Classification of (n+3)(n+3)-dimensional metric nn-Lie algebras

In this paper, we focus on $(n+3)$-dimensional metric $n$-Lie algebras. To begin with, we give some properties on $(n+3)$-dimensional $n$-Lie algebras. Then based on the properties, we obtain the classification of $(n+3)$-dimensional metric $n$-Lie algebras

Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing'an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type

Emergence of central recirculation zone in a V-shaped premixed swirling flame

This paper presents an experimental study on the emergence of the central recirculation zone (CRZ) in a V-shaped premixed swirling flame, using simultaneous measurement of particle image velocimetry (PIV) and CH* chemiluminescence. The results show that either increasing the Reynolds number (Re) or decreasing the equivalence ratio ({\phi}) would facilitate the emergence of CRZ, and the inner shear layer (ISL) plays an essential role in governing the characteristics of CRZ. Further analysis demonstrates that the CRZ emergence can be promoted by higher ISL intensity but suppressed by enhanced viscous diffusion owing to higher flame temperature. As such, the CRZ formation can be interpreted as the outcome of a competition between the ISL intensity, i.e., circulation, and the vorticity consumption due to viscous diffusion. This competition physically corresponds to a special Reynolds number, Re_s, defined as the ratio between the ISL circulation ({\Gamma}) and the ISL effective viscosity ({\nu}_s), with a simplified heat loss model proposed for the temperature and viscosity estimations of the ISL. The outputting {\Gamma}-{\nu}_s plot yields a single boundary line separating the cases with and without CRZ, which points to a common critical Re_s of about 637, justifying the generality of the present criterion for lean-premixed V-shaped swirling flames of various operating conditions. Unlike most previous works which study the CRZ of a swirling flame from the point of vortex breakdown, the present work reveals the importance of enhanced viscous diffusion, caused by flame heating, in suppressing the CRZ emergence

Learning World Models with Identifiable Factorization

Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle these information remains a challenging problem. In this paper, we propose IFactor, a general framework to model four distinct categories of latent state variables that capture various aspects of information within the RL system, based on their interactions with actions and rewards. Our analysis establishes block-wise identifiability of these latent variables, which not only provides a stable and compact representation but also discloses that all reward-relevant factors are significant for policy learning. We further present a practical approach to learning the world model with identifiable blocks, ensuring the removal of redundants but retaining minimal and sufficient information for policy optimization. Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the DeepMind Control Suite and RoboDesk showcase the superior performance of our approach over baselines

Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI Images

Plant invasion imposes significant threats to biodiversity and ecosystem function. Thus, monitoring the spatial pattern of invasive plants is vital for effective ecosystem management. Spartina alterniflora (S. alterniflora) has been one of the most prevalent invasive plants along the China coast, and its spread has had severe ecological consequences. Here, we provide new observation from Landsat operational land imager (OLI) images. Specifically, 43 Landsat-8 OLI images from 2014 to 2016, a combination of object-based image analysis (OBIA) and support vector machine (SVM) methods, and field surveys covering the whole coast were used to construct an up-to-date dataset for 2015 and investigate the spatial variability of S. alterniflora in the coastal zone of mainland China. The classification results achieved good estimation, with a kappa coefficient of 0.86 and 96% overall accuracy. Our results revealed that there was approximately 545.80 km2 of S. alterniflora distributed in the coastal zone of mainland China in 2015, from Hebei to Guangxi provinces. Nearly 92% of the total area of S. alterniflora was distributed within four provinces: Jiangsu, Shanghai, Zhejiang, and Fujian. Seven national nature reserves invaded by S. alterniflora encompassed approximately one-third (174.35 km2) of the total area of S. alterniflora over mainland China. The Yancheng National Nature Reserve exhibited the largest area of S. alterniflora (115.62 km2) among the reserves. Given the rapid and extensive expansion of S. alterniflora in the 40 years since its introduction and its various ecological effects, geospatially varied responding decisions are needed to promote sustainable coastal ecosystems

Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China

Soil is the largest pool of terrestrial organic carbon in the biosphere and interacts strongly with the atmosphere, climate and land cover. Remote sensing (RS) and geographic information systems (GIS) were used to study the spatio-temporal dynamics of croplands and soil organic carbon density (SOCD) in the Sanjiang Plain, to estimate soil organic carbon (SOC) storage. Results show that croplands increased with 10,600.68 km2 from 1992 to 2012 in the Sanjiang Plain. Area of 13,959.43 km2 of dry farmlands were converted into paddy fields. Cropland SOC storage is estimated to be 1.29 ± 0.27 Pg C (1 Pg = 103 Tg = 1015 g) in 2012. Although the mean value of SOCD for croplands decreased from 1992 to 2012, the SOC storage of croplands in the top 1 m in the Sanjiang Plain increased by 70 Tg C (1220 to 1290). This is attributed to the area increases of cropland. The SOCD of paddy fields was higher and decreased more slowly than that of dry farmlands from 1992 to 2012. Conversion between dry farmlands and paddy fields and the agricultural reclamation from natural land-use types significantly affect the spatio-temporal patterns of cropland SOCD in the Sanjiang Plain. Regions with higher and lower SOCD values move northeast and westward, respectively, which is almost consistent with the movement direction of centroids for paddy fields and dry farmlands in the study area. Therefore, these results were verified. SOC storages in dry farmlands decreased by 17.5 Tg·year−1 from 1992 to 2012, whilst paddy fields increased by 21.0 Tg·C·year−1

Cardiorespiratory Benefits of Exercise

Abundant evidence proved that the amount of habitual exercise and the level of cardiorespiratory fitness (CRF) are inversely related to the risk of cardiovascular morbidity and mortality. In this chapter, you can learn about the cardiorespiratory benefits of exercise, involving: (1) delay the development of cardiovascular disease (CVD) affecting many of the standard cardiorespiratory diseases risk factors, such as plasma lipids, especially high-density lipoprotein cholesterol, fasting glucose levels, blood and hypertension control; (2) improve the cardiac output (CO) and the CRF of different ages. However, certain kind of exercise might not be applicable to cardiac patients, since high-intensity, high-volume exercise may increase all-cause mortality among these patients. At present, the American College of Sports Medicine (ACSM) recommends that aerobic exercise (AE) and resistance exercise (RE) two or three times a week is related to better physical function at different ages, improvement of muscle strength, body composition and, especially, CRF