Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

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.     

Analysis of operating limits and combustion state regulation for low-calorific value gases in industrial burners

The effective and efficient utilization of low-calorific value (LCV) gases has gained increasing attention in scientific research and industrial fields. In this study, the combustion characteristics of three LCV gases in practical devices are analyzed by using a nonadiabatic perfectly stirred reactor model. The complete steady-state solution in the temperature-residence time parameter space is obtained with arc-length continuation. The stable operation region is quantified by the eigenvalue analysis. The transition of solution curves is quantified with heat loss coefficient. Five key system parameters are systematically investigated on their effects on stability limits. With the combustion performance being quantified by a combustion state index, a combustion state regulation method is proposed to find the optimal regulation path of system parameters. Active subspace method is further applied to shorten the regulation step by identifying the active direction. The proposed method and findings are useful for optimal regulation of burning LCV gases in industrial burners.     

Full AC analysis of value of DER to the grid and optimal DER scheduling

This paper presents part of the work ComEd and Quanta Technology have performed to quantify the locational and temporal value of DER to avoid distribution grid upgrade investments. It focuses on the formulation of a robust and efficient algorithm for DER optimal dispatch on a distribution feeder to mitigate the violation of current and voltage limits using the allocated cost of capacity and locational marginal value of real and reactive DER injection/withdrawal.     

Effect of cooking temperature on texture and flavour binding of braised sauce porcine skin

To investigate the effect of cooking temperature (55, 65, 75, 85 and 95 °C) on texture and flavour binding of braised sauce porcine skin (BSPS), sensory acceptance, microstructure and flavour-binding capacity were investigated during the processing of BSPS. Samples cooked at 85 and 95 °C showed better texture and aroma scores. Hardness and chewiness of BSPS were obviously improved at 85 and 95 °C than control group. Collagen structure was significantly destroyed over 85 °C. The porcine skin collagen heated at 85 and 95 °C showed relatively higher flavour-binding capacity than other samples. The improvement of texture of BSPS was mainly attributed to the degradation of collagen. Higher aroma scores of BSPS were related to intense binding abilities with aroma compounds at 85 and 95 °C. Cooking at 85 or 95 °C could be an optimal cooking temperature for BSPS.     

珠江口盆地珠一坳陷古近系高自然伽马砂岩形成机制及油气地质意义

运用放射性元素寻找油气是一种非常规油气勘探手段。近年来,在珠江口盆地珠一坳陷富烃凹陷周边古近系钻遇高自然伽马(GR)砂岩,其GR值(100~300 API)甚至大于同区泥岩的GR值(100~200 API)。为了弄清该特殊现象背后的地质意义,对珠江口盆地珠一坳陷古近系高自然伽马砂岩开展了铀(U)、钍(Th)、钾(K)等3种元素含量与GR值的相关趋势线分析,从井震特征、岩性特征及矿物成分特征等入手分析了砂岩GR值增高的主要原因及成因机制,探讨了放射性元素聚集的条件、运移通道、驱动力以及油气意义。结果表明:西江、惠州地区由U含量增高导致砂岩GR值偏高,恩平、番禺地区由K,Th含量增高导致砂岩GR值偏高;砂岩GR值增高有两大成因机制,一是地下流体带来的放射性元素离子U4+在氧化-还原面处富集后导致地层GR值偏高,这种特殊现象说明在具有连通基底大断裂旁的圈闭中,U4+的富集指示了曾经油气的存在,证实了研究区油气运移通道的有效性,对于油气藏的预测有着非常重要的指导性意义,二是地表流体带来的含放射性元素的矿物大量沉积后导致地层GR值偏高,含放射性元素矿物性质不稳定,可指示近源供给的存在,对于判断物源及沉积环境有着非常重要的意义。该研究成果为预测研究区油气成藏有利区带提供了依据。     

我国水-能源-粮食耦合关系及影响因素

为缓解我国水、能源和粮食资源紧张问题，促进资源可持续利用，构建水-能源-粮食系统，利用耦合协调度模型对我国的30个省（自治区、直辖市）进行测算,并利用空间杜宾模型分析主要影响因素。结果表明：2003—2017年，我国能源、粮食评价[JP]指数高于水资源评价指数，系统综合评价指数逐年递增；大部分省份耦合协调度处于初级协调水平且呈现逐年上升的态势，个别省份耦合协调度濒临失调；耦合协调度空间自相关性较强，虽有明显波动，但是呈现逐年加强的态势；影响耦合协调度的主要因素有从业人口数、固定资产投资额、人均生产总值、人口总数、[JP]文盲人口占比、工业污染排放、城镇化。     

Effect of heat treatment on structural,morphological, dielectric and magnetic properties of Mg–Zn ferrite nanoparticles

Green combustion was used to prepare a ferrite composition of Mg_0.4Zn_0.6Fe₂O₄ using a blend of fresh lemon juice as a natural fuel-reductant. Effect of heat treatment on phase, morphological, dielectric, and humidity sensor properties is discussed. The formation of a cubic spinel ferrite has been established by XRD-diffraction and vibrational spectroscopic studies. The experimental lattice parameter ranges from 8.3721 to 8.3631 Å. The broadening of octahedral band (υ₂) in the vibrational spectra is an identification for the existence of ferrite nanoparticles in various sizes. The typical crystallite size ranges from 10.2 to 36.9 nm. Using micrographs obtained from field-effect scanning electron microscopy (FESEM), researchers observed a spherical-shaped microstructure with agglomerated nanoparticles. Dielectric investigations have shown that the current ferrite composition has typical dielectric dispersion. The highest reported value for saturation magnetization (M_s) in the present study is 33 emu/g. Magnetic behaviour is primarily influenced by magnetocrystalline anisotropy, cation distribution, and crystallite size. The existence of void spaces in the sintered samples, as well as their porous nature, rendered them suitable for humidity sensor applications. Sintered samples have good sensing capability at 900 °C. The current findings are integrated in terms of cation distribution and magnetocrystalline anisotropy, assuming fine size effects of ferrite nanoparticles.     

中国智能机械制造评价及发展研究

以中国规模以上工业企业数据库为基础，结合在智能制造主要区域调研的结果，筛选出15145家智能制造企业，经同行评议后，生成322家候选企业名单；再由资深产业专家从“开发式创新”和“探索式创新”两个方面去考察候选企业的能力，得到中国智能制造百强排行榜；随后根据行业分类，得到智能机械制造企业排行榜。在概述了智能机械制造上榜企业的基本情况后，着重分析了前20强企业，并提出了中国智能机械制造发展路径和未来的发展趋势。