Nanjing University of Finance and Economics

Consumption of whole grains has been associated with reduced risk of developing major chronic diseases. These health benefits have been attributed in part to their unique phytochemicals. Little is known about the complete profiles of phytochemicals and antioxidant activities of different adlay varieties. The objectives of this study were to determine the phytochemicals profiles of the three adlay varieties, including both free and bound of total phenolics and total flavonoids, and to determine the total antioxidant activity of adlay. The free, bound, and total phenolic contents of adlay samples ranged from 31.23 to 45.19 mg of gallic acid equiv/100 g of sample, from 28.07 to 30.86 mg of gallic acid equiv/100 g of sample, and from 59.30 to 76.04 mg of gallic acid equiv/100 g of sample, respectively. On average, the bound phenolics contributed 45.3% of total phenolic content of the adlay varieties analyzed. The free, bound, and total flavonoid contents of adlay samples ranged from 6.21 to 18.24 mg of catechin equiv/100 g, from 18.68 to 35.27 mg of catechin equiv/100 g, and from 24.88 to 52.86 mg of catechin equiv/100 g, respectively. The average values of bound flavonoids contributed 71.1% of total flavonoids of the adlay varieties analyzed. The percentage contribution of flavonoid content to phenolic content of free, bound, and total ranged from 11.6 to 35.2%, from 50.5 to 66.8%, and from 24.6 to 50.5%. The free, bound, and total oxygen radical absorbance capacity (ORAC) values of adlay samples ranged from 231.9 to 316.6 mg of Trolox equiv/100 g, from 209.0 to 351.4 mg of Trolox equiv/100 g, and from 440.9 to 668.0 mg of Trolox equiv/100 g, respectively. The average ORAC values of bound phytochemicals contributed 48.1% of total antioxidant activity of the adlay varieties analyzed. The content of total polyphenol and the antioxidant capacity are obviously different among different species. Liaoning 5 adlay and Longyi 1 adlay are significantly better than Guizhou heigu adlay. The adlay extracts have obvious proliferate inhibition on human liver cancer cells, and substantially in the experimental concentration range, the adlay sample itself has no cytotoxicity. Knowing the phytochemical profiles and antioxidant activity of adlay gives insights to its potential application to promote health.

This paper investigates the problem of event-triggered H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> load frequency control (LFC) for multiarea power systems under hybrid cyber attacks, including denial-of-service (DoS) attacks and deception attacks. An event-triggered transmission scheme is developed under the DoS attacks to lighten the load of network bandwidth while preserving a satisfactory system performance. Then, a new switched system model accounting for the simultaneous presence of DoS attacks and stochastic deception attacks is established with respect to the LFC for multiarea power system. On the basis of the new model, sufficient conditions ensuring multiarea power system exponentially mean-square stable with prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance are obtained by using Lyapunov stability theory. Furthermore, criteria for simultaneously designing the weighting matrix in event-triggered scheme and the controller gain matrix are derived by utilizing the linear matrix inequality technique. Finally, a three-area power system is simulated to demonstrate the usefulness of the approaches proposed in this paper.

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.

In this study, rapeseed protein isolate was hydrolyzed with various proteases to obtain hydrolysates that were separated by membrane ultrafiltration into four molecular size fractions (<1, 1–3, 3–5, and 5–10 kDa). Alcalase hydrolysis significantly (p < 0.05) produced the highest yield of protein hydrolysate while Flavourzyme produced the least. The <1 kDa fraction was the most abundant after the membrane ultrafiltration of the protein hydrolysates, which indicates that the proteases were efficient at reducing the native rapeseed proteins into low molecular weight peptides. Antioxidant properties of the resulting hydrolysates and membrane fractions were characterized and results showed the Pepsin + Pancreatin (P + P) protein hydrolysate had significantly highest (p < 0.05) scavenging activity against DPPH radical among the unfractionated enzymatic hydrolysates. But the P + P hydrolysate was not as effective as other hydrolysates during long-term inhibition of linoleic acid oxidation. For most of the samples, fractionation into the <1 kDa peptides significantly (p < 0.05) improved DPPH and superoxide scavenging properties when compared to the unfractionated protein hydrolysates. Only the <1 kDa fraction showed ferric reducing antioxidant power and the effect was dose-dependent. Overall, Alcalase and Proteinase K seem to be more efficient proteases to release antioxidant peptides from rapeseed proteins when compared to P + P, Flavourzyme and Thermolysin.

