Mixing of low-dose cohesive drug and overcoming of pre-blending step using a new gentle-wing high-shear mixer granulator

The objective of this study was to examine the influence of drug amount and mixing time on the homogeneity and content uniformity of a low-dose drug formulation during the dry mixing step using a new gentle-wing high-shear mixer. Moreover, the study investigated the influence of drug incorporation mode on the content uniformity of tablets manufactured by different methods. Albuterol sulfate was selected as a model drug and was blended with the other excipients at two different levels, 1% w/w and 5% w/w at impeller speed of 300?rpm and chopper speed of 3000?rpm for 30?min. Utilizing a 1?ml unit side-sampling thief probe, triplicate samples were taken from nine different positions in the mixer bowl at selected time points. Two methods were used for manufacturing of tablets, direct compression and wet granulation. The produced tablets were sampled at the beginning, middle, and end of the compression cycle. An analysis of variance analysis indicated the significant effect (p?相似文献   

Double emulsions (W/O/W): physical characteristics and perceived intensity of salty taste

This study addressed the correlation between physical characteristics of double emulsions and sensory perception, as the microstructure of these systems may provide the mechanism to understanding the initial cause of the altered salty taste. Thus, double emulsions (W/O/W) were prepared using different volumes of the internal aqueous phase while maintaining the same fat and sodium contents in the evaluated systems. Polyglycerol polyricinoleate (PGPR) and tween 80 were used as hydrophobic and hydrophilic emulsifiers, respectively. After preparation, the samples were stored at 25 °C for 4 days and submitted to analysis of optical microscopy, distribution and polydispersity of the oil droplets size, electrical conductivity, rheological behaviour and sensorial analysis. It was found that the use of different emulsifier concentrations (PGPR) did not influence the physical characteristics of the emulsions with the same formulation, but emulsions with different internal aqueous phase concentrations presented different results. These distinct characteristics may have influenced sensorial perception, as the emulsion with higher internal phase concentration was considered saltier. Thus, it can be concluded that structural differences of the double emulsions can be used to decrease the sodium contents without perceivable changes in salty taste.     

Precise Extraction of Charge Carrier Mobility for Organic Transistors

Unreliable mobility values, and particularly greatly overestimated values and severely distorted temperature dependences, have recently hampered the development of the organic transistor field. Given that organic field‐effect transistors (OFETs) have been routinely used to evaluate mobility, precise parameter extraction using the electrical properties of OFETs is thus of primary importance. This review examines the origins of the various mobilities that must be determined for OFET applications, the relevant extraction methods, and the data selection limitations, which help in avoiding conceptual errors during mobility extraction. For increased precision, the review also discusses device fabrication considerations, calibration of both the specific gate‐dielectric capacitance and the threshold voltage, the contact effects, and the bias and temperature dependences, which must actually be handled with great care but have mostly been overlooked to date. This review serves as a systematic overview of the OFET mobility extraction process to ensure high precision and will also aid in improving future research.     

An architecture for digital hate content reduction with mobile edge computing

Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content. The objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's level. In this regard, the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative analyses. Profiling resulted in different categories of hate content caused by gender, religion, race, and disability. Based on this information, an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing environment. The proposed architecture will be a novel idea to reduce hate content generation and its impact.     

超声提取柿果皮总三萜酸工艺优化

目的采用正交法优化柿果皮中总三萜酸的超声提取工艺。方法以"牛心柿"柿果皮为原料,利用紫外分光光度计法测定柿果皮中总三萜酸的含量,并从提取溶剂选择及其浓度、料液比、超声时间和温度等方面,通过单因素实验和正交试验确定超声提取柿果皮总三萜酸的最佳条件。结果最佳超声提取工艺为:在超声功率240 W的前提下,提取溶剂为75%(V/V)的乙醇、料液比1:15(m/V)、时间45 min、温度50℃。在此条件下,柿果皮总三萜得率最高可达39.33mg/g。结论优化后的方法简单、快速、准确、提取率高,适合提取柿果皮的总三萜酸。     

生物预处理技术在天然产物提取中的应用研究进展

生物预处理技术运用于天然产物提取是借助微生物或生物酶的作用，在适当条件下对天然资源进行处理，破坏植物原料的细胞壁，促进天然产物释放到提取介质中，以提高分离效率，有助于保证天然产物活性。生物预处理技术按形式可以分为微生物发酵技术和生物酶处理技术。针对2种生物预处理技术的作用机制和应用进行综述，旨在为天然产物开发利用提供参考。     

