Instacart Market Basket Analysis

Instacart is a company that operates as a fast grocery delivery service in America. Groceries will be delivered by personal shoppers based on customers’ product lists from various retailers. The service is mainly available from a smartphone app as well as website. The purpose of this project was to use anonymized data on customer orders over time to predict which previously purchased product will be in a user’s next order. Data was provided by Instacart open source: 3 million orders from more than 200,000 users. The project was processed by following steps: features were created by data mining and exploratory data analysis, features were transformed through the feature tools in python library, constructed positive and negative examples, constructed train and test sets, modeling and performance evaluation. XGboost model and Logistic Regression accuracies are 82.38% and 74.99%. A review of results and further suggestions were conducted at last.   Keywords:data mining, feature creation, feature engineering, Logistic Regression, XGBoost</p

Additional file 6 of DNA damage repair system in C57BL/6 J mice is evolutionarily stable

Additional file 6: Supplementary Table 6. Coding-variants in Eve6B differing from 1998 C57BL/6J genome

Additional file 5 of DNA damage repair system in C57BL/6 J mice is evolutionarily stable

Additional file 5: Supplementary Table 5. Sanger-validated non-DDR variants in 2018 C57BL/6

Additional file 4 of DNA damage repair system in C57BL/6 J mice is evolutionarily stable

Additional file 4: Supplementary Table 4. Variants in Non-DDR genes detected in C57BL/6J from 1998 to 201

Additional file 2 of DNA damage repair system in C57BL/6 J mice is evolutionarily stable

Additional file 2: Supplementary Table 2. Variants detected in 2018 C57BL/6J present in > 22 (50%) cases

Additional file 1 of DNA damage repair system in C57BL/6 J mice is evolutionarily stable

Additional file 1: Supplementary Table 1. Total variants detected in 2018 C57BL/6J genome

Additional file 3 of DNA damage repair system in C57BL/6 J mice is evolutionarily stable

Additional file 3: Supplementary Table 3. A. Absence of coding variants in DDR genes by refering to mm7. B. Absence of coding variants in DDR genes by refering to mm1

Spinel-Oxide-Based Laccase Mimics for the Identification and Differentiation of Phenolic Pollutants

Phenol and its derivatives, known as persistent organic pollutants, have long threatened human health and environmental safety. There is an urgent need to develop convenient, low-cost, and multiplex analytical methods. Since phenols are substrates of laccase, they can be detected via laccase-catalyzed colorimetric assays. Nevertheless, the laccase-based assays cannot distinguish different phenols. Moreover, natural laccases suffer from high cost and low stability issues. To meet these needs, here we developed a laccase-like nanozyme sensor array for phenol detection and differentiation, which takes advantage of both nanozymes and cross-reactive sensor arrays. First, we examined a series of spinel-type transition metal oxides and found that manganese on octahedral sites profoundly affects the laccase-like activity of the materials. Based on the developed manganese-based spinel oxides (i.e., Mn3O4, Zn0.4Li0.6Mn2O4, and LiMn2O4), a colorimetric sensor array was constructed. The sensor array could effectively identify and discriminate phenol and its derivatives and showed good performance in the identification and differentiation of phenols in tap water samples. This work provides an important guidance for the development of laccase-like nanozymes and a promising methodology for pollutant monitoring

Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes

Understanding the impact of extractant functionalization on metal-binding energetics in liquid–liquid extraction is essential to guide the development of better separation processes. Traditionally, computational extractant design uses electronic structure calculations on metal–ligand clusters to determine the metal-binding energy of the lowest energy state. Although highly accurate, this approach does not account for all of the relevant physics encountered under experimental conditions. Such methodologies often neglect entropic contributions such as temperature effects and ligand flexibility, in addition to approximating solvent–extractant interactions with implicit solvent models. In this study, we use classical molecular dynamics simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. We generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the headgroup and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal-binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the headgroup has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal-binding energetics based on experimentally reported distribution ratios. We also consider a different bidentate extractant molecule, carbamoylmethylphosphine oxide, and show how the choice of solvent can further reshape the conformational energetic landscape. This study demonstrates the feasibility of using molecular dynamics simulations with advanced sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation

DataSheet_1_Can the development of digital construction reduce enterprise carbon emission intensity? New evidence from Chinese construction enterprises.pdf

IntroductionWith the rapid development of digital technology and its deep integration with the engineering and construction field, digital construction has become an effective way for low-carbon transformation in the construction industry. However, there is a gap of empirical research between digital construction and carbon emissions. MethodsThis paper empirically investigates the impact of digital construction level on carbon emission intensity and the mechanism of action by using the two-way fixed effects model and mechanism testing based on the panel data of 52 Shanghai and Shenzhen A-share listed companies in China’s construction industry from 2015 to 2021. ResultsThe findings indicate that the improvement of digital construction level can significantly decrease the carbon emission intensity of construction enterprises, and the conclusions still hold after robustness tests and discussions on endogeneity issues such as replacing core explanatory variables, replacing models, using instrumental variables method, system GMM model and difference in differences model. According to a mechanism analysis, digital construction can curb carbon emission intensity by enhancing the R&D innovation capacity and total factor productivity of enterprises. Furthermore, the heterogeneity analysis shows that the improvement of digital construction level in state-owned enterprises as well as civil engineering construction enterprises can better contribute to reducing carbon emission intensity. DiscussionThis paper will provide a reference for the synergistic optimization of digital construction development and carbon emissions reduction in construction enterprises. The research conclusions are going to promote the digital transformation of the construction industry to accelerate the achievement of the carbon peaking and carbon neutrality goals.</p