The electronic absorption spectra of more than 20 crude oils and asphaltenes are examined. The spectral location of the electronic absorption edge varies over a wide range, from the near-infrared for heavy oils and asphaltenes to the near-UV for gas condensates. The functional form of the electronic absorption edge for all crude oils (measured) is characteristic of the “Urbach tail,” a phenomenology which describes electronic absorption edges in wide-ranging materials. The crude oils all show similar Urbach widths, which are significantly larger than those generally found for various materials but are similar to those previously reported for asphaltenes. Monotonically increasing absorption at higher photon energy continues for all crude oils until the spectral region is reached where single-ring aromatics dominate absorption. However, the rate of increasing absorption at higher energies moderates, thereby deviating from the Urbach behavior. Fluorescence emission spectra exhibit small red shifts from the excitation wavelength and small fluorescence peak widths in the Urbach regions of different crude oils, but show large red shifts and large peak widths in spectral regions which deviate from the Urbach behavior. This observation implies that the Urbach spectral region is dominated by lowest-energy electronic absorption of corresponding chromophores. Thus, the Urbach tail gives a direct measure of the population distribution of chromophores in crude oils. Implied population distributions are consistent with thermally activated growth of large chromophores from small ones.
The exponential attenuation of electronic absorption in spectral regions removed from the absorption maxima, the “Urbach tail,” has been observed in a variety of materials and has been ascribed to thermal and structural disorder. Here, we report, to our knowledge, the first observation of the Urbach tail in a multicomponent organic system (the asphaltenes) which is due, in part, to the overlapping absorption spectra of the diverse component chromophores within the tail spectral region. The distribution of chromophores produces an unusually large, nonthermal width in the Urbach tail.
Explosive growth in the number of users on various social media platforms has transformed the way firms strategize their marketing activities. To take advantage of the vast size of social networks, firms have now turned their attention to influencer marketing wherein they employ independent influencers to promote their products on social media platforms. Despite the recent growth in influencer marketing, the problem of network seeding (i.e., identification of influencers to optimally post a firm’s message or advertisement) neither has been rigorously studied in the academic literature nor has been carefully addressed in practice. We develop a data-driven optimization framework to help a firm successfully conduct (i) short-horizon and (ii) long-horizon influencer marketing campaigns, for which two models are developed, respectively, to maximize the firm’s benefit. The models are based on the interactions with marketers, observation of firms’ message placements on social media, and model parameters estimated via empirical analysis performed on data from Twitter. Our empirical analysis discovers the effects of collective influence of multiple influencers and finds two important parameters to be included in the models, namely, multiple exposure effect and forgetting effect. For the short-horizon campaign, we develop an optimization model to select influencers and present structural properties for the model. Using these properties, we develop a mathematical programming based polynomial time procedure to provide near-optimal solutions. For the long-horizon problem, we develop an efficient solution procedure to simultaneously select influencers and schedule their message postings over a planning horizon. We demonstrate the superiority of our solution strategies for both short- and long-horizon problems against multiple benchmark methods used in practice. Finally, we present several managerially relevant insights for firms in the influencer marketing context. This paper was accepted by J. George Shanthikumar, big data analytics.
The effective local reuse of physical cash by depository institutions (DIs) is the primary goal of the new cash recirculation policy of the Federal Reserve System (Fed) of the United States. These guidelines, implemented since July 2007, encourage the reuse of cash by (i) penalizing a DI for the practice of cross shipping, the near-simultaneous deposit of used cash to—and withdrawal of fit cash from—the Fed; and (ii) offering a custodial inventory program that enables a DI to transfer fit cash to the Fed's books, but physically hold it within the DI's secured facility. The effective management of the inventory of cash under these new guidelines is both a challenging and important issue for DIs. We introduce two new multiperiod models—designed specifically to capture the operations of a medium-size DI—that emerge from the DI's objective to minimize the total cost incurred in managing the inventory of cash over a finite planning horizon. The Basic Model (BM) captures the DI's mode of operations if it chooses not to locally reuse cash and, instead, incur the cross-shipping penalty. Using two important structural properties, we provide a polynomial-time dynamic programming algorithm for BM. The Reuse Model (RM) represents the DI's actions when it locally recirculates cash. We first prove the hardness of RM and then develop an integer programming formulation. A comprehensive test bed—based on our interaction with a leading secure-logistics provider—helps us to develop several useful insights into the relative impacts of the DI-specific parameters and the Fed's cross-shipping fee on the effective management of cash. In particular, we show that the Value of Local Reuse for a DI, measured as the percentage cost saving between the optimal solutions of BM and RM, is substantial, and we analyze the forces that influence the volume of cross shipping. We also develop a rolling-horizon procedure to adapt the optimal solutions of BM and RM for obtaining near-optimal real-time solutions in the presence of a modest amount of uncertainty. Finally, we provide a comparative analysis of a DI's decisions under the Fed's mechanism and those under a socially optimal mechanism.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.