Abstract
This study identifies and recommends key cues in brand community and public behavioral data. It proposes a research framework to strengthen social monitoring and data analysis, as well as to ...review digital commercial brands and competition through continuous data capture and analysis. The proposed model integrates multiple technologies, analyzes unstructured data through ensemble learning, and combines social media and text exploration technologies to examine key cues in public behaviors and brand communities. The results reveal three main characteristics of the six major digital brands: notification and diversion module; interaction and diversion module; and notification, interaction, and diversion module. This study analyzes data to explore consumer focus on social media. Prompt insights on public behavior equip companies to respond quickly and improve their competitive advantage. In addition, the use of community content exploration technology combined with artificial intelligence data analysis helps grasp consumers’ information demands and discover unstructured elements hidden in the information using available Facebook resources.
This study explores the interactive characteristics of the public, referencing existing data mining methods. This research attempts to develop a community data mining and integration technology to ...investigate the trends of global retail chain brands. Using social media mining and ensemble learning, it examines key image cues to highlight the various reasons motivating participation by fans. Further, it expands the discussion on image and marketing cues to explore how various social brands induce public participation and the evaluation of information efficiency. This study integrates random decision forests, extreme gradient boost, and adaboost for statistical verification. From 1 January 2011 to 31 December 2019, the studied brands published a total of 25,538 posts. The study combines community information and participation in its research framework. The samples are divided into three categories: retail food brand, retail home improvement brand, and retail warehouse club brand. This research draws on brand image and information cue theory to design the theoretical framework, and then uses behavior response factors for the theoretical integration. This study contributes a model that classifies brand community posts and mines related data to analyze public needs and preferences. More specifically, it proposes a framework with supervised and ensemble learning to classify information users′ behavioral characteristics.
This study conducted the first extensive and comprehensive investigation of the whole-scale sedimentary antibiotic concentration, possible drivers, environmental fate, and potential ecological risks ...in the Yangtze River. Totally, 20 antibiotics were detected in the sediments. Results revealed that the order of antibiotic abundance in sediment was fluoroquinolones > tetracyclines > macrolides > sulfonamides > amphenicols. The total antibiotic concentrations were 0.10–134.4 ng/g (mean: 11.88 ng/g). Of these, fluoroquinolones and tetracyclines were the two dominant antibiotic categories. The dominant occurrence of fluoroquinolones and tetracyclines in sediments suggested that the distribution coefficient (K d) was one of the important factors to determine their fate. Correlation analysis demonstrated that antibiotic contamination was largely influenced by the local scale of animal husbandry, and the positive correlation between antibiotics and heavy metals was likely driven by their common source of contamination and the complexation. Environmental risk assessment showed that tetracycline and chlortetracycline exhibited potential risks from medium to high in the Yangtze River, although most of the compounds posed minimal and low risks. This work provided a valuable large-scale data set across the whole Yangtze River and revealed the contamination profile of antibiotics. Mitigation and management measures to reduce antibiotic inputs are needed for the Yangtze River basin.
The discoveries of intrinsically magnetic topological materials, including semimetals with a large anomalous Hall effect and axion insulators
, have directed fundamental research in solid-state ...materials. Topological quantum chemistry
has enabled the understanding of and the search for paramagnetic topological materials
. Using magnetic topological indices obtained from magnetic topological quantum chemistry (MTQC)
, here we perform a high-throughput search for magnetic topological materials based on first-principles calculations. We use as our starting point the Magnetic Materials Database on the Bilbao Crystallographic Server, which contains more than 549 magnetic compounds with magnetic structures deduced from neutron-scattering experiments, and identify 130 enforced semimetals (for which the band crossings are implied by symmetry eigenvalues), and topological insulators. For each compound, we perform complete electronic structure calculations, which include complete topological phase diagrams using different values of the Hubbard potential. Using a custom code to find the magnetic co-representations of all bands in all magnetic space groups, we generate data to be fed into the algorithm of MTQC to determine the topology of each magnetic material. Several of these materials display previously unknown topological phases, including symmetry-indicated magnetic semimetals, three-dimensional anomalous Hall insulators and higher-order magnetic semimetals. We analyse topological trends in the materials under varying interactions: 60 per cent of the 130 topological materials have topologies sensitive to interactions, and the others have stable topologies under varying interactions. We provide a materials database for future experimental studies and open-source code for diagnosing topologies of magnetic materials.
The development of polymers that can spontaneously repair themselves after mechanical damage would significantly improve the safety, lifetime, energy efficiency and environmental impact of man-made ...materials. Most approaches to self-healing materials require the input of external energy, healing agents, solvent or plasticizer. Despite intense research in this area, the synthesis of a stiff material with intrinsic self-healing ability remains a key challenge. Here, we show a design of multiphase supramolecular thermoplastic elastomers that combine high modulus and toughness with spontaneous healing capability. The designed hydrogen-bonding brush polymers self-assemble into a hard-soft microphase-separated system, combining the enhanced stiffness and toughness of nanocomposites with the self-healing capability of dynamic supramolecular assemblies. In contrast to previous self-healing polymers, this new system spontaneously self-heals as a single-component solid material at ambient conditions, without the need for any external stimulus, healing agent, plasticizer or solvent.
