Concealed geological formations that induce disasters frequently result in incidents, such as water and gas outburst, during coal mine roadway excavation, thereby underscoring the critical necessity for precise advanced exploration of geological structures and their spatial positions to ensure safe excavation practices. Full waveform inversion (FWI) presents several advantages, including high-resolution imaging of complex geological structures and the effective inversion of essential physical parameters. However, the unique observational conditions, such as the linear arrangement of vertically excavated working faces in the roadway whole space and the implementation of single-offset detection, significantly exacerbate the challenge of non-uniqueness in the inversion process. To mitigate the problem of cycle skipping in the FWI of seismic advanced detection within roadways and to enhance inversion accuracy, a study was undertaken to investigate the impact characteristics of FWI under these unique observational conditions present. Initiating from the model structure and incorporating a time-domain multiscale inversion strategy, a novel approach was proposed for constructing a single-scale initial model based on wave velocity structure correction, thereby yielding a more accurate initial model for subsequent scales of inversion. Furthermore, a convolutional wavelet-independent inversion method, along with a total variation regularization approach, was introduced to effectively tackle the challenges of wavelet estimation in actual data inversion, as well as to more effectively mitigate the nonlinearity and ill-posedness inherent in the inversion process. Research findings indicate that, in comparison to conventional elastic wave FWI methods, the newly developed roadway FWI methodology can enhance inversion accuracy by approximately 20% in roadway scenarios, thereby providing high-precision imaging results.

Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images. However, accurately representing objects with glossy surfaces remains a challenge for existing methods. In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections. To achieve this, we first propose a novel neural renderer with decomposed rendering components to learn the interaction between surface and environment lighting. This renderer is trained using existing physically based renderers and is decoupled from actual scene representations. We then propose an SDF-based neural surface model that leverages this learned neural renderer to represent general scenes. Our model additionally synthesizes indirect illuminations caused by inter-reflections from shiny surfaces by marching surface-reflected rays. We demonstrate that our method outperforms state-of-art methods on challenging shiny scenes, providing high-quality rendering of specular reflections while also enabling material editing and scene relighting.

African swine fever virus (ASFV) poses serious threats to the pig industry. The multigene family (MGF) proteins are extensively distributed in ASFVs and are generally classified into five families, including MGF-100, MGF-110, MGF-300, MGF-360 and MGF-505. Most MGF proteins, however, have not been well characterized and classified within each family. To bridge this gap, this study first classified MGF proteins into 31 groups based on protein sequence homology and network clustering. A web server for classifying MGF proteins was established and kept available for free at http://www.computationalbiology.cn/MGF/home.html. Results showed that MGF groups of the same family were most similar to each other and had conserved sequence motifs; the genetic diversity of MGF groups varied widely, mainly due to the occurrence of indels. In addition, the MGF proteins were predicted to have large structural and functional diversity, and MGF proteins of the same MGF family tended to have similar structure, location and function. Reconstruction of the ancestral states of MGF groups along the ASFV phylogeny showed that most MGF groups experienced either the copy number variations or the gain-or-loss changes, and most of these changes happened within strains of the same genotype. It is found that the copy number decrease and the loss of MGF groups were much larger than the copy number increase and the gain of MGF groups, respectively, suggesting the ASFV tended to lose MGF proteins in the evolution. Overall, the work provides a detailed classification for MGF proteins and would facilitate further research on MGF proteins.