Generalized Implicit Factorization Problem

The Implicit Factorization Problem was first introduced by May and Ritzenhofen at PKC'09. This problem aims to factorize two RSA moduli $N_1=p_1q_1$ and $N_2=p_2q_2$ when their prime factors share a certain number of least significant bits (LSBs). They proposed a lattice-based algorithm to tackle this problem and extended it to cover $k>2$ RSA moduli. Since then, several variations of the Implicit Factorization Problem have been studied, including the cases where $p_1$ and $p_2$ share some most significant bits (MSBs), middle bits, or both MSBs and LSBs at the same position. In this paper, we explore a more general case of the Implicit Factorization Problem, where the shared bits are located at different and unknown positions for different primes. We propose a lattice-based algorithm and analyze its efficiency under certain conditions. We also present experimental results to support our analysis

Analyses of representative elementary volume for coal using X-ray μ-CT and FIB-SEM and its application in permeability predication model

We acknowledge financial support from the National Natural Science Foundation of China (41872123; 41830427), the Petro China Innovation Foundation (2018D-5007-0101), the Key research and development project of Xinjiang Uygur Autonomous Region (2017B03019-1), the Royal Society Edinburgh through the international cost share scheme and National Natural Science Foundation China (NSFC 41711530129).Peer reviewedPostprin

A Note on the Density of the Multiple Subset Sum Problems

It is well known that the general subset sum problem is NP-complete. However, almost all subset sum problems with density less than $0.9408\ldots$ can be solved in polynomial time with an oracle that can find the shortest vector in a special lattice. In this paper, we give a similar result for the multiple subset sum problems which has $k$ subset sum problems with the same solution. Some extended versions of the multiple subset sum problems are also considered. In addition, a modified lattice is involved to make the analysis much simpler than before

Generalized Implicit Factorization Problem

The Implicit Factorization Problem (IFP) was first introduced by May and Ritzenhofen at PKC\u2709, which concerns the factorization of two RSA moduli $N_1=p_1q_1$ and $N_2=p_2q_2$, where $p_1$ and $p_2$ share a certain consecutive number of least significant bits. Since its introduction, many different variants of IFP have been considered, such as the cases where $p_1$ and $p_2$ share most significant bits or middle bits at the same positions. In this paper, we consider a more generalized case of IFP, in which the shared consecutive bits can be located at $any$ positions in each prime, not necessarily required to be located at the same positions as before. We propose a lattice-based algorithm to solve this problem under specific conditions, and also provide some experimental results to verify our analysis

Unveiling the potential of Butylphthalide: inhibiting osteoclastogenesis and preventing bone loss

Osteoporosis, resulting from overactive osteoclasts and leading to elevated fracture risk, has emerged as a global public health concern due to the aging population. Therefore, inhibiting osteoclastogenesis and bone resorption function represents a crucial approach for preventing and treating osteoporosis. The purpose of this study was to examine the effects and molecular mechanisms of Butylphthalide (NBP) on the differentiation and function of osteoclasts induced by RANKL. Osteoclastogenesis was assessed through TRAP staining and bone slice assay. An animal model that underwent ovariectomy, simulating postmenopausal women’s physiological characteristics, was established to investigate the impact of Butylphthalide on ovariectomy-induced bone loss. To delve deeper into the specific mechanisms, we employed Western blot, PCR, immunofluorescence, and immunohistochemical staining to detect the expression of proteins that are associated with the osteoclast signaling pathway. In this study, we found that Butylphthalide not only suppressed osteoclastogenesis and bone resorption in vitro but also significantly decreased TRAcP-positive osteoclasts and prevented bone loss in vivo. Further mechanistic experiments revealed that Butylphthalide reduces intracellular ROS in osteoclasts, inhibits the MAPK and NFATc1 signaling pathways, and downregulates the key genes and proteins of osteoclasts. This inhibits osteoclast formation and function. The reduction in ROS in osteoclasts is intricately linked to the activity of Butylphthalide-modulated antioxidant enzymes. Overall, NBP may offer a alternative treatment option with fewer side effects for skeletal diseases such as osteoporosis

