Search

Under geological conditions, the initial generation of oil and gas in source rocks reaches saturation before being expelled and migrating, with the adsorption of hydrocarbons by kerogen being a key factor influencing oil saturation. The hydrocarbons produced by pyrolysis interact with kerogen macromolecules. Understanding the solvency and adsorption capacities of solid kerogen organic matter for liquid hydrocarbons can clarify the selective retention of hydrocarbons by source rocks and their characteristics in hydrocarbon generation and expulsion. Aromatic hydrocarbons are crucial components of petroleum hydrocarbons. Based on a three-dimensional model of kerogen, this study employed Autodock software to perform semi-flexible docking calculations between different types of aromatic hydrocarbon molecules (including benzene, polycyclic aromatic hydrocarbons, and their derivatives) and kerogen molecules of varying maturities. The Gibbs free energy required for their binding was calculated to study the characte-risticsof the interaction between aromatic hydrocarbons and kerogen. This study investigated the mechanism of kerogen adsorption of aromatic hydrocarbons at the molecular level, revealing the nature of solid and liquid organic matter interactions. When binding with kerogen of the same maturity, the larger the molecular weight of the polycyclic aromatic hydrocarbons, the greater the number of methyl groups in the compound, and the higher the degree of molecular condensation, the lower the Gibbs free energy required for binding with kerogen molecules. The interaction between aromatic hydrocarbons and kerogen molecules was influenced by three factors: the molecular mass of the aromatic hydrocarbons, the degree of molecular condensation, and the number of methyl groups in the system. After reaching the peak of hydrocarbon generation, kerogen with higher content of aromatic carbon methyl groups showed a stronger adsorption capacity for aromatic hydrocarbons. Polycyclic aromatic hydrocarbons and their derivatives with larger molecular mass and higher degrees of condensation demonstrated stronger binding abilities with kerogen. Conversely, smaller aromatic molecules with conventional connectivity exhibited weaker retention capacities in kerogen, making them more prone to hydrocarbon expulsion, migration, and accumulation into reservoirs.

Developing metal structural materials with a good combination of strength-ductility is of great significance for the rapid development of the aerospace industry and the energy industry. High-entropy alloys (HEAs) have attracted widespread attention due to their excellent mechanical properties, especially the excellent ductility and toughness of face-centered cubic (FCC) high-entropy alloys. However, similar to traditional alloys, there is a trade-off between strength and ductility in HEAs. In this paper, we report the successful preparation of Fe-HEA (Fe62Ni16Co9Mn9Ti3Si1 (atomic percentage, %)) with high strength-ductility synergy at room temperature by cryo-rolling and annealing at 600 °C for 10 min (CR600), which increases the yield strength by 59% compared to room temperature rolling (RR600), while the uniform elongation decreases by only 20%. The ultra-high yield strength and the not significant reduction in uniform elongation are mainly attributed to the formation of high-density dislocations, dual-phase bimodal ultra-fine grain heterostructures, (NiMn)3-xTix and (Fe, Ni)2SiTi composite nanoprecipitates, precipitation-induced chemical composition heterogeneity, and BCC→HCP phase transformation during tensile deformation during CR and subsequent annealing at a reduced temperature (600 °C). In addition, by increasing the annealing temperature of the alloy after CR to 800 °C, its uniform elongation can be increased to 53%, which is mainly due to the excellent strain hardening ability induced by changes in heterogeneous structures, nanoprecipitates, and martensitic phase transformation. Compared with other similar alloys, our CR alloy exhibits higher strength-ductility synergy. The results of this study provide a new perspective for developing HEAs with higher strength-ductility synergy.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Objective To establish and forensically verify a 42 microhaplotypes （mircohaps, MHs） multiplex assay system based on next-generation sequencing （NGS）, and to explore the application value of this system in the practice of forensic genetics. Methods A total of 42 highly polymorphic MHs were selected from previous studies, and sequenced by the MiSeq FGxTM platform to verify the repeata-bility, sensitivity, specificity, stability, and mixture analysis ability of the detection system. Through population genetic investigation of 102 unrelated Chinese Han individuals in Liyang City, Jiangsu Province, China, the application value of this system in forensic genetics was evaluated. Results The sequencing repeatability of the 42-plex MHs assay was 100% and the sensitivity was as low as 0.062 5 ng. The system had the ability to withstand the interference of indigo （≤2 500 ng/μL）, humic acid （≤9 ng/μL）, hemoglobin（≤20 μmol）, and urea （≤200 ng/μL） and to detect mixtures of 2 people （1∶19）， 3 people （1∶1∶9） and 4 people （1∶1∶1∶9）. Based on 102 individual data, the combined power of discrimination and the combined power of exclusion were 1-3.45×10-30 and 1-3.77×10-11, respectively, and the average effect value of alleles was 2.899. Conclusion The 42-plex MHs assay was successfully established in this study and this system has high repeatability and sensitivity, good anti-jamming ability and mixture analysis ability. The 42 MHs are highly polymorphism and have good application value in individual identification and paternity testing.

