the rural area. The SUHI hotpots were clustered in regions with intensive human activities, forming archipelagos. Due to the different blue-green landscape densities, the cooling capacity had spatial heterogeneity in different urban rural belts (URBs), and the cooling capacity of URB

Gibberellins are important phytohormones that regulate many developmental processes in plants. Garlic bulbs are not only the reproductive organs of garlic but also a popular condiment and vegetable with high nutritional and medicinal value. However, the roles of gibberellins in garlic bulb development are unknown. In this study, we investigated the effects of injecting garlic plants with 1 mM L-1 gibberellic acid 3 (GA3) on the bulb development of ten garlic cultivars and determined the effects of treatment with different concentrations of GA3 on the bulb development of cv. G064. The results showed that GA3 treatment dramatically stimulated lateral bud formation but inhibited the growth of plants and bulbs in ten garlic cultivars, and lateral buds formation occurred twice in garlic plant treated with 1 mM L-1 GA3. Bulb nutritional traits were improved by applying 1 mM L-1 GA3 in five cultivars (cv. G024, G064, G096, G2011-2-1 and G2011-4) and were worsened by applying 1 mM L-1 GA3 in cv. G2012-3. Meanwhile, the number of cloves and whorls per bulb increased with increased concentration of GA3. Compared to the control and low concentration of GA3 treatment (0.1, 0.5, 1, 2 and 5 mM L-1), the number of cloves per bulb and the soluble sugar content were significantly increased, but the mean bulb weight significantly decreased for plants treated with a high concentration of GA3 (10 mM L-1). To our knowledge, this is the first report that injecting plants with 1 mM L-1 GA3 led to a sharp increase in the number of cloves and whorls per bulb in garlic. Meanwhile, GA3 treatment improved bulb nutritional traits for the majority of garlic cultivars. This provided new insights into the induction of plant lateral buds by GA3 treatment and improving the reproduction efficiency of garlic via exogenous GA3.

The perennial life style is a successful ecological strategy, and Arabis alpina is a recently developed model Brassicaceae species for studying it. One aspect, poorly investigated until today, concerns the differing patterns of allocation, storage, and metabolism of nutrients between perennials and annuals and the yet unknown signals that regulate this process. A. alpina has a complex lateral stem architecture with a proximal vegetative perennial (PZ) and a distal annual flowering zone (AZ) inside the same stems. Lipid bodies (LBs) with triacylglycerols (TAGs) accumulate in the PZ. To identify potential processes of lipid metabolism linked with the perennial lifestyle, we analyzed lipid species in the PZ versus AZ. Glycerolipid fractions, including neutral lipids with mainly TAGs, phospholipids, and glycolipids, were present at higher levels in the PZ as compared to AZ or roots. Concomitantly, contents of specific long‐chain and very long‐chain fatty acids increased during formation of the PZ. Corresponding gene expression data, gene ontology term enrichment, and correlation analysis with lipid species pinpoint glycerolipid‐related genes to be active during the development of the PZ. Possibilities that lipid metabolism genes may be targets of regulatory mechanisms specifying PZ differentiation in A. alpina are discussed.

The number of cloves in a garlic bulb is controlled by axillary meristem differentiation, which directly determines the propagation efficiency. Our previous study showed that injecting garlic plants with gibberellins (GA3) solution significantly increased clove number per bulb. However, the physiological and molecular mechanism of GA-induced axillary bud formation is still unknown. Herein, dynamic changes in histology, phytohormones, sugars and related genes expression at 2, 4, 8, 16 and 32 days after treatment (DAT) were investigated. Histological results indicated two stages (axillary meristem initiation and dormancy) were in the period of 0&ndash, 30 days after GA3 treatment. Application of GA3 caused a significant increase of GA3 and GA4, and the downregulation of AsGA20ox expression. Furthermore, the change trends in zeatin riboside (ZR) and soluble sugar were the same, in which a high level of ZR at 2 DAT and high content of soluble sugar, glucose and fructose at 4 DAT were recorded, and a low level of ZR and soluble sugar arose at 16 and 32 DAT. Overall, injection of GA3 firstly caused the downregulation of AsGA20ox, a significant increase in the level of ZR and abscisic acid (ABA), and the upregulation of AsCYP735 and AsAHK to activate axillary meristem initiation. Low level of ZR and soluble sugar and a high level of sucrose maintained axillary meristem dormancy.

