A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.     

Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores

Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores.     

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.     

火试金法配料过程自动化的探索和应用

火试金法中配料方式采用的是传统的人工配料,针对人工配料方式的不足,设计了一款自动化程度较高的自动配料机,通过对配料机的准确性、效率及可靠性实验,结果表明配料机检测称量误差在±0.2 g以内,效率为人工配料的2~3倍,检测结果与人工配料的相一致。与此同时,应用此设备改善了工作环境,避免了人为失误造成的检测质量问题,降低了复检率,为企业降低了成本,提高了效益,在业内具有广泛应用前景。     

高师生实验探究洋葱中挥发性化学物质

利用实验室一般仪器,对洋葱中挥发性化学成分如含硫化合物、醇类、醛类、羧酸进行了定性检测,旨在让高师生体验探究学习,掌握探究方法,发展实验设计和探究能力。     

高中学业水平考试试题难度模型建构*

利用主成分分析方法厘清试题难度影响因素之间的关系,进而从问题表征、问题解决和结果输出等3个维度上建立了影响高中学业水平考试试题难度的主要因素。通过编写高中学业水平考试测试题进行实证研究,建立影响因素常见呈现类型的难易赋值规则。选用机器学习的线性回归方法建构试题难度分析模型,并使用高考化学江苏卷部分试题的实测难度数据进行校验,模型难度预测值和高考难度实测值具有较好的拟合度,表明模型具有较好的应用价值和推广意义。     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.     

The Cradle-to-Cradle Life Cycle Assessment of Polyethylene terephthalate: Environmental Perspective

Over the last several years, the number of concepts and technologies enabling the production of environmentally friendly products (including materials, consumables, and services) has expanded. One of these ways is cradle-to-cradle (C2C) certified^TM. Life cycle assessment (LCA) technique is used to highlight the advantages of C2C and recycling as a method for reducing plastic pollution and fossil depletion by indicating the research limitations and gaps from an environmental perspective. Also, it estimates the resources requirements and focuses on sound products and processes. The C2C life cycle measurements for petroleum-based poly (ethylene terephthalate) (PET) bottles, with an emphasis on different end-of-life options for recycling, were taken for mainland China, in brief. It is considered that the product is manufactured through the extraction of crude oil into ethylene glycol and terephthalic acid. The CML analysis method was used in the LCIA for the selected midpoint impact categories. LCA of the product has shown a drastic aftermath in terms of environmental impacts and energy use. But the estimation of these consequences is always dependent on the system and boundary conditions that were evaluated throughout the study. The impacts that burden the environment are with the extraction of raw material, resin, and final product production. Minor influences occurred due to the waste recycling process. This suggests that waste degradation is the key process to reduce the environmental impacts of the production systems. Lowering a product’s environmental impact can be accomplished in a number of ways, including reducing the amount of materials used or choosing materials with a minimal environmental impact during manufacture processes.