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Although companies are investing more in avatars to improve interactivity and engage their customers better, the effectiveness of avatars in online markets needs more evidences. This study explores the influence mechanism of anthropomorphism on consumers' willingness to accept virtual live streamers, grounded in social identity theory and construal levels theory. Three research hypotheses were formulated and tested through an online scenario experiment employing a 2 (high anthropomorphism vs. low anthropomorphism) and 2 (utilitarian products vs. hedonic products) between-subjects design (N = 214). The results reveal that anthropomorphism positively influences consumers' willingness to accept virtual live streamers. This effect is mediated through the chain mediation effects of psychological distance and trust (effect = 0.181, 95% confidence interval = [0.075, 0.299]). Additionally, product type serves as a moderator in this process. Specifically, the mediation is significant for utilitarian products (effect = 0.410, 95% confidence interval = [0.203, 0.604]) but not for hedonic products (effect = 0.102, 95% confidence interval = [-0.106, 0.310]). These findings contribute to understanding the effectiveness of avatar marketing in live streaming commerce, specifically consumers' willingness to accept virtual live streamers, and enrich the literature on anthropomorphism in online marketplaces. Furthermore, they assist live streaming commerce operators in developing effective anthropomorphism strategies to enhance the utilization efficiency of virtual live streamers. • Anthropomorphism positively influences consumers' willingness to accept virtual live streamers. • Psychological distance and trust form a chain mediation effect. • Product type plays a crucial moderator, the mediation is significant for utilitarian products but not for hedonic products. • Aid operators in developing anthropomorphism strategies to optimize the efficiency of using virtual live streamers. [ABSTRACT FROM AUTHOR]

• Extends the streamline-based inversion algorithm to incorporate DTS data and thermal process. • Proposes a hierarchical history matching workflow combining multi-objective Genetic Algorithm (MOGA) and streamline-based inversion. • Better monitoring of the CO 2 plume propagation is achieved by integration of temperature and pressure measurements. The scientific community has become increasingly interested in geological CO 2 sequestration and CO 2 enhanced oil recovery (EOR). The tracking of the CO 2 propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO 2 injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO 2 plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO 2 propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO 2 geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO 2 movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO 2 -EOR and/or associated geological storage projects. [ABSTRACT FROM AUTHOR]

With the widespread adoption of artificial intelligence (AI) powered voice assistants (VAs) and increasingly homogeneous competition among enterprises, it is critically important to understand the driving factors behind consumer VA evaluations. Drawing on attachment theory and socio-technical systems theory, this study proposes a theoretical model to examine how social and technical attributes of VAs impact consumer evaluation behavior. To test the model, a two-wave longitudinal survey was conducted among 462 valid samples in China and analyzed using a multi-method approach, including partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). PLS-SEM findings reveal that consumers' VA evaluations (continuance and word-of-mouth intentions) are primarily influenced by individuals' emotional and functional attachments toward VAs. These attachments, in turn, are determined by social attributes (interactivity, natural speech, and design aesthetics) and technical attributes (accuracy, connectivity, and personalization). Furthermore, the results indicate that social and technology anxiety play a moderating role in the relationship between VA attributes and attachments. The fsQCA analysis supports the PLS-SEM findings and identifies three configuration paths for high continuance intention and three configuration paths for positive word-of-mouth intention toward VAs. These findings provide novel insights into both theory and practice. • This study examines how social and technical attributes of voice assistants (VAs) impact consumer evaluation behavior. • Consumers' VA evaluations (continuance and word-of-mouth intentions) are primarily influenced by individuals' emotional and functional attachments. • Interactivity, natural speech, and design aesthetic positively influence emotional attachment. • Accuracy, connectivity, and personalization positively influence functional attachment. [ABSTRACT FROM AUTHOR]

