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The present study reports the biomimetic synthesis of silver nanoparticles (AgNPs) using a simple, cost effective and eco-friendly method. In this method, the flavonoid extract of Perilla frutescens (PFFE) was used as a bioreduction agent for the reduction of metallic silver into nanosilver, called P. frutescens flavonoid extract silver nanoparticles (PFFE-AgNPs). The Ultraviolet–Visible (UV-Vis) spectrum showed a characteristic absorption peak at 440 nm that confirmed the synthesis of PFFE-AgNPs. A Fourier transform infrared spectroscopic (FTIR) analysis of the PFFE-AgNPs revealed that flavonoids are involved in the bioreduction and capping processes. X-ray diffraction (XRD) and selected area electron diffraction (SAED) patterns confirmed the face-centered cubic (FCC) crystal structure of PFFE-AgNPs. A transmission electron microscopic (TEM) analysis indicated that the synthesized PFFE-AgNPs are 20 to 70 nm in size with spherical morphology and without any aggregation. Dynamic light scattering (DLS) studies showed that the average hydrodynamic size was 44 nm. A polydispersity index (PDI) of 0.321 denotes the monodispersed nature of PFFE-AgNPs. Further, a highly negative surface charge or zeta potential value (−30 mV) indicates the repulsion, non-aggregation, and stability of PFFE-AgNPs. PFFE-AgNPs showed cytotoxic effects against cancer cell lines, including human colon carcinoma (COLO205) and mouse melanoma (B16F10), with IC50 concentrations of 59.57 and 69.33 μg/mL, respectively. PFFE-AgNPs showed a significant inhibition of both Gram-positive (Listeria monocytogens and Enterococcus faecalis) and Gram-negative (Salmonella typhi and Acinetobacter baumannii) bacteria pathogens. PFFE-AgNPs exhibited in vitro antioxidant activity by quenching 1,1-diphenyl-2-picrylhydrazyl (DPPH) and hydrogen peroxide (H2O2) free radicals with IC50 values of 72.81 and 92.48 µg/mL, respectively. In this study, we also explained the plausible mechanisms of the biosynthesis, anticancer, and antibacterial effects of PFFE-AgNPs. Overall, these findings suggest that PFFE-AgNPs have potential as a multi-functional nanomaterial for biomedical applications, particularly in cancer therapy and infection control. However, it is important to note that further research is needed to determine the safety and efficacy of these nanoparticles in vivo, as well as to explore their potential in other areas of medicine. [ABSTRACT FROM AUTHOR]

Drought is a major limiting factor affecting grain production. Drought-tolerant crop varieties are required to ensure future grain production. Here, 5597 DEGs were identified using transcriptome data before and after drought stress in foxtail millet (Setaria italica) hybrid Zhangza 19 and its parents. A total of 607 drought-tolerant genes were screened through WGCNA, and 286 heterotic genes were screened according to the expression level. Among them, 18 genes overlapped. One gene, Seita.9G321800, encoded MYBS3 transcription factor and showed upregulated expression after drought stress. It is highly homologous with MYBS3 in maize, rice, and sorghum and was named SiMYBS3. Subcellular localization analysis showed that the SiMYBS3 protein was located in the nucleus and cytoplasm, and transactivation assay showed SiMYBS3 had transcriptional activation activity in yeast cells. Overexpression of SiMYBS3 in Arabidopsis thaliana conferred drought tolerance, insensitivity to ABA, and earlier flowering. Our results demonstrate that SiMYBS3 is a drought-related heterotic gene and it can be used for enhancing drought resistance in agricultural crop breeding. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes; A dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions; An improved self-adaptive dynamic migrating PSO is proposed in which a novel environment change detection and response strategy is applied. What are the implications of the main findings? Solving the joint optimization problem of spare part management and spare part supply chain network optimization under multiple supply periods; The improved dynamic particle swarm optimization algorithm has better computation efficiency and performance than the traditional algorithm. Spare parts are the critical operation asset for ensuring a production line keeps going, which significantly improves the performance of manufacturing enterprises. This article pays attention to the joint optimization of spare part management and spare part supply chain network optimization in multiple supply periods. An extended (T, s, S) inventory control strategy is utilized to manage spare parts in customer nodes which can determine supply time, consumption and demand. In this spare part supply chain, the supply environment is different in different periods, so the mathematical model and solution method should be able to respond to and detect the environment change quickly. Hence, a dynamic nonlinear programming model is developed for optimizing inventory control decisions and spare part supply decisions so as to minimize the total cost. Furthermore, an improved self-adaptive dynamic migrating particle swarm optimization algorithm is proposed to solve the optimization problem. In this algorithm, a novel environment change detection and response strategy is applied to deal with the dynamic period in the spare part supply chain network. The results obtained show that the improved algorithm improves the computation time by eight percent and has better computational efficiency compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Sustainable closed-loop supply chain (SCLSC) network design and decision-making is a critical problem for enterprises and organizations' operations because of its excellent economic, environmental, and social performance. This article proposes a multi-objective mixed-integer programming model with targets for minimum total cost, reduction in environmental damage, and maximum social responsibility. In order to deal with the uncertainty caused by the dynamic business environment, a fuzzy robust programming (FRP) approach is applied. Furthermore, an efficiency-oriented optimization methodology, hybridizing meta-heuristics and efficiency evaluation, is proposed to solve the developed multi-objective model and functions as auxiliary decision-making. Data envelopment analysis is applied to evaluate the sustainability performance of feasible solutions and calculate their efficiency. The efficiency can comprehensively reflect the sustainability performance and guide the evolution process of meta-heuristic algorithms. A numerical case validates the proposed FRP model and efficiency-oriented optimization methodology. The results demonstrate that with the proposed methodology, decision-makers not only can obtain a set of efficient schemes but also can determine the optimal scheme with the best sustainability performance. [ABSTRACT FROM AUTHOR]

