Clustering algorithm in initialization of multi-hop wireless sensor networks

In most application scenarios of wireless sensor networks (WSN), sensor nodes are usually deployed randomly and do not have any knowledge about the network environment or even their ID's at the initial stage of their operations. In this paper, we address the clustering problems with a newly deployed multi-hop WSN where most existing clustering algorithms can hardly be used due to the absence of MAC link connections among the nodes. We propose an effective clustering algorithm based on a random contention model without the prior knowledge of the network and the ID's of nodes. Computer simulations have been used to show the effectiveness of the algorithm with a relatively low complexity if compared with existing schemes

A new production prediction model for multistage fractured horizontal well in tight oil reservoirs

 To better evaluate the production performance of tight oil reservoirs, it is urgent to solve the multistage fractured horizontal well production enigma. It is paramount to develop new models to analyze the well performance for tight oil reservoirs. In this paper, a new production prediction model of multistage fractured horizontal well in tight oil reservoir was established. In this model, unsteady transfer flow between fracture and matrix was considered. This model was solved by using Laplace transform method, line source function and Stehfest method comprehensively. The production prediction type curves including pressure transient analysis curves and rate transient analysis curves were then obtained. According to these type curves, eight flow regimes were obtained as early wellbore storage period, skin factor period, bi-linear flow regime, linear flow regime, first radial flow regime, transition flow regime, transfer flow regime and later radial flow regime. In the end, a field case history matching result was given and four key parameters’ effect on tight formation well production was analyzed. This research is of both theoretical significance and practical value for tight oil development.Cited as: Zhao, K., Du, P. A new production prediction model for multistage fractured horizontal well in tight oil reservoirs. Advances in Geo-Energy Research, 2020, 4(2): 152-161, doi: 10.26804/ager.2020.02.0

Statistical Mechanics for Non-Hermitian Quantum Systems

We present a systematic study of statistical mechanics for non-Hermitian quantum systems. Our work reveals that the stability of a non-Hermitian system necessitates the existence of a single path-dependent conserved quantity, which, in conjunction with the system's Hamiltonian, dictates the equilibrium state. By elucidating the relationship between the Hamiltonian and the supported conserved quantity, we propose criteria for discerning equilibrium states with finite relaxation times. Although our findings indicate that only non-Hermitian systems with real energy spectrum precisely possess such conserved quantities, we also demonstrate that an effective conserved quantity can manifest in certain systems with complex energy spectra. The effective conserved quantity, alongside the effective transitions within their associated subspace, collectively determines the system's equilibrium state. Our results provide valuable insights into non-Hermitian systems across various contexts and hold potential for applications in a diverse range of physical systems

Non-Hermitian Maxwell's Demon

Maxwell's demon was first introduced by Maxwell in 1871 to discuss the limitations of the second law of thermodynamics due to addition information flow. In this paper, an alternative type of Maxwell's demon -- non-Hermitian Maxwell's demon is uncovered that shows quite different properties as the original Maxwell's demon and leads to rich physics phenomena in non-Hermitian systems, such as mismatch between single-body and many-body properties, Bose-Einstein condensation at arbitrary high temperature, phase transition that violates the Goldstone theorem. This provides an alternative degree of freedom to tune quantum many-body systems and realize exotic quantum phases and phase transitions

Masked Diffusion Models Are Fast and Privacy-Aware Learners

Diffusion models have emerged as the \emph{de-facto} technique for image generation, yet they entail significant computational overhead, hindering the technique's broader application in the research community. We propose a prior-based denoising training framework, the first to incorporate the pre-train and fine-tune paradigm into the diffusion model training process, which substantially improves training efficiency and shows potential in facilitating various downstream tasks. Our approach centers on masking a high proportion (e.g., up to 90\%) of the input image and employing masked denoising score matching to denoise the visible areas, thereby guiding the diffusion model to learn more salient features from training data as prior knowledge. By utilizing masked learning in a pre-training stage, we efficiently train the ViT-based diffusion model on CelebA-HQ $256 \times 256$ in the pixel space, achieving a 4x acceleration and enhancing the quality of generated images compared to denoising diffusion probabilistic model (DDPM). Moreover, our masked pre-training technique can be universally applied to various diffusion models that directly generate images in the pixel space, aiding in the learning of pre-trained models with superior generalizability. For instance, a diffusion model pre-trained on VGGFace2 attains a 46\% quality improvement through fine-tuning with merely 10\% data from a different distribution. Moreover, our method shows the potential to serve as a training paradigm for enhancing the privacy protection capabilities of diffusion models. Our code is available at \url{https://github.com/jiachenlei/maskdm}

Performance of horizontal wells in composite tight gas reservoirs considering stress sensitivity

 Tight gas reservoir (TGR) plays an important role in unconventional oil and gas resources. The existing seepage models for TGR rarely consider the effects of heterogeneity, stress-sensitivity, and the unsteady fluid exchange between matrix and fracture. Heterogeneity is common for tight gas reservoir which should be carefully considered in geological model. The stress-sensitivity effect of fracture is an important factor influencing the transient flow behavior of TGR. Ultra-low porosity and permeability cause the unsteady flow between matrix and fractures systems. So this paper introduced a mathematical model for the horizontal well in a dual-porosity composite tight gas reservoir with considering the stress-sensitivity effect and unsteady flow between matrix and fractures systems. Some mathematical methods including the finite Fourier cosine transform, perturbation technique, Laplace transform, superposition principle, Stehfest numerical inversion algorithm are used to solve the nonlinear partial differential equation. Different flow regimes are divided based on pressure transient analysis curves. The sensitivity analysis of related parameters is studied according to pressure transient analysis and rate transient analysis curves. The presented model and obtained results in this paper give better understanding on pressure and rate transient behaviors of composite TGR.Cited as: Zhao, K., Du, P. Performance of horizontal wells in composite tight gas reservoirs considering stress sensitivity. Advances in Geo-Energy Research, 2019, 3(3): 287-303, doi: 10.26804/ager.2019.03.0