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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Firms can derive insights not only from their own failures but also from the innovation failures of counterparts in the same industry. This study investigates how 109 Chinese A‐share listed high‐tech firms (2013–2022) learn from counterparts' innovation failures to improve their success rate of innovation and emphasizes the crucial role of venture capital (VC) syndication networks in facilitating knowledge transfer. Central VCs within the network notably enhance vicarious learning from others' failures. Further empirical analysis shows a significant increase in the success rate of exploitative innovations, while the impact on exploratory innovations is minimal. [ABSTRACT FROM AUTHOR]

China has abundant shale gas resources with good exploration value and development potential, making it a recent hotspot for exploration and development. It is widely agreed that large-scale hydraulic fracturing is essential for reservoir enhancement in shale formations. However, the evolution of fractures during hydraulic fracturing is highly complex, necessitating research on the influence of various factors on the vertical propagation of hydraulic fractures. Based on geological and engineering parameters from the Luzhou block in southern Sichuan, this study employed the finite element method (FEM) and the cohesive element method to establish a coupled fluid-solid model for the vertical propagation of hydraulic fractures. Numerical simulations were conducted to investigate the interaction between hydraulic fractures and natural weak planes, clarifying the mechanisms involved. This study elucidates how different rock and natural weak plane properties affect the vertical propagation of hydraulic fractures and draws diagrams illustrating these interactions. The research indicated three fracture distribution patterns after the intersection of hydraulic fractures with natural weak planes: passive fractures, 'I'-shaped fractures, and crossing fractures. The main fractures in these patterns exhibit initial damage and damage evolution characterized by tensile failure. Specifically, in passive fractures, the initial damage and damage evolution of natural weak planes manifest as shear failure. In 'I'-shaped fractures, the initial damage in natural weak planes is characterized by shear failure, with damage evolution showing tensile failure. Crossing fractures show minimal damage in the weak planes. Under conditions of high natural weak plane cohesive strength, high Young's modulus, low interlayer rock cohesive strength, high vertical stress difference, low interlayer stress difference, and high intersection angles, crossing fractures tend to form. Conversely, conditions of low natural weak plane cohesive strength, low Young's modulus, high interlayer rock cohesive strength, low vertical stress difference, high interlayer stress difference, and low intersection angles favor the formation of 'I'-shaped fractures. Passive fractures form under conditions of low natural weak plane cohesive strength and high vertical stress difference. This study found that Poisson's ratio has a minimal effect on the vertical expansion of hydraulic fractures under the studied conditions, with natural weak plane strength being the primary control factor for fracture patterns. These findings enhance the theoretical foundation for the vertical propagation of hydraulic fractures in deep shale formations, facilitating the development and implementation of strategies for enhancing production in shale reservoirs with natural weak planes and better optimizing production in different types of shale reservoirs. [ABSTRACT FROM AUTHOR]

It has become a consensus that large-scale hydraulic fracturing is adopted to achieve the stimulation of unconventional oil and gas reservoir. The complex fracture network formed by fracturing is closely related to the effect of reservoir stimulation, which has extremely complicated evolution process. Therefore, it is necessary to study the evolution law of fracture network in large-scale hydraulic fracturing of unconventional reservoirs. In this article, the geological engineering parameters of horizontal well H in shale gas reservoir in southern Sichuan are taken as an example, a three-dimensional fracture network expansion model is established based on the boundary element method and finite volume method, and the simulation of the complex fracture network in a whole well section is carried out to analyze the evolution law of reservoir fracture network under different geological and engineering parameters. The results show that the horizontal stress field distribution has a significant effect on fracture geometric form. Hydraulic fractures in reservoirs with larger horizontal stress difference have stronger directivity, while the horizontal wellbore tends to obtain better reservoir stimulation results when it is parallel to the minimum horizontal principal stress setting. The conjugated natural fracture developed in the reservoir inhibits the hydraulic expansion fractures in both directions. Although it increases the complexity of the fractures, it is not necessarily conducive to improving the reservoir stimulation effectiveness. The lower the strength of natural fracture is, the more complex the fracture geometric form becomes, and the smaller the stimulated reservoir volume is. Correspondingly, the higher the strength of natural fracture is, the simpler the fracture geometric form becomes, and the larger the stimulated reservoir volume is. Suitable fracturing construction displacement can not only contribute to form a more complex fracture distribution, but also help to obtain a larger stimulated reservoir volume. The optimal construction displacement ranges from 10 to 14 m3/min. Low viscosity fracturing fluids are suitable for the formation of long-narrow fractures and able to connect with the remote reservoir and form complex fracture networks. Lower viscosity fluids can be used to achieve better reservoir stimulation effectiveness when sand-carrying capacity is met. [ABSTRACT FROM AUTHOR]

Generative networks are effective tools for digital materials (DM) inverse design. However, the optimization performance of generative networks is restricted by the increasing discrepancy between the optimized input and the prescribed input domain as the design loop increases. Herein, a correction technique is incorporated into generative deep neural network (GDNN) and generative deep convolutional neural network (GDCNN). The correction is performed by pulling the machine learning (ML)‐optimized inputs back to the prescribed domain at certain interval during the optimization process instead of only postprocessing at the end. A DM system with two phases, i.e., the matrix phase and the pore phase, is used for the structural design. The datasets are produced using a numerical model and describe the relationship between the material structure and the elastic modulus and tensile strength. The results show that the optimization effectiveness of corrected GDNN/GDCNN significantly improves given the fact that more structures converge to the best structures and fewer nonrepetitive structures are left after optimization, which helps to search for the best structures and decreases the computational burden when verifying the ML‐recommended structures. The corrected GDNN and GDCNN also manage to find structures with higher tensile strength in the new inverse design. [ABSTRACT FROM AUTHOR]