• Monitoring CO 2 plumes during geologic CO 2 sequestration projects is essential. • High-fidelity simulations can be prohibitively expensive for history matching. • A deep learning framework is developed for efficient CO 2 plume visualization. • Onset time is used for visualization of a propagating CO 2 saturation front. • Variational autoencoder is used to compress high dimentional image data. Monitoring CO 2 plumes throughout the operation of geologic CO 2 sequestration projects is essential to environmental safety. The evolution of underground CO 2 saturation can be predicted using high-fidelity numerical simulations. However, high-fidelity simulations can be prohibitively expensive to compute. As a result of recent developments in data-driven models, rapid predictions of the CO 2 plume can now be made using readily available pressure and temperature measurements. This study presents a novel deep learning-based workflow for efficiently visualizing CO 2 plumes in near real-time while considering their uncertainties. In our deep learning workflow, we visualize the CO 2 plume images in the reservoir as a propagating saturation front, represented by 'onset time', using field measurements, including downhole pressure and temperature. At a given location, the 'onset time' is the calendar time when the CO 2 saturation exceeds a certain threshold. Therefore, a single image of the CO 2 front propagation is captured using the 'onset time' rather than storing multiple CO 2 saturation images at different time steps. The use of 'onset time' significantly reduces memory and computational cost of deep learning-based framework, enabling large-scale field applications. We use a variational autoencoder-decoder (VAE) network to compress high dimensional 'onset time' images into low dimensional latent variables while considering uncertainties of the predicted images. The use of VAE and onset time, simplifies the overall neural network architecture and significantly enhances the training efficiency. To estimate the latent variables of the VAE network, we train a feed forward neural network model that incorporates available monitoring data such as downhole pressure and temperature measurements. The estimated latent variables are then fed into a trained decoder network to generate 3D onset time images, visualizing the propagation of CO 2 plume in near real time. The proposed workflow is applied to both synthetic and field cases, where the field application is a large-scale geological carbon storage project in a carbonate reef reservoir in the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The field measurements include distributed temperature sensing (DTS) and distributed pressure responses from down-hole gauges along the monitoring well. The predicted CO 2 plume images provided by the proposed workflow are shown to be consistent with the simulation results of traditional history matched model using genetic algorithm. Our deep learning-based framework can predict the CO2 front propagation in terms of onset time in seconds, making it well-suited for real time decision-making and operational optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Calculate ecological quality using the Ensemble Learning method. • Gridding the regional space and proposing an Ecological Information State Layer (EISL). • Semi-supervised learning has better portability in ecological computing. • Analyze the weight of ecological factor characteristics to provide recommendations for ecological conservation. Habitat quality is an indicator of the ecological evolution of a region, and evaluating habitat quality utilizing Machine Learning approaches can reflect the ecological status of a region more objectively. Gridded statistical ecological factors in the study region were utilized to build the Ecological Information State Layer (EISL) in the grid space. Calculate the Environmental Quality Index (EQI) of the sampled grid to evaluate the performance index of the models on the sampled grid. The model with the optimal performance index is selected for the task of habitat quality classification in the research region, and then especially decision-making advice is offered by inverting the degree of influence of ecological elements on habitat quality. The results show that: (1) The Semi-supervised Ensemble Learning model (Tri-training) Accuracy, Kappa coefficient, and F1-score are 0.93, 0.89, and 0.92, respectively, as the optimal model for the habitat quality classification task. (2) Based on the results of the ecological categories calculation, the current ecosystem quality of the southwestern section of the Daxia River Basin is maintained as a positive indicator. the ecosystem quality inside the living space centered on the cities of Linxia and Hezuo shows negative indicator changes. (3) The calculation results of the inverse importance of ecological factors show that vegetation index and human population density are the key factors impacting the habitat quality of the Daxia River Basin. In the ecological management of Daxia River Basin, the degree of spatial aggregation of people's settlements should be reduced while preserving and expanding vegetation cover. Using Tri-training's comprehensive analysis of multiple ecological factors, regional habitat quality can be accurately assessed. [ABSTRACT FROM AUTHOR]

