Creating Value Through Corporate Social Responsibility: The Role of Foreign Institutional Investors in Chinese Listed Firms

This study examines the interplay between two major global trends – the growing role of foreign institutional investors (FIIs) due to financial liberalization and the rise of corporate social responsibility (CSR) as an investment ethos. We choose the setting of China, the world’s second-largest economy that has recently experienced substantial growth in foreign portfolio investment and increased its commitment to CSR. We document that CSR performance significantly influences the portfolio allocation decisions of certain types of FIIs. Crucially, our analysis reveals that firms with a higher level of ownership by FIIs are associated with a more positive relation between CSR performance and firm value. This finding is robust to endogeneity examinations, including quasi-natural experiments and instrumental variable estimations. The finding is stronger for non-state-owned enterprises, firms with higher customer awareness, firms with more foreign directors, and firms with more frequent corporate site visits from FIIs. Monitoring and advising are two likely channels through which FIIs enhance the CSR-value relation. Finally, we demonstrate that FIIs enhance firms’ ability to harness the power of CSR as a driver of innovation

Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios

Design, synthesis and antimycobacterial activity of novel nitrobenzamide derivatives

We report herein the design and synthesis of a series of novel nitrobenzamide derivatives. Results reveal that many of them display considerable in vitro antitubercular activity. Four N-benzyl or N-(pyridine-2-yl)methyl 3,5-dinitrobenzamides A6, A11, C1 and C4 have not only the same excellent MIC values of 1500), opening a new direction for further development

Genomic characterization of bZIP gene family and patterns of gene regulation on Cercospora beticola Sacc resistance in sugar beet (Beta vulgaris L.)

Sugar beet (Beta vulgaris L.) is one of the most important sugar crops, accounting for nearly 30% of the world’s annual sugar production. And it is mainly distributed in the northwestern, northern, and northeastern regions of China. However, Cercospora leaf spot (CLS) is the most serious and destructive foliar disease during the cultivation of sugar beet. In plants, the bZIP gene family is one of important family of transcription factors that regulate many biological processes, including cell and tissue differentiation, pathogen defense, light response, and abiotic stress signaling. Although the bZIP gene family has been mentioned in previous studies as playing a crucial role in plant defense against diseases, there has been no comprehensive study or functional analysis of the bZIP gene family in sugar beet with respect to biotic stresses. In this study, we performed a genome-wide analysis of bZIP family genes (BvbZIPs) in sugar beet to investigate their phylogenetic relationships, gene structure and chromosomal localization. At the same time, we observed the stomatal and cell ultrastructure of sugar beet leaf surface during the period of infestation by Cercospora beticola Sacc (C. beticola). And identified the genes with significant differential expression in the bZIP gene family of sugar beet by qRT-PCR. Finally we determined the concentrations of SA and JA and verified the associated genes by qRT-PCR. The results showed that 48 genes were identified and gene expression analysis indicated that 6 BvbZIPs were significantly differential expressed in C. beticola infection. It is speculated that these BvbZIPs are candidate genes for regulating the response of sugar beet to CLS infection. Meanwhile, the observation stomata of sugar beet leaves infected with C. beticola revealed that there were also differences in the surface stomata of the leaves at different periods of infection. In addition, we further confirmed that the protein encoded by the SA signaling pathway-related gene BVRB_9g222570 in high-resistant varieties was PR1, which is closely related to systemic acquired resistance. One of the protein interaction modes of JA signal transduction pathway is the response of MYC2 transcription factor caused by JAZ protein degradation, and there is a molecular interaction between JA signal transduction pathway and auxin. Despite previous reports on abiotic stresses in sugar beet, this study provides very useful information for further research on the role of the sugar beet bZIP gene family in sugar beet through experiments. The above research findings can promote the development of sugar beet disease resistance breeding

Naturally Fermented Acid Slurry of Soy Whey: High-Throughput Sequencing-Based Characterization of Microbial Flora and Mechanism of Tofu Coagulation

Tofu processing generates large quantities of whey as waste water. Although naturally fermented whey serves as a coagulant, the critical constituents remain unknown. High-throughput sequencing identified predominant Lactobacillus in the naturally fermented acid slurry. Lactobacillus casei YQ336 with high coagulating ability and lactic acid production was isolated and its soy protein coagulating mechanism was determined. The acid in YQ336 fermented acid slurry lowered soy milk pH and reduced negatively charged groups of denatured soy protein, leading to coagulation. Acid slurry metal ions also promoted pH decline; moreover, YQ336-produced protease might partially hydrolyse soy protein, further promoting coagulation. Thus, organic acids, metal ions, and enzymes together promote coagulation, with the former acting as the main contributing factor. This study will pave the way for future industrial application of L. casei YQ336 in acid slurry tofu processing and food manufacturing, thereby potentially reducing resource waste and environmental pollution

Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model

In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Pacific-Arctic sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally-varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the SIC anomaly correlation coefficient (ACC), increased by 32% in the Bering Sea and 18% in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. Sea ice concentration (SIC) trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions up to 7 month lead times in the Bering Sea and the Sea of Okhotsk. We find that subsurface ocean heat content (OHC) provides a crucial source of prediction skill in all seasons, especially in the ice-growing season, and adding sea ice thickness (SIT) to the regional Markov model has a negative contribution to the prediction skill in the cold season and substantial contribution in the warm season. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model

Understanding Arctic Sea Ice Thickness Predictability by a Markov Model

The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model- assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7–0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model’s skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper-ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. Sea ice concentration (SIC) is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions

A review of synoptic weather effects on sea ice outflow through Fram Strait: cyclone vs. anticyclone

Sea ice outflow through Fram Strait is a vital component of the sea ice mass balance of the Arctic Ocean. Previous studies have examined the role of large-scale modes of atmospheric circulation variability such as the Arctic Oscillation, North Atlantic Oscillation, and Dipole Anomaly in the movement of sea ice. This review emphasizes the distinct impacts of synoptic weather on sea ice export as well as on other relevant fields (i.e., sea ice concentration and sea ice drift). We identify deficiencies in previous studies that should be addressed, and we summarize potential research subjects that should be investigated to further our understanding of the relationship between synoptic weather and sea ice export via Fram Strait. For example, the connection between summertime anticyclones and weakened potential vorticity related to the observed extensive spring Eurasian snow and Siberian Ocean sea ice loss is of considerable interest. In-depth exploration of this type of geophysical mechanism will be particularly useful in assessment of the robustness of such linkages inferred through statistical analyses

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn and propagate streamlines directly from T1 and anatomical contexts. Training for this network has previously relied on high-resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical protocols. Then, we define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. Under this metric, T1 tractography generated by CoRNN reproduces diffusion tractography with approximately two millimeters of error