Quantum gravity effects on compact star cores

Using the Tolman-Oppenheimer-Volkoff equation and the equation of state of zero temperature ultra-relativistic Fermi gas based on generalized uncertainty principle (GUP), the quantum gravitational effects on the cores of compact stars are discussed. Our results show that ${2m(r)}/ {r}$ varies with $r$. Quantum gravity plays an important role in the region $r\sim 10^3 r_0$, where $r_0\sim \beta_0 l_p$, $l_p$ is the Planck length and $\beta_0$ is a dimensionless parameter accounting for quantum gravity effects. Furthermore, near the center of compact stars, we find that the metric components are $g_{tt}\sim r^4$ and $g_{rr}=[1-{r}^2/(6r_0^2)]^{-1}$. All these effects are different from those obtained from classical gravity. These results can be applied to neutron stars or denser ones like quark stars. The observed masses of neutron stars ($\leq 2M_\odot$) indicate that $\beta_0$ can not exceed $10^{37}$, not as good as the upper bound $\beta_0<10^{34}$ from simple electroweak consideration. This means that incorporating either quantum gravity effects or nuclear interactions, one obtains almost the same mass limits of neutron stars.Comment: 12 pages, 1 figure, added brief review on compact stars configurations, abstract expanded, references added, typo corrected, published versio

Temporal analysis of physico-chemical and biological indicators of water quality in a lowland river in Northern Germany

Management of water resources is among the most important issues humans have been dealing with in the last decades and will remain one of the most concerning topics in the coming years. Anthropogenic activities have heavily impacted surface water bodies and groundwater, the main sources of fresh water supply. The quality of aquatic ecosystems has been globally degraded by industrial waste disposal, pollution from urban expansion, and the intensification of agriculture. Under the circumstances of climate change, awareness of the interactions between environmental parameters and water quality becomes even more necessary. It is important to better understand the transportation and conditions of nutrients in agricultural catchments to assess the status of both the abiotic and biotic factors. Nitrogen (N) and phosphorus (P) were chosen for this study as they are the major sources of water pollution and eutrophication in aquatic systems. They are the sources of nutrients, supporting the growth of aquatic organisms, such as diatoms. Diatoms are one of the most common groups of phytoplankton in rivers and are one of the most important primary producers in the food web. They were chosen as the bio-indicators as they are sensitive and quickly respond to environmental changes in their surroundings. Physico-chemical parameters which can affect the growth of diatoms have been widely investigated, though the influences of hydrological processes in rivers are still not fully understood. In addition, high frequent (i.e., minute resolution) data sets in hydrology are common but rare for hydro-biological research. Therefore, the scope of this thesis is to study the water quality variation from daily samples to analyze the interactions between physico-chemical factors and diatom communities in the study area

Dynamic Equation Formulations Based on First Physical Principle of Energy Conservation

In this work, a methodology is proposed for formulating general dynamic equations. Under the umbrella of the first principle of energy conservation, the Lagrange's equation, Hamilton's canonical equations are formulated without touching on the existing Hamilton's variational principles or Newton's laws. We argue that, all of the formulations for characterizing the dynamic behaviors of a system can be formulated based on the first principles of physics, i.e., the energy conservation. Since the Hamiltonian principle can also be derived from energy conservation, the proposed methodology may be extended to those areas where Hamiltonian principle reaches, such as acoustics, elastodynamics, fluid mechanics, electrodynamics, and quantum mechanics, and so on. The proposed methodology provides an efficient way to tackle the dynamic problems in dissipation continuum system, especially in characterizing multi-physical field interaction and coupling. The major results obtained from Hamiltonian mechanics are in agreement with those derived from the proposed methodology. On the count ray to the existing Hamiltonian mechanics, it is pointed out that, the real physics meaning of Hamilton's variational principle, Lagrange's equation, and Newtonian second law of motion le are the consequence of the law of conservation of energy. Our proposed methodology is easier to understand with clear physical meanings, and is able to explain the existing mechanical principles or theorems, with energy, work done by various forces, and their conservation.Comment: 5pages,1figur

Improvement of Market Economy Management Measures for Innovative Enterprises under Block Chain Technology

In order to solve the financing difficulties of innovative Small and Medium Enterprise (SMEs) in the financial and economic field, this research proposes a market economy management measure for innovative enterprises, namely the enterprise credit information sharing model based on block chain technology. Firstly, the problems existing in the sharing model based on block chain technology are analyzed, and the basic model framework of block chain is adopted to improve the sharing model. Secondly, according to the improved Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, the simulation experiment design of the credit information sharing model of enterprise market economy management measures is carried out. Finally, the improved sharing model proposed in this research is evaluated in terms of fault tolerance and throughput. The results show that the improved market economy management measures based on block chain technology in this research can meet certain fault tolerance rate, and the throughput is relatively stable. To some extent, it can meet the needs of credit information trading and sharing, and solve the difficulties of enterprise information sharing and low efficiency of data exchange

Learning to Reconstruct Shapes from Unseen Classes

From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes. Here we present an algorithm, Generalizable Reconstruction (GenRe), designed to capture more generic, class-agnostic shape priors. We achieve this with an inference network and training procedure that combine 2.5D representations of visible surfaces (depth and silhouette), spherical shape representations of both visible and non-visible surfaces, and 3D voxel-based representations, in a principled manner that exploits the causal structure of how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe performs well on single-view shape reconstruction, and generalizes to diverse novel objects from categories not seen during training.Comment: NeurIPS 2018 (Oral). The first two authors contributed equally to this paper. Project page: http://genre.csail.mit.edu