The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

The Helix project is a multiyear longitudinal research study focus on understanding what makes systems engineers effective. The Helix team gathered data through interviews, site visits, and surveys with individuals who are doing systems engineering (SE) work on programs, supervising systems engineers, using products from systems engineers, and are knowledgeable about SE. The responses provided by the participants were documented and analyzed using text mining techniques, such as topic modeling and word similarity. To interpret the results, the team developed several visualizations from three scenarios, full corpus, by organization type, and by domain. The visuals show the common language among systems engineers within topics and the relationship between words from the three scenarios mentioned above. Also, the article provides insights into what enables systems engineers to be proficient or effective in their organizations.

In times of shrinking margins, researchers are looking for more cost-effective and profitable ways to improve the post-production support of large-scale, complex systems. There is an inherent tradeoff between the system design and the design’s long-term support. A system’s design largely determines its reliability that, in turn, influences the demands on its post-production support network necessary to maintain the proper use of the system over its intended, economic useful-life. Performance-based contracts are a successful financial instrument between suppliers and buyers for long-term support contracts. This research leverages the tenets of performance-based contracting, especially its foundation in transactional cost economics and management control theory, and agency theory, to develop and test an analytical model. This research proposes a novel, analytical model that maximizes the profit margin of a large-scale, complex system simultaneously considering its design and post-production support network. To date, these decisions are largely understudied. This model uses redundancy allocation to represent a design decision, and the post-production support network decisions are the location, quantity of spares, and logistics footprint. The post-production support network is a non-arboreal, multi-echelon sustainment network, and the design is a series-parallel configuration. The model is constrained by customer-specified, minimum reliability, mean-time-between-failure (MTBF), and a maximum logistics footprint (LF) measured in pounds. A meta-heuristic algorithm was used to address the nonlinearity of the objective function and constraints. Afterward, a numerical example was solved, and comparative experiments were conducted to test the algorithm. The results showed a profit for the supplier of $14,505.12 with 78.45 hrs of MTBF out of the 75 hours minimum allowed and a logistic footprint of 6,285.25 lb. out of the 10,000 lb allowed. The solution demonstrates the economic importance of system engineers, contract personnel, and program managers in understanding the inherent tradeoff space connecting the design and support of a system.

The Systems Engineering Research Center (SERC) is a University Affiliated Research Center (UARC) of the US Department of Defense (DoD) formed in 2008 with more than 20 collaborator universities in the United States. Over the last decade, SERC has conducted research with Principal Investigators from universities within the SERC network, as reflected in technical reports (TR). These reports describe detailed information and analysis of the conducted research for every project under SERC support, such as written records of experiments or results of a scientific project. We analyzed the TRs from 2009 to early 2023 to identify research streams, topics, and evolution in systems engineering (SE) research using text mining and network analysis techniques, such as Louvain Community Detection and word similarity. As a result, we identified four major research streams over a decade of research projects, along with insights about topics and the evolution of SE within this time frame. Finally, we distinguished most profile authors and their most significant collaborations and networks.

Oil-in-water nanoemulsions (NE) can increase the water solubility of plant-derived bioactive molecules as drug candidates. Machine learning-guided NE design can prevent the expensive, time-consuming trial-and-error process. NE composition data was aggregated into a dataset; a predictive machine learning model identified improved self-nanoemulsifying system formulations (olive oil and combinations of Tween 20, Tween 80, glycerol, and soy lecithin). Predictive power was assessed by estimating successful self-nanoemulsification through transmittance and Dynamic Light Scattering. NEs were loaded with an organic extract containing anacardic acid. Encapsulation efficiency was measured by UHPLC. Antiproliferative activity was evaluated on human hepatic cancer (Hep G2) and normal-like human embryonic kidney (HEK-293) cell lines. The model showed an accuracy of 81%. The best-performing formulation, consisting of 10% olive oil, 60% Tween 20, and 30% glycerol, exhibited an average particle size of 162.8 ± 26 nm, a polydispersity index of 0.234 ± 0.03, and high encapsulation efficiency. While HEK-293 cells remained unaffected, naked NE exhibited a selective growth inhibitory effect on the Hep G2 cell line. Loaded NE increased the cytotoxic effect on Hep G2 (IC50: 5.9 ± 1.27 µM). Machine learning-guided NE formulation was a successful carrier for the plant extract and the molecule of interest, providing a proof of concept for how artificial intelligence can shorten the development pipeline for NE drug delivery systems.