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The weakly bound Ne·HCl van der Waals dimer is a prototype for studying large-amplitude motion in noncovalently bound systems. We report the microwave spectrum of eight isotopologues of Ne·HCl, five of which have not previously been observed by high-resolution spectroscopy. We examine trends in experimentally derived van der Waals stretching force constants and effective bond lengths that are strongly sensitive to isotopologue-dependent motional averaging along the hindered rotation coordinate. Finally, we report an updated semi-empirical three-dimensional morphed potential for the Ne·HCl dimer that is fitted to a large dataset including previous microwave, near-IR, and sub-millimeter wave data in addition to our new microwave data. 

A detailed understanding of water’s remarkable ability to solvate small molecules is a central theme in chemistry. In this work, we have identified 12 new partially and fully deuterated isotopologues of the hydrochloric acid dihydrate cluster, (H2O)2 · HCl, by chirped-pulse Fourier transform microwave spectroscopy. We also performed high-level ab initio calculations relevant to the structure and dynamics of the cluster. The observation of singly substituted isotopologues at each unique hydrogen position enables a more detailed experimental characterization of the geometry than was possible previously. In addition, an analysis of splittings in the spectrum that are caused by population of more than one vibration-tunneling level enables a detailed characterization of hydrogen bond-breaking bifurcation tunneling motions that occur in the complex. Not only do our results confirm theoretical predictions about hydrogen atoms that are permuted by the tunneling motions—they also provide experimental evidence for concerted large-amplitude motions of other atoms. Finally, an analysis of the chlorine nuclear quadrupole coupling tensor suggests that the HCl bonding character might be influenced to a small extent by isotopic substitution of atoms involved in the Cl–H⋯O hydrogen bond due to zero-point vibrational effects. 

The proliferation of distributed energy resources has heightened the interactions between transmission and distribution (T&D) systems, necessitating novel analyses for the reliable operation and planning of interconnected T&D networks. A critical gap is an analysis approach that identifies and localizes the weak spots in the combined T&D networks, providing valuable information to system planners and operators. The research goal is to efficiently model and simulate infeasible (i.e. unsolvable in general settings) combined positive sequence transmission and three-phase distribution networks with a unified solution algorithm. We model the combined T&D network with the equivalent circuit formulation. To solve the overall T&D network, we build a Gauss-Jacobi-Newton (GJN) based distributed primal dual interior point optimization algorithm capable of isolating weak nodes. We validate the approach on large combined T&D networks with 70k+ T and 15k+ D nodes and demonstrate performance improvement over the alternating direction method of multipliers (ADMM) method. 

Abstract In this paper we present a study of distribution polarization doped Al_xGa_1−xN layers and their use in quasi-vertical configuration pn-diodes which exhibited a high breakdown field of ∼8.5 MV cm⁻¹and a large forward current density (∼23 kA cm⁻²). We also establish their potential use in UVC light emitters by studying the optical emission from a quantum well inserted at the distribution polarization doped pn-junction interface. 

Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. Two anthropogenic geomorphic feature extraction problems are explored: the extraction of agricultural terraces and the mapping of surface coal mine reclamation-related valley fill faces. Light detection and ranging (LiDAR)-derived DTMs were used to generate LSPs. We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. Transfer learning between the different geomorphic datasets offered minimal benefits. We suggest that pre-trained models developed using large, image-based datasets may be of value for anthropogenic geomorphic feature extraction from LSPs even given the data and task disparities. More specifically, ImageNet-based parameters should be considered as an initialization state for the encoder component of semantic segmentation architectures applied to anthropogenic geomorphic feature extraction even when using non-RGB image-based predictor variables, such as LSPs. The value of transfer learning between the different geomorphic mapping tasks may have been limited due to smaller sample sizes, which highlights the need for continued research in using unsupervised and semi-supervised learning methods, especially given the large volume of digital terrain data available, despite the lack of associated labels. 

Hydrohalic acid dimers provide a fundamental opportunity to study hydrogen bond rearrangement dynamics at a high level of detail. The (HCl)2 and (DCl)2 homodimers do not have a pure rotational spectrum due to rapid geared tunneling motions that interchange the role of the hydrogen bond donor and acceptor. In this work, we report the pure rotational spectrum of HCl–DCl, which has a preference for deuterium in the hydrogen bond donor position. However, the quadrupole coupling constants indicate a significant amount of geared tunneling, consistent with significant zero-point wavefunction amplitude in the less stable DCl–HCl configuration. A comparison of experimental results with previously published wavefunction calculations based on model and ab initio potentials is consistent with a picture in which about 14% of the probability density distribution is located in the less stable well. 

An updated fit to the interacting levelsν₃andν₆of CF₃I has been evaluated in this work. 

Targeted advertising remains an important part of the free web browsing experience, where advertisers' targeting and personalization algorithms together find the most relevant audience for millions of ads every day. However, given the wide use of advertising, this also enables using ads as a vehicle for problematic content, such as scams or clickbait. Recent work that explores people's sentiments toward online ads, and the impacts of these ads on people's online experiences, has found evidence that online ads can indeed be problematic. Further, there is the potential for personalization to aid the delivery of such ads, even when the advertiser targets with low specificity. In this paper, we study Facebook--one of the internet's largest ad platforms--and investigate key gaps in our understanding of problematic online advertising: (a) What categories of ads do people find problematic? (b) Are there disparities in the distribution of problematic ads to viewers? and if so, (c) Who is responsible--advertisers or advertising platforms? To answer these questions, we empirically measure a diverse sample of user experiences with Facebook ads via a 3-month longitudinal panel. We categorize over 32,000 ads collected from this panel (n = 132); and survey participants' sentiments toward their own ads to identify four categories of problematic ads. Statistically modeling the distribution of problematic ads across demographics, we find that older people and minority groups are especially likely to be shown such ads. Further, given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms (advertising platforms themselves) played a significant role in the biased distribution of these ads.