Shaping the Future of Animation towards Role of 3D Simulation Technology in Animation Film and Television

The application of 3D simulation technology has revolutionized the field of animation film and television art, providing new possibilities and creative opportunities for visual storytelling. This research aims to explore the various aspects of applying 3D simulation technology in animation film and television art. It examines how 3D simulation technology enhances the creation of realistic characters, environments, and special effects, contributing to immersive and captivating storytelling experiences. The research also investigates the technical aspects of integrating 3D cloud simulation technology into the animation production pipeline, including modeling, texturing, rigging, and animation techniques. This paper explores the application of these optimization algorithms in the context of cloud-based 3D environments, focusing on enhancing the efficiency and performance of 3D simulations. Black Widow and Spider Monkey Optimization can be used to optimize the placement and distribution of 3D assets in cloud storage systems, improving data access and retrieval times. The algorithms can also optimize the scheduling of rendering tasks in cloud-based rendering pipelines, leading to more efficient and cost-effective rendering processes. The integration of 3D cloud environments and optimization algorithms enables real-time optimization and adaptation of 3D simulations. This allows for dynamic adjustments of simulation parameters based on changing conditions, resulting in improved accuracy and responsiveness. Moreover, it explores the impact of 3D cloud simulation technology on the artistic process, examining how it influences the artistic vision, aesthetics, and narrative possibilities in animation film and television. The research findings highlight the advantages and challenges of using 3D simulation technology in animation, shedding light on its potential future developments and its role in shaping the future of animation film and television art

FeS2 monolayer: a high valence and high-TCT_{\rm C} Ising ferromagnet

Two-dimensional (2D) magnetic materials are of current great interest for their promising applications in spintronics. Strong magnetic coupling and anisotropy are both highly desirable for the achievement of a high temperature magnetic order. Here we propose the unusual high valent FeS$_2$ hexagonal monolayer as such a candidate for a strong Ising 2D ferromagnet (FM), by spin-orbital state analyses, first-principles calculations, and the renormalized spin-wave theory (RSWT). We find that very importantly, the high valent Fe$^{4+}$ ion is in the low-spin state ($t_{2g}^{4}$, $S$=1) with degenerate $t_{2g}$ orbitals rather than the high-spin state ($t_{2g}^{3}e_g^{1}$, $S$=2). It is the low-spin state that allows to carry a large perpendicular orbital moment and then produces a huge single ion anisotropy (SIA) of 25 meV/Fe. Moreover, the negative charge transfer character associated with the unusual high valence, strong Fe $3d$-S $3p$ hybridization, wide bands, and a small band gap all help to establish a strong superexchange. Indeed, our first-principles calculations confirm the strong FM superexchange and the huge perpendicular SIA, both of which are further enhanced by a compressive strain. Then, our RSWT calculations predict that the FM $T_{\rm C}$ is 261 K for the pristine FeS$_2$ monolayer and could be increased to 409 K under the compressive --5\% strain. The high $T_{\rm C}$ is also reproduced by our Monte Carlo (MC) simulations. Therefore, it is worth exploring the high-$T_{\rm C}$ Ising FMs in the high valent 2D magnetic materials with degenerate orbitals.Comment: 13 pages, 5 figure

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August, 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses

Association between ideal cardiovascular health metrics and suboptimal health status in Chinese population

Suboptimal health status (SHS) is a physical state between health and illness, and previous studies suggested that SHS is associated with majority components of cardiovascular health metrics defined by American Heart Association (AHA). We investigated the association between SHS and cardiovascular health metrics in a cross-sectional analysis of China suboptimal health cohort study (COACS) consisting of 4313 participants (60.30% women) aged from 18 to 65 years old. The respective prevalence of SHS is 7.10%, 9.18%, 10.04% and 10.62% in the first, second, third and fourth quartiles of ideal cardiovascular health (CVH) metrics (P for trend = 0.012). Participants in the largest quartile of ideal CVH metrics show a lower likelihood of having optimal SHS score compared to those in the smallest quartile (odds ratio (OR), 0.43; 95% confidence interval (CI), 0.32–0.59), after adjusting for age, gender, marital status, alcohol consumption, income level and education. Four metrics (smoking, physical inactivity, poor dietary intake and ideal control of blood pressure are significantly correlated with the risk of SHS. The present study suggests that ideal CVH metrics are associated with a lower prevalence of SHS, and the combined evaluation of SHS and CVH metrics allows the risk classification of cardiovascular disease, and thus consequently contributes to the prevention of cardiovascular diseases

Association between ideal cardiovascular health metrics and suboptimal health status in Chinese population

