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This article proposes a combination model, which is composed of latent Dirichlet allocation model, TF-IDF feature extraction algorithm and Euclidean distance measurement method, to identify and judge whether the similarities between multiple policy texts exist or not. With the help of actual data result, this will drive the relevant government agencies to figure out problems in a timely manner and provide a decision-making basis for them to formulate and optimise appropriate economic policies. To this end, this article analyses and studies the four types of economic texts that are classified as Insurance, Banking, Tax and Finance from the Central Government of Hebei province and Shijiazhuang city levels. Also, we consider Beijing, Shanghai and Guangdong. Experimental results show that (1) the combination model can quickly and effectively recognise and determine whether there are similarities between multiple economic policy texts; (2) similarities exist or not between the central, provincial and municipal level policy texts depending on the comparison of the distance values across them; (3) the smaller the distance value between economic policy texts of the same kind, the higher the similarity in them; and (4) the distance values between the six policy texts in Finance, Insurance, Bank and Tax categories are ranked from low to high. In terms of similarity, the Finance category is the highest, followed by Insurance and Bank, and the Tax category is the lowest. [ABSTRACT FROM AUTHOR]

We present a new Hawkes-Contact model that combines a Hawkes process and a finite-range contact process to model the stock price movements, especially under the impact of news and other information flows that could lead to contagious effects. To fully capture the underlying price process, we take the Hawkes process to track the full pathway of historical prices on their future movements and the contact process to capture the impact from news/investment sentiment. We compare this full model to a univariate Hawkes process that works as a benchmark model through analyzing their statistical properties using both simulated returns and the real 5-min returns of the crude oil index (Wind CZCE-TA). The statistical properties include probability density function, complementary cumulative distribution function, and Lempel-Ziv Complex. Our results show that the real returns' distribution is often far from normal, but the simulated returns through the Hawkes or Hawke-Contact model can achieve close fit to the real returns and exhibit similar statistical properties. More importantly, the Hawkes-Contact model performs better than the simple Hawkes model in capturing characteristics in the return movements, which indicates that the price evolution is also driven by the news sentiment created after them. [ABSTRACT FROM AUTHOR]

This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China's Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with other dynamic forecast systems within different time windows, which involves the Hidden Markov Model (HMM), Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and LSTM-HMM with an input of monthly Consumer Price Index (CPI) or quarterly CPI within 4-year, 6-year, 8-year and 10-year time window. These forecasting models employed in our empirical analysis share the basic HMM structure but differ in the generation of observable CPI fluctuation states. Our forecasting results suggest that (1) among all the models, LSTM-HMM generally performs better than the other models; (2) the model performance can be improved when model input transforms from quarterly to monthly; (3) among all the time windows, models within 10-year time window have better overall performance; (4) within 10-year time window, the LSTM-HMM, with either quarterly or monthly input, has the best accuracy and consistency. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal hedging strategy in the presence of market frictions using the Long Short Term Memory Recurrent Neural Network (LSTM-RNN) method, which is a modification of the method proposed in Buehler et al. (Deep hedging. Quant. Finance, 2019, 19(8), 1271–1291). The market frictions are transaction costs, liquidity constraints, trading limits and cost of funds. The loss function is a spectral risk measure. We first make an empirical analysis of the LSTM-RNN model of real option markets, which are the Asian market (domestic market 50 ETF option, Hong Kong Hang Seng Index Option, Nikkei Index Option), the North American market (S&P 500 Index Option) and the European market (FTSE 100 Index Option). The benchmark models are from Leland (Option pricing and replication with transaction costs. J. Finance., 1985, 40(5), 1283–1301), Boyle and Vorst (Option replication in discrete time with transaction costs. J. Finance, 1992, 47(1), 271–293) and Whalley and Wilmott (A hedging strategy and option valuation model with transaction costs. OCIAM Working Paper, Mathematical Institute, Oxford, 1993). Finally, we compare the results from the LSTM-RNN model with benchmark models involving transaction costs for both simulated market data generated by Geometric Brownian Motion (GBM) and the Heston model and real market data. The results show that the LSTM-RNN model outperforms benchmark models for low or medium volatility (<0.8), OTM moneyness and under a certain risk level (<80%) in the GBM market setting. For the 50ETF option market, the LSTM-RNN model outperforms benchmark models for ATM and under a certain risk level (<15%). For the HSI option market, the LSTM-RNN model outperforms benchmark models when transaction costs are smaller than 1.5%. For the Nikkei and S&P 500 option markets, the LSTM-RNN model always outperforms benchmark models. For the FTSE option market, the LSTM-RNN model outperforms benchmark models when moneyness is not too deeply ITM. [ABSTRACT FROM AUTHOR]