Perennial plants maintain their life span through several growth seasons. Arabis alpina serves as model Brassicaceae species to study perennial traits. A. alpina lateral stems have a proximal vegetative zone with a dormant bud zone, and a distal senescing seed-producing inflorescence zone. We addressed the questions of how this zonation is distinguished at the anatomical level, whether it is related to nutrient storage, and which signals affect the zonation. We found that the vegetative zone exhibits secondary growth, which we termed the perennial growth zone (PZ). High-molecular weight carbon compounds accumulate there in cambium and cambium derivatives. Neither vernalization nor flowering were requirements for secondary growth and sequestration of storage compounds. The inflorescence zone with only primary growth, termed annual growth zone (AZ), or roots exhibited different storage characteristics. Following cytokinin application, cambium activity was enhanced and secondary phloem parenchyma was formed in the PZ and also in the AZ. In transcriptome analysis cytokinin-related genes represented enriched gene ontology terms and were expressed at higher level in PZ than AZ. Thus, A. alpina uses primarily the vegetative PZ for nutrient storage, coupled to cytokinin-promoted secondary growth. This finding lays a foundation for future studies addressing signals for perennial growth.HighlightArabis alpina stems have a perennial zone with secondary growth, where cambium and derivatives store high-molecular weight compounds independent of vernalization. Cytokinins are signals for the perennial secondary growth zone.

The strategic role of phytohormones and arbuscular mycorrhizal fungi (AMF) to overcome various stress conditions is gaining popularity in sustainable agricultural practices. This current study aims to investigate and identify the protective roles of 28-homobrassinolide (HBL) and Glomus versiforme on two cucumber cultivars (salt sensitive Jinyou 1# and tolerant Chanchun mici (CCMC)) grown under saline conditions (100 mM NaCl). HBL and AMF were applied as individual and combined treatments on two cucumber cultivars and their effects were observed on the morphological growth and physiology under control and saline conditions. Findings revealed that the treated plants showed better performance under saline conditions through improved photosynthesis, leaf relative water content, and decreased electrolyte leakage in tolerant cultivar (CCMC) and to a lesser extent in sensitive (Jinyou 1#) cultivar. Comparable differences were noticed in the antioxidant enzymes activity such as superoxide dismutase, catalase, and peroxidase after every 10 days in both cultivars. Treating the plants with HBL and AMF also improved the mineral uptake regulation and lowered sodium concentration in roots compared to that in the non-treated plants. Current findings suggest that the protective role of HBL and AMF involves the regulation of antioxidants and lowers the risk of ion toxicity in the cucumber and hence enhance tolerance to salinity. These results are promising, but further studies are needed to verify the crop tolerance to stress and help in sustainable agricultural production, particularly vegetables that are prone to salinity.

The real estate industry is booming with the accelerating process of urbanization. The elevator is widely used as an important part of the building and its sales volume and usage have continued to grow. The frequent occurrence of elevator accidents aroused people’s attention in real life. Therefore, it is very important to strengthen the elevator maintenance process. This paper studies the optimal maintenance strategy of elevators within finite life from the perspective of preventive maintenance to reduce the failure rate of elevators. The number of elevator preventative maintenance and the time interval were found by establishing a target programming optimization model. Finally, the validity of the model is verified by numerical example.

Garlic (Allium sativum L.) bulb, as the main propagation organ, is consumed worldwide for its flavor and medicinal value. Our previous work showed that injecting plant with gibberellic acid (GA3) can dramatically increase the clove number per bulb to improve propagation efficiency of garlic. However, how the mode and time of application of GA3 affect plant architecture and bulb structure remains largely unknown. Herein, we investigated the effect of both soaking seed clove in GA3 solution for different durations (6, 12, 24 and 48 h, respectively) and injecting plant with GA3 at seven growth stages (30, 60, 90, 120, 150, 180 and 210 days after planting) on plant growth and bulb development in garlic cultivar G024. It was evident that soaking seed clove in GA3 solution for 48 h induced secondary plant (equated with tiller or lateral branch) formation and significantly increased the incidence rate of secondary plant, clove number per bulb and bulb weight. In 2016–2017 and 2017–2018, clove number per bulb (19.59 and 19.64, respectively) and bulb weight (50.72 g and 48.82 g, respectively) were sharply increased by injection of GA3 at 30 days after planting (DAP), as compared to control (12.14 and 11.75, respectively; 37.53 g and 36.72 g, respectively). No difference in clove number per bulb among control, “GA3 180 DAP” and “GA3 210 DAP” was recorded. Furthermore, soluble sugar content in stem was higher at 4 days after GA3 treatment and was lower at 32 days after GA3 treatment, as compared to control. At 8, 16 and 32 days after GA3 treatment, soluble protein content in stem (4.28, 4.55 and 7.32 mg g−1 FW, respectively) was always higher than control (2.52, 3.44 and 5.81 mg g−1 FW, respectively). In summary, soaking seed clove in 1 mM L−1 GA3 solution for 48 h is a better way to increase bulb weight but caused a decrease in bulb commodity quality. While injecting plant with 1 mM L−1 GA3 at 60, 90 or 120 DAP can be used for increasing clove number per bulb to improve propagation efficiency of garlic. Exogenous GA3 induced axillary bud formation of garlic via the changes of soluble sugar content and soluble protein content in stem.

