TAP: A static analysis model for PHP vulnerabilities based on token and deep learning technology.

With the widespread usage of Web applications, the security issues of source code are increasing. The exposed vulnerabilities seriously endanger the interests of service providers and customers. There are some models for solving this problem. However, most of them rely on complex graphs generated from source code or regex patterns based on expert experience. In this paper, TAP, which is based on token mechanism and deep learning technology, was proposed as an analysis model to discover the vulnerabilities of PHP: Hypertext Preprocessor (PHP) Web programs conveniently and easily. Based on the token mechanism of PHP language, a custom tokenizer was designed, and it unifies tokens, supports some features of PHP and optimizes the parsing. Besides, the tokenizer also implements parameter iteration to achieve data flow analysis. On the Software Assurance Reference Dataset(SARD) and SQLI-LABS dataset, we trained the deep learning model of TAP by combining the word2vec model with Long Short-Term Memory (LSTM) network algorithm. According to the experiment on the dataset of CWE-89, TAP not only achieves the 0.9941 Area Under the Curve(AUC), which is better than other models, but also achieves the highest accuracy: 0.9787. Further, compared with RIPS, TAP shows much better in multiclass classification with 0.8319 Kappa and 0.0840 hamming distance

An Efficient Synthesis of Spiro[indoline-3,9′-xanthene]trione Derivatives Catalyzed by Magnesium Perchlorate

A simple and efficient method for the synthesis of spiro[indoline-3,9′-xanthene]trione derivatives by means of condensation between isatins and 1,3-cyclohexanedione in the presence of a catalytic amount of magnesium perchlorate at 80 °C in 50% aqueous ethanol medium has been described. Notably, the present method offers desirable advantages of good yields, simplicity of workup procedure, easy purification, and reduced reaction times

Assessing climate/air quality synergies and cost-effectiveness for Beijing transportation:Insights into sustainable development

CO2 emission reduction policies can simultaneously reduce air pollutant emissions in the transport sector, but the extent of their cost-effective is under-evaluated. To explore sustainable transportation in a mega-city, the potential synergies and cost-effectiveness of CO2 and air pollutant emission reductions in Beijing transportation are quantitatively evaluated by radar chart analysis, total cost of ownership (TCO) and marginal abatement cost curves (MACC) under various mitigation measures. The findings show that the adoption of electric light-duty vehicles can achieve a significant synergistic effect and cost-effectiveness of emission reductions, and would be implemented as a high priority. Moreover, improving emission standards and fuel economy have an obvious cost-effectiveness and effective mitigation for air pollutants, but with poor synergies due to low CO2 mitigation. In contrast, the clean energy for large and heavy vehicles, and bio-fuel for aviation are essential measures for achieving carbon neutrality, but with high costs. Furthermore, changes of transport modes have good cost-effectiveness, but there are no synergies due to the emission increments of PM2.5 and NOx. Given that transportation plays a crucial role in achieving carbon neutrality and enhancing air quality, more stringent and effective environmental policies targeting emission reduction can expedite the sustainable transition in Beijing transportation