Search

After decades of development, China's stock market is now at a critical stage of scale expansion. However, China's stock market is dominated by retail investors. Such an immature market is vulnerable to investor sentiment. Based on the current realistic background, this paper studies the relationship between investor sentiment and stock returns in The Chinese market and forecasts the development trend of the future market. This paper firstly combs the literature on investor sentiment at home and abroad and adopts the classical research method of investor sentiment index & principal component analysis. The investor sentiment index was constructed using weekly data of the Shanghai composite index from 2020 to 2021. Select the turnover rate, China SSE 50ETF turnover ratio, closed-end fund discount, Teng Fall Index (TFL) and financing net buying turnover ratio of these five variables to construct. Finally, the multiple linear regression is used to analyze the relationship between investor sentiment and returns. We find that the yield of Shanghai composite index is positively correlated with investor sentiment, which will affect the return of stock market. The significance of this conclusion lies in that it can help to understand the interaction between investor behavior and market activities after the COVID-19 and help to test whether the use of investor sentiment indicators can provide guidance for investors' decisions.

Multitasking optimization can achieve better performance than traditional single-tasking optimization by leveraging knowledge transfer between tasks. However, the current multitasking optimization algorithms suffer from some deficiencies. Particularly, on high similar problems, the existing algorithms might fail to take full advantage of knowledge transfer to accelerate the convergence of the search, or easily get trapped in the local optima. Whereas, on low similar problems, they tend to suffer from negative transfer, resulting in performance degradation. To solve these issues, this article proposes an evolutionary multitasking optimization algorithm for multiobjective/many-objective optimization with two-stage adaptive knowledge transfer based on population distribution. The resultant algorithm named EMT-PD can improve the convergence performance of the target optimization tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population. At the first stage of knowledge transfer, an adaptive weight is used to adjust the search step size of each individual, which can reduce the impact of negative transfer. At the second stage of knowledge transfer, the search range of each individual is further adjusted dynamically, which can improve the population diversity and be beneficial for jumping out of the local optima. Experimental results on multitasking multiobjective optimization test suites show that EMT-PD is superior to other state-of-the-art evolutionary multitasking/single-tasking algorithms. To further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multitasking many-objective optimization test suite is also designed in this article. The experimental results on the new test suite also demonstrate the competitiveness of EMT-PD.

In contrast to the traditional single-tasking evolutionary algorithms, evolutionary multitasking (EMT) travels in the search space of multiple optimization tasks simultaneously. Through sharing knowledge across the tasks, EMT is able to enhance solving the optimization tasks. However, if knowledge transfer is not properly carried out, the performance of EMT might become unsatisfactory. To address this issue and improve the quality of knowledge transfer among the tasks, a novel multiobjective EMT algorithm based on subspace alignment and self-adaptive differential evolution (DE), namely, MOMFEA-SADE, is proposed in this article. Particularly, a mapping matrix obtained by subspace learning is used to transform the search space of the population and reduce the probability of negative knowledge transfer between tasks. In addition, DE characterized by a self-adaptive trial vector generation strategy is introduced to generate promising solutions based on previous experiences. The experimental results on multiobjective multi/many-tasking optimization test suites show that MOMFEA-SADE is superior or comparable to other state-of-the-art EMT algorithms. MOMFEA-SADE also won the Competition on Evolutionary Multitask Optimization (the multitask multiobjective optimization track) within IEEE 2019 Congress on Evolutionary Computation.

This article studies the functions of the opposition ‘one’s own – other’s’ in the system of characters in M. A. Bulgakov’s plays Days of the Turbins, Zoyka’s Apartment and Flight. A group of characters is singled out, from whose perspective this opposition is not relevant, not significant. Particular attention is paid to their common features. The conclusion is made that the opposition in question is marked in the problems of Bulgakov’s plays of the 1920s.

Photocatalytic ammonia (NH3) synthesis from N2 is a strategy conducive to carbon neutrality because it avoids high energy consumption and high carbon emission process in the industrial synthesis of NH3. However, the structure-activity relationship of bimetallic active centers in heterogeneous catalysts is still dimness for high-performance photocatalytic nitrogen reduction reaction (pNRR). Here, a rational N2-bridging strategy for heterogeneously pNRR has been expanded through regulating bimetallic Ti-Pd active centers at TiO2 (101)/Pd (111) interface and the structure-activity correlation during the pNRR was explored. The side-on adsorption with “*N ≡ N*” configuration (*presents the adsorption site) on Ti-Pd centers induces a considerable polarization and potent activation of N2, enabling to achieve an impressive rate of NH3 production (635.73 µg gcat.−1 h− 1) on the optimal se-Pd/m-TiO2 NRs (2.71%). More importantly, nearly ~ 92.37% of initial activity could be retained following 8 successive reaction rounds. Further mechanism studies revealed that the side-on bridging mode of N2 with bimetallic Ti-Pd centers possesses an ultra-high adsorption energy, which can effectively weaken the triple bond of N2 and reduce the activation energy barrier of pNRR process. This study provides insights into the electronic structure regulation of bimetallic active centers and structure-property relationship at the atomic level.

