Quantifying the Effect of Mobile Channel Visits on Firm Revenue

The explosive penetration of mobile devices is one of the most prominent trends in e-business. Although the importance of mobile channel has prompted growing literature, little is known about the revenue implications of customer visit toward mobile channel. This study examines (1) the differential effect of mobile visits in affecting firm revenue (i.e. mobile vs. desktop visits), and (2) which type of mobile visits are more effective (i.e., direct vs. search engine and referral traffic; visits for high vs. low involvement products). We collect an unique objective daily data from a leading online travel agency in China. With a vector autoregressive (VAR) method, we find that, compared with desktop channel, mobile channel visits have shorter carryover effect, but larger short-term effect on firm revenues. Further, mobile channel has larger short-term effect on firm revenues for search engine traffic and lower involvement products. Our findings provide important theoretical contributions and notable implications for mobile commerce strategy

RESEARCH ON UNBALANCED WEIGHING EXPERIMENT OF MULTI-POINT BRACED SWIVEL CABLE-STAYED BRIDGE

To guarantee the safety of the swivel process, the weighing experiment before the swivel is especially important. Based on this, this paper takes a twin-tower, double-cable prestressed concrete swivel cable-stayed bridge as the background and suggests a multi-point braced swivel weighing experiment involving the joint force of the arm-brace and the spherical hinge to solve problems such as a particular obstacle in the relying project's swivelling process. Firstly, the relevant weighing experiment formulas for various circumstances were theoretically derived. The field test results were then used to calculate the jacking force at the limit state during the jacking process, which was then substituted into the relevant formulae, and the relevant parameters of the weighing experiment were calculated. Finally, the counterweight is adjusted based on the weighing results to carry out the structural rotation. The angular velocity was stable during the swivelling process, and the structure was successfully swivelled. The successful practice of a multi-point braced swivel weighing experiment involving the joint force of the arm-brace, and the spherical hinge can provide a reference for the design and construction of similar bridges

Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications

Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air.In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Internet of things (IoT) and Internet of underwater things (IoUT). OoT device not only can collect data through underwater methods, but also can utilize radio frequence over the air. For underwater communications, underwater acoustic communications (UWA COMMs) is the most effective way for OoT devices to exchange information, but it is always tormented by doppler shift and synchronization errors. In this paper, in order to overcome UWA tough conditions, a deep neural networks based receiver for underwater acoustic chirp communication, called C-DNN, is proposed. Moreover, to improve the performance of DL-model and solve the problem of model generalization, we also proposed a novel federated meta learning (FML) enhanced acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer. Particularly, tractable expressions are derived for the convergence rate of FML in a wireless setting, accounting for effects from both scheduling ratio, local epoch and the data amount on a single node.From our analysis and simulation results, it is shown that, the proposed C-DNN can provide a better BER performance and lower complexity than classical matched filter (MF) in underwater acoustic communications scenario. The ARC/FML framework has good convergence under a variety of channels than federated learning (FL). In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air

Correlated states in twisted double bilayer graphene

Electron-electron interactions play an important role in graphene and related systems and can induce exotic quantum states, especially in a stacked bilayer with a small twist angle. For bilayer graphene where the two layers are twisted by a "magic angle", flat band and strong many-body effects lead to correlated insulating states and superconductivity. In contrast to monolayer graphene, the band structure of untwisted bilayer graphene can be further tuned by a displacement field, providing an extra degree of freedom to control the flat band that should appear when two bilayers are stacked on top of each other. Here, we report the discovery and characterization of such displacement-field tunable electronic phases in twisted double bilayer graphene. We observe insulating states at a half-filled conduction band in an intermediate range of displacement fields. Furthermore, the resistance gap in the correlated insulator increases with respect to the in-plane magnetic fields and we find that the g factor according to spin Zeeman effect is ~2, indicating spin polarization at half filling. These results establish the twisted double bilayer graphene as an easily tunable platform for exploring quantum many-body states

TOC interpretation of lithofacies-based categorical regression model: A case study of the Yanchang formation shale in the Ordos basin, NW China