Cloud computing becomes increasingly popular for data owners to outsource their data to public cloud servers while allowing intended data users to retrieve these data stored in cloud. This kind of computing model brings challenges to the security and privacy of data stored in cloud. Attribute-based encryption (ABE) technology has been used to design fine-grained access control system, which provides one good method to solve the security issues in cloud setting. However, the computation cost and ciphertext size in most ABE schemes grow with the complexity of the access policy. Outsourced ABE (OABE) with fine-grained access control system can largely reduce the computation cost for users who want to access encrypted data stored in cloud by outsourcing the heavy computation to cloud service provider (CSP). However, as the amount of encrypted files stored in cloud is becoming very huge, which will hinder efficient query processing. To deal with above problem, we present a new cryptographic primitive called attribute-based encryption scheme with outsourcing key-issuing and outsourcing decryption, which can implement keyword search function (KSF-OABE). The proposed KSF-OABE scheme is proved secure against chosen-plaintext attack (CPA). CSP performs partial decryption task delegated by data user without knowing anything about the plaintext. Moreover, the CSP can perform encrypted keyword search without knowing anything about the keywords embedded in trapdoor.

Large capital investment, extended R&D cycle, and high uncertainties characterize green innovations. Consequently, financial risks easily emerge during firms’ green innovation process. This study utilizes data from Chinese A-share listed companies from 2011 to 2019 to examine the effects of digital finance on firms’ green innovation. The findings reveal that digital finance exerts significant and positive influence on green innovation. Digital finance institutions alleviate information asymmetry in the green innovation market through digital technologies such as big data analysis of firm behavior to directly promote firms’ innovation behavior. The internal mechanism analysis reveals that digital finance indirectly promotes green innovation by improving the quality of firms’ environmental information disclosure and reducing financial constraints. The heterogeneity analysis indicates that the promotional effect of digital finance on green innovation is more prominent in larger and state-owned enterprises.

This study has attempted to address prior knowledge gaps in the environmental economics literature by integrating the innovation shocks into the Environment Kuznets Curve (EKC) equation for twenty-six OECD economies using data from 1990 to 2014. Foreign direct investment (FDI), exports (EXP), renewable energy consumption (REC), and GDP per capita were included as control variables. The results from multiple empirical analyses indicated that positive shocks to innovation improve, but the negative shocks disrupt environmental quality. Data analyses also showed that a positive correlation exists between income per capita of OECD economies. From the negative coefficient of income per capita (squared) and the existence of a negative nexus between FDI and CO2e, both the EKC and the Pollution Halo Hypothesis (PHH) were confirmed in sampled economies, respectively. The paper offers empirical support for the favourable impacts of REC on the quality of the environment and calls for the adoption of innovation shocks as a policy instrument to formulate better environmental policies for a sustainable future.

With the development of cloud computing, outsourcing data to cloud server attracts lots of attentions. To guarantee the security and achieve flexibly fine-grained file access control, attribute based encryption (ABE) was proposed and used in cloud storage system. However, user revocation is the primary issue in ABE schemes. In this article, we provide a ciphertext-policy attribute based encryption (CP-ABE) scheme with efficient user revocation for cloud storage system. The issue of user revocation can be solved efficiently by introducing the concept of user group. When any user leaves, the group manager will update users' private keys except for those who have been revoked. Additionally, CP-ABE scheme has heavy computation cost, as it grows linearly with the complexity for the access structure. To reduce the computation cost, we outsource high computation load to cloud service providers without leaking file content and secret keys. Notably, our scheme can withstand collusion attack performed by revoked users cooperating with existing users. We prove the security of our scheme under the divisible computation Diffie-Hellman assumption. The result of our experiment shows computation cost for local devices is relatively low and can be constant. Our scheme is suitable for resource constrained devices.

This study uses text mining technology to construct an index of digital transformation and discusses the impact of digital transformation on enterprise innovation and its mechanisms from theoretical and empirical perspectives. It also analyzes whether digital transformation can significantly enhance enterprises’ value through innovation. The findings are presented as follows: first, digital transformation has a positive and significant impact on enterprise innovation, and this finding holds true when we conduct robustness testing and endogeneity processing. Second, the influence of digital transformation on enterprise innovation varies significantly according to enterprise size, ownership, and industry. Third, risk-taking plays an intermediary role between digital transformation and innovation. Fourth, the innovation incentive effect of digital transformation has a value enhancement function with a two-year lag, while it lacks a value enhancement function in the current year, following year, or next three years. In the modern era of innovation-driven and cross-border integration, this study deepens the theoretical understanding of innovation-driven and digital transformation. It also promotes the deeper integration of real and digital economies in practice.

