Identification of critical causes of construction accidents in China using a hybrid HFACS-CN model.

Construction safety is of significance since construction accidents can result in loss of property and large numbers of casualties. This research aims to identify the critical causes of construction accidents by introducing a hybrid approach. The hybrid approach is developed to identify the critical causes of construction accidents by combining the human factors analysis and classification system (HFACS) model with complex network (CN) theory. A total of 863 construction accident cases were collected, and 46 causal factors were identified. Subsequently, the accident causal network was established, and six critical causal factors were extracted. The hybrid analysis approach is demonstrated with a real construction accident case, and the results demonstrate that the hybrid approach could better identify the critical causal factors. Consequently, this research enables the enhancement of understanding the HFACS framework and CN theory, as well as a contribution to safety management in the construction industry at different levels.

Near-infrared spectroscopy combined with pattern recognition algorithms to quickly classify raisins.

With the development of commodity economy, the emergence of fake and shoddy raisin has seriously harmed the interests of consumers and enterprises. To deal with this problem, a classification method combining near-infrared spectroscopy and pattern recognition algorithms were proposed for adulterated raisins. In this study, the experiment was performed by three kinds of raisins in Xinjiang (Hongxiangfei, Manaiti, Munage). After collecting and normalizing the spectral data, we compared the spectra of three kinds of raisins. Next the principal component analysis (PCA) was preformed to compress the dimension of the spectral data, and then classification models including support vector machine (SVM), multiscale fusion convolutional neural network (MCNN) and improved AlexNet were established to identify raisins. The accuracy of SVM, MCNN, and improved AlexNet is 100%, 92.83%, and 97.78% respectively. This study proves that near-infrared spectroscopy combined with pattern recognition is feasible for the raisin inspection.

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey.

Zhejiang Suichang native honey, which is included in the list of China's National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky-Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.

Online probing quantum dots and engineered enzyme self-assembly in a nanoliter scale.

Nanoparticles provide significantly enhanced binding characteristics. However, fast online probing of the self-assembly process remains hard to achieve in practice. Herein, we report a fluorescence coupled CE method for probing the self-assembly events between quantum dots (QDs) and engineered Jumonji domain-containing protein 6 (Jmjd6) enzyme. QDs and Jmjd6 were sequentially injected into the capillary, where the self-assembly took place in a nanoliter scale. In particular, we showed that the Jmjd6/QD ratio, the interval time, and the injection volume had a great effect on the online self-assembly. The current approach may allow for a better understanding of QDs-enzyme self-assembly and enzymatic activity detection.

In-capillary probing QDs and HAT tag self-assembly and displacement using Förster resonance energy transfer.

HAT tag, a natural His affinity tag, is one of the most popular fusion tags. However, HAT tag containing three positive charges limited its self-assembly with quantum dots (QDs). Herein, ATTO 590-labeled HAT peptide was synthesized to self-assemble with QDs inside the capillary. QDs and ATTO 590-labeled HAT peptide were sequentially injected into the capillary and probed by fluorescence-coupled CE (CE-FL), showing an obvious Förster resonance energy transfer signal. Online self-assembly, separation, and detection were achieved within 10 min. CE-FL further revealed that imidazole and H6 G6 peptide could partially outcompete with HAT tag on the QDs surface inside the capillary. The displacement intermediates were separated clearly using CE-FL. Our study demonstrates the power of online CE-FL in analyzing the binding interaction between ligands containing positive charges and QDs.

Capillary electrophoretic studies on quantum dots and histidine appended peptides self-assembly.

Herein, we designed four peptides appended with different numbers of histidine (Hisn -peptide). We launched a systematic investigation on quantum dots (QDs) and Hisn -peptide self-assembly in solution using fluorescence coupled CE (CE-FL). The results indicated that CE-FL was a powerful method to probe how ligands interaction on the surface of nanoparticles. The self-assembly of QDs and peptide was determined by the numbers of histidine. We also observed that longer polyhistidine tags (n ≤ 6) could improve the self-assembly efficiency. Furthermore, the formation and separation of QD-peptide assembly were also studied by CE-FL inside a capillary. The total time for the mixing, self-assembly, separation, and detection was less than 10 min. Our method greatly expands the application of CE-FL in QDs-based biolabeling and bioanalysis.