Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

A Seasonal and Multilevel Association Based Approach for Market Basket Analysis in Retail Supermarket

Market Basket Analysis is an observational data mining methodology to investigate the consumer buying behavior patterns in retail Supermarket. It analyzes customer baskets and explores the relationship among products that helps retailers to design store layouts, make various strategic plans and other merchandising decisions that have a big impact on retail marketing and sales. Frequent itemsets mining is the first step for market basket analysis. The association rules mining uncovers the relationship among products by looking what products the customers frequently purchase together. In retail marketing, the transactional database consists of many itemsets that are frequent only in a particular season however not taken into consideration as frequent in general. In some cases, association rules mining at lower data level with uniform support doesn't reflect any significant pattern however there is valuable information hiding behind it. To overcome those problems, we propose a methodology for mining seasonally frequent patterns and association rules with multilevel data environments. Our main contribution is to discover the hidden seasonal itemsets and extract the seasonal associations among products in additionally with the traditional strong regular rules in transactional database that shows the superiority for making season based merchandising decisions. The dataset has been generated from the transaction slips in large supermarket of Bangladesh that discover 442 more seasonal patterns as well as 1032 seasonal association rules in additionally with the regular rules for 0.1% minimum support and 50% minimum confidence.

Tunable Multilevel Data Storage Bioresistive Random Access Memory Device Based on Egg Albumen and Carbon Nanotubes

In this paper, a tuneable multilevel data storage bioresistive memory device is prepared from a composite of multiwalled carbon nanotubes (MWCNTs) and egg albumen (EA). By changing the concentration of MWCNTs incorporated into the egg albumen film, the switching current ratio of aluminium/egg albumen:multiwalled carbon nanotubes/indium tin oxide (Al/EA:MWCNT/ITO) for resistive random access memory increases as the concentration of MWCNTs decreases. The device can achieve continuous bipolar switching that is repeated 100 times per cell with stable resistance for 104 s and a clear storage window under 2.5 × 104 continuous pulses. Changing the current limit of the device to obtain low-state resistance values of different states achieves multivalue storage. The mechanism of conduction can be explained by the oxygen vacancies and the smaller number of iron atoms that are working together to form and fracture conductive filaments. The device is nonvolatile and stable for use in rewritable memory due to the adjustable switch ratio, adjustable voltage, and nanometre size, and it can be integrated into circuits with different power consumption requirements. Therefore, it has broad application prospects in the fields of data storage and neural networks.

Active emotions and personal growth initiative fuel employees’ daily job crafting: A multilevel study

In this study, we expand on the existing work on job crafting by focusing on (1) within-person fluctuation in affective experiences in relation to job crafting and person-job fit and (2) between-person fluctuations in personal growth initiative (PGI) as an important boundary condition of these relationships. Using multilevel data from 116 employees (341 observations), our results showed that fluctuations in positive active emotions (PAE) and negative active emotions (NAE) related positively to daily job crafting; this relationship was moderated by overall PGI levels. Next, we found a positive association between daily job crafting and daily person-job fit. Finally, we found indirect effects from NAE and PGI to daily fluctuations in person-job fit via daily fluctuations in job crafting; NAE and PGI energized employees to engage in daily job crafting, which contributed to their daily person-job alignment. We discuss implications for theory and practice. JEL CLASSIFICATION: M0

Missing Data in Research on Youth and Family Programs

Background Multilevel data can be missing at the individual level or at a nested level, such as family, classroom, or program site. Increased knowledge of higher-level missing data is necessary to develop evaluation design and statistical methods to address it. Methods Participants included 9,514 individuals participating in 47 youth and family programs nationwide who completed multiple self-report measures before and after program participation. Data were marked as missing or not missing at the item, scale, and wave levels for both individuals and program sites. Results Site-level missing data represented a substantial portion of missing data, ranging from 0–46% of missing data at pre-test and 35–71% of missing data at post-test. Youth were the most likely to be missing data, although site-level data did not differ by the age of participants served. In this dataset youth had the most surveys to complete, so their missing data could be due to survey fatigue. Conclusions Much of the missing data for individuals can be explained by the site not administering those questions or scales. These results suggest a need for statistical methods that account for site-level missing data, and for research design methods to reduce the prevalence of site-level missing data or reduce its impact. Researchers can generate buy-in with sites during the community collaboration stage, assessing problematic items for revision or removal and need for ongoing site support, particularly at post-test. We recommend that researchers conducting multilevel data report the amount and mechanism of missing data at each level.