Abstract Cervical cancer is a significant public health concern, particularly in low‐ and middle‐income countries where resources for prevention and treatment are limited. Routine screening, such as the Papanicolaou test (Pap smears) and human papillomavirus (HPV) testing, plays a crucial role in the early detection and prevention of cervical cancer. However, the participation rate in cervical cancer screening programs remains below optimal levels due to various factors. This study aimed to evaluate the reliability and acceptability of the HygeiaTouch Self Sampling Kit for Women in collecting vaginal samples for HPV typing, comparing the results with samples collected by physicians. The study included 1210 women aged 21–65 from three medical centers in Taiwan. The findings indicated that the self‐sampling kit was as effective as physician‐collected specimens in terms of obtaining valid samples and identifying HPV. The agreement between the two methods was 88%, with a κ value of 0.75. Furthermore, the study assessed the mechanical characteristics of the self‐sampling applicator through tensile, bending, and torque tests, and determined that it was safe for intravaginal use. Additionally, the study evaluated the safety and satisfaction of self‐sampling and found a low rate of adverse events (0.7%) and high levels of satisfaction (over 90%) among participants. Overall, we demonstrated that the HygeiaTouch Self Sampling Kit for Women is a reliable and acceptable device for HPV testing and cervical screening, providing a convenient, safe, and effective alternative for women.

Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction of the porosity of high-performance concrete with added cementitious elements is the goal of this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, and curing conditions were considered as inputs in the database. The aim of this study is to create ML models that could evaluate concrete porosity. Gene expression programming (GEP) and multi-expression programming (MEP) were used to develop these models. Statistical tests, Taylor’s diagram, R 2 values, and the difference between experimental and predicted readings were the metrics used to evaluate the models. With R 2 = 0.971, mean absolute error (MAE) = 0.348%, root mean square error (RMSE) = 0.460%, and Nash–Sutcliffe efficiency (NSE) = 0.971, the MEP provided a slightly better-fitted model and improved prediction performance when contrasted with the GEP, which had R 2 = 0.925, MAE = 0.591%, RMSE = 0.745%, and NSE = 0.923. Binder, water/binder ratio, curing conditions, and aggregate content had a direct (positive) relationship with the porosity of concrete, while SP, fly ash, and slag had an indirect (negative) association, according to the SHapley Additive exPlanations study.

Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor’s diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R 2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash–Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.

Renewable energy promotes the sustainable development of the environmental protection industry (EPI) and the economy. Meanwhile, the EPI provides various technologies and services for renewable energy, which is an important guarantee for the renewable energy market. This paper combines the DY and BK spillover indexes to discuss the cross-market risk contagion effect between EPI and renewable energy market from the perspective of time-frequency spillover. The results show that: (1) The spillover effect between the EPI and renewable energy market is cyclical from the perspective of frequency domain, and the total market spillover is dominated by high-frequency spillover. And the risk contagion has the characteristics of time-varying and vulnerability from the perspective of time domain. (2) The net spillover effect shows that the EPI plays the role of a net exporter in different frequency domains, the solar energy and the hydro market play more roles as risk receivers, and the role of the wind energy market is frequently converted. (3) The risk spillover network structure shows that EPI acts as an important node in the cross-market risk contagion path. Driven by EPI, the short-term risk transmission follows the path of “EPI-biomass energy-other four renewable energy markets”, and the medium and long-term follows the path of “EPI-wind energy-other four renewable energy markets”.