Merging Process Research Articles

Visible-Light Imaging of the Super-Alfvénic/Sonic Collisional Merging Process of Field-Reversed Configurations with a Contrast Medium-Mixed Plasmoid

Visible-Light Imaging of the Super-Alfvénic/Sonic Collisional Merging Process of Field-Reversed Configurations with a Contrast Medium-Mixed Plasmoid

Formation of Ultracold Molecules by Merging Optical Tweezers.

We demonstrate the formation of a single RbCs molecule during the merging of two optical tweezers, one containing a single Rb atom and the other a single Cs atom. Both atoms are initially predominantly in the motional ground states of their respective tweezers. We confirm molecule formation and establish the state of the molecule formed by measuring its binding energy. We find that the probability of molecule formation can be controlled by tuning the confinement of the traps during the merging process, in good agreement with coupled-channel calculations. We show that the conversion efficiency from atoms to molecules using this technique is comparable to magnetoassociation.

Giant hail in Poland produced by a supercell merger in extreme instability – A sign of a warming climate?

Severe thunderstorms that occurred on 11 June 2019 over eastern Germany and western Poland caused severe material losses primarily due to large hail. This work documents a detailed inventorization of 79 large hail reports collected in the densely populated area of Gorzów Wielkopolski in Poland, including the largest with a diameter of 12 cm and heaviest with a weight of 380 g. Evaluation of environmental and Doppler radar data indicate that three unique aspects characterize this event. Firstly, the storm benefited from a favorable convective environment including a record-high CAPE exceeding 4000 J kg−1 that was the highest ever measured value at a proximity rawinsonde station of Lindenberg, and the highest for this region according to ERA5 since 1950. Secondly, moisture pooling along the convergence zone led to convective initiation of 3 isolated cells that merged together. Two of them with already embedded rotation. This merger subsequently evolved into a single powerful mesocyclone that was a main cause of giant hail. Subtle differences in the orography within Warta river valley could have played a role in the merging process. Third, a supercell had the biggest intensity over a densely populated area, which led to considerable material losses, but also allowed collection of a large number of hail reports. Distribution of these reports indicates that peak hail size varied significantly on very small distances. Storm has been producing giant hail for around 20-min over a distance of around 10 km and was preceded by a well-developed velocity couplet, bounded weak echo region and hook-echo signatures on the Doppler radar scans.

Forest GUMP: a tool for verification and explanation

AbstractIn this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests. Besides pre/post-condition-based verification and equivalence checking, Forest GUMP also supports three concepts of explanation, the well-known model explanation and outcome explanation, as well as class characterization, i.e., the precise characterization of all samples that are equally classified. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and demonstrates the use of Forest GUMP along illustrative examples. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. As Forest GUMP is publicly available all experiments can be reproduced, modified, and complemented using any dataset that is available in the ARFF format.

Dynamic Evolution and Quantitative Characterization of Fractures in Coal at the Eastern Edge of Ordos Basin under Axial Loading

Understanding the evolution of pore-fracture networks in coal during loading is of paramount importance for coalbed methane exploration. To shed light on these dynamic changes, this study undertook uniaxial compression experiments on coal samples collected from the eastern edge of the Ordos Basin, complemented by μ-CT scanning to obtain a 3D visualization of the crack network model. The compression process was divided into three stages, namely, micro-crack compaction, linear elasticity, and peak failure. An increase in stress resulted in greater concentration and unevenness in fractal dimensions, illustrating the propagation of initial cleats and micro-cracks in the dominant crack direction and the ensuing process of crack merging. These results provide valuable insights into the internal structure and behavior of coal under stress, informing more efficient strategies for coalbed methane extraction.

A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block

As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image.

A novel merging strategy model considering the remaining distance in the acceleration lane

AbstractThe driver of a merging vehicle must account for both the lane‐changing risk and the remaining distance in the acceleration lane () during a merging decision‐making process. To investigate the impact of on merge decisions, a typical freeway merging section was selected and monitored with a millimetre radar and a high‐resolution digital camera, which were mounted on the guardrail in the gore area. More than 2000 merging vehicles were captured during the data collection process. The effects of the surrounding vehicles on the merging behaviour were analyzed, and a merging strategy model considering that was based on the random forest algorithm was constructed. The results show that the following vehicle in the target lane is the main factor that affects the merging behaviour of the merging vehicle. When the decision time window was set to 0.6 s, the proposed merging decision model could distinguish ‘Merge’ events and ‘Wait’ events with accuracies of 97.2% and 89.4%, respectively. The overall accuracy of the model was 94.9%, which was 3.9% higher than for a corresponding merging decision model that excluded influence. The proposed merging decision model can aid merging processes and give cues for human‐like merge decisions of automated vehicles.