In this paper, taking the shale of Chang 7-Chang 9 oil formation in Yanchang Formation in the southeastern Ordos Basin as an example, through the study of shale heterogeneity characteristics, starting from the preprocessing of supervision data set, a logging interpretation method of total organic carbon content (TOC) on the lithofacies-based Categorical regression model (LBCRM) is proposed. It is show that: 1) Based on core observation, and Differences of sedimentation and structure, five lithofacies developed in the Yanchang Formation: shale shale facies, siltstone/ultrafine sandstone facies, tuff facies, argillaceous shale facies with silty lamina and argillaceous shale facies with tuff lamina. 2) The strong heterogeneity of shale makes it difficult to accurately explain the TOC distribution of shale intervals in the application of model-based interpretation methods. The LBCRM interpretation method based on the understanding of shale heterogeneity can effectively reduce the influence of formation factors other than TOC on the prediction accuracy by studying the characteristics of shale heterogeneity and constructing a TOC interpretation model for each lithofacies category. At the same time, the degree of unbalanced distribution of data is reduced, so that the data mining algorithm achieves better prediction effect. 3) The interpretability of lithofacies logging ensures the wellsite application based on the classification and regression model of lithofacies. Compared with the traditional homogeneous regression model, the prediction performance has been greatly improved, TOC segment prediction is more accurate. 4) The LBCRM method based on shale heterogeneity can better understand the reasons for the deviation of the traditional model-based interpretation method. After being combined with the latter, it can make logging data provide more useful information

Evaluation of petrophysical classification of strongly heterogeneous reservoirs based on the MRGC algorithm

The target formation in the study area of the Pearl River Mouth Basin is characterized by complex lithology and thin interbedded layers, with a large pore-permeability distribution range and strongly heterogeneous characteristics, which makes the reservoir pore structure and production capacity significantly different and brings research difficulties for reservoir logging evaluation and desert identification. The conventional reservoir classification method is mainly based on physical research, which requires developing extremely accurate formulas for calculating porosity and permeability; the calculation accuracy of pore permeability of low-porosity and low-permeability reservoirs is difficult to guarantee; and the conventional logging data cannot be comprehensively applied in reservoir classification. In this paper, taking Zhujiang and Zhuhai Formation reservoirs in the Huizhou M oilfield as an example, we integrated core analysis data such as core cast thin section, pore permeability data, rock electrical parameters, grain size, and relative permeability curves and combined with petrophysical parameters and pore structure characteristics to classify the reservoirs. The artificial neural network is used to predict the resistivity of saturated pure water (R0) to remove the influence of oil and gas on reservoir resistivity. The natural gamma ray (GR) “fluctuation” is used to calculate the variance root of variation (GS) to reflect the lithological variability and sedimentary heterogeneity of the reservoir, and then the conventional logging preferences, R0 and Gs (based on GR), are classified based on the automatic clustering MRGC algorithm to classify the logging facies. To classify the petrophysical phase reservoirs under the constraint of pore structure classification, we proposed a petrophysical classification logging model based on the natural gamma curve “fluctuation” intensity for strongly heterogeneous reservoirs. The learning model is extended to the whole area for training and prediction of desert identification, and the prediction results of the model are in good agreement with the actual results, which is important for determining favorable reservoirs in the area and the adjustment of oilfield development measures

Unusual Fermi Surface Sheet-Dependent Band Splitting in Sr2RuO4 Revealed by High Resolution Angle-Resolved Photoemission

High resolution angle-resolved photoemission measurements have been carried out on Sr2RuO4. We observe clearly two sets of Fermi surface sheets near the (\pi,0)-(0,\pi) line which are most likely attributed to the surface and bulk Fermi surface splitting of the \beta band. This is in strong contrast to the nearly null surface and bulk Fermi surface splitting of the \alpha band although both have identical orbital components. Extensive band structure calculations are performed by considering various scenarios, including structural distortion, spin-orbit coupling and surface ferromagnetism. However, none of them can explain such a qualitative difference of the surface and bulk Fermi surface splitting between the \alpha and \beta sheets. This unusual behavior points to an unknown order on the surface of Sr2RuO4 that remains to be uncovered. Its revelation will be important for studying and utilizing novel quantum phenomena associated with the surface of Sr2RuO4 as a result of its being a possible p-wave chiral superconductor and a topological superconductor.Comment: 13 pages, 4 figure