Spectroscopic Studies of Brooker\u27s Merocyanine in Zeolite L

Zeolites are porous, crystalline substances that have very unique atomic organizations which allow for the formation of complex channels within the crystals. Each type of zeolite has a distinct shape and structure. To better understand the properties of zeolite channels, a dye molecule known as Brooker’s merocyanine was inserted into Zeolite L. Maximum dye loading into the zeolite channels was achieved by altering different experimental variables, such as heat, solution concentration, stirring, cation exchange, and light exposure. X-ray diffraction was used to verify the synthesis of zeolites, the cation exchange process, and dye loading. UV-Vis spectroscopy was used to measure the amount of dye adsorbed by the zeolite. By using the UV-Vis absorbance values and Beer’s Law, the concentration of dye in the zeolites was determined. The results showed that an increase of heat and stirring correlated to an increase of adsorption of dye by the zeolite. Due to the light sensitivity of Brooker’s merocyanine, it was found that limiting the amount of light exposure of the dye solutions also resulted in higher dye adsorption by the zeolites. An increase of the concentration of the dye solution increased the rate of adsorption in the channels. However, exchanging the potassium ions found within the synthesized Zeolite L channels with smaller hydrogen ions did not have an affect on the adsorption of dye in the channels. Characterizing how to achieve a maximum of dye adsorption in the zeolites allows for a better understanding of how dye molecules interact within the zeolite channels

Error bounds for discrete minimizers of the {G}inzburg--{L}andau energy in the high-κ\kappa regime

In this work, we study discrete minimizers of the Ginzburg–Landau energy in finite element spaces. Special focus is given to the influence of the Ginzburg–Landau parameter $\kappa$. This parameter is of physical interest as large values can trigger the appearance of vortex lattices. Since the vortices have to be resolved on sufficiently fine computational meshes, it is important to translate the size of $\kappa$ into a mesh resolution condition, which can be done through error estimates that are explicit with respect to $\kappa$ and the spatial mesh width $h$. For that, we first work in an abstract framework for a general class of discrete spaces, where we present convergence results in a problem-adapted $\kappa$-weighted norm. Afterwards we apply our findings to Lagrangian finite elements and a particular generalized finite element construction. In numerical experiments we confirm that our derived $L^2$- and $H^1$-error estimates are indeed optimal in $\kappa$ and $h$

Error bounds for discrete minimizers of the Ginzburg-Landau energy in the high-κ\kappa regime

In this work, we study discrete minimizers of the Ginzburg-Landau energy in finite element spaces. Special focus is given to the influence of the Ginzburg-Landau parameter $\kappa$. This parameter is of physical interest as large values can trigger the appearance of vortex lattices. Since the vortices have to be resolved on sufficiently fine computational meshes, it is important to translate the size of $\kappa$ into a mesh resolution condition, which can be done through error estimates that are explicit with respect to $\kappa$ and the spatial mesh width $h$. For that, we first work in an abstract framework for a general class of discrete spaces, where we present convergence results in a problem-adapted $\kappa$-weighted norm. Afterwards we apply our findings to Lagrangian finite elements and a particular generalized finite element construction. In numerical experiments we further explore the asymptotic optimality of our derived $L^2$- and $H^1$-error estimates with respect to $\kappa$ and $h$. Preasymptotic effects are observed for large mesh sizes $h$

Deep Denoising for Hearing Aid Applications

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.Comment: submitted to IWAENC 201

Harvest Timing and Moisture Determination: Forage Drying Rates and Moisture Probe Accuracy

Scott County Kentucky currently has a beef cattle herd of 28,509 head (USDA, 2017). These cattle utilize forage as a large part of their diets. Baleage is bales of wilted, high moisture forage which have been wrapped in several layers of UV‐resistant plastic and allowed to ensile like traditional chopped silage (Henning et al., 2021). Baleage has become another way for farmers to harvest and store forage to be used in cattle diets. It has some advantages over the traditional hay production in Kentucky. One advantage is it can be harvested, baled, and stored in a shorter period of time, which is ideal with Kentucky’s weather conditions. Baleage keeps a higher level of forage quality over time, unlike hay when it is loses nutrients through the drying process. Producers are still learning about best practices in producing baleage for livestock. If done incorrectly it can cause issues with storage, forage quality, spoilage, and even cattle death

Uniform Diagonalization Theorem for Complexity Classes of Promise Problems including Randomized and Quantum Classes

Diagonalization in the spirit of Cantor's diagonal arguments is a widely used tool in theoretical computer sciences to obtain structural results about computational problems and complexity classes by indirect proofs. The Uniform Diagonalization Theorem allows the construction of problems outside complexity classes while still being reducible to a specific decision problem. This paper provides a generalization of the Uniform Diagonalization Theorem by extending it to promise problems and the complexity classes they form, e.g. randomized and quantum complexity classes. The theorem requires from the underlying computing model not only the decidability of its acceptance and rejection behaviour but also of its promise-contradicting indifferent behaviour - a property that we will introduce as "total decidability" of promise problems. Implications of the Uniform Diagonalization Theorem are mainly of two kinds: 1. Existence of intermediate problems (e.g. between BQP and QMA) - also known as Ladner's Theorem - and 2. Undecidability if a problem of a complexity class is contained in a subclass (e.g. membership of a QMA-problem in BQP). Like the original Uniform Diagonalization Theorem the extension applies besides BQP and QMA to a large variety of complexity class pairs, including combinations from deterministic, randomized and quantum classes.Comment: 15 page