Inhibitory effects of curcumin and piperine on fluorescent advanced glycation end products formation in a bovine serum albumin–fructose model

This study investigated the inhibitory effects of curcumin and piperine on fluorescent advanced glycation end products (fAGEs) formation in a bovine serum albumin (BSA)–fructose model. Model systems of BSA and fructose were prepared, and curcumin or piperine was added. fAGEs and BSA oxidation product (dityrosine, kynurenine and N'-formylkynurenine) contents were determined. The results showed that fAGEs content decreased with increasing concentration of curcumin and piperine (P < 0.05). Addition of curcumin and piperine at 160 µg mL⁻¹ could inhibit fluorescent AGEs by 100% and 93% respectively. Dityrosine and N'-formylkynurenine contents decreased as curcumin and piperine concentration increased (P < 0.05). Furthermore, the result of principal component analysis indicated that curcumin and piperine markedly impeded BSA oxidation, resulting in a lower level of fAGEs in model systems. Therefore, adding curcumin and piperine may facilitate reduced fAGEs levels in BSA–fructose model.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

A dotted nanowire arrayed by 5 nm sized palladium and nickel composite nanopaticles showing significant electrocatalytic activity towards ethanol oxidation reaction (EOR)

To the best of our knowledge, this is the first time to report the preparation of a dotted nanowire arrayed by 5 nm sized palladium and nickel composite nanoparticles (denoted as Pd_xNi_y NPs) via a hydrothermal method using NU and PdO·H₂O as the starting materials. The samples prepared at the mass ratio of NU to PdO·H₂O 1:1, 1:2 and 2:1 were, respectively, nominated as catalyst c₁, c₂ and c₃. The chemical compositions of all synthesized catalysts were mainly studied by using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), revealing that metallic Ni was one main component of all prepared catalysts. Surprisingly, the main diffraction peaks appearing in the XRD patterns of all prepared catalysts were assigned to the metallic Ni rather than the metallic Pd. Very interestingly, as indicated by the TEM images, a large number of dotted nanowires arrayed by numerous equidistant 5 nm sized nanoparticles were distinctly exhibited in catalyst c₁. More importantly, when being used as electrocatalysts for EOR, all prepared catalysts exhibited an evident electrocatalytic activity towards EOR. In the cyclic voltammetry (CV) test, the peak current density of the forward peak of EOR on catalyst c₁ measured at 50 mV s^?1 was as high as 56.1 mA cm^?2, being almost 9 times higher than that of EOR on catalyst c₃ (6.3 mA cm^?2). Particularly, the polarized current density of EOR on catalyst c₁ at 3600 s, as indicated by the chronoamperometry (CA) experiment, was still maintained to be around 1.47 mA cm^?2, a value higher than the latest reported data of 1.3 mA cm^?2 (measured on the pure Pd/C electrode). Presenting a novel method to prepare dotted nanowires arranged by 5 nm sized nanoparticles and showing the significant eletrocatalytic activities of the newly prepared dotted nanowires towards EOR were the major contributions of this preliminary work.     

A red code triggers an unintended approach motivation toward sweet ultra-processed foods: Possible implications for front-of-pack labels

Front-of-package labels (FOPL) are recommended to reduce consumer intake of ultra-processed food products (UPP). The multiple traffic-light label is one example of FOPL that indicates the content of target nutrients in products by displaying red (high), amber (medium), and/or green (low) color-coding. The red code may implicitly enhance sweetness perception and approach dispositions toward sweet UPP via cross-modal visual-taste interactions. We conducted two experiments to examine the possibility of contradictory influence of explicitly learned and implicit cross-modal associations on the emotional responses evoked by UPP pictures. In both experiments, we first explicitly associated the color codes with health-related meanings. In Experiment I (n = 78), a psychometric tool estimated the emotional responses (pleasantness and arousal ratings) evoked by UPP pictures when preceded by red, amber, or green color-codes. In Experiment II (n = 24), we recorded participants’ electrocortical brain activity to assess the early posterior negativity (EPN) component as an index of the emotional responses to UPP. The reported pleasantness (Experiment I) and the EPN amplitude (Experiment II) were greater for sweet UPP relative to salty UPP when primed with red codes but not when primed with green or amber. A red code increased positive emotions toward sweet UPP despite its explicit association with increased health-risks. Thus, the use of multiple traffic-lights might lead to an unintended implicit approach behavior toward sweet UPP. Designers, researchers, and policy makers may consider color-taste cross-modal associations when designing, testing, and applying FOPL.     

基于物联网的智慧林业可持续发展策略研究

随着我国科技的不断进步,在“互联网+”大数据的背景下,林业的智慧化发展是必经之路。虽然我国智慧林业的发展已经取得了一定的突破,但是仍然存在着许多问题。基于此,文章在物联网技术的基础上,对智慧林业的可持续发展策略进行探讨。     

儿童用品TBT通报数据可视化预警研究

探索采用数据可视化技术分析儿童用品TBT通报数据,以可视化图形图像呈现通报热点并揭示趋势信息,提出对策与建议,助力为儿童用品产业升级、TBT预警数据分析和信息传播工作提供新思路,提高中小企业的国外市场准入机会。     

Deep reinforcement learning-based cooperative interactions among heterogeneous vehicular networks

Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution.     

A survey on fog computing for the Internet of Things

Fog computing has emerged to support the requirements of IoT applications that could not be met by today’s solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.