Nanoliposomes are ideal nanocarriers for encapsulated active compounds used in the food industry as they provide stability and controlled release. However, cholesterol may pose risks in large intake, which is the commonly-used nanoliposome stabilizers. In this study, resveratrol was used instead of cholesterol as a novel nanoliposome stabilizer to construct a resveratrol blank liposome (RBL) system. The RBL system was used to protect the bacteriocin CAMT6 to create bacteriocin-loaded nanoliposomes (BLLs). The RBLs and BLLs had favourable particle sizes (172.71 nm and 150.47 nm), polydispersity index (PDI) values (0.150 and 0.120) and zeta potentials (-41.54 mV and -43.53 mV, respectively). According to Differential scanning colourimetry (DSC) and X-ray diffraction (XRD) analyses, resveratrol altered the structure of the phospholipid layer. The phospholipid layers of the RBLs and BLLs had higher mobility when resveratrol was used as a stabilizer instead of cholesterol. Structurally, resveratrol was inserted egg yolk lecithin to constitute an RBL. CAMT6 was loaded in BLLs with spherical and shell-core structures. The BLL encapsulation efficiency was 97.32 % and exhibited three release phases, with the release rates reaching 62 %. In experiments with milk, the BLLs effectively protected the anti-Listeria activity of CAMT6. In summary, resveratrol is a suitable nanoliposome stabilizer and the proposed RBL system is an excellent way to improve the stability of water-soluble preservatives, such as bacteriocins, in complex food environments.

Background The tumor microenvironment (TME) plays an important role in the occurrence and development of gastric cancer (GC) and is widely used to assess the treatment outcomes of GC patients. Immunohistochemistry (IHC) and gene sequencing are the main analysis methods for the TME but are limited due to the subjectivity of observers, the high cost of equipment and the need for professional analysts. Methods The ImmunoScore (IS) was developed in the TCGA cohort and validated in GEO cohorts. The Radiomic ImmunoScore (RIS) was developed in the TCGA cohort and validated in the Nanfang cohort. A nomogram was developed and validated in the Nanfang cohort based on RIS and clinical features. Results For IS, the area under the curves (AUCs) were 0.798 for 2-year overall survival (OS) and 0.873 for 4-year overall survival. For RIS, in the TCGA cohort, the AUCs distinguishing High-IS or Low-IS and predicting prognosis were 0.85 and 0.81, respectively; in the Nanfang cohort, the AUC predicting prognosis was 0.72. The nomogram performed better than the TNM staging system according to the ROC curve (all P Conclusions The RIS and the nomogram can be used to assess the TME, prognosis and adjuvant chemotherapy benefit of GC patients after radical gastrectomy and are valuable additions to the current TNM staging system. High-nomogram GC patients may benefit more from adjuvant chemotherapy than Low-nomogram GC patients.

In order to make a scientific evaluation on the development potential of the listed high-tech enterprises of Guangdong-Hong Kong-Macao Greater Bay Area, providing useful advice for investors, enterprises and government, this paper put forward the significant concept of development potential evaluation system of the listed high-tech enterprise in Guangdong-Hong Kong-Macao Greater Bay Area. Combined with the analytic hierarchy process and the entropy method, we determined each indicator with weight, summarized the information of indicators and examined the status of developmental potential of the high-tech enterprises for each year.

SummaryEvolutionary dynamics of the conflict between transposable elements (TEs) and their host genome remain elusive. This conflict would be intense in stress-adapted plants as stress can often reactivate TEs. Mangroves reduce TE load convergently in their adaptation to intertidal environments and thus provide a unique opportunity to address the host-TE conflict and its interaction with stress adaptation.Using the mangroveRhizophora apiculataas a model, we investigated methylation and short interfering RNA (siRNA) targeting patterns in relation to the abundance and age of long terminal repeat (LTR) retrotransposons. We also examined LTR retrotransposons’ distance to genes, impact on neighboring gene expression, and population frequencies.We found differential accumulation among classes of LTR retrotransposons despite high overall methylation levels. This can be attributed to 24-nt siRNA-mediated CHH methylation preferentially targetingGypsyelements, particularly in their LTR regions. OldGypsyelements possess unusually abundant siRNAs which show cross-mapping to young copies.Gypsyelements appear to be closer to genes and under stronger purifying selection than other classes.Our results suggest a continuous host-TE battle masked by the TE load reduction inR. apiculata. This conflict may enable mangroves likeR. apiculatato maintain genetic diversity and thus evolutionary potential during stress adaptation.