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Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE). However, it requires a large amount of simulated data, which can be costly to collect. This can be avoided by learning physics from the physics-constrained loss, which we refer to it as mean squared residual (MSR) loss constructed by the discretized PDE. We investigate the physical information in the MSR loss, which we called long-range entanglements, and identify the challenge that the neural network requires the capacity to model the long-range entanglements in the spatial domain of the PDE, whose patterns vary in different PDEs. To tackle the challenge, we propose LordNet, a tunable and efficient neural network for modeling various entanglements. Inspired by the traditional solvers, LordNet models the long-range entanglements with a series of matrix multiplications, which can be seen as the low-rank approximation to the general fully-connected layers and extracts the dominant pattern with reduced computational cost. The experiments on solving Poisson's equation and (2D and 3D) Navier–Stokes equation demonstrate that the long-range entanglements from the MSR loss can be well modeled by the LordNet, yielding better accuracy and generalization ability than other neural networks. The results show that the Lordnet can be 40 × faster than traditional PDE solvers. In addition, LordNet outperforms other modern neural network architectures in accuracy and efficiency with the smallest parameter size. [ABSTRACT FROM AUTHOR]

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore. [ABSTRACT FROM AUTHOR]

This paper reports on a corpus-based study aimed at reexamining the typological status and diachronic change of motion expressions in Chinese, drawing on parallel texts consisting of autonomous motion expressions in Old Chinese (OC) and its Modern Chinese (MoC) translation. The results show that MoC significantly differs from OC both in the preference of lexicalization patterns (Talmyan typology) and semantic components distributed in discourse (Slobinian typology) when narrating similar motion scenes. However, these results fail to support the viewpoint that Chinese has undergone a change from a verb- to a satellite-frame (Li 1993; Talmy 2000; Peyraube 2006; Shi & Wu 2014). It is argued that (i) the Talmyan typology and the Slobinian typology should be treated separately. In Talmyan typology, the diachrony of Chinese demonstrates the change of a V- to a parallel-frame, in that satellite- and verb-framed constructions in MoC have equal frequency and show no bias for the encoding of subtypes of autonomous motion. In Slobinian typology, MoC remains as a Path-salient language, as it gives considerable weight to the expression of Path; (ii) the dominant lexicalization pattern in a language varies from one sub-domain of motion to another (see also Lamarre 2003), and thus the typology of motion expressions is sub-domain-specific; and (iii) motivating forces and blocking forces, furthermore, co-exist diachronically for the typological evolution of motion encoding due to the idiosyncrasy of the morphosyntactic system. [ABSTRACT FROM AUTHOR]

This paper re-examines from an evolutionary perspective the typological status of Chinese, with regard to the issue of how the information of motion events is encoded (Talmy 2000; Slobin 2004). We investigate, with emphasis on the roles of both language structure and language use, the four periods of Chinese (Old, Middle, Pre-Modern and Modern) in terms of parameters such as path, manner and ground, and compare with typologically different languages, namely, verb-framed languages like Spanish and satellite-framed languages like English. Our statistical study shows that (i) Chinese has been undergoing a typological shift from a verb-framed language to a satellite-framed language, and Pre-Modern Chinese is a stepped-up period with respect to the speed of evolution; (ii) Modern Chinese adopts diverse patterns to encode motion events, which are different from both typical verb-framed languages and typical satellite-framed languages. We thus conclude that (i) contrary to Peyraube's (2006) claim, the typological shift in Chinese has not yet been achieved; (ii) there is little justification for classifying Chinese as an equipollently framed language as in Slobin (2004) and Chen and Guo (2009). Therefore, there is no need to posit an equipollent type for Chinese; and (iii) Modern Chinese is in a transitional state from a V-type to an S-type due to the evolution of its motion expressions. It also favors the S-type in both its morphosyntactic properties and language use. [ABSTRACT FROM AUTHOR]

• Shi and de share the same core semantics of being verum operators. • They indicate the speaker's certainty towards updating the prejacent into the CG. • They differ in discourse licensing conditions. • Shi is defined iff there is a salient or hinted p in the previous discourse. • De is defined if the QUD is a yes – no question about p. In this paper, we examine the semantics of two types of constructions in Mandarin that are widely believed to realize the emphatic effects, namely, verum shi and sentence-final de. It aims to address three crucial questions: (i) what are the emphatic effects that shi and de convey? (ii) Where do these emphatic effects originate from? (iii) How should the semantics of sentences with shi and de be analyzed respectively? Based on an experiment investigating the discourse licensing conditions of these two constructions, it is argued that (i) both shi and de are overt realizations of the verum operator according to Romero & Han (2004) and Romero (2015) but they differ in their discourse licensing conditions; (ii) verum shi can only be licensed by affirmative contexts and positive biased yes – no questions, whereas sentence-final de can be licensed by affirmative, corrective, positive biased, negative biased and neutral yes – no questions; and therefore, (iii) [ shi p ] is defined if and only if there is a contextually salient or hinted p (p is its prejacent) in the previous discourse whereas [ de p ] only calls for a yes – no question about the truth value of p. The current analysis offers not only an explanation of the complex patterns observed but also sheds light on the analyses of the emphatic effects associated with other constructions. [ABSTRACT FROM AUTHOR]

• Enhanced residential hourly water-energy consumption prediction by a hybrid model. • Water-energy nexus was considered by taking one resource as a proxy for the other. • Fewer variables were used in the predictions compared with existing studies. • R2 was improved by 29.2 % and 48.5 % in water and electricity predictions. • The mechanism and common laws of household water-energy nexus were discussed. Predicting water and energy consumption at high resolution over a short-term horizon is critical for water and energy resource management. Water and energy are shown to be closely interlinked in household consumption. However, hourly predictions are often based only on historical consumption data for the resource being predicted, with activity or appliance information and household attribution as additional information. Few studies have used aggregated water and energy consumption for predictions. Within this context, the current study proposed a novel hybrid machine learning model based on the Prophet time-series model, Gated Recurrent Unit network, and self-adaptive weights, called the Prophet-GRU model, which could jointly include historical water and electricity consumption as inputs for hourly water or electricity prediction. Data on hourly water and electricity consumption in six households in Beijing during January–March 2020 were used to train and validate the Prophet-GRU model. The goodness of fit indicator (R 2) and prediction accuracy (mean squared error and mean absolute error) for the water and electricity predictions were evaluated. Compared with the single input of water or electricity, with the combined input of data of these two resources, the proposed Prophet-GRU model achieved improvements of 29.2 % and 48.5 % in R 2, for water and electricity consumption prediction, respectively. Our results could help better understand water-energy linkages and promote collaborative water and energy management practices. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract With the development of society, plastics play a significant role in daily supplies owing to their advantages. Whereas, insufficient scratch resistance and vulnerable plastic surfaces result in the constraint of their range of application fields, for instance, electronic products. Hence, it is a highly desirable objective of researchers to investigate hardening coatings for protecting plastic surfaces, by selecting polyurethane acrylate (PUA) as film-forming materials attributing to their adjustable features. This article reveals components of PUA and principles for its hardening modification, and summaries various methods of hardening modification of PUA-based coatings, such as improving the crosslinking density, strengthening hydrogen bonding, incorporating rigid groups into molecular structure, introducing inorganic nanoparticles into resin matrix and transferring linear PUA into hyperbranched analogs. Moreover, optimal strategies for the preparation of PUA-based hardening films from above five tactics are discussed. [ABSTRACT FROM AUTHOR]