Cryptocurrencies including Bitcoin are known to be vulnerable to so-called 'double-spending' attacks, where the same digital currency is used to execute multiple different transactions simultaneously. Little is known, however, about the underlying reasons for this vulnerability. Here we develop an agent-based model to study how features of cryptocurrency networks contribute to their vulnerability to double-spending attacks. Perhaps surprisingly, we find neither the number of network nodes nor its path length seem to influence the probability of successful attacks. We find robust evidence that the network's clustering coefficient has substantial influence. In particular, scale-free networks, with their small clustering coefficients, are more than twice as likely to succumb to double-spending attacks than are networks with larger coefficients, such as regular networks. The implication is that cryptocurrency networks, which are scale-free, may be uniquely susceptible to double-spending attacks. [ABSTRACT FROM AUTHOR]

This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simulation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders. [ABSTRACT FROM AUTHOR]

Abstract: As the origin center of apricots, China has the richest apricot germplasm resources. The establishment of core collections is very important for a better evaluation and utilization of apricot germplasm. In this study, 1501 apricot accessions from the initial collections in China were used to sample and establish a primary core collection. Data of 18 traits including both quantitative and qualitative were collected and used to design sampling strategy. The overall sampling strategy includes principles for grouping, sampling proportion within group and sampling method from each group. Three sampling proportions 5%, 10% and 15% were used in the study. Our results suggest that 10% was the best entire sampling ratio for primary core collection of apricots. With 10% entire sampling ratio, the optimal sampling strategy was to group samples based on their growing regions, in combination with logarithmic sampling proportion within each group. Using this sampling strategy, we have established a primary core collection with 150 accessions, and the primary core collection can well represent the genetic diversities of the entire collection. The genetic diversity of the apricot primary core collection was further analyzed using Simple Sequence Repeats (SSRs) molecular marker technique. A total of 22 polymorphic specific primers were developed and 196 alleles were detected. The average number of alleles in each locus was 8.9, suggesting that there were very rich genetic variances among the prime core collection. This also confirms that our primary core collection is a good representation of the genetic diversities of the whole collections at DNA level. [Copyright &y& Elsevier]

In this paper, we analyze and compare long-range power-law correlations of returns, absolute returns, squared returns, cubed returns and square waved returns for sixteen individual stocks from the block of energy sources of Chinese stock market and five stock indices (Shanghai Composite Index, Shenzhen Component Index, Dow Jones Industrial Average index, Nasdaq Composite Index, the Standard and Poor's 500 Index) by using a detrended fluctuation analysis approach. The empirical evidence suggests that Shanghai Composite Index is very close to Shenzhen Component Index and Nasdaq, DJIA is very close to S&P 500 in all cases. And the exponent trends of the returns are close to that of square waved returns. Also, five indices deviate from other sixteen individual energy stocks in all cases except square waved returns. Further, there are long-range correlations and persistence in volatility series of absolute returns and squared returns. Moreover, we investigate the long-term memory of these returns by applying Lo's modified rescaled range statistic. We find that the China energy market exhibits fractal and persistence properties. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we model a new random stock price model for the stock markets based on the finite range contact process, which is a model for epidemic spreading that mimics the interplay of local infections and recovery of individuals, it is a member of a class of stochastic processes known as interacting particle systems. Then, we analyze the statistical behaviors of Shanghai Stock Exchange (SSE) Composite Index, Shenzhen Stock Exchange (SZSE) Composite Index, Dow Jones Industrial Average Index (DJIA), Nasdaq Composite Index (IXIC), the standard and Poor’s 500 Index (S&P500) and the simulative data derived from the finite range contact model by comparison. And six individual Chinese stocks from large-cap, mid-cap and small-cap categories are discussed. Furthermore, we investigate the long range correlations of the returns for these indices and the corresponding simulative data by applying the detrended fluctuation analysis. At last, the positive part of the probability distributions of the logarithmic returns for the actual data and the simulative data are studied by the q-Gaussian dynamic systems. The main objective of this work is to discuss the impact on the returns with the different range financial models. [Copyright &y& Elsevier]

