Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence

Application of evolution-based uncertainty design on gear

The evolution of mechanical parameters, a factor affecting the mechanical reliability, has gathered more attention nowadays. However, studies on time varying uncertainty can hardly be found. A new method based on evolution-based uncertainty design (EBUD) is applied to the design of gear in this paper. Considering the wear evolution over the lifetime, a tooth wear’s time-varying uncertainty model based on the continuous-time model and Ito lemma is established. Drift and volatility functions dependent on the drift rate and volatility rate of rotational speed and torque are used to express the time-varying uncertainty of tooth thickness. The method can predict the reliability and provide an instruction in reliability improving, maintenance and repair of the gear system

Mechanical Properties and Failure Behavior of Mortar-Encased Coal Bodies under Impact Loads: Insights from Experimental Investigation

AbstractFailure of residual coal pillars under dynamic load disturbances can induce goaf collapse, ground subsidence, or coalbursts. Encasing the residual coal pillar in mortar is an effective method for reinforcing the residual coal pillar. However, the mechanical behaviors of mortar-encased coal bodies under impact loads remain poorly investigated. In this study, impact tests were conducted on coal, mortar, and mortar-encased coal specimens using a split Hopkinson pressure bar (SHPB) system. The mechanical properties and failure behavior of the mortar-encased coal specimens under impact loading were systematically investigated in terms of several metrics including dynamic stress-strain curves, failure patterns, strength change characteristics, and energy consumption laws. Results show that, owing to the different mechanical properties of the coal and mortar elements in the composite specimens, the mortar-encased specimen has a nonlinear deformation characteristic. The mortar has a higher energy absorption rate compared to the coal. Additionally, increasing the thickness of the external mortar body is helpful for absorbing more stress wave energy and increasing the dynamic strength of the mortar-encased coal specimens. Furthermore, under low strain rate loading, the external mortar body of the composite specimen initially experienced axial splitting failure. With increasing strain rate, axial splitting failure occurred in both the external mortar body and inner coal body. This study provides useful guidelines for reinforcing residual coal pillars in underground engineering

Tet and TDG Mediate DNA Demethylation Essential for Mesenchymal-to-Epithelial Transition in Somatic Cell Reprogramming

SummaryTet-mediated DNA oxidation is a recently identified mammalian epigenetic modification, and its functional role in cell-fate transitions remains poorly understood. Here, we derive mouse embryonic fibroblasts (MEFs) deleted in all three Tet genes and examine their capacity for reprogramming into induced pluripotent stem cells (iPSCs). We show that Tet-deficient MEFs cannot be reprogrammed because of a block in the mesenchymal-to-epithelial transition (MET) step. Reprogramming of MEFs deficient in TDG is similarly impaired. The block in reprogramming is caused at least in part by defective activation of key miRNAs, which depends on oxidative demethylation promoted by Tet and TDG. Reintroduction of either the affected miRNAs or catalytically active Tet and TDG restores reprogramming in the knockout MEFs. Thus, oxidative demethylation to promote gene activation appears to be functionally required for reprogramming of fibroblasts to pluripotency. These findings provide mechanistic insight into the role of epigenetic barriers in cell-lineage conversion

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°–93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°–118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid

CircUBAP2 Promotes MMP9-Mediated Oncogenic Effect via Sponging miR-194-3p in Hepatocellular Carcinoma

