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Granular materials possess multiscale structures, i.e. micro-scales involving atoms and molecules in a solid particle, meso-scales involving individual particles and their correlated structure, and macroscopic assembly. Strong and abundant dissipations are exhibited due to mesoscopic unsteady motion of individual grains, and evolution of underlying structures (e.g. force chains, vortex, etc.), which defines the key differences between granular materials and ordinary objects. The purpose of this paper is to introduce the major studies have been conducted in recent two decades.

The main properties at individual scale are introduced, including the coordination number, pair-correlation function, force and mean stress distribution functions, and the dynamic correlation function. The relationship between meso- and macro-scales is analyzed, such as between contact force and stress, the elastic modulus, and bulk friction in granular flows. At macroscales, conventional engineering models (i.e. elasto-plastic and hypo-plastic ones) are introduced. In particular, the so-called granular hydrodynamics theory, derived from thermodynamics principles, is explained.

On the basis of recent study the authors conducted, the multiscales (both spatial and temporal) in granular materials are first explained, and a multiscale framework is presented for the mechanics of granular materials.

It would provide a paramount view on the multiscale studies of granular materials.

This paper aims to address the limitations of current deep learning algorithms for sound source localization (SSL), which focus on a single feature and frequency scale, neglecting the integration of multi-scale information. The method developed in this study enhances localization accuracy by effectively using the spatial information and spectral diversity provided by microphone arrays.

The method is based on a multi-scale cross-short-time Fourier transform (STFT) complex-valued convolutional neural network (CCNN). It uses cross-STFT spectra at different scales to capture detailed acoustic information across various frequencies. The effectiveness of the algorithm was validated through both simulations and experimental studies.

Experimental results demonstrate that the proposed multi-scale cross-STFT CCNN not only outperforms the single-scale cross-STFT model but also delivers superior localization performance compared to other advanced methods, achieving consistently higher accuracy. The method shows excellent robustness across various signal-to-noise ratio (SNR) conditions and performs well even on imbalanced datasets, confirming its strong generalization capabilities.

This paper introduces a novel approach to SSL that integrates multi-scale information, addressing a key limitation of existing methods. The findings offer significant value to researchers and practitioners in the field of acoustic signal processing, particularly those focused on deep learning-based localization techniques.

Prior literature indicates conflicting effects of online product information, which may complicate or simplify consumer purchase decisions. Therefore, the purpose of this paper is to investigate how different online product information (i.e. the choice set size and the popularity information and its presentation) affect consumers’ decision making and the related market outcomes.

This research relies on information-processing theories and social learning theory. By stepwise conducting two 2×2 within-subject factorial design experiments, this research examines the effects of the choice set size, product popularity information and product presentation on consumers’ decision making and the aggregated market outcomes.

The results show that product popularity information led consumers to either simplify or complicate their decision strategy, depending on the size of the choice sets. Additionally, presenting products by their popularity in descending order resulted in consumers making decisions with a larger decision bias. The results also show that the presence of product popularity was more likely to forge a “superstar” structure in a large market.

The research suggests that e-retailers and e-marketplace operators should carefully utilize product popularity information. Multiple mechanisms that shape different shopping environments with different orders are necessary to create a long-tailed market structure.

This study found the mixed effects of product popularity information when it is presented in different environments (i.e. the large/small choice set and the sorted/randomized product presentation). The overuse of popularity information may induce consumers’ decision bias.

With its worldwide fame for making action films, Hong Kong cinema has been defined as masculine. Action films, including the costumed martial arts films and the modern gangster films, have been a major genre in Hong Kong cinema from the 1960s on. Despite the dominant masculinity, women still play significant roles in some of these films. In fact, fighting women leave footprints in the history of Hong Kong cinema, which precede their counterparts in the West and even provide models for Hollywood after 2000.

This chapter focuses on the female characters portrayed by the acclaimed Hong Kong director Wong Kar-wai, whose works have an ambiguous connection to mainstream genres. He modifies Hong Kong action films and creates unconventional female characters such as the drug dealer in Chungking Express (1994), the killer dispatcher in Fallen Angels (1995), the swordswoman in Ashes of Time (1994), and the kung fu master in The Grandmaster (2013). Wong's films have been mush discussed in academia, but the gender images therein are quite ignored. With high intertextuality, these characters are used to question mainstream action films and redefine women's roles in male's cinematic space. In addition, via the writing of these women, Wong constructs an open and ambivalent post-colonial Hong Kong identity. This paper contextualises the figures of sword-wielding and gun-shooting women and examines how Wong Kar-wai deploys these images to articulate the cultural identity of a post-colonial city.

Automatic interpretation of Nominal Compounds is a crucial issue for many applications, for example, sentence understanding, machine translation, question-answering system and so forth. Many automatic interpretation models of Nominal Compounds use the strategies based on verbs or rules to obtain the interpretation of compounds. However, the performances of these models are still limited. The purpose of this paper is to propose an effective approach for automatic interpretation of Chinese nominal compounds.

The authors propose a top-down and bottom-up model based on rules and large-scale corpus for automatic interpretation of Nominal Compounds.

Experimental results demonstrate that the proposed model outperforms the state-of-the-art automatic interpretation model.

The paper is an up-to-date study of automatic interpretation for Nominal Compounds. It can help people understand the meaning of Nominal Compounds in reading. With a better understanding of Nominal Compounds, we can discover more hidden knowledge in them.