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Degumming is the vital and critical step in the preparation of hemp fiber for textile application. However, the traditional chemical degumming processes use large amounts of harmful chemicals, especially strong alkalis, which have caused severe challenges to the environment. The reaction conditions of alkaline pectinase and alkaline pectinase were studied in this research, and the alkaline pectinase-xylanase system was successfully applied to the degumming of hemp fibers at mild conditions (T=55 ℃ and pH=8.0) without strong alkali. A comparative analysis of hemp fibers treated under different conditions showed that the gum removal ratio could reach about 50% within 5.5 h of alkaline pectin-xylanase system degumming, making fiber smoother and stronger. After alkaline pectin-xylanase system treatment (0.6 g pectinase, 0.3 g xylanase, 55 ℃, pH 8.0, 5.5 h), the removal ratio of pectin and hemicellulose reached 75 % and 40 %, respectively. And linear density and tenacity of the fiber was 17.4 dtex and 5.62 cN/dtex, respectively. SEM, FT-IR and XRD analysis furthermore demonstrated the excellent effects of the proposed process. The degumming fiber had better water retention performance (513 %) and moisture sorption (8.9 %), which has more excellent application prospects in the textile industry. Moreover, the method abandons the use of acid and alkali and can provide an eco-friendly degumming process for hemp fiber.

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis methods.We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified.

In order to study the high temperature rheological properties of high modulus asphalt cement (HMAC) and clarify its definition criteria, firstly, three kinds of HMAC binders were prepared with high modulus modifiers and subjected to dynamic shear rheological test (DSR), zero shear viscosity test (ZSV) and multi-stress creep recovery test (MSCR). Based on the tests and analysis, the complex shear modulus (G*), rutting factor (G*/sin δ), zero shear viscosity (ZSV) and unrecoverable creep compliance (Jnr) were taken as technical indexes for the performance evaluation of HMAC. Further, through analyzing the consistency of evaluation results of different evaluation indicators and the correlation between evaluation indicators of HMAC binders and HMAC mixtures, the definition criteria of HMAC binder are eventually given. The test results show that the three high modulus modifiers can improve the viscoelastic properties and high temperature properties of the matrix asphalt, but the modification effect is slightly different; the rheological properties index can basically make a consistent evaluation of the high temperature performance of three kinds of HMAC binders, but the correlation degree is different. The dynamic stability (DS) of the mixture is well correlated with G* (60 °C, 10 Hz, 0.1%) (refers to the complex shear modulus measured at 60 °C, loading frequency 10 Hz and strain level 0.1%), G*/sin δ (76 °C, 5 Hz, 0.1%) (refers to the rutting factor measured at 76 °C, loading frequency 5 Hz and strain level 0.1%), zero shear viscosity (60 °C) and Jnr.3.2 (76 °C) (refers to the unrecoverable creep compliance measured at the stress level of 3.2kpa and the temperature of 76 °C), but the correlation of dynamic modulus (E*) with zero shear viscosity (60 °C) and Jnr.3.2 (76 °C) is poor. In summary, it is recommended that HMAC binder should meet both G*(60 °C, 10 Hz, 0.1%) ≥ 60.0 kPa and G*/sin δ (76 °C, 5 Hz, 0.1%) ≥ 5.0 kPa, and the definition criteria can be as a design basis for HMAC binder. At the same time, it is recommended to use Jnr.3.2 (76 °C) to evaluate the high temperature performance of HMAC.

Cadaverine is a biogenic amine that has the potential to become an important platform chemical for the production of industrial polymers, such as polyamides and polyurethanes. We reported here a lysine decarboxylase from Klebsiella oxytoca. The lysine decarboxylase from Klebsiella oxytoca was cloned to Escherichia coli to get the strain LN18. The specific activity of the crude protein from LN18 reached 30 000 U. The molecular weight was about 80 kDa. The optimum temperature and pH of the crude protein were 55 ℃ and 5.5 respectively. The specific activity could keep over 30% at pH 8.0 compared the one at pH 5.5, much difference from Escherichia coli lysine decarboxylase CadA. Mg²⁺ was positive to the specific activity, whereas Fe²⁺, Zn²⁺ and Ca²⁺ were negative.赖氨酸脱羧酶，可以催化赖氨酸脱羧生成戊二胺。戊二胺是重要的平台化合物，可以合成新型聚酰胺材料、脂肪族异氰酸酯等新材料。本研究对来自于产酸克雷伯氏菌的赖氨酸脱羧酶进行异源表达。以pUC18质粒为载体，将来源于产酸克雷伯氏菌的赖氨酸脱羧酶基因ldc 克隆到大肠杆菌，得到菌株LN18。在添加0.5 mmol/L IPTG 的LB 培养基中，对LN18 进行摇瓶培养，发酵液酶活可达到35 U/g 发酵液，从发酵液制备的赖氨酸脱羧酶粗酶蛋白的酶活可以达到30 000 U/g 粗蛋白。产酸克雷伯氏菌赖氨酸脱羧粗酶蛋白大小约80 kDa，粗酶的最适温度和pH 值分别为55 ℃和5.5，与文献中报道的大肠杆菌的赖氨酸脱羧酶CadA 在pH 8.0几乎没有酶活不同，产酸克雷伯氏菌的赖氨酸脱羧酶在pH 8.0 的酶活达到最优pH 下酶活的30%以上。金属离子对酶活有一定的影响，Mg²⁺对酶活有促进作用，Fe²⁺ 、Zn²⁺、Ca²⁺有一定的抑制作用。.

Cadaverine, as a biogenic amine, is an important platform chemical for the production of industrial polymers, such as polyamides, polyurethanes, and nylon. Previous efforts focused on the bio-based production of cadaverine using two lysine decarboxylases of Escherichia coli CadA and LdcC. In this study, we report the biotransformation of cadaverine using a lysine decarboxylase from Klebsiella oxytoca. Codon optimization of the gene encoding this enzyme was carried on for the heterologous expression in E. coli, which led to a system that converted more than 24% lysine-HCl to cadaverine compared to the same system expressing CadA. The system was further optimized by using three different inducible promoters to control the expression of lysine decarboxylase gene of K. oxytoca in E. coli. The final optimized system converted lysine-HCl to cadaverine at a conversion rate of 0.133%/min/g. When the optimized system described above is coupled to an industrial process, the combined process has the potential to produce cadaverine with high conversion efficiency (46%) from sugar.

Traffic sign recognition is an important topic in driver assistant system and intelligent autonomous vehicles. Traffic sign detection is a critical step, whose performance greatly affect the performance and computation cost of traffic sign recognition. In this paper, we propose a traffic sign detection method based on a scoring SVM model. First, traffic sign color and color gradient are extracted according to their color characteristics. Then, the shape of traffic sign is computed by a voting scheme, yielding shape score maps. After that, the score maps of traffic signs are used to train a SVM model. Compared with single traffic sign score value, the scoremapis more efficient to verify the existence of a traffic sign. Finally, the trained SVM model is used to detect traffic signs. Experiments show that the proposed method is more effective than the voting based method to detect traffic signs.