BackgroundThe physiological regulatory functions of circRNAs have become a topic of intensive research in recent years. Increasing evidence supports a significant role of circRNAs during cancer initiation and progression, including hepatocellular carcinoma (HCC).Materials and MethodsA bioinformatics analysis from three independent Gene Expression Omnibus (GEO) databases was performed to profile and screen the dysregulated circRNAs in HCC. RT-qPCR was used to examine the expression level of circUBAP2 in HCC and adjacent non-tumor tissues. Then, proliferation assays (CCK8 and colony formation) and migration assays (transwell and wound healing) were performed to examine effect of circUBAP2 in vitro. Immunoprecipitation, RNA pulldown, FISH, and dual-luciferase reporter assay was conducted to explore the circUBAP2-related mechanism for regulating HCC progression. Moreover, a mouse xenograft model and a mouse lung metastasis model confirmed the effect of circUBAP2 in vivo.ResultsIn this study, we found a novel circRNA: circUBAP2, which was identified by bioinformatics analysis. Among 91 HCC patients, circUBAP2 was significantly upregulated in HCC tissues, and negatively correlated with aggressive clinical characteristics and prognosis. Functional assays demonstrated that circUBAP2 promoted cell proliferation, colony formation, migration, and invasion in vitro. Moreover, circUBAP2 enhanced tumor growth and pulmonary metastasis in vivo. Mechanistically, circUBAP2 acts as a competing endogenous RNA (ceRNA) for miR-194-3p, a tumor suppressor in HCC. We confirmed that MMP9 was direct target for miR-194-3p, which was regulated by circUBAP2.ConclusionCircUBAP2 plays a significant role in promoting HCC via the miR-194-3p/MMP9 pathway and could serve as a promising prognostic biomarker and novel therapeutic target for HCC patients

Use of a Generalized Additive Model to Investigate Key Abiotic Factors Affecting Microcystin Cellular Quotas in Heavy Bloom Areas of Lake Taihu

Lake Taihu is the third largest freshwater lake in China and is suffering from serious cyanobacterial blooms with the associated drinking water contamination by microcystin (MC) for millions of citizens. So far, most studies on MCs have been limited to two small bays, while systematic research on the whole lake is lacking. To explain the variations in MC concentrations during cyanobacterial bloom, a large-scale survey at 30 sites across the lake was conducted monthly in 2008. The health risks of MC exposure were high, especially in the northern area. Both Microcystis abundance and MC cellular quotas presented positive correlations with MC concentration in the bloom seasons, suggesting that the toxic risks during Microcystis proliferations were affected by variations in both Microcystis density and MC production per Microcystis cell. Use of a powerful predictive modeling tool named generalized additive model (GAM) helped visualize significant effects of abiotic factors related to carbon fixation and proliferation of Microcystis (conductivity, dissolved inorganic carbon (DIC), water temperature and pH) on MC cellular quotas from recruitment period of Microcystis to the bloom seasons, suggesting the possible use of these factors, in addition to Microcystis abundance, as warning signs to predict toxic events in the future. The interesting relationship between macrophytes and MC cellular quotas of Microcystis (i.e., high MC cellular quotas in the presence of macrophytes) needs further investigation

Fault Diagnosis of Rotating Machinery Based on Stochastic Resonance with a Bistable Confining Potential

Since the weak fault characteristics of mechanical equipment are often difficult to extract in strong background noise, stochastic resonance (SR) is widely used to extract the weak fault characteristics, which is able to utilize the noise to amplify weak fault characteristics. Although classical bistable stochastic resonance (CBSR) can enhance the weak characteristics by adjusting the parameters of potential model, when potential barrier height is adjusted potential well width is also changed and vice versa. The simultaneous change of both potential well width and barrier height is difficult to obtain a suitable potential model for better weak fault characteristic extraction and further fault diagnosis of machinery. For this reason, the output signal-to-noise ratio (SNR) of CBSR is greatly reduced, and the corresponding enhancement ability of weak fault characteristics is limited. In order to avoid the shortcomings, a new SR method is proposed to extract weak fault characteristics and further diagnose the faults of rotating machinery, where the classical bistable potential is replaced with a bistable confining potential to get the optimal SR. The bistable confining potential model not only has the characteristics of the classical bistable potential model but also has the ability to adjust the potential width, barrier height, and wall steepness independently. Simulated data are used to demonstrate the proposed new SR method. The results indicate that the weak fault characteristics can be effectively extracted from simulated signals with heavy noise. Experiments on the bearings and planetary gearboxes demonstrate that the proposed SR method can correctly diagnose the faults of rotating machinery and moreover has higher spectrum peak and better recognition degree compared with the CBSR method

Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments