Electrospinning offers exceptional flexibility in fabricating nanofibers, yet achieving precise control over fiber diameter, which is crucial for tailoring material properties, remains a challenge due to the complex and nonlinear interactions among various process parameters. This study presents a literature-derived, multi-material predictive-optimization strategy for the computational control of electrospun fiber diameter. A dataset containing 3000 data points was collected from published scientific research articles on electrospinning, covering a wide range of polymers, solvents, and process parameters, and was used to develop predictive machine learning (ML) models for fiber diameter prediction. Among the developed models, the extreme gradient boosting (XGB) model performed best, achieving a coefficient of determination (R2) value of 0.94 and delivering low prediction errors (root mean square error [RMSE]: 275.02 nm, mean absolute error [MAE]: 75.40 nm) on the unseen data. Experimental validation using polystyrene (PS) and polyvinyl chloride (PVC) nanofibers showed close alignment between predicted and measured fiber diameters, supporting the practical reliability of the predictive model under real electrospinning conditions. To move beyond forward prediction, the trained XGB model was integrated with a genetic algorithm (GA) to enable surrogate-based inverse design of electrospinning parameters for user-defined target fiber diameters. The GA identified parameter combinations corresponding to target diameters between 100 nm and 4000 nm with low surrogate-model fitness errors. For target diameters of 400 nm and 600 nm, the GA achieved fitness errors of 0.03 nm and 0.35 nm, respectively. Furthermore, the integrated XGB-GA approach yielded a perfect linear correlation (R2 = 1.00) between the target fiber diameters and the predicted fiber diameters within the GA-optimized subset, demonstrating the effectiveness of the optimization strategy for precise diameter control across various electrospinning conditions. Overall, this study provides a data-driven approach for predicting fiber diameter and guiding electrospinning parameter selection while reducing dependence on trial-and-error experimentation within the material systems represented in the dataset.
Helical carbon nanotubes (HCNTs) offer unique geometrical characteristics and capabilities; however, their properties, functionalization, and applications have not been sufficiently explored, compared to the straight CNTs that have different crystallinity and structural characteristics. The coil-shaped geometries of HCNTs can substantially increase their mechanical entanglement/interlocking with solidified host-resins and the microfiber-reinforcements in fiber-reinforced composites. As a result, it can considerably improve the mechanical, thermal, electrical, and magnetic properties of the composites. To further improve their effectiveness, HCNTs should be chemically treated to promote their molecular interactions and bonding-effectiveness with the resin molecules, as well as to enhance their dispersion-uniformity and suspension-stability in the host-resin. In this study, a reflux method was deployed to chemically functionalize HCNTs with a low-molarity nitric acid-solution and then effects of reflux time and temperature on surface-modification and dispersion-homogeneity of the functionalized HCNTs (FHCNTs) were investigated. The results from SEM, FTIR, XRD, Raman spectroscopy, and visual dispersion-test showed that changes in reflux time and temperature were mostly effective in atomic scale structural alteration of the HCNTs. Except for the FHCNTs that were treated at higher temperatures for a longer time, the rest showed improvements in their dispersion, an increase in ID/IG Raman ratios, and changes in FTIR spectra.
Biological invaders, such as Sericea lespedeza, cause over $21 billion (about $65 per person in the US) in annual losses for the US, necessitating effective control methods. To our knowledge, this article is the first to integrate random occurrences of an invader that are not attributable to biophysical impacts, within an integrated simulation-optimisation model to control Sericea. Specifically, we introduce a novel dispersal framework that integrates predictable Gaussian seed spread with a random sprout algorithm, explicitly addressing the long-standing question of random pop-ups of new invaders and capturing long-distance establishment events that traditional models miss. The simulation models the species' biological growth and integrates both predictable dispersal and unpredictable establishment events into a unified framework. Our optimisation model minimises economic damage by determining optimal search and treatment locations under budget constraints. The case study data and parameter calibration are based on large-scale field data collected in Kansas and Oklahoma. We simulate Sericea growth over a 2,500-acre landscape for 25 years, representing a 25-fold increase in spatial coverage and more than double the temporal scope compared to former studies, substantially increasing problem complexity while demonstrating the scalability of our model. Results, averaged over 10 independent replications, show that prioritising searches in low-density areas and treating infestations immediately upon detection yield the greatest benefits. The framework highlights the value of early detection, search speed, and cost-effective control, offering a generalisable tool for invasive species management.
This paper examines how the Kansas tax experiment, which eliminated state income taxes on pass-through entities between 2012 and 2017, affected firms’ debt payment behavior, a key indicator of liquidity constraint. Using establishment-level data from the National Establishment Time Series (NETS) and exploiting the geographic discontinuity in the Kansas City metropolitan area, we estimate the causal effect of the reform using a spatially anchored difference-in-differences framework. The results show that eliminating pass-through income led to a measurable, but temporary, improvement in the timeliness of debt payments. The average duration of delayed payments was reduced by one-third in the baseline model. The effects were heterogeneous and significant in small and non-publicly listed establishments.
Background: As educators seek innovative ways to engage students in transformative learning, immersive service-learning provides unique opportunities to develop leadership and stewardship. The program explored in our research study provides undergraduate students with intensive volunteer experience with the National Parks Service (NPS). Purpose: Our research study is designed to describe and investigate how a service-learning experience transforms students' understanding of leadership and stewardship. Method: The program integrates formal learning with experiential community-based learning through interactions with university faculty and park rangers within the national park. These course-based experiences help solidify the main concepts of servant leadership, stewardship, and service throughout the experience. Qualitative data used in the study was collected from impact videos and written reflection questions from ePortfolio workbooks of 23 undergraduate participants. Findings: In analyzing the data, it was clear that transformational experiences helped students in their understanding of leadership, stewardship, and building of cultural awareness and community. Through experiential learning, students were able to immerse themselves in the beforementioned concepts in interactions throughout the trip with many crediting these interactions for a shift in their perspective. Implications: These findings stress the importance of immersive, interdisciplinary experiential learning on students' personal development and understanding of course content.