The decision-making of sustainable supply chain network (SSCN) design is a strategy capacity for configuring network facility and product flow. When optimizing conflicting economic, environmental, and social performance objectives, it is difficult to select the optimal scheme from obtained feasible decision schemes. In this article, according to the triple bottom line of sustainability, a multi-objective sustainable supply chain network optimization model is developed, and a novel performance-oriented optimization framework is proposed. This framework, referred to as performance-oriented optimization framework, integrates multi-objective meta-heuristic algorithms and entropy-weighted technique for order preference by similarity to an ideal solution (EW-TOPSIS). The optimization framework can comprehensively evaluate the performance of overall SSCN by EW-TOPSIS and guide the evolution process of algorithms. In this framework, decision-makers can obtain the feasible schemes calculated by meta-heuristics and determine the optimal one according to the performance value evaluated by EW-TOPSIS. This article combines three performance evaluation strategies with four meta-heuristic algorithms, namely, non-dominated Sorting Genetic Algorithm-II (NSGA-2), multi-objective differential evolutionary (MODE), multi-objective particle swarm optimization (MOPSO), and multi-objective gray wolr optimization (MOGWO), for verifying the effectiveness of the performance-oriented optimization framework. The results validate that the proposed framework has much better sustainability performance than the traditional optimization algorithms and evaluation methods. Furthermore, the proposed performance-oriented optimization framework can provide managers with a special optimal scheme with the best sustainability performance. Finally, some research prospects are presented such as more multi-criteria decision making methods. [ABSTRACT FROM AUTHOR]

At present, most investigations involving the Maillard reaction models have focused on free amino acids (FAAs), whereas the effects of peptides on volatile products are poorly understood. In our study, the formation mechanism of pyrazines, which were detected as characteristic volatiles in sunflower seed oil, from the reaction system of glucose and lysine-containing dipeptides and tripeptides was studied. The effect of the amino acid sequences of the dipeptides and tripeptides on pyrazine formation was further highlighted. Four different dipeptides and six tripeptides were selected. The results showed that the production of pyrazines in the lysine-containing dipeptide models was higher than that in the tripeptide and control models. Compounds 2,5(6)-Dimethylpyrazine and 2,3,5-trimethylpyrazine were the main pyrazine compounds in the dipeptide models. Furthermore, the C- or N-terminal amino acids of lysine-containing dipeptides can exert an important effect on the formation of pyrazines. In dipeptide models with lysine at the C-terminus, the content of total pyrazines followed the order of Arg−Lys > His−Lys; the order of the total pyrazine content was Lys−His > Lys−Arg in dipeptide models with N-terminal lysine. Additionally, for the tripeptide models with different amino acid sequences, more pyrazines and a greater variety of pyrazines were detected in the tripeptide models with N-terminal lysine/arginine than in the tripeptide models with N-terminal histidine. However, the total pyrazine content and the percentage of pyrazines in the total volatiles were similar in the tripeptide models with the same amino acids at the N-terminus. This study clearly illustrates the ability of dipeptides and tripeptides containing lysine, arginine and histidine to form pyrazines, improving volatile formation during sunflower seed oil processing. [ABSTRACT FROM AUTHOR]

Identity-based encryption (IBE), and its hierarchical extension (HIBE), are interesting cryptographic primitives that aim at the implicit authentication on the users' public keys by using users' identities directly. During the past several decades, numerous elegant pairing-based (H)IBE schemes were proposed. However, most pairing-related security assumptions suffer from known quantum algorithmic attacks. Therefore, the construction of lattice-based (H)IBE became one of the hot directions in recent years. In the setting of most existing lattice-based (H)IBE schemes, each bit of a user's identity is always associated with a parameter matrix. This always leads to drastic but unfavorable increases in the sizes of the system public parameters. To overcome this issue, we propose a flexible trade-off mechanism between the size of the public parameters and the involved computational cost using the blocking technique. More specifically, we divide an identity into l ′ segments and associate each segment with a matrix, while increasing the lattice modulo slightly for maintaining the same security level. As a result, for the setting of 160-bit identities, we show that the size of the public parameters can be reduced by almost 89.7% (resp. 93.8%) while increasing the computational cost by merely 5.2% (resp. 12.25%) when l ′ is a set of 16 (resp. 8). Finally, our IBE scheme is extended to an HIBE scheme, and both of them are proved to achieve the indistinguishability of ciphertexts against adaptively chosen identity and chosen plaintext attack (IND-ID-CPA) in the standard model, assuming that the well-known ring learning with error (RLWE) problem over the involved ideal lattices is intractable, even in the post-quantum era. [ABSTRACT FROM AUTHOR]