Small Files Problem Resolution via Hierarchical Clustering Algorithm.

The Small Files Problem in Hadoop Distributed File System (HDFS) is an ongoing challenge that has not yet been solved. However, various approaches have been developed to tackle the obstacles this problem creates. Properly managing the size of blocks in a file system is essential as it saves memory and computing time and may reduce bottlenecks. In this article, a new approach using a Hierarchical Clustering Algorithm is suggested for dealing with small files. The proposed method identifies the files by their structure and via a special Dendrogram analysis, and then recommends which files can be merged. As a simulation, the proposed algorithm was applied via 100 CSV files with different structures, containing 2-4 columns with different data types (integer, decimal and text). Also, 20 files that were not CSV files were created to demonstrate that the algorithm only works on CSV files. All data were analyzed via a machine learning hierarchical clustering method, and a Dendrogram was created. According to the merge process that was performed, seven files from the Dendrogram analysis were chosen as appropriate files to be merged. This reduced the memory space in the HDFS. Furthermore, the results showed that using the suggested algorithm led to efficient file management.

Majorana tunneling in a non-Hermitian double-quantum-dot structure

In this paper, we investigate a non-Hermitian quantum transport system, which is formed by two quantum dots possessing PT-symmetric complex potentials and side coupled to Majorana fermions. We study the transmission coefficient first. It is found that the conductance is reduced by a factor of 1/4, i.e., G=e2/4h, which can be seen as a signature of Majorana fermion in this double quantum dots structure. It is also found that the joint effects of Majorana tunneling, PT-symmetric complex potentials and dot energy level are mainly as follows: merging process, asymmetry and exceptional point. Then we study the current and conductance driven by a voltage or temperature gradient. Due to the merging process and asymmetry, the thermal current and conductance change sign drastically near the exceptional point. With large fixed temperature difference, the relationship between thermal current and dot level becomes linear, which is a manifestation of Majorana fermion’s robustness. These results can be useful in understanding the quantum transport behaviors modified by the interplay between PT symmetry and Majorana fermions.

Smart bridge maintenance using cluster merging algorithm based on self-organizing map optimization

Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners.

Generalizing Simultaneously to Support Smooth Zooming: Case Study of Merging Area Objects

When users zoom in or out on a digital map, the map should change correspondingly to present geographical information at proper levels. A way to help map users better keep track of their interested objects is to change the map smoothly instead of discretely switching between several levels of detail. This paper focuses on the problem of providing smooth merging of area objects. We propose to merge multiple areas simultaneously to share their animation durations. In this way, each merging operation can be prolonged, and it is visually smoother. We present a greedy algorithm to decide which areas should be merged at each step. The merging process is pre-computed and is recorded into a space-scale cube (SSC). When a user accesses our web map, the SSC is sent to the client side so that the map can be generated by slicing the SSC in the graphics processing unit (GPU). We also explain how to snap the zooming to valid states so that the zooming will not stop halfway of the merging operations. Our case study shows that it is visually smoother to merge simultaneously than to sequentially merge each pair of areas.

Fast and Accurate Framework for Ontology Matching in Web of Things

The Web of Things (WoT) can help with knowledge discovery and interoperability issues in many Internet of Things (IoT) applications. This article focuses on semantic modeling of WoT and proposes a new approach called Decomposition for Ontology Matching (DOM) to discover relevant knowledge by exploring correlations between WoT data using decomposition strategies. The DOM technique adopts several decomposition techniques to order highly linked ontologies of WoT data into similar groups. The main idea is to decompose the instances of each ontology into similar groups and then match instances of similar groups instead of entire instances of two ontologies. Three main algorithms for decomposition have been developed. The first algorithm is based on radar scanning, which determines the distribution of distances between each instance and all other instances to determine the cluster centroid. The second algorithm is based on adaptive grid clustering, where it focuses on distribution information and the construction of spanning trees. The third algorithm is based on split index clustering, where instances are divided into groups of cells from which noise is removed during the merging process. Several studies were conducted with different ontology databases to illustrate the use of the DOM technique. The results show that DOM outperforms state-of-the-art ontology matching models in terms of computational cost while maintaining the quality of the matching. Moreover, these results demonstrate that DOM is capable of handling various large datasets in WoT contexts.