Because precisions of different multiple-regression methods are different for the same samples, how to improve accuracy of forecasting has therefore generated wide interest. This paper attempted to improve precision of forecast by combining multiple linear regression and three artificial intelligence regressive models. In our study, a novel frame of model combination is proposed by fluctuating degree, complementarity and compatibility. Complementarity is available to judge which models can be combined to decease the errors and establish its sets. The assigned weights of each single model in complementary sets are calculated by fluctuating degree. Compatibility, superiors and inferiors of a combined model are evaluated by MAE, RMSE and MAPE. The empirical case of predicting electric demand demonstrated that the combined models based on fluctuating degree increase predicting precision of sample period and extrapolative forecast if there exists complement between different single-models.

In order to predict the price of candidates in acquisition and evaluate its feasibility, this paper puts forward a model of price prediction of candidates based on support vector machine. The model is trained by the data of market deals which were made in the past. The result of simulation and test indicates that average error of prediction is percent 7.71. It also proves that SVM has better performance than BP neural network under the condition of small samples. SVM can overcome the limitation of hypotheses of econometrics and shortcomings of BP neural network that is low efficient and easy to fall into local minimum. Therefore, prediction model based on SVM has more advantages and wider prospect of application.

In order to predict price of candidates in acquisition, in this paper, we design a new model of price prediction of target based on radial basis function neural network. The model is trained by the financial data of acquisition market deals which were made in the past. The result of simulation and test indicates that average error of price prediction is percent 12. It is a suggestive reference of evaluating the feasibility of acquisitions. The result also advises more non-fanance index and training samples should be increased to improve the model.

In this paper, we apply Self-Organized Mapping and Hopfield neural network to cluster characteristics of financial performance and predict the performance of post-acquisition. Financial characteristics of samples of targets are divided into three sorts very evidently by clustering of SOM. According to the financial variables of every sort, we build Hopfield network model to predict the sort of performance of test samples. Demonstration indicates the accuracy is 92.11%.

The purpose of the article is to provide an overview of brand management for brand strategy. Through making use of game theory, the article analyzes the methods of brand competition. The article will put forward the train of brand competition strategy. Although the competition of market is fierce, company can gain the advantage in the competition only if the company has a good strategic.

Forecasting cash flow objectively is very important in discounted cash flow analysis. In order to predict cash flow after mergers, this paper puts forward a model based on genetic-algorithm and BP neural network, designs the steps of algorithm, build the structure of BP neural network. By simulation of data, the result indicates the model has good performance of prediction. It can overcome the flaws of statistics linear models.

In order to know how the motive of acquisition influences on its performance, multi-Dimensional analysis and regression analysis are used to analyze the sort of motives and relation between motives and long-term performance. Research indicates performance of acquirers in pre-acquisition and post-acquisition increases remarkably based on synergy motive, acquisitions based on agency motive do not create value in long term, acquirers with high ROE and ROA is more possibly based on synergy motive than those based on agency motive.

Appropriate methods are helpful to measure risk. The risk is objective in acquisitions. It resources from subjective risk, morality risk, information asymmetry risk, law risk, system risk, policy risk and market risk during the process of mergers and acquisitions decision-making. In the different stages of decision-making, the emphases of risks are different Index of measuring risks can be established by industrial experts. The case demonstrates that genetic algorithm and BP neural network are good tools to measure the risks.

In order to predict the price of candidates in acquisition and evaluate its feasibility, this paper puts forward a model of price prediction of candidates based on genetic-algorithm and BP neural network. The model is trained by the data of market deals which were made in the past. The result of simulation and test indicates that average error of prediction is percent 11.03. It can be as the reference of evaluating the feasibility of mergers & acquisitions. More index and training samples should be increased to improve the model.

Target selection is one of the most important steps of during the process of mergers and acquisitions. Hopfield neural network is very strong in pattern recognition which can simulate the criteria of acquirer and remind it. The network model overcomes the shortcomings of classic statistic and fuzzy models and embodies the requirements of acquirer. Demonstration shows that Hopfield network is an effective tool to choose targets with good performance if the standard of mergers and acquisition is feasible and reasonable.

The result of value appraisal decides whether an enterprise acquires target or not. Discounted Cash Flow is a theoretical, widely applied method of value appraisal. However, it predicts cash flow by linear model, static appraisal and ignores integration cost. This paper tries to amend the flaws through combining games and Discounted Cash Flow method, considering integration cost. The amendment optimizes Discounted Cash Flow method, increases its practicability and science. Its improvement is available to reduce the risk of value appraisal and complete integration successfully.

The result of value appraisal decides whether an enterprise acquires target or not. Discounted cash flow is a theoretical, widely applied method of value appraisal. However, there are flaws of the theory in predicting cash flow by linear model, static appraisal and ignoring integration cost. This paper tries to amend the flaws in predicting cash flow by genetic neural network, combining games and discounted cash flow method, considering integration cost. The amendment optimizes discounted cash flow method, increases the practicability and science of the method. The improvement of model is available to reduce the risk of value appraisal and complete integration successfully.