The effects of pH and protein concentration on some structural and functional properties of hemp seed protein isolate (HPI, 84.15% protein content) and defatted hemp seed protein meal (HPM, 44.32% protein content) were determined. The HPI had minimum protein solubility (PS) at pH 4.0, which increased as pH was decreased or increased. In contrast, the HPM had minimum PS at pH 3.0, which increased at higher pH values. Gel electrophoresis showed that some of the high molecular weight proteins (>45 kDa) present in HPM were not well extracted by the alkali and were absent or present in low ratio in the HPI polypeptide profile. The amino acid composition showed that the isolation process increased the Arg/Lys ratio of HPI (5.52%) when compared to HPM (3.35%). Intrinsic fluorescence and circular dichroism data indicate that the HPI proteins had a well-defined structure at pH 3.0, which was lost as pH value increased. The differences in structural conformation of HPI at different pH values were reflected as better foaming capacity at pH 3.0 when compared to pH 5.0, 7.0, and 9.0. At 10 and 25 mg/mL protein concentrations, emulsions formed by the HPM had smaller oil droplet sizes (higher quality), when compared to the HPI-formed emulsions. In contrast at 50 mg/mL protein concentration, the HPI-formed emulsions had smaller oil droplet sizes (except at pH 3.0). We conclude that the functional properties of hemp seed protein products are dependent on structural conformations as well as protein concentration and pH.

This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR systems for MR images is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional feature extraction methods focus only on low-level or high-level features and use some handcrafted features to reduce this gap. It is necessary to design a feature extraction framework to reduce this gap without using handcrafted features by encoding/combining low-level and high-level features. Deep learning is very powerful for feature representation that can depict low-level and high-level information completely and embed the phase of feature extraction in self-learning. Therefore, we propose a deep convolutional neural network VGG19-based novel feature extraction framework and apply closed-form metric learning to measure the similarity between the query image and database images. Furthermore, we adopt transfer learning and propose a block-wise fine-tuning strategy to enhance the retrieval performance. The extensive experiments are performed on a publicly available CE-MRI dataset that consists of three types of brain tumors (i.e., glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Our method is more generic, as we do not use any handcrafted features; it requires minimal preprocessing, tested as robust on fivefold cross-validation, can achieve a fivefold mean average precision of 96.13%, and outperforms the state-of-the-art CBIR systems on the CE-MRI dataset.

During the past decades, numerous achievements concerning luminescent zinc oxide nanoparticles (ZnO NPs) have been reported due to their improved luminescence and good biocompatibility. The photoluminescence of ZnO NPs usually contains two parts, the exciton-related ultraviolet (UV) emission and the defect-related visible emission. With respect to the visible emission, many routes have been developed to synthesize and functionalize ZnO NPs for the applications in detecting metal ions and biomolecules, biological fluorescence imaging, nonlinear multiphoton imaging, and fluorescence lifetime imaging. As the biological applications of ZnO NPs develop rapidly, the toxicity of ZnO NPs has attracted more and more attention because ZnO can produce the reactive oxygen species (ROS) and release Zn2+ ions. Just as a coin has two sides, both the drug delivery and the antibacterial effects of ZnO NPs become attractive at the same time. Hence, in this review, we will focus on the progress in the synthetic methods, luminescent properties, and biological applications of ZnO NPs.

Extensive elucidations focusing on the efficient health promoting properties and high nutritional values of mushrooms have been expanded dynamically from the past few decades. Due to its high quality of proteins, polysaccharides, unsaturated fatty acids, mineral substances, triterpenes sterols and secondary metabolites, mushrooms have always been appreciated for their vital role in protecting and curing various health problems, such as immunodeficiency, cancer, inflammation, hypertension, hyperlipidemia, hypercholesterolemia and obesity. Moreover, investigations in recent years have revealed the correlations between the health-promoting benefits and gut microbiota regulating effects induced by the mushrooms intake. Researches on the immense role in the nutritional and health benefits displayed by mushrooms have become an emergent task to study. The present article overviewed and compiled the latest information correlated to the health benefits and underlying functional mechanisms of mushroom nutraceuticals, and concluded that the supplementation of mushrooms as a dietary composition could become a natural adjuvant for the prevention and treatment of several health diseases.

The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, however the existing research efforts are still preliminary and fragmented. To that end, in this paper, we provide a systematic study of K-means-based consensus clustering (KCC). Specifically, we first reveal a necessary and sufficient condition for utility functions which work for KCC. This helps to establish a unified framework for KCC on both complete and incomplete data sets. Also, we investigate some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC. Experimental results on various realworld data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with many missing values.

