A Two-Stage Real-time Prediction Method for Multiplayer Shooting E-Sports

E-sports is an industry with a huge base and the number of people who pay attention to it continues to rise. The research results of E-sports prediction play an important role in many aspects. In the past game prediction algorithms, there are mainly three kinds: neural network algorithm, AdaBoost algorithm based on Naïve Bayesian (NB) classifier and decision tree algorithm. These three algorithms have their own advantages and disadvantages, but they cannot predict the match ranking in real time. Therefore, we propose a real-time prediction algorithm based on random forest model. This method is divided into two stages. In the first stage, the weights are trained to obtain the optimal model for the second stage. In the second stage, each influencing factor in the data set is corresponded to and transformed with the data items in the competition log. The accuracy of the prediction results and its change trend with time are observed. Finally, the model is evaluated. The results show that the accuracy of real-time prediction reaches 92.29%, which makes up for the shortage of real-time in traditional prediction algorithm

Extracting Triangular 3D Models, Materials, and Lighting From Images

We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, which typically produce entangled 3D representations encoded in neural networks, we output triangle meshes with spatially-varying materials and environment lighting that can be deployed in any traditional graphics engine unmodified. We leverage recent work in differentiable rendering, coordinate-based networks to compactly represent volumetric texturing, alongside differentiable marching tetrahedrons to enable gradient-based optimization directly on the surface mesh. Finally, we introduce a differentiable formulation of the split sum approximation of environment lighting to efficiently recover all-frequency lighting. Experiments show our extracted models used in advanced scene editing, material decomposition, and high quality view interpolation, all running at interactive rates in triangle-based renderers (rasterizers and path tracers). Project website: https://nvlabs.github.io/nvdiffrec/ .Comment: Project website: https://nvlabs.github.io/nvdiffrec

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications. Prior works on 3D generative modeling either lack geometric details, are limited in the mesh topology they can produce, typically do not support textures, or utilize neural renderers in the synthesis process, which makes their use in common 3D software non-trivial. In this work, we introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. We bridge recent success in the differentiable surface modeling, differentiable rendering as well as 2D Generative Adversarial Networks to train our model from 2D image collections. GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings, achieving significant improvements over previous methods.Comment: NeurIPS 2022, Project Page: https://nv-tlabs.github.io/GET3D

Fullerenol inhibits tendinopathy by alleviating inflammation

Tendinopathy is a common disease in orthopaedics, seriously affecting tendon functions. However, the effects of non-surgical treatment on tendinopathy are not satisfactory and surgical treatments possibly impair the function of tendons. Biomaterial fullerenol has been proved to show good anti-inflammatory effects on various inflammatory diseases. For in vitro experiments, primary rat tendon cells (TCs) were treated by interleukin-1 beta (IL-1β) combined with aqueous fullerenol (5, 1, 0.3 μg/mL). Then inflammatory factors, tendon-related markers, migration and signaling pathways were detected. For in vivo experiments, rat tendinopathy model was constructed by local injection of collagenase into Achilles tendons of rats and fullerenol (0.5, 1 mg/mL) was locally injected 7 days after collagenase injection. Inflammatory factors and tendon-related markers were also investigated. Fullerenol with good water-solubility showed excellent biocompatibility with TCs. Fullerenol could increase expression of tendon-related factors (Collagen I and tenascin C) and decrease expression of inflammatory factors (matrix metalloproteinases-3, MMP-3, and MMP-13) and reactive oxygen species (ROS) level. Simultaneously, fullerenol slowed the migration of TCs and inhibited activation of Mitogen-activated protein kinase (MAPK) signaling pathway. Fullerenol also attenuated tendinopathy in vivo, including reduction of fiber disorders, decrease of inflammatory factors and increase of tendon markers. In summary, fullerenol is a promising biomaterial that can be used to treat tendinopathy

Combined Effect of IL-12Rβ2 and IL-23R Expression on Prognosis of Patients with Laryngeal Cancer

