Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine

Evaluating Modules in Graph Contrastive Learning

The recent emergence of contrastive learning approaches facilitates the research on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature. These methods contrast semantically similar and dissimilar sample pairs to encode the semantics into node or graph embeddings. However, most existing works only performed model-level evaluation, and did not explore the combination space of modules for more comprehensive and systematic studies. For effective module-level evaluation, we propose a framework that decomposes GCL models into four modules: (1) a sampler to generate anchor, positive and negative data samples (nodes or graphs); (2) an encoder and a readout function to get sample embeddings; (3) a discriminator to score each sample pair (anchor-positive and anchor-negative); and (4) an estimator to define the loss function. Based on this framework, we conduct controlled experiments over a wide range of architectural designs and hyperparameter settings on node and graph classification tasks. Specifically, we manage to quantify the impact of a single module, investigate the interaction between modules, and compare the overall performance with current model architectures. Our key findings include a set of module-level guidelines for GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an MLP encoder associated with Sum readout could achieve competitive performance on graph classification. Finally, we release our implementations and results as OpenGCL, a modularized toolkit that allows convenient reproduction, standard model and module evaluation, and easy extension

Slope Stability Analysis Considering Sliding Effect of Upper Body

Owing to the human engineering activities, the phenomenon that new landslide happen on the upper part of the old sliding body can be found everywhere. This kind of slope consisting of two sliding bodies, which are upper body and lower body, is named as double-sliding-body slope. Its stability is usually analyzed according to two slopes. However, the effect of new landslide movement on the stability of entire slope system is not taken into account. In this paper, sliding effect of upper body is analyzed, and the formula considering sliding effect of upper body is derived based on Sarma method for analysis of entire slope. Theoretical analysis and a case history indicate that the spasmodic motion of upper body has bad effect on the lower body stability; Sliding along the top face of lower body, the effect on the lower body is disadvantageous during starting instant. Nevertheless, the bad effect will disappear and transform as the advantageous effect as descending of the slope gradient and sliding acceleration. For the slope controlling, the sliding effect of upper body should be considered in the stability analysis of double-sliding-body slope, thus it will help us recognizing and mastering or forecasting the evaluative trend of double-siding-body slope. Further reliable scientific basis can be provided in order to make effective controlling measures.特集 : 「資源、新エネルギー、環境、防災研究国際セミナー

深部軟岩坑道用ハイコンベックス・ストリップボルトを用いた複合支保技術に関する研究

Based on the systematic analysis of mechanical characteristics for deep roadway excavated in soft strata, the high convex strip-bolting support technology was put forward, and a numerical analysis was carried out by FLAC3D. The process of general bolting support and high convex strip-bolting support of deep soft rock roadway were simulated. The results indicate that the convex bed of strip-band can adapt to large deformation of high pre-stressed bolting, and the high strength and high rigidity of strip can bear large axial force and moment of flexion. In addition, bolting and anchoring combined support can control the deformation of rock masses by transferring the strength of deep strata, and bolt-grouting can form stress-relief region in deep and generates high strength invert arch at shallow which can afford some support resistances. A case study is also presented and the results of field measurement show that the new support system is suitable for roadways excavated in weak rocks.特集 : 「資源、新エネルギー、環境、防災研究国際セミナー

Histone ubiquitination-related gene CUL4B promotes lung adenocarcinoma progression and cisplatin resistance

Background: The role of the histone ubiquitination-related gene in the cisplatin resistance of lung adenocarcinoma (LUAD) remains an intricate subject.Methods: We accessed transcriptome data of both wild type and cisplatin-resistant cells from the GSE108214 dataset, and garnered transcriptome and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) database. Utilizing the R software, we analyzed these public datasets in depth. Real-time Quantitative PCR (qPCR) was used to detect the RNA level of CUL4B. Effect of CUL4B on cell proliferation was evaluated using CCK8 and colony formation assay. Effect of CUL4B on cell invasion was evaluated using transwell assay. Cisplatin sensitivity was evaluated by calculating IC50.Results: Our analysis shed light on the significance of the histone ubiquitination-related gene, CUL4B, in relation to cisplatin resistance and the overall survival rates of LUAD patients. Notably, CUL4B was found to be overexpressed in both lung cancer tissues and cells. Meanwhile, in vitro experiments indicated can CUL4B significantly promote the proliferation, invasion and migration of lung cancer cells. Furthermore, suppressing CUL4B expression led to a noticeable reduction in the IC50 value of cisplatin in lung cancer cells. A deep dive into biological enrichment analysis revealed that among patients exhibiting high CUL4B expression, there was a pronounced activation of the G2M checkpoint and the PI3K/AKT/mTOR signaling pathways. Immune microenvironment analysis has revealed that patients with elevated CUL4B expression may exhibit increased infiltration of M2 macrophages, coupled with a reduced infiltration of CD8+ T cells and activated NK cells. Notably, we observed higher CUL4B expression among those who responded positively to immunotherapy.Conclusion: These findings underscore the significance of CUL4B in the resistance to cisplatin in lung cancer, highlighting its potential as a therapeutic target

Chemical ordering suppresses large-scale electronic phase separation in doped manganites

For strongly correlated oxides, it has been a long-standing issue regarding the role of the chemical ordering of the dopants on the physical properties. Here, using unit cell by unit cell superlattice growth technique, we determine the role of chemical ordering of the Pr dopant in a colossal magnetoresistant (La1-yPry)1-xCaxMnO3 (LPCMO) system, which has been well known for its large length-scale electronic phase separation phenomena. Our experimental results show that the chemical ordering of Pr leads to marked reduction of the length scale of electronic phase separations. Moreover, compared with the conventional Pr-disordered LPCMO system, the Pr-ordered LPCMO system has a metal–insulator transition that is ~100 K higher because the ferromagnetic metallic phase is more dominant at all temperatures below the Curie temperature

A Recombinant Vaccine of H5N1 HA1 Fused with Foldon and Human IgG Fc Induced Complete Cross-Clade Protection against Divergent H5N1 Viruses

Development of effective vaccines to prevent influenza, particularly highly pathogenic avian influenza (HPAI) caused by influenza A virus (IAV) subtype H5N1, is a challenging goal. In this study, we designed and constructed two recombinant influenza vaccine candidates by fusing hemagglutinin 1 (HA1) fragment of A/Anhui/1/2005(H5N1) to either Fc of human IgG (HA1-Fc) or foldon plus Fc (HA1-Fdc), and evaluated their immune responses and cross-protection against divergent strains of H5N1 virus. Results showed that these two recombinant vaccines induced strong immune responses in the vaccinated mice, which specifically reacted with HA1 proteins and an inactivated heterologous H5N1 virus. Both proteins were able to cross-neutralize infections by one homologous strain (clade 2.3) and four heterologous strains belonging to clades 0, 1, and 2.2 of H5N1 pseudoviruses as well as three heterologous strains (clades 0, 1, and 2.3.4) of H5N1 live virus. Importantly, immunization with these two vaccine candidates, especially HA1-Fdc, provided complete cross-clade protection against high-dose lethal challenge of different strains of H5N1 virus covering clade 0, 1, and 2.3.4 in the tested mouse model. This study suggests that the recombinant fusion proteins, particularly HA1-Fdc, could be developed into an efficacious universal H5N1 influenza vaccine, providing cross-protection against infections by divergent strains of highly pathogenic H5N1 virus