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  1. Abstract

    The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the “pattern effect.” To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.

     
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  2. Strong electron correlation plays an important role in transition-metal and heavy-metal chemistry, magnetic molecules, bond breaking, biradicals, excited states, and many functional materials, but it provides a significant challenge for modern electronic structure theory. The treatment of strongly correlated systems usually requires a multireference method to adequately describe spin densities and near-degeneracy correlation. However, quantitative computation of dynamic correlation with multireference wave functions is often difficult or impractical. Multiconfiguration pair-density functional theory (MC-PDFT) provides a way to blend multiconfiguration wave function theory and density functional theory to quantitatively treat both near-degeneracy correlation and dynamic correlation in strongly correlated systems; it is more affordable than multireference perturbation theory, multireference configuration interaction, or multireference coupled cluster theory and more accurate for many properties than Kohn–Sham density functional theory. This perspective article provides a brief introduction to strongly correlated systems and previously reviewed progress on MC-PDFT followed by a discussion of several recent developments and applications of MC-PDFT and related methods, including localized-active-space MC-PDFT, generalized active-space MC-PDFT, density-matrix-renormalization-group MC-PDFT, hybrid MC-PDFT, multistate MC-PDFT, spin–orbit coupling, analytic gradients, and dipole moments. We also review the more recently introduced multiconfiguration nonclassical-energy functional theory (MC-NEFT), which is like MC-PDFT but allows for other ingredients in the nonclassical-energy functional. We discuss two new kinds of MC-NEFT methods, namely multiconfiguration density coherence functional theory and machine-learned functionals. 
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    Abstract Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6. 
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  5. Free, publicly-accessible full text available May 22, 2024
  6. The strong couplings between electronic states in conical intersection regions are among the most challenging problems in quantum chemistry. XMS-CASPT2, a second-order multireference quasidegenerate perturbation theory, has been successful in describing potential energy surfaces near the conical intersections. We have recently proposed a less expensive method for this problem, namely state-interaction pair-density functional theory (SI-PDFT), which considers the coupling between electronic states described by multiconfiguration pair-density functional theory (MC-PDFT). Here we test the accuracy of SI-PDFT for closely coupled potential energy surfaces of methylamine along five different reaction paths for N–H bond fission. We choose paths that pass close to a conical intersection of the ground and first excited states. We find that SI-PDFT predicts potential energy curves and energy splittings near the locally avoided crossing in close proximity to those obtained by XMS-CASPT2. This validates the method for application to photochemical simulations. 
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  7. ABSTRACT

    Product recalls are often associated with quality failures and may negatively affect customer satisfaction and firm performance. Motivated by counterintuitive anecdotal evidence on the positive relationship between recalls and sales, we develop an endogenous consumer reference model with stochastic quality levels and consumer valuation of gain‐loss utility to examine the consumer's willingness to buy in the event of a recall. A consumer makes purchasing decisions by evaluating gain‐loss values against a quality reference point. The theory of endogenous reference points incorporates the consumer's expectations of buying into forming a reference point based on the consumer's beliefs about quality outcomes (Kőszegi & Rabin, 2006, 2007). The uncertainty of the consumer's quality expectations combined with the consumer's personal equilibrium makes the reference point endogenous, generating predictions different from those under conventional exogenous reference point and in the case of deterministic quality expectations. We find that a product recall may revise the consumer's belief toward a more negative quality outcome, and therefore, a lower reference point, when quality expectations are uncertain and the consumer's reference point is endogenous. The consumer expects a greater gain from buying the product against the lower reference point, leading to a higher willingness to buy. In addition, we find that supply chain offshoring, which may be associated with consumer evaluation of gain‐loss utility or consumer loss aversion, may complicate the effect of recalls on a consumer's willingness to buy. These results suggest that firms should consider consumer preferences under uncertainty when designing and developing their global supply chain strategies in quality management.

     
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  8. Abstract Motivation

    Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.

    Results

    To address this, we propose a novel predictor named Anti-Cancer peptide Predictor with Feature representation Learning (ACPred-FL) for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using sequence-based feature descriptors. By doing so, the class label information of data samples is fully utilized. To improve the feature representation, we further employ a two-step feature selection technique, resulting in a most informative five-dimensional feature vector for the final peptide representation. Experimental results show that such five features provide the most discriminative power for identifying ACPs than currently available feature descriptors, highlighting the effectiveness of the proposed feature representation learning approach. The developed ACPred-FL method significantly outperforms state-of-the-art methods.

    Availability and implementation

    The web-server of ACPred-FL is available at http://server.malab.cn/ACPred-FL.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  9. Abstract

    A method for the stereoselective [4+2]‐cycloaddition of alkenylboranes and dienes is presented. This transformation was accomplished through the introduction of a new strategy that involves the use of chiral N‐protonated alkenyl oxazaborolidines as dieneophiles. The reaction leads to the formation of products that can be readily derivatized to more complex structural motifs through stereospecific transformations of the C−B bond such as oxidation and homologation. Detailed computation evaluation of the reaction has uncovered a surprising role of the counterion on stereoselectivity.

     
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