Background/Aims: This study aimed to pathologically elucidate the roles of interleukin-12 receptor (IL-12R) β2 and interleukin-23 receptor (IL-23R) expression in tumor cells and tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment and to determine their combined effect on prognosis of laryngeal cancer (LC). Methods: The tumor-cell expression scores and TIL positivity ratiosof IL-12Rβ2 and IL-23R in matched LC and normal laryngeal tissue samples from 61 LC patients were measured via immunohistochemistry (IHC). We adopted a linear regression model to analyze the correlation between IL-12Rβ2 and IL-23R expression in tumor cells and TIL ratios. TheKaplan-Meier log-rank test and Cox regression hazard ratios were used to analyze survival. Results: LC tumor cells had a higher IL-12Rβ2 expression and TIL ratio than IL-23R expression and TIL ratio. The significant correlations between IL-12Rβ2 and IL-23R expression and TIL ratios were identified in LC tissues, particularly in well-differentiated LC. Furthermore, either high tumor cell IL-12Rβ2 or low IL-23R expression had better survival than its corresponding low or high expression, respectively. Similar results did for IL-12Rβ2 ratio and IL-23R ratio. Finally, patients with both high IL-12Rβ2 and low IL-23R had the best prognosis among any other combined groups with both gene expression (HR, 0.1; 95% CI, 0.0-0.8). Likewise, patients with positive ratios of high IL-12Rβ2 and low IL-23R TILs had the best survival (HR, 0.1; 95% CI, 0.0-0.4). Conclusion: IL-12Rβ2 and IL-23R create a homeostasis within the tumor cells and TILs, and this homeostasis affects prognosis. While the intrinsic mechanisms of epigenetic immunoediting for IL-12Rβ2 and IL-23R remain unknown, additional larger and functional studies are warranted for validation

Examining and prioritizing the effect of sustainable energy on the job market to advance China's green workforce

China, the world's greatest emitter of greenhouse gases, has committed to reaching peak carbon dioxide emissions by 2030 and carbon neutrality by 2060. One strategy to accomplish this aim is switching to a low-carbon economy via advancing renewable energy (RE) projects. Therefore, this study focuses on solar, wind, biomass, and hydropower and examines the beneficial employment effects of RE projects in China. The study uses fuzzy-based Multi-Criteria Decision Making (MCDM) methodologies, such as the Analytical Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS), to evaluate numerous employment effect criteria and sub-criteria. Based on these evaluations, the report prioritizes four main types of RE projects. According to the fuzzy AHP technique results, direct employment, skill requirements, and local employment are the most crucial employment effect factors. The study then employed the fuzzy WASPAS approach to assessing various RE initiatives' employment prospects. According to the findings, hydropower is the best choice for creating jobs, followed by wind and solar power initiatives. In recent years, the hydro, wind, and solar power industries have experienced rapid expansion, creating numerous job possibilities in production, installation, operation, and maintenance. Additionally, creating RE projects can boost regional economic growth and lessen poverty

Numerical Simulation of Passenger Evacuation and Heat Fluxes in the Waiting Hall of an Ultralarge Railway Station Hub

The resurgence of passenger flows after the pandemic poses a significant challenge to the safe operation of rail transit. Therefore, adopting the waiting hall of an ultralarge railway station hub as an example, thermal radiation and evacuation simulations were conducted by the Fire Dynamics Simulator and Pathfinder, respectively. Island-style shops, known for their high crowd density and fire load, were defined as fire sources, and the effectiveness of a 6 m wide fire isolation zone was validated via the adoption of the dual-validation model. By comparing the relationships between the total evacuation population after passenger flow recovery and various evacuation parameters, it was shown that passengers were not evenly distributed among the exits in the waiting hall during an emergency, leading to uneven utilization. Furthermore, to gain a comprehensive understanding of the evacuation process under simulated fire conditions, an evacuation simulation involving 10,000 evacuees over a duration of 324.8 s was conducted. This study provides a theoretical basis for optimizing fire emergency evacuation plans for ultralarge railway station hubs