• An novel stochastic method is proposed to conduct multi-objective optimization. • Proxy-based optimization is involved to improve computational efficiency. • Two applications on polymer flooding cases are discussed and compared with other multi-objective methods. Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective global and local surrogate-assisted particle swarm optimization (MO-GLSPSO) method, which consists of alternative steps: global population prescreen and local population search. The global steps use generalized regression neural network (GRNN) to prescreen a better population, and the local steps use radial basis function (RBF) as proxy to search for the next generation. The global steps aim to reduce the chance of generations being trapped in local minima, and the local steps obtain the optimal solutions with a fast convergence rate. The rates (liquid production rate and water injection rate) and polymer injection concentration of wells are tuned to obtain a Pareto-front that maximizes cumulative oil production and minimizes cumulative polymer injection. The MO-GLSPSO method is tested using both synthetic and Brugge benchmark cases. The iterations generally improve the oil production or reduce polymer injection and are stabilized at a Pareto-front of the two objectives. Improved sweep efficiency and polymer utility are also observed in the optimal results. The proposed method is also compared with other two methods, multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), to examine the pros and cons. The results indicate that MO-GLSPSO has a better pareto-front than others. [ABSTRACT FROM AUTHOR]

• Megaprojects provide potential opportunities for technological capability expansion. • There is more than one path leading to high expansion performance for participants. • The antecedent conditions show relationships of complementarity and substitutability. Megaprojects are considered to be platforms providing participants with potential opportunities for technological innovation and capability development, but it is unclear which participants can benefit more from them and how. Taking the expansion performance in technological capability as the specific objective, this paper seeks to investigate what combination of antecedent conditions can enable participants in megaprojects to achieve high performance. Applying fuzzy-set Qualitative Comparative Analysis (fsQCA) to the sample of main participating companies in the Hong Kong–Zhuhai–Macao Bridge project, we find that megaprojects do not automatically lead to a significant expansion in technological capability of participants, and no single condition is sufficient in isolation or absolutely necessary for high performance. We uncover three pathways to high expansion performance, among which the conditions show relationships of complementarity and substitutability. This study enhances the theoretical understanding of the process of megaprojects promoting participants' growth, and offers new insights into the complex causality of performance differences in capability development. [ABSTRACT FROM AUTHOR]

• Well connectivity detection in CCUS project. • Data-driven gas production forecasting based on gas injection schedule. • Statistical recurrent unit used as a forecast model. • Loss regularization for selecting influential injectors for well connectivity. • Variable importance as a proxy for interwell connectivity. Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO 2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. The study results in a reliable and efficient data analytics framework that is well-suited for large field applications. [ABSTRACT FROM AUTHOR]

Abstract: Wing rock prevention is an important objective for an aircraft that needs to fly and maneuver at moderate to high angles of attack. This phenomenon is characterized as a periodic motion (or limit cycle), and needs to be avoided at all costs. A candidate mechanism for the wing rock limit cycle is the inertia coupling between an unstable lateral-directional (Dutch roll) mode with a stable longitudinal (Short period) mode. The coupling mechanism is provided by the nonlinear interaction of motion related terms in the complete set of motion equations. A methodology for preventing the limit cycle accomplished by adding a control function to the original equation of wing rock motion is presented in this paper. To analyze the state variables of the system, the complete set of nonlinear equations of motion including effective linear control function was solved. Numerical model constructed for A-4D and a Mig-21 Aircraft is solved to illustrate the results. The numerical results show that it was sufficient to use a linear control function including both roll attitude and roll rate for suppressing wing rock motion without any error in desired time. A good agreement between the numerical results and the published work is obtained for limit cycle oscillation existence at different values of damping ratio for almost the same roll angle. [Copyright &y& Elsevier]

Abstract: This paper deals with the heat equation in which the source term involves a Dirac function and describes the elastic reaction of the immersed boundary. We analyze the existence of the approximate solution in two-dimensional case with Dirac function approximated by differentiable function. We obtain the result via finite element method, the Banach Fixed-Point Theorem and a theorem in nonlinear ordinary differential equations in abstract space. [Copyright &y& Elsevier]

Abstract: In this paper, we obtain an existence theorem of a connected set of solutions to a nonlinear variational inequality with explicit nonlinear constraints. This result follows in a constructive way by designing a simplicial algorithm. The algorithm operates on a triangulation of the unbounded regions and generates a piecewise linear path of parametrized stationary points. Each point on the path is an approximate solution. [Copyright &y& Elsevier]

