Filling Pattern of Volcanostratigraphy of Cenozoic Volcanic Rocks in the Changbaishan Area and Possible Future Eruptions

The Cenozoic volcanostratigraphy in the Changbaishan area had complex building processes.Twenty-two eruption periods have been determined from the Wangtian＇e, Touxi, and Changbaishan volcanoes. The complex volcanostratigraphy of the Changbaishan area can be divided into four types of filling patterns from bottom to top. They are lava flows filling in valleys（LFFV）, lava flows filling in platform（LFFP）, lava flows formed the cone（LFFC）, and pyroclastic Flow filling in crater or valleys（PFFC/V）. LFFV has been divided into four layers and terminates as a lateral overlap. The topography of LFFV, which is controlled by the landform, is lens shaped with a wide flat top and narrow bottom.LFFP has been divided into three layers and terminates as a lateral downlap. The topography of LFFP is sheet and tabular shaped with a narrow top and wide bottom. It has large width to thickness ratio. It was built by multiple eruptive centers distributed along the fissure. The topography of LFFC, which is located above the LFFP, has a hummocky shape with a narrow sloping top and a wide flat bottom. It terminates as a later downlap or backstepping. It has large width to thickness ratio. It was built by a single eruptive center. The topography of PFFC/V, which located above the LFFC, LFFP, or valley, has the shape of fan and terminates as a lateral downlap or overlap. It has a small width to thickness ratio and was built by a single eruptive center. The filling pattern is controlled by temperature, SiO_2 content,volatile content, magma volume, and the paleolandform. In the short term, the eruptive production of the Changbaishan area is comenditic ash or pumice of a Plinian type eruption. The eruptive volume in future should be smaller than that of the Baguamiao period, and the filling pattern should be PFFC/V,which may cause huge damage to adjacent areas.

Multi-View Feature Fusion Model for Software Bug Repair Pattern Prediction

Many search-based Automatic Program Repair(APR)techniques employ a set of repair patterns to generate candidate patches.Regarding repair pattern selection,existing search-based APR techniques either randomly select a repair pattern from the repair pattern set to apply or prioritize all repair patterns based on the bug's context information.In this paper,we introduce PatternNet,a multi-view feature f usion model capable of predicting the repair pattern for a reported software bug.To accomplish this task,PatternNet first extracts multiview features from the pair of buggy code and bug report using different models.Specifically,a transformer-based model(i.e.,UniXcoder)is utilized to obtain the bimodal feature representation of the buggy code and bug report.Additionally,an Abstract Syntax Tree(AST)-based neural model(i.e.,ASTNN)is employed to learn the feature representation of the buggy code.Second,a co-attention mechanism is adopted to capture the dependencies between the statement trees in the AST of the buggy code and the textual tokens of the reported bug,resulting in co-attentive features between statement trees and reported bug's textual tokens.Finally,these multi-view features are combined i nto a unified representation using a feature fusion network.We quantitatively demonstrate the effectiveness of PatternNet and the feature fusion network for predicting software bug repair patterns.

A Study of the Genesis of Karst-type Subtle Reservoir in Tahe Oilfield

The genesis of the Ordovician karst in the Tahe Oilfield is hypothesized on the basis of many analyses of the geo-chemical data and the sporopollen. Ordovician carbonate rocks experienced multistage karst reconstructions under the condition of low-temperature oxidation meteoric water on the surface or near-surface. The karst occurred mainly in the early Hercynian, which has undergone three stages of karst cycle and developed three sets of cave reservoirs under the influence of the tectonic uplift, and the upper of the cave was formed earlier. The evidence of karst was found in the Caledonian. On the above basis, the genetic prediction pattern of the Ordovician palaeokarst reservoir in this region was set up.

Tuning the Spatially Controlled Growth,Structural Self-Organizing and Cluster-Assembling of the Carbyne-Enriched Nano-Matrix during Ion-Assisted Pulse-Plasma Deposition

Carbyne-enriched nanomaterials are of current interest in nanotechnology-related applications.The properties of these nanomaterials greatly depend on their production process.In particular,structural self-organization and auto-synchronization of nanostructures are typical phenomena observed during the growth and heteroatom-doping of carbyne-enriched nanostructured metamaterials by the ion-assisted pulse-plasma deposition method.Accordingly,fine tuning of these processes may be seen as the key step to the predictive designing of carbyneenriched nano-matrices with improved properties.In particular,we propose an innovative concept,connected with application of the vibrational-acoustic effects and based on universal Cymatics mechanisms.These effects are used to induce vibration-assisted self-organized wave patterns together with the simultaneous manipulation of their properties through an electric field.Interaction between the inhomogeneous electric field distribution generated on the vibrating layer and the plasma ions serves as the additional energizing factor controlling the local pattern formation and self-organization of the nano-structures.

Structure prediction for error pattern and extension of Welch-Berlekamp theorem

As the Welch-Berlekamp (W-B) theorem accurately predicts structure of error locator polynomials of the error patterns, it results in the Welch-Berlekamp algorithm of decoding cyclic codes. However, it is only valid within the BCH bound. Now, a prediction formula for error locator determination is presented based on the study of theory of minimal homogeneous interpolation problem, which extends the Welch-Berlekamp theorem and expands the Welch-Berlekamp algorithm so that the constraint from the BCH

Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].