Ionic liquid-assisted micellar extraction for the quantitative determination of sesquiterpenic acids in Valeriana officinalis L. (Caprifoliaceae)

ABSTRACT

A series of hydrophilic ionic liquids (ILs) were investigated as extractants for the quantitative determination of valerenic and acetoxyvalerenic acids in Valerianae radix. The influence of different factors, e.g. type of cation and anion, alkyl chain length, concentration, temperature, extraction time and solid–liquid ratio, was investigated. The results obtained showed that the extraction yield is strongly dependent on the cation type. The best outcome was obtained with IL capable of forming micelles in aqueous solution, e.g. 1-decyl-3-methylimidazolium chloride. As a result, an alternative protocol for the quantification of sesquiterpenic acids in valerian was developed and validated.     

碎软煤层条带瓦斯地面高效抽采技术研究

为了提高碎软煤层条带瓦斯抽采效率和效果,基于目前地面瓦斯抽采主要采用垂直井或从式井的方式抽采效果差、效率低的现状,通过理论和实验分析论证了穿岩层压裂改造煤储层的可行性,提出了在目标煤层顶板岩层中钻水平井,并通过垂直向下射孔以及采用泵送桥塞分段进行压裂的方式进行地面瓦斯抽采。试验结果表明:顶板分段压裂水平井单井产量高、高稳产期更长、产量衰减更慢;有效水平井段控制区域内瓦斯下降均匀,更有利于进行条带瓦斯抽采;相同投资条件下,采用水平井的方式瓦斯抽采效率和投入产出比更高。     

Effect of recycled content and rPET quality on the properties of PET bottles,part II: Migration

Polyethylene terephthalate (PET) bottles were produced from three types of recycled PET (rPET) with four levels of recycled content. The migration of substances from these bottles to water was studied. Several migrated substances were detected. The migrated amounts of acetaldehyde and ethylene glycol complied with the limits given in the food contact material (FCM) legislation. Migration of 2-methyl-1,3-dioxolane was below the limit of 10 μg·L⁻¹, which is conventionally applied for non-intentionally added substances (NIAS) not classified as ‘carcinogenic’, ‘mutagenic’ or ‘toxic to reproduction’ (CMR). Limonene, acetone, butanone and furan were also detected as migrants, of which limonene is a natural fragrant, and the other three are probably residues from solvents used to clean and protect the mould at the small-scale production facility. Finally, benzene and styrene were also found as migrants from rPET. These migrants appear to originate from heat-induced reactions within the PET matrix, which involve contaminants. The formation of benzene in rPET is attributed to polyvinylchloride as contaminant. The migrated amounts of benzene from the PET bottles with recycled content to the water simulant are relatively small (0.03–0.44 μg·L⁻¹) after 10 days at 40°C. Consequently, the margin of exposure is 3.10⁵–8.10⁶. Hence, the level of concern for the public health is low, and the migrated amount represents a low priority for risk management. The FCM legislation demands a risk assessment for migrating NIAS. Depending on the underlying data and exposure scenario, different threshold limits in the food can be derived which can still be considered as safe.     

基于深度学习的安全缺陷报告预测方法实证研究

软件安全问题的发生在大多数情况下会造成非常严重的后果，及早发现安全问题，是预防安全事故的关键手段之一.安全缺陷报告预测可以辅助开发人员及早发现被测软件中潜藏的安全缺陷，从而尽早得以修复.然而，由于安全缺陷在实际项目中的数量较少，而且特征复杂（即安全缺陷类型繁多，不同类型安全缺陷特征差异性较大），这使得手工提取特征相对困难，并随后造成传统机器学习分类算法在安全缺陷报告预测性能方面存在一定的瓶颈.针对该问题，提出基于深度学习的安全缺陷报告预测方法，采用深度文本挖掘模型TextCNN和TextRNN构建安全缺陷报告预测模型；针对安全缺陷报告文本特征，使用skip-grams方式构建词嵌入矩阵，并借助注意力机制对TextRNN模型进行优化.所构建的模型在5个不同规模的安全缺陷报告数据集上展开了大规模实证研究，实证结果表明：深度学习模型在80%的实验案例中都要优于传统机器学习分类算法，性能指标F1-score平均可提升0.258，在最好的情况下甚至可以提升0.535.除此之外，针对安全缺陷报告数据集存在的类不均衡问题，对不同采样方法进行了实证研究，并对结果进行了分析.