Purpose—Using a sample of universities from Europe and North America the research herein seeks to understand the content trends of university brand pages through an exploration of the social ...community and the measurement of user participation and behavior. The analysis relies on an artificial intelligence approach. Through the verification of interactions between users and content on the university brand pages, recommendations are made, which aim to ensure the pages meet the needs of users in the future. Design/methodology/approach—The study sample was drawn from six well-known universities in Europe and North America. The content of 23,158 posts made over the course of nine years between 1 January 2011 to 31 December 2019 was obtained by a web crawler. Concepts in the fields of computer science, data mining, big data and ensemble learning (Random Decision Forests, eXtreme Gradient Boosting and AdaBoost) were combined to analyze the results obtained from social media exploration. Findings—By exploring community content and using artificial intelligence analysis, the research identified key information on the university brand pages that significantly affected the cognition and behavior of users. The results suggest that distinct levels of user participation arise from the use of different key messages on the university fan page. The interactive characteristics identified within the study sample were classified as one of the following module-types: (a) information and entertainment satisfaction module, (b) compound identity verification module or (c) compound interactive satisfaction module. Research limitations/implications—The study makes a contribution to the literature by developing a university community information interaction model, which explains different user behaviors, and by examining the impact of common key (image) clues contained within community information. This work also confirms that the behavioral involvement of users on the university’s brand page is closely related to the information present within the university community. A limitation of the study was the restriction of the sample to only European and North American cultural and economic backgrounds and the use of Facebook as the sole source of information about the university community. Practical implications—Practically, the research contributes to our understanding of how, in official community interactions, user interactions can be directed by features such as information stimuli and brand meanings. In addition, the work clarifies the relationship between information and user needs, explaining how the information characteristics and interaction rules particular to a given school can be strengthened in order to better manage the university brand page and increase both the attention and interaction of page users. Originality/value—This research provides an important explanation of the role of key information on the university fan pages and verifies the importance of establishing key (image) clues in the brand community, which in turn affect user cognition and interaction. Although related research exists on information manipulation and the importance of online communities, few studies have directly discussed the influence of key information on the fan pages of university brands. Therefore, this research will help to fill gaps in the literature and practice by examining a specific context, while at the same time providing a valuable and specific reference for the community operation and management of other related university brands.
This research proposes a framework for the fashion brand community to explore public participation behaviors triggered by brand information and to understand the importance of key image cues and ...brand positioning. In addition, it reviews different participation responses (likes, comments, and shares) to build systematic image and theme modules that detail planning requirements for community information. The sample includes luxury fashion brands (Chanel, Hermès, and Louis Vuitton) and fast fashion brands (Adidas, Nike, and Zara). Using a web crawler, a total of 21,670 posts made from 2011 to 2019 are obtained. A fashion brand image model is constructed to determine key image cues in posts by each brand. Drawing on the findings of the ensemble analysis, this research divides cues used by the six major fashion brands into two modules, image cue module and image and theme cue module, to understand participation responses in the form of likes, comments, and shares. The results of the systematic image and theme module serve as a critical reference for admins exploring the characteristics of public participation for each brand and the main factors motivating public participation.
A topological insulator is a material with an insulating interior but time-reversal symmetry-protected conducting edge states. Since its prediction and discovery almost a decade ago, such a ...symmetry-protected topological phase has been explored beyond electronic systems in the realm of photonics. Electrons are spin-1/2 particles, whereas photons are spin-1 particles. The distinct spin difference between these two kinds of particles means that their corresponding symmetry is fundamentally different. It is well understood that an electronic topological insulator is protected by the electron’s spin-1/2 (fermionic) time-reversal symmetry
T
f
2
=
−
1
. However, the same protection does not exist under normal circumstances for a photonic topological insulator, due to photon’s spin-1 (bosonic) time-reversal symmetry
T
b
2
=
−
1
. In this work, we report a design of photonic topological insulator using the Tellegen magnetoelectric coupling as the photonic pseudospin orbit interaction for left and right circularly polarized helical spin states. The Tellegen magnetoelectric coupling breaks bosonic time-reversal symmetry but instead gives rise to a conserved artificial fermionic-like-pseudo time-reversal symmetry,
T
p
(
T
p
2
=
−
1
)
, due to the electromagnetic duality. Surprisingly, we find that, in this system, the helical edge states are, in fact, protected by this fermionic-like pseudo time-reversal symmetry Tp
rather than by the bosonic time-reversal symmetry Tb
. This remarkable finding is expected to pave a new path to understanding the symmetry protection mechanism for topological phases of other fundamental particles and to searching for novel implementations for topological insulators.
We synthesized a series of brush copolymers having glassy polymethylmethacrylate (PMMA) backbone and flexible polyacrylate-amide (PA-amide) brushes that exhibit thermoplastic elastomer properties. ...Importantly, the dynamic hydrogen bonds in the soft PA-amide matrix enables the material to self-heal after mechanical damage at room temperature without the need of any external stimulus.
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•Brush copolymers with PMMA backbone and H-bonding PA-amide brushes were prepared.•The brush copolymers were able to self-assemble into spherical nanoparticles.•The materials showed thermoplastic elastomers with tunable mechanical properties.•The materials show a combination of strong mechanical and self-healing properties.
Infrared light detection and sensing is deeply embedded in modern technology and human society and its development has always been benefitting from the discovery of various photoelectric materials. ...The rise of two-dimensional materials, thanks to their distinct electronic structures, extreme dimensional confinement and strong light-matter interactions, provides a material platform for next-generation infrared photodetection. Ideal infrared detectors should have fast respond, high sensitivity and air-stability, which are rare to meet at the same time in one two-dimensional material. Herein we demonstrate an infrared photodetector based on two-dimensional Bi
O
Se crystal, whose main characteristics are outstanding in the whole two-dimensional family: high sensitivity of 65 AW
at 1200 nm and ultrafast photoresponse of ~1 ps at room temperature, implying an intrinsic material-limited bandwidth up to 500 GHz. Such great performance is attributed to the suitable electronic bandgap and high carrier mobility of two-dimensional oxyselenide.
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