Abstract: Proton pumps play an important role in the physiological activities of plants. Changes in membrane-associated H+- and Ca2+-ATPase activities in heat-shocked plants after heat acclimation (HA) or salicylic acid (SA) pre-treatment in annual young grape plants (Vitis vinifera L. cv. Jingxiu) were investigated. ATPase activity was assayed through biochemical analysis in which two-phase partitioning was used to purify plasma membranes and cerium trichloride was precipitated with an electromicroscopic cytochemical method. The plasmalemma H+- and Ca2+-ATPase activities were higher in HA- or SA-pre-treated plants than in controls. The stability of H+- and Ca2+-ATPase activities in pre-treated plants always remained at higher levels during subsequent heat shock treatment, which was consistent with the observations made using an electromicroscope. A number of cerium phosphate grains representing enzyme activity in HA- or SA-pre-treated plants were observed 6h following heat shock, whereas no grains were found in control plants under the same conditions. These results suggest that the changes in the activities of plasma membrane H+- and Ca2+-ATPase contributed to the thermotolerance induced by either HA or SA pre-treatment in young grape plants and the two pre-treatments may have had the some same regulatory mechanism. [Copyright &y& Elsevier]

Abstract: Grape berries (Vitis vinifera cv. Jingxiu) were pretreated for 10h in air either at 38°C (heat pretreatment) or at 25°C (control) and then transferred to −2°C for 0, 3, 6,12, 24, 48 and 72h, respectively. Compared with the control without heat pretreatment, membrane permeability and malondialdehyde (MDA) contents were reduced and the activities of peroxidase, superoxide dismutase (SOD) and catalase increased in the heat-pretreated berries under chilling stress. Heat pretreatment could protect the ultrastructure of the pericarp cells against subsequent chilling injury. In the control berries, the ultrastructure of pericarp cells was significantly damaged under chilling stress, with disordered stroma lamellae in the chloroplast or plastid, an indistinct nuclear membrane, looser cell walls, disorganized or absent middle lamella and a less identifiable nuclear membrane. After 72h of chilling stress, the cellular organelles could no longer be identified. These results offered cytological evidence that berries subjected to a mild heat stress could acquire tolerance to subsequent lethal low temperatures. The heat pretreatment also induced expression of Hsp70 transcripts. The results suggest that cross-adaptation could be induced in grape berries and the fruit could adapt, at least partly, to low temperature through heat pretreatment-induced tolerance. [Copyright &y& Elsevier]

Highlights: [•] We determine distributions of sugars and acids with compartmental analysis method. [•] We examine the alteration of sugars and acids in vacuole, cytoplasm and free space. [•] Membrane permeability to sugars and acids are measured at different growing stages. [•] We analyze distributions of sweetness and sour in different compartments of cells. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Background: The nuclear DNA is conventionally used to assess the diversity and relatedness among different species, but variations at the DNA genome level has also been used to study the relationship among different organisms. In most species, mitochondrial and chloroplast genomes are inherited maternally; therefore it is anticipated that organelle DNA remains completely associated. Many research studies were conducted simultaneously on organelle genome. The objectives of this study was to analyze the genetic relationship between chloroplast and mitochondrial DNA in three Chinese Prunus genotypes viz., Prunus persica, Prunus domestica, and Prunus avium., Results: We investigated the genetic diversity of Prunus genotypes using simple sequence repeat (SSR) markers relevant to the chloroplast and mitochondria. Most of the genotypes were genetically similar as revealed by phylogenetic analysis. The Y2 Wu Xing (Cherry) and L2 Hong Xin Li (Plum) genotypes have a high similarity index (0.89), followed by Zi Ye Li (0.85), whereas; L1 Tai Yang Li (plum) has the lowest genetic similarity (0.35). In case of cpSSR, Hong Tao (Peach) and L1 Tai Yang Li (Plum) genotypes demonstrated similarity index of 0.85 and Huang Tao has the lowest similarity index of 0.50. The mtSSR nucleotide sequence analysis revealed that each genotype has similar amplicon length (509 bp) except M5Y1 i.e., 505 bp with CCB256 primer; while in case of NAD6 primer, all genotypes showed different sizes. The MEHO (Peach), MEY1 (Cherry), MEL2 (Plum) and MEL1 (Plum) have 586 bps; while MEY2 (Cherry), MEZI (Plum) and MEHU (Peach) have 585, 584 and 566 bp, respectively. The CCB256 primer showed highly conserved sequences and minute single polymorphic nucleotides with no deletion or mutation. The cpSSR (ARCP511) microsatellites showed the harmonious amplicon length. The CZI (Plum), CHO (Peach) and CL1 (Plum) showed 182 bp; whileCHU (Peach), CY2 (Cherry), CL2 (Plum) and CY1 (Cherry) showed 181 bp amplicon lengths., Conclusions: These results demonstrated high conservation in chloroplast and mitochondrial genome among Prunus species during the evolutionary process. These findings are valuable to study the organelle DNA diversity in different species and genotypes of Prunus to provide in depth insight in to the mitochondrial and chloroplast genomes.