Application of machine learning for risky sexual behavior interventions among factory workers in China

IntroductionAssessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models.MethodsThe survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12.ResultsAbout a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%).DiscussionMachine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services

Self-Assembly of Lipid Molecules under Shear Flows: A Dissipative Particle Dynamics Simulation Study

The self-assembly of lipid molecules in aqueous solution under shear flows was investigated using the dissipative particle dynamics simulation method. Three cases were considered: zero shear flow, weak shear flow and strong shear flow. Various self-assembled structures, such as double layers, perforated double layers, hierarchical discs, micelles, and vesicles, were observed. The self-assembly behavior was investigated in equilibrium by constructing phase diagrams based on chain lengths. Results showed the remarkable influence of chain length, shear flow and solution concentration on the self-assembly process. Furthermore, the self-assembly behavior of lipid molecules was analyzed using the system energy, particle number and shape factor during the dynamic processes, where the self-assembly pathways were observed and analyzed for the typical structures. The results enhance our understanding of biomacromolecule self-assembly in a solution and hold the potential for applications in biomedicine

Type division and controlling factor analysis of 3rd-order sequences in marine carbonate rocks

Type division and controlling factor analysis of 3rd-order sequence are of practical significance to tectonic analysis, sedimentary environment identification, and other geological researches. Based on the comprehensive analysis of carbon and oxygen isotope trends, paleobathymetry and spectral-frequency of representative well logs, 3rd-order sequences can be divided into 3 types: (a) global sea level (GSL) sequence mainly controlled by GSL change; (b) tectonic sequence mainly controlled by regional tectonic activity; and (c) composite sequence jointly controlled by GSL change and regional tectonic activity. This study aims to identify the controlling factors of 3rd-order sequences and to illustrate a new method for classification of 3rd-order sequences of the middle Permian strata in the Sichuan Basin, China. The middle Permian strata in the Sichuan Basin consist of 3 basin-contrastive 3rd-order sequences, i.e., PSQ1, PSQ2 and PSQ3. Of these, PSQ1 is a GSL sequence while PSQ2 and PSQ3 are composite sequences. The results suggest that the depositional environment was stable during the deposition of PSQ1, but was activated by tectonic activity during the deposition of the middle Permian Maokou Formation

Middle and Upper Ordovician Chitinozoans from the Kuruktag Area, northeastern Tarim, China

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