Insights into the formation and diversification of a novel chiropteran wing membrane from embryonic development

BackgroundThrough the evolution of novel wing structures, bats (Order Chiroptera) became the only mammalian group to achieve powered flight. This achievement preceded the massive adaptive radiation of bats into diverse ecological niches. We investigate some of the developmental processes that underlie the origin and subsequent diversification of one of the novel membranes of the bat wing: the plagiopatagium, which connects the fore- and hind limb in all bat species.ResultsOur results suggest that the plagiopatagium initially arises through novel outgrowths from the body flank that subsequently merge with the limbs to generate the wing airfoil. Our findings further suggest that this merging process, which is highly conserved across bats, occurs through modulation of the programs controlling the development of the periderm of the epidermal epithelium. Finally, our results suggest that the shape of the plagiopatagium begins to diversify in bats only after this merging has occurred.ConclusionsThis study demonstrates how focusing on the evolution of cellular processes can inform an understanding of the developmental factors shaping the evolution of novel, highly adaptive structures.

Water Carrying Capacity Determination based on Ecosystem Services in GKS Metropolitan Area

Gerbangkertasusila (GKS) Metropolitan Area is a strategic area with an urban nature that has a very fast economic, physical, and demographic acceleration. This rapid development has caused pressure on the environment, and has caused the depletion of natural resources, especially water resources. Water resources are the most important natural resources and are related to a series of ecosystem services such as flood management, water purification, recreation and especially water supply. Water supply can be assessed from a supply and demand perspective. In Indonesia, the process of calculating the carrying capacity of water is calculated spatially using the scale grid system method. The use of this method aims to reduce the limitations of data that has different scales, to facilitate the process of merging or aggregating data to get more in-depth results. Therefore, it is interesting to use this method to assess the status of water carrying capacity in the GKS Area, given the importance of the GKS role on a local and regional scale in East Java Province. In addition, this method is relatively new, so there are not many cities in Indonesia that apply this method to assess water carrying capacity.

Face Editing Using Part‐Based Optimization of the Latent Space

AbstractWe propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non‐linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub‐vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part‐based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces.

Chronology of the chemical enrichment of the old Galactic stellar populations

Context. Over its history, the Milky Way has accreted several smaller satellite galaxies. These mergers added stars and gas to the Galaxy and affected the properties of the pre-existing stellar populations. Stellar chemical abundances and ages are needed to establish the chronological order of events that occur before, during, and after such mergers. Aims. We report the precise ages (∼6.5%) and chemical abundances for the TITANS, a sample of old metal-poor dwarfs and subgiants with accurate atmospheric parameters. We also obtain ages with an average precision of 10% for a selected sample of dwarf stars from the GALAH survey. We use these stars, located within ∼1 kiloparsec of the Sun, to analyse the chronology of the chemical evolution of in situ and accreted metal-poor stellar populations. Methods. We determined ages via isochrone fitting. For the TITANS, we determined Mg, Si, Ca, Ti, Ni, Ba, and Eu abundances using spectrum synthesis. The [Mg/Fe] abundances of the GALAH stars were re-scaled to be consistent with the abundances of the TITANS. We separated stellar populations by primarily employing chemical abundances and orbits. Results. We find that star formation in the so-called Gaia-Enceladus or Gaia-Sausage galaxy, the last major system to merge with the Milky Way, lasted at least 3 billion years and got truncated 9.6 ± 0.2 billion years ago. This marks with a very high level of precision the last stage of its merging process. We also identified stars of a heated metal-poor in-situ population with virtually null net rotation, probably disturbed by several of the early Milky Way mergers. We show that this population is more metal-rich than Gaia-Enceladus at any moment in time. Conclusions. The sequence of events uncovered in our analysis supports the hypothesis that Gaia-Enceladus truncated the formation of the high-α disc and caused the gas infall that forms the low-α disc.