The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, such frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Data sets of different survey frequencies are generated by sampling at various time intervals from the complete data sets. Onset time maps based on different survey frequencies are calculated. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. In terms of robustness of the inversion methods, nonlinearity of the onset time method can be smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. • Infrequent seismic surveys can also generate onset time map by interpolation between seismic surveys. • Onset time can be interpreted from infrequent seismic surveys when dominant physics is not changed. • The nonlinearity of the onset time method can be smaller than that of the amplitude inversion method. • Onset time interpretation are not sensitive to the choices of petro-elastic model. [ABSTRACT FROM AUTHOR]

Fractures play important role in unconventional resources by influencing the fluid flow pattern underground. Streamline-based flow diagnostic is important to the history matching or operational optimization process of fractured reservoirs. This paper presents a robust streamline tracing framework for use in the Embedded Discrete Fracture Models (EDFM) and several applications including flow visualization, flow diagnostics, and rate allocation optimization. The proposed streamline tracing framework is based on a boundary layer method that can honor the flux communications between the matrix and fractures or between fracture and fracture. The proposed method is benchmarked with a semi-analytical solution and its robustness is illustrated by a series of numerical examples encompassing different levels of geologic and geometrical complexity. Streamlines in complex fracture networks provide flow diagnostics such as sweep efficiency and connectivity of wells and fractures. The streamlines are then utilized to develop a workflow for rate allocation optimization for waterflood in naturally fractured reservoirs. We apply a streamline-based gradient free algorithm whereby both injection and production rates are adjusted under realistic operational constraints to a field scale fractured reservoir. This approach only requires a few forward simulations and therefore offers significant advantage in terms of computational efficiency. It is confirmed that the optimized schedule provides improvements in oil recovery and sweep efficiency compared to the base scenario with uniform injection and production rates. • Robust streamline tracing framework for the EDFM is developed based on boundary layer method. • The proposed method is validated by a semi-analytical solution and the grid-based solution. • The robustness of the framework is examined by numerical examples of significant geologic and geometrical complexity. • A streamline-based rate allocation optimization algorithm has been applied to a benchmark EDFM model. [ABSTRACT FROM AUTHOR]

Embedded Discrete Fracture Model (EDFM) is a promising approach to describe the reservoirs with fractures. Conventional streamline-based inversion method has been limited to the dual-porosity models where the natural fractures are modeled implicitly and flow between matrix blocks is not accounted for. To address this challenge, we propose a novel parameterization and hierarchical multi-scale history matching formulation for EDFM's. We sequentially include basis functions, from large to small scale, to calculate basis coefficient sensitivity combined with streamline-based analytical sensitivity, for updating matrix and fracture properties to match the reservoir dynamic response. In EDFM dominant fractures are explicitly represented within the matrix domain. The matrix-fracture and fracture-fracture interactions are modeled using non-neighbor connections (NNCs) with corresponding transmissibility. In this research, grid connectivity information including NNCs and the reservoir properties in the prior model are first used to construct a grid Laplacian matrix. Next, the eigenvectors of the Laplacian matrix are used as the transformation basis vectors through which matrix and fracture properties are mapped to a low-dimensional transform domain. This step significantly reduces the number of unknowns and also regularizes the inverse problem. Finally, the basis coefficient sensitivity in the transform domain is analytically calculated using streamlines and the updated basis coefficients are then used to reconstruct the reservoir property field. We first illustrate the proposed parameterization of the EDFM and its effectiveness by reconstructing low rank approximations of the spatial distribution of the matrix and fracture properties. Conventional streamline-based inversion method typically leads to large property changes along the streamlines. With the proposed parameterization approach, the basis coefficient sensitivities enable us to effectively calibrate the EDFM in a more geologically continuous manner on both matrix domain and fracture planes. We demonstrate the power and efficacy of our method through application to a field scale reservoir model with complex fault structure, channels, and dominant natural fractures. The example involves waterflood history matching with water-cut and bottom-hole pressure data. The proposed approach effectively updates the prior permeability field along the fracture planes and the matrix domain, resulting in significantly improved history match. The parameterization of EDFM has high compression power to represent important geological trend and fracture properties with significantly reduced number of parameters. The new model calibration method extends the capability of the streamline-based inversion method to explicitly model flow in natural fractures and also flow between matrix blocks. • The parameterization of EDFM has high compression power for important geological trend and fracture properties. • The new model calibration method extends the capability of the streamline-based inversion method in EDFM. • We demonstrated the power, efficacy, and practicability of the new approach. [ABSTRACT FROM AUTHOR]