Suboptimal health status (SHS) is a physical state between health and illness, and previous studies suggested that SHS is associated with majority components of cardiovascular health metrics defined by American Heart Association (AHA). We investigated the association between SHS and cardiovascular health metrics in a cross-sectional analysis of China suboptimal health cohort study (COACS) consisting of 4313 participants (60.30 % women) aged from 18 to 65 years old. The respective prevalence of SHS is 7.10 %, 9.18 %, 10.04 % and 10.62 % in the first, second, third and fourth quartiles of ideal cardiovascular health (CVH) metrics (P for trend = 0.012). Participants in the largest quartile of ideal CVH metrics show a lower likelihood of having optimal SHS score compared to those in the smallest quartile (odds ratio (OR), 0.43; 95% confidence interval (CI), 0.32 – 0.59), after adjusting for age, gender, marital status, alcohol consumption, income level and education. Four metrics (smoking, physical inactivity, poor dietary intake and ideal control of blood pressure are significantly correlated with the risk of SHS. The present study suggests that ideal CVH metrics are associated with a lower prevalence of SHS, and the combined evaluation of SHS and CVH metrics allows the risk classification of cardiovascular disease, and thus consequently contributes to the prevention of cardiovascular diseases

Automated Extraction of Forest Burn Severity Based on Light and Small UAV Visible Remote Sensing Images

Identification of forest burn severity is essential for fire assessments and a necessary procedure in modern forest management. Due to the low efficiency and labor intensity of the current post-fire field survey in China’s Forestry Standards, the limitation of temporal resolution of satellite imagery, and poor objectivity of manual interpretations, a new method for automatic extraction of forest burn severity based on small visible unmanned aerial vehicle (UAV) images is proposed. Taking the forest fires which occurred in Anning city of Yunnan Province in 2019 as the study objects, post-fire imagery was obtained by a small, multi-rotor near-ground UAV. Some image recognition indices reflecting the variations in chlorophyll loss effects in different damaged forests were developed with spectral characteristics customized in A and C, and the texture features such as the mean, standard deviation, homogeneity, and shape index of the length–width ratio. An object-oriented method is used to determine the optimal segmentation scale for forest burn severity and a multilevel rule classification and extraction model is established to achieve the automatic identification and mapping. The results show that the method mentioned above can recognize different types of forest burn severity: unburned, damaged, dead, and burnt. The overall accuracy is 87.76%, and the Kappa coefficient is 0.8402, which implies that the small visible UAV can be used as a substitution for the current forest burn severity survey standards. This research is of great practical significance for improving the efficiency and precision of forest fire investigation, expanding applications of small UAVs in forestry, and developing an alternative for forest fire loss assessments in the forestry industry

Automated Extraction of Forest Burn Severity Based on Light and Small UAV Visible Remote Sensing Images

Identification of forest burn severity is essential for fire assessments and a necessary procedure in modern forest management. Due to the low efficiency and labor intensity of the current post-fire field survey in China’s Forestry Standards, the limitation of temporal resolution of satellite imagery, and poor objectivity of manual interpretations, a new method for automatic extraction of forest burn severity based on small visible unmanned aerial vehicle (UAV) images is proposed. Taking the forest fires which occurred in Anning city of Yunnan Province in 2019 as the study objects, post-fire imagery was obtained by a small, multi-rotor near-ground UAV. Some image recognition indices reflecting the variations in chlorophyll loss effects in different damaged forests were developed with spectral characteristics customized in A and C, and the texture features such as the mean, standard deviation, homogeneity, and shape index of the length–width ratio. An object-oriented method is used to determine the optimal segmentation scale for forest burn severity and a multilevel rule classification and extraction model is established to achieve the automatic identification and mapping. The results show that the method mentioned above can recognize different types of forest burn severity: unburned, damaged, dead, and burnt. The overall accuracy is 87.76%, and the Kappa coefficient is 0.8402, which implies that the small visible UAV can be used as a substitution for the current forest burn severity survey standards. This research is of great practical significance for improving the efficiency and precision of forest fire investigation, expanding applications of small UAVs in forestry, and developing an alternative for forest fire loss assessments in the forestry industry

Application of Downhole Micro-seismic Technique in Shale Gas Fracturing Optimization of Fuling, China

The downhole micro-seismic method has successfully applied to well fractory fracturing development in Jiaoshiba block, Fuling shale gas reservoir in September 2015. The new concept and understanding have accumulated. Combing the actual construction condition of micro-seismic monitoring in JY AA-BB well groups, we reviewed briefly the downhole micro-seismic monitoring technology and then summarized multi-disciplinary experience and understanding. The staged fracturing process level improved and productivity increased with the further development of shale gas field. Results show that the understanding of micro-seismic method has played a guiding role in the optimization of fracturing process and site construction adjustment of other wells in the same area, which increased the productivity and created a huge economic benefits with a broad market prospects