This article concentrates on event-based secure leader-following consensus control for multiagent systems (MASs) with multiple cyber attacks, which contain replay attacks and denial-of-service (DoS) attacks. A new multiple cyber-attacks model is first built by considering replay attacks and DoS attacks simultaneously. Different from the existing researches on MASs with a fixed topological graph, the changes of communication topologies caused by DoS attacks are considered for MASs. Besides, an event-triggered mechanism is adopted for mitigating a load of network bandwidth by scheduling the transmission of sampled data. Then, an event-based consensus control protocol is first developed for MASs subjected to multiple cyber attacks. In view of this, by using the Lyapunov stability theory, sufficient conditions are obtained to ensure the mean-square exponential consensus of MASs. Furthermore, the event-based controller gain is derived by solving a set of linear matrix inequalities. Finally, an example is simulated for confirming the effectiveness of the theoretical results.

The relationship between resilience and mental health was examined in three phases over 4 years in a sample of 314 college students in China. The present study aimed to gain insight into the reciprocal relationship of higher levels of resilience predicting lower levels of mental ill-being, and higher levels of positive mental health, and vice versa, and track changes in both resilience, mental ill-being and positive mental health over 4 years. We used the Depression Anxiety Stress, the Positive Mental Health, and the Resilience Scales. Results revealed that first-year students and senior year students experienced higher negative mental health levels and lower positive mental health levels than junior year students. Cross-lagged structural equation modeling analyses showed that resilience could significantly predict mental health status in the short term, namely within 1 year from junior to senior year. However, the predicting function of resilience for mental health is not significant in the long term, namely within 2 years from freshman to junior year. Additionally, the significant predicting function of individuals' mental health for resilience is fully verified for both the short and long term. These results indicate that college mental health education and interventions could be tailored based on students' year in college.

The problem of secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack is investigated in this article. First, a new model of hybrid cyber attack, which considers a deception attack, a replay attack, and a denial-of-service (DoS) attack, is established for filter design. Second, an adaptive event-triggered scheme is applied to the filter design to save the limited communication resource. In addition, a novel adaptive-event-triggered filtering error model is established with the consideration of hybrid cyber attack and input constraint. Moreover, based on the Lyapunov stability theory and linear matrix inequality technique, sufficient conditions are obtained to guarantee the augmented system stability, and the parameters of the designed filter are presented with explicit forms. Finally, the proposed method is validated by simulation examples.

Attribute-based encryption (ABE) can guarantee confidentiality and achieve fine-grained data access control in a cloud storage system. Due to the fact that every attribute in ABE may be shared by multiple users and each user holds multiple attributes, any single-attribute revocation for some user may affect the other users with the same attribute in the system. Therefore, how to revoke attribute efficiently is an important and challenging problem in ABE schemes. In order to solve above problems, we first give a concrete attack to the existing ABE scheme with attribute revocation. Then, we formalize the definition and security model, which model collusion attack executed by the existing users cooperating with the revoked users. Finally, we present a user collusion avoidance ciphertext-policy ABE scheme with efficient attribute revocation for the cloud storage system. The problem of attribute revocation is solved efficiently by exploiting the concept of an attribute group. When an attribute is revoked from a user, the group manager updates other users’ secret keys. Furthermore, we prove that the proposed scheme is secure against collusion attack launched by the existing users and the revoked users. The security of the proposed scheme is reduced to the computational Diffie–Hellman assumption.

This paper examines quantized stabilization for Takagi-Sugeno (T-S) fuzzy systems with a hybrid-triggered mechanism and stochastic cyber-attacks. A hybrid-triggered scheme, which is described by a Bernoulli variable, is adopted to mitigate the burden of the network. By taking the effect of the hybrid-triggered scheme and stochastic cyber-attacks into consideration, a mathematical model for a closed-loop control system with quantization is constructed. Theorems for main results are developed to guarantee the asymptotical stability of networked control systems by using Lyapunov stability theory and linear matrix inequality techniques. Based on the derived sufficient conditions in theorems, the controller gains are presented in an explicit form. Finally, two practical examples demonstrate the feasibility of designed algorithm.

This article focuses on the security control for Takagi-Sugeno (T-S) fuzzy systems with adaptive event-triggered mechanism (AETM) and multiple cyber-attacks, which include deception attacks and denial-of-service (DoS) attacks. A multiple cyber-attacks model is first established for T-S fuzzy systems by considering deception attacks and DoS attacks at the same time. An AETM is introduced to relieve the network load, where the threshold of event-triggering condition can be adaptively adjusted while preserving the system performance. Then a novel mathematical model for T-S fuzzy systems with multiple cyber-attacks and AETM is proposed first. Based on the built model, sufficient conditions to guarantee the exponentially mean square stability of the system are achieved by utilizing the Lyapunov stability theory. Moreover, the controller gains are derived with the help of a linear matrix inequality technique. Finally, simulated examples are presented for illustrating the effectiveness of the proposed method.