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ABSTRACT: Ethyl cellulose (EC) is a biocompatible, renewable, and recyclable material with diverse sources, making it an attractive candidate for industrial applications. Electrospinning has gained significant attention for the production of EC fibers. However, conventional electrospinning methods face challenges such as bead formation, low yield, and the absence of porous internal structures, limiting both the functional performance and scalability. This study presents an optimized approach for producing EC fibers by using a gravity-driven ultrahigh-speed electrospinning (GUHS-ES) system. This system leverages gravity to reshape the Taylor cone morphology during electrospinning, enhancing stability and dramatically increasing throughput. As flow rates increase, the Taylor cone contracts inward, while the tip structure expands and stabilizes, reaching maximum size at ultrahigh flow rates (100−150 mL/h). This unique Taylor cone structure enables a fiber production rate of 24.5 g/h, hundreds of times greater than conventional electrospinning techniques. Another advantage of the GUHS-ES system is its ability to achieve both high diameter uniformity and adjustable porosity. At ultrahigh flow rates, the pore sizes of the EC fibers reached 321 nm. The highly porous structure of EC fibers exhibited an absorption capacity of 56.6 to 110.7 times their weight, exceeding most previously reported oil-absorbing materials and demonstrating high efficacy for rapid waste oil absorption. This green, efficient technology represents a promising advancement for the large-scale production and application of natural polymer fibers with broad implications for sustainable industrial processes. 

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov Chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them. 

We study the differentially private (DP) empirical risk minimization (ERM) problem under the semi-sensitive DP setting where only some features are sensitive. This generalizes the Label DP setting where only the label is sensitive. We give improved upper and lower bounds on the excess risk for DP-ERM. In particular, we show that the error only scales polylogarithmically in terms of the sensitive domain size, improving upon previous results that scale polynomially in the sensitive domain size (Ghazi et al., 2021). 

Xu, H., Liu, M., Bu, Y., Sun, S., Zhang, Y., Zhang, C., Acuna, DE, Gray S., Meyer, E., & Ding, Y. (2024). The impact of heterogeneous shared leadership in scientific teams. Information Processing & Management, 61(1), 103542. 

The rooftop is a default location for photovoltaic solar panels and is often not enough to offset increasing building energy consumption. The vertical surface of urban buildings offers a prime location to harness solar energy. The overall goal of this research is to evaluate power production potentials and multi-functionalities of a 3D building integrated photovoltaic (BIPV) facade system. The traditional BIPV which is laminated with window glass obscures the view-out and limits daylight penetration. Unlike the traditional system, the 3D solar module was configured to reflect the sun path geometry to maximize year-round solar exposure and energy production. In addition, the 3D BIPV façade offers multiple functionalities – solar regulations, daylighting penetration, and view-out, resulting in energy savings from heating, cooling, and artificial lighting load. Its ability to produce solar energy offsets building energy consumption and contributes to net-zero-energy buildings. Both solar simulations and physical prototyping were carried out to investigate the promises and challenges of the 3D BIPV façade system compared to a traditional BIPV system. With climate emergency on the rise and the need for clean, sustainable energy becoming ever more pressing, the 3D BIPV façade in this paper offers a creative approach to tackling the problems of power production, building energy savings, and user health and wellbeing.