Research on Water Absorption and Frost Resistance of Concrete Coated with Different Impregnating Agents for Ballastless Track Structure

In consideration of performance requirement of ballastless track concrete in cold regions of China, 3 types of commercially available impregnating agents were employed to research their effect on water absorption and frozen resistance of concrete, containing silanes, potassium silicate and osmotic curing agent. The results presented that coating silanes was the most effective on the reduction of water absorption among all employed impregnating agents, because of the most significant character change of concrete surface from hydrophilicity to hydrophobicity which could be proved by the contact angle test of concrete. The promotion on frozen resistance of concrete was not as significant as that for water absorption by coating 3 commercially available types of impregnant agents, because of the spalling damage on concrete surface during the freezing-thawing cycles

SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.Comment: 11 pages, 5 figures, ICAIS 202

Cloning and Comparative Studies of Seaweed Trehalose-6-Phosphate Synthase Genes

The full-length cDNA sequence (3219 base pairs) of the trehalose-6-phosphate synthase gene of Porphyra yezoensis (PyTPS) was isolated by RACE-PCR and deposited in GenBank (NCBI) with the accession number AY729671. PyTPS encodes a protein of 908 amino acids before a stop codon, and has a calculated molecular mass of 101,591 Daltons. The PyTPS protein consists of a TPS domain in the N-terminus and a putative TPP domain at the C-terminus. Homology alignment for PyTPS and the TPS proteins from bacteria, yeast and higher plants indicated that the most closely related sequences to PyTPS were those from higher plants (OsTPS and AtTPS5), whereas the most distant sequence to PyTPS was from bacteria (EcOtsAB). Based on the identified sequence of the PyTPS gene, PCR primers were designed and used to amplify the TPS genes from nine other seaweed species. Sequences of the nine obtained TPS genes were deposited in GenBank (NCBI). All 10 TPS genes encoded peptides of 908 amino acids and the sequences were highly conserved both in nucleotide composition (>94%) and in amino acid composition (>96%). Unlike the TPS genes from some other plants, there was no intron in any of the 10 isolated seaweed TPS genes

A laboratory nanoseismological study on deep-focus earthquake micromechanics

Global earthquake occurring rate displays an exponential decay down to ~300 km and then peaks around 550 to 600 km before terminating abruptly near 700 km. How fractures initiate, nucleate, and propagate at these depths remains one of the greatest puzzles in earth science, as increasing pressure inhibits fracture propagation. We report nanoseismological analysis on high-resolution acoustic emission (AE) records obtained during ruptures triggered by partial transformation from olivine to spinel in Mg2GeO4, an analog to the dominant mineral (Mg,Fe)2SiO4 olivine in the upper mantle, using state-of-the-art seismological techniques, in the laboratory. AEs’ focal mechanisms, as well as their distribution in both space and time during deformation, are carefully analyzed. Microstructure analysis shows that AEs are produced by the dynamic propagation of shear bands consisting of nanograined spinel. These nanoshear bands have a near constant thickness (~100 nm) but varying lengths and self-organize during deformation. This precursory seismic process leads to ultimate macroscopic failure of the samples. Several source parameters of AE events were extracted from the recorded waveforms, allowing close tracking of event initiation, clustering, and propagation throughout the deformation/transformation process. AEs follow the Gutenberg-Richter statistics with a well-defined b value of 1.5 over three orders of moment magnitudes, suggesting that laboratory failure processes are self-affine. The seismic relation between magnitude and rupture area correctly predicts AE magnitude at millimeter scales. A rupture propagation model based on strain localization theory is proposed. Future numerical analyses may help resolve scaling issues between laboratory AE events and deep-focus earthquakes