Search

Sulfur dioxide ($\mathrm{SO}_2$) is an important precursor for the formation of atmospheric sulfate aerosol and acid rain. We present an instrument using Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS) for the measurement of $\mathrm{SO}_2$ with a minimum limit of detection of 0.75 ppbv (3-$\sigma$) using the spectral range 305.5--312 nm and an averaging time of 5 min. The instrument consists of high-reflectivity mirrors (0.9985 at 310 nm) and a deep UV light source (Light Emitting Diode). The effective absorption path length of the instrument is 610 m with a 0.966 m base length. Published reference absorption cross sections were used to fit and retrieve the $\mathrm{SO}_2$ concentrations and were compared to fluorescence standard measurements for $\mathrm{SO}_2$. The comparison was well correlated ($R^2 = 0.9998$) with a correlation slope of 1.04. Interferences for fluorescence measurements were tested, and the BBCEAS showed no interference, while ambient measurements responded similarly to standard measurement techniques.

Absorption cross-sections for the 5th (6 $\leftarrow$ 0) and 6th (7 $\leftarrow$ 0) OH overtones for gas-phase methanol, ethanol, and isopropanol were measured using a slow flow cell and Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBBCEAS). Measurements were performed in two wavelength regions, 447-457 nm, and 508-518 nm, using two different instruments. The experimental results are consistent with previous computational predictions of the excitation energies for these transitions. Treating the OH stretch as a local mode allowed for calculation of the fundamental vibrational frequency ($\omega_e$), anharmonicity constant ($\omega_e x_e$), and the vertical dissociation energy (VDE) for each alcohol studied. The fundamental vibrational frequency is $3848 \pm 18 \, \text{cm}^{-1}$, $3807 \pm 55 \,\text{cm}^{-1}$, and $3813 \pm 63 \, \text{cm}^{-1}$ for methanol, ethanol, and isopropanol, respectively. The anharmonicity constant was measured to be $84.8 \pm 2.1 \, \text{cm}^{-1}$, $80.2 \pm 5.9 \, \text{cm}^{-1}$, and $84.4 \pm 6.8 \, \text{cm}^{-1}$ for methanol, ethanol, and isopropanol, respectively. The OH vertical dissociation energy was measured to be $499.4 \pm 18.4$ kJ/mol, $518.0 \pm 56.7$ kJ/mol, and $492.7 \pm 59.9$ kJ/mol. The spectroscopically measured values are compared to thermodynamically measured OH bond dissociation energies. The observed differences in previous measurements of the bond dissociation energies compared to the values reported herein can be explained due to the difference between vertical dissociation energies and bond dissociation energies. If the OH overtone stretching mode is excited in methanol to either the 5th or 6th overtone, the bimolecular reaction between methanol and O$_2$ becomes thermodynamically feasible and could contribute to formation of methoxy and HO$_2$ radicals under the proper combination of pressure and temperature.

Sketching is an important activity in any design process. Designers and stakeholders share their ideas through hand-drawn sketches. These sketches are further used to create 3D models. Current methods to generate 3D models from sketches are either manual or tightly coupled with 3D modeling platforms. Therefore, it requires users to have an experience of sketching on such platform. Moreover, most of the existing approaches are based on geometric manipulation and thus cannot be generalized. We propose a novel AI based ensemble approach, SingleSketch2Mesh, for generating 3D models from hand-drawn sketches. Our approach is based on Generative Networks and Encoder-Decoder Architecture to generate 3D mesh model from a hand-drawn sketch. We evaluate our solution with existing solutions. Our approach outperforms existing approaches on both - quantitative and qualitative evaluation criteria., Comment: Working on some updates

Fashion is a fast-changing industry where designs are refreshed at large scale every season. Moreover, it faces huge challenge of unsold inventory as not all designs appeal to customers. This puts designers under significant pressure. Firstly, they need to create innumerous fresh designs. Secondly, they need to create designs that appeal to customers. Although we see advancements in approaches to help designers analyzing consumers, often such insights are too many. Creating all possible designs with those insights is time consuming. In this paper, we propose a system of AI assistants that assists designers in their design journey. The proposed system assists designers in analyzing different selling/trending attributes of apparels. We propose two design generation assistants namely Apparel-Style-Merge and Apparel-Style-Transfer. Apparel-Style-Merge generates new designs by combining high level components of apparels whereas Apparel-Style-Transfer generates multiple customization of apparels by applying different styles, colors and patterns. We compose a new dataset, named DeepAttributeStyle, with fine-grained annotation of landmarks of different apparel components such as neck, sleeve etc. The proposed system is evaluated on a user group consisting of people with and without design background. Our evaluation result demonstrates that our approach generates high quality designs that can be easily used in fabrication. Moreover, the suggested designs aid to the designers creativity.