• Enhanced adsorption property of bimetallic RuNi catalyst was emphatically studied. • 100% yield of hydrocarbon products was obtained at 260 °C in guaiacol HDO. • RuNi had a good ability of hydrogen adsorption, guaiacol adsorption and activation. • An experimental and modeling study was presented and the results were consistent. • The hydrocarbon content in lignin-oil increased from 9.2% to 22.6% after upgrade. Bimetallic catalysts are usually superior to monometallic catalysts due to the synergistic effect of two metal species, and the enhanced adsorption properties by the interaction between two metal species played an important role in catalytic reaction. In this study, the promotion relationship between the adsorption properties and the hydrodeoxygenation (HDO) performances of a bimetallic RuNi/SiO 2 -ZrO 2 catalyst were investigated based on an experimental and modeling study, by comparison to the monometallic catalysts. RuNi catalyst presented a much better performance on guaiacol HDO than Ru and Ni catalysts, with the yield of cyclohexane product close to 100%. Characterization results exhibited a strong interaction between two metal species and an electron transfer from Ru to Ni, which led to a higher spin-up d -band center value of surface atoms and thereby a different adsorption property from the monometallic catalysts. Adsorption property mainly included the adsorption of substrate and the adsorption of hydrogen. The former one is the first step, and the latter one is the second step to trigger the occurrence of HDO reaction. The good adsorption of substrate and the strong adsorption of hydrogen in RuNi catalyst were the crucial factors for its better catalytic HDO performance than the monometallic catalysts. The modeling results by DFT calculation confirmed the experimental results and revealed the promotion mechanism. Moreover, bimetallic RuNi catalyst also showed a good performance on the HDO of other phenolic compounds and raw lignin-oil. This work highlighted the promotion effect of adsorption properties on the HDO reaction. [ABSTRACT FROM AUTHOR]

Fowl adenovirus serotype-4 (FAdV-4) is highly lethal to poultry, making it one of the leading causes of economic losses in the poultry industry. However, a small proportion of poultry can survive after FAdV-4 infection. It is unclear whether there are genetic factors that protect chickens from FAdV-4 infection. Therefore, the livers from chickens uninfected with FAdV-4 (Normal), dead after FAdV-4 infection (Dead) or surviving after FAdV-4 infection (Survivor) were collected for RNA-seq, and 2,649 differentially expressed genes (DEGs) were identified. Among these, many immune-related cytokines and chemokines were significantly upregulated in the Dead group compared with the Survivor group, which might indicate that death is related to an excessive inflammatory immune response (cytokine storm). Subsequently, the KEGG results for DEGs specifically expressed in each comparison group indicated that cell cycle and apoptosis-related DEGs were upregulated and metabolism-related DEGs were downregulated in the Dead group, which also validated the reliability of the samples. Furthermore, GO and KEGG results showed DEGs expressed in all three groups were mainly associated with cell cycle. Among them, BRCA1, CDK1, ODC1 , and MCM3 were screened as factors that might influence FAdV-4 infection. The qPCR results demonstrated that these 4 factors were not only upregulated in the Dead group but also significantly upregulated in the LMH cells after 24 h infection by FAdV-4. Moreover, interfering with BRCA1, CDK1, ODC1 , and MCM3 significantly attenuated viral replication of FAdV-4. And interfering of BRCA1, CDK1 , and MCM3 had more substantial hindering effects. These results provided novel insights into the molecular changes following FAdV-4 infection but also shed light on potential factors driving the survival of FAdV-4 infection in chickens. [ABSTRACT FROM AUTHOR]