Li Madou: Combining Chinese and Western performers

This dissertation explores ways in which Chinese and Western instruments and musicians can be combined in order to develop a greater understanding of each other’s music from a variety of different perspectives. It documents the sequence of events and contemporary influences that encouraged the author to set up the ‘Li Madou Ensemble’, named after the historical figure important in the development of the interaction between China and the West. Alongside an exploration of the issues involved in the process of preparing the Li Madou Ensemble and delivering its first concert performance, there is analysis of feedback received through interviews with participants. A literature review contextualizes all this activity within a historical perspective of the interactions between Chinese and Western musicians. Within the project the idea is advanced that through a combination of music from two different cultures, a new way of seeing and understanding music from different perspectives may be provided

The Impact of Green Supply Chain Management (GSCM) on Company’s Operational Performance-Based on the Mediation Effect of Operation Capabilities

Practitioners and researchers are increasingly paying great attention to green supply chain management (GSCM). However, no agreement has been reached on whether GSCM can directly improve company’s operational performance. From the perspective of resource-based view, this paper divides GSCM into internal environment management (IEM) and supplier environment management (SEM), and studies the mechanism of operation capabilities in the relationship between GSCM and company’s operational performance. Our findings suggest that (1) IEM partially improves company’s operational performance through operation capabilities. (2) SEM has positive impact on company’s operational performance through operation capabilities. The conclusion reveals the role of operation capabilities in the relationship between GSCM and company’s operational performance, opening up the black box of the relationship to some extent, which provides guidance for manufacturing companies

The Effects of Shape-taste Congruence on Product Evaluations

The background design of a product presented online is important to attract consumers’ attention and further help them make proper judgments about the product. Although many studies have investigated factors of advertising background design, few focus on the effect of shape feature in the background on product evaluations. The present research investigates the influence of congruency between the shape in the background design and product taste perception on consumer product evaluations by using a pretest and three experiments. The results show that shape-taste congruency intensifies product evaluations with an increase evaluation of sweet-taste products and a decreases evaluation of sour-taste products (study1a & 1b). We explain the effect of shape-taste congruency through positive affect (study 2). In addition, an individual’s design sensitivity moderates the effect of shape-taste congruency on product evaluations, which means the effect of shape-taste congruency will disappear for people with low design sensitivity (study 3). The research provides important implications for online retailers in product display design

A Transformer-based Framework for POI-level Social Post Geolocation

POI-level geo-information of social posts is critical to many location-based applications and services. However, the multi-modality, complexity and diverse nature of social media data and their platforms limit the performance of inferring such fine-grained locations and their subsequent applications. To address this issue, we present a transformer-based general framework, which builds upon pre-trained language models and considers non-textual data, for social post geolocation at the POI level. To this end, inputs are categorized to handle different social data, and an optimal combination strategy is provided for feature representations. Moreover, a uniform representation of hierarchy is proposed to learn temporal information, and a concatenated version of encodings is employed to capture feature-wise positions better. Experimental results on various social datasets demonstrate that three variants of our proposed framework outperform multiple state-of-art baselines by a large margin in terms of accuracy and distance error metrics.Comment: Full papers are 12 pages in length plus additional 4 pages for references (turns to 18 pages in total after submitting to arxiv). One figure and 5 tables are contained. This paper was submitted to ECIR 2023 for revie

HICF: Hyperbolic Informative Collaborative Filtering

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation lies in that its exponentially increasing capacity is well-suited to describe the power-law distributed user-item network whereas the Euclidean equivalent is deficient. Nonetheless, it remains unclear which kinds of items can be effectively recommended by the hyperbolic model and which cannot. To address the above concerns, we take the most basic recommendation technique, collaborative filtering, as a medium, to investigate the behaviors of hyperbolic and Euclidean recommendation models. The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models. Driven by the above observations, we design a novel learning method, named hyperbolic informative collaborative filtering (HICF), aiming to compensate for the recommendation effectiveness of the head item while at the same time improving the performance of the tail item. The main idea is to adapt the hyperbolic margin ranking learning, making its pull and push procedure geometric-aware, and providing informative guidance for the learning of both head and tail items. Extensive experiments back up the analytic findings and also show the effectiveness of the proposed method. The work is valuable for personalized recommendations since it reveals that the hyperbolic space facilitates modeling the tail item, which often represents user-customized preferences or new products.Comment: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22

Global Satellite Observations for Smart Cities

The smart city approach requires collection of interdisciplinary data and information from multiple sources and integration with modern technologies to provide a new and cost-effective way for researchers and decision makers to study and manage cities. In this book chapter, we introduce NASA satellite-based global and regional observations with emphasis on the hydrologic cycle (e.g., precipitation, wind, temperature, soil moisture) for smart cities. These products, consisting of both near-real-time and historical datasets, are publicly available free of charge and can be used for global and regional research and applications. Examples of using these datasets in smart cities are included. The chapter is organized as follows, first, a brief overview of NASA global satellite-based data products, followed by data services and tools, two examples of using satellite-based datasets in megacities, and finally summary and future plans

Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

Landslide is a natural disaster that can easily threaten local ecology, people's lives and property. In this paper, we conduct modelling research on real unidirectional surface displacement data of recent landslides in the research area and propose a time series prediction framework named VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode decomposition, which can predict the landslide surface displacement more accurately. The model performs well on the test set. Except for the random item subsequence that is hard to fit, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the trend item subsequence and the periodic item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for the periodic item prediction module based on XGBoost\footnote{Accepted in ICANN2023}

Multiphase Porous Electrochemical Catalysts Derived from Iron-Based Metal–Organic Framework Compounds

Herbicide use has attracted attention recently due to potential damage to human health and lethality to the honey bees and other pollinators. Fenton reagent treatment processes can be applied for the degradation of herbicidal contaminants from water. However, the need to carry out the normal Fenton reactions under acidic conditions often hinders their practical application for pollution control. Herein, we report on the synthesis and application of multiphasic porous electro-Fenton catalysts prepared from calcinated metal–organic framework compounds, CMOF@PCM, and their application for the mineralization of herbicides in aqueous solution at circum-neutral pH. CMOF nanoparticles (NPs) are anchored on porous carbon monolithic (PCM) substrates, which allow for binder-free application. H_2O_2 is electrochemically generated on the PCM substrate which serves as a cathode, while ·OH is generated by the CMOF NPs at low applied potentials (−0.14 V). Results show that the structure and reactivity of the CMOF@PCM electro-Fenton catalysts are dependent on the specific MOF precursor used during synthesis. For example, CMIL-88-NH_2, which is prepared from MIL-88(Fe)–NH_2, is a porous core–shell structured NP comprised of a cementite (Fe_3C) intermediate layer that is sandwiched between a graphitic shell and a magnetite (Fe_3O_4) core. The electro-Fenton production of hydroxyl radical on the CMOF@PCM composite material is shown to effectively degrade an array of herbicides