Local Popularity: A Double-edged Tool in Platform Operation

Although displaying local popularity is wildly adopted by major platforms, the actual effect of such information cues on motivating users has not been documented. Findings from a field experiment suggest that local popularity effectively motivates users to invite more friends but surprisingly reduces users’ self-participation. Social conformity theory may account for such effects: local information encourages users to invite their local friends, but such effect is limited to users from small cities since users in a relatively small community are more bonded and less likely to reject the invitation due to social pressure. Meanwhile, local information attenuates the power of popularity (e.g., fewer registered users in the local area) and ultimately discourages users\u27 self-participation. This study deepens our understanding of displaying popularity cue in improving platform operation, based on which we suggest that practitioners should be cautious about the persuasive power of such information cues in location-based marketing

A comprehensive review of computation-based metal-binding prediction approaches at the residue level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods

Enhanced room-temperature Na+ ionic conductivity in Na4.92_{4.92}Y0.92_{0.92}Zr0.08_{0.08}Si4_{4}O12_{12}

Developing cost-effective and reliable solid-state sodium batteries with superior performance is crucial for stationary energy storage. A key component in facilitating their application is a solid-state electrolyte with high conductivity and stability. Herein, we employed aliovalent cation substitution to enhance ionic conductivity while preserving the crystal structure. Optimized substitution of Y3+ with Zr4+ in Na5YSi4O12 introduced Na+ ​ion vacancies, resulting in high bulk and total conductivities of up to 6.5 and 3.3 ​mS ​cm−1, respectively, at room temperature with the composition Na4.92Y0.92Zr0.08Si4O12 (NYZS). NYZS shows exceptional electrochemical stability (up to 10 ​V vs. Na+/Na), favorable interfacial compatibility with Na, and an excellent critical current density of 2.4 ​mA ​cm−2. The enhanced conductivity of Na+ ​ions in NYZS was elucidated using solid-state nuclear magnetic resonance techniques and theoretical simulations, revealing two migration routes facilitated by the synergistic effect of increased Na+ ​ion vacancies and improved chemical environment due to Zr4+ substitution. NYZS extends the list of suitable solid-state electrolytes and enables the facile synthesis of stable, low-cost Na+ ion silicate electrolytes

Chemical design of optical metamaterials through self-assembly of plasmonic and phosphorescent nanocrystal superlattices

A simple, one-pot method has been developed for the shape-controlled synthesis of highly monodisperse β-NaYF4-based and LiYF 4-based UCNPs. The UCNPs with distinct morphologies (spheres, rods, hexagonal prisms and plates) can be assembled into large-area superlattices displaying simultaneous positional and orientational order. Moreover, a systematic study of lanthanide trifluoride nanocrystal growth reveals a correlation between nanocrystal phase stability and lanthanide contraction and yields a series of monodisperse faceted nanocrystals including circular, rhombic and irregular hexagonal plates as well as tetragonal bipyramids. The rhombic and irregular hexagonal nanoplates represent a fascinating class of planar nanotiles with rich and subtle self-assembly phase behavior. The seeded growth of colloidal gold nanorods has been dramatically improved through the use of aromatic compounds and fatty acid salts as additives. Better nanorod shape purity and dimensional tunability can be achieved with reduced amount of surfactants present in the growth solutions compared to the standard methods. In addition, we have also demonstrated that monodisperse gold nanorods with tunable aspect ratios can be synthesized in the presence of high concentration of chloride as opposed to bromide ions. This observation represents an important step towards a better understanding of nanorod formation in seed-mediated growth. Self-assembly of nanocrystals into multi-component superlattices represents a versatile bottom-up approach for the design of nanocrystal-based metamaterials and functional devices. We have developed a systematic structural characterization framework that allows rigorous assignment of the three-dimensional crystal structure of binary nanocrystal superlattices (BNSLs). Several new BNSL phases have been identified, both crystalline and quasicrystalline. We have also studied experimentally the plasmonic resonance of self-assembled noble metal-nonmetallic BNSLs. An interfacial assembly method is used to organize these NCs into superlattices over centimeter-scale areas, which were then transferred onto optically-transparent substrates for microspectrophotometric measurements on individual superlattice domains. By changing the NC composition and size ratio between the large and small NCs, we demonstrate that the plasmonic resonance of BNSLs is strongly dependent upon the lattice constants and symmetry and is broadly tunable over the entire visible spectrum

A Covariate-regulated Sparse Subspace Learning Model and Its Application to Process Monitoring and Fault Isolation

Technometrics652269-28

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

The information level of the urban public transport system is constantly improving, which promotes the use of smart cards by passengers. The OD (origination–destination) travel time of passengers reflects the temporal and spatial distribution of passenger flow. It is helpful to improve the flow efficiency of passengers and the sustainable development of the city. It is an urgent problem to select appropriate indexes to evaluate OD travel time and analyze the correlation of these indexes. More than one million OD records are generated by the AFC (Auto Fare Collection) system of Nanjing metro every day. A complex network method is proposed to evaluate and analyze OD travel time. Five working days swiping data of Nanjing metro are selected. Firstly, inappropriate data are filtered through data preprocessing. Then, the OD travel time indexes can be divided into three categories: time index, complex network index, and composite index. Time index includes use time probability, passenger flow between stations, average time between stations, and time variance between stations. The complex network index is based on two models: Space P and ride time, including the minimum number of rides, and the shortest ride time. Composite indicators include inter site flow efficiency and network flow efficiency. Based on the complex network model, this research quantitatively analyzes the Pearson correlation of the indexes of OD travel time. This research can be applied to other public transport modes in combination with big data of public smart cards. This will improve the flow efficiency of passengers and optimize the layout of the subway network and urban space