Chain Dynamics of Partially Disentangled UHMWPE around Melting Point Characterized by 1H Low-Field Solid-State NMR.

Melts of ultrahigh molecular weight polyethylene (UHMWPE) entangled significantly, suffering processing difficulty. In this work, we prepared partially disentangled UHMWPE by freeze-extracting, exploring the corresponding enchantment of chain mobility. Fully refocused 1H free induction decay (FID) was used to capture the difference in chain segmental mobility during the melting of UHMWPE with different degrees of entanglement by low-field solid-state NMR. The longer the polyethylene (PE) chain is in a less-entangled state, the harder the process of merging into mobile parts after detaching from crystalline lamella during melting. 1H double quantum (DQ) NMR was further used to obtain information caused by residual dipolar interaction. Before melting, the DQ peak appeared earlier in intramolecular-nucleated PE than in intermolecular-nucleated PE because of the strong constraints of crystals in the former one. During melting, less-entangled UHMWPE could keep disentangled while less-entangled high density polyethylene (HDPE) could not. Unfortunately, no noticeable difference was found in DQ experiments between PE melts with different degrees of entanglement after melting. It was ascribed to the small contribution of entanglements compared with total residual dipolar interaction in melts. Overall, less-entangled UHMWPE could reserve its disentangled state around the melting point long enough to achieve a better way of processing.

The luminosity functions of kilonovae from binary neutron star mergers under different equation of states

ABSTRACTKilonovae produced by mergers of binary neutron stars (BNSs) are important transient events to be detected by time domain surveys with the alerts from the ground-based gravitational wave detectors. The observational properties of these kilonovae depend on the physical processes involved in the merging processes and the equation of state (EOS) of neutron stars (NSs). In this paper, we investigate the dependence of kilonova luminosities on the parameters of BNS mergers, and estimate the distribution functions of kilonova peak luminosities (KLFs) at the u, g, r, i, y, and z bands as well as its dependence on the NS EOS, by adopting a comprehensive semi-analytical model for kilonovae (calibrated by the observations of GW170817), a population synthesis model for the cosmic BNSs, and the ejecta properties of BNS mergers predicted by numerical simulations. We find that the kilonova light curves depend on both the BNS properties and the NS EOS, and the KLFs at the considered bands are bimodal with the bright components mostly contributed by BNS mergers with total mass $\lesssim 3.2\, \mathrm{M}_\odot$/$2.8\, \mathrm{M}_\odot$ and fainter components mostly contributed by BNS mergers with total mass $\gtrsim 3.2\, \mathrm{M}_\odot$/$2.8\, \mathrm{M}_\odot$ by assuming a stiff/soft (DD2/SLy) EOS. The emission of the kilonovae in the KLF bright components is mostly due to the radiation from the wind ejecta by the remnant discs of BNS mergers, while the emission of the kilonovae in the KLF faint components is mostly due to the radiation from the dynamical ejecta by the BNS mergers.

Mathematical modelling of coalescence of viscous particles: An overview

AbstractViscous particles of polymer melts and glasses coalesce under the action of surface tension. Resistance is due to viscosity, while inertia is not a contributing factor, with the Ohnesorge number being very high. Russian physicist Yakov Frenkel developed a model for neck growth during the initial stage of the merging process of two spherical particles, assuming uniform biaxial extensional flow. Frenkel's model was extended for prediction of neck size as a function of time to the completion of coalescence, expressed by an ordinary differential equation. The time t is expressed in dimensionless form as (tγ/ηR), where η, γ, and R denote the viscosity, surface tension, and particle radius, respectively. Models were also developed for viscoelastic effects, non‐isothermal conditions, and unequal diameter particles. For the coalescence of infinitely long cylinders, planar extensional flow is assumed. Other investigators presented numerical solutions of the Navier–Stokes equations, which include shear flow components, but the predictions of neck growth are not much different from those of the Frenkel‐based models. Comparisons to experiments are also discussed, involving polymers, glasses, animal tissue cells, and biomacromolecules. The models are also used in additive manufacturing applications for the determination of bonding and pore shrinkage.

Almost Rerere: Learning to Resolve Conflicts in Distributed Projects

The concurrent development of applications requires reconciling conflicting code updates by different developers. Recent research on the nature of merge conflicts in open source projects shows that a significant fraction of merge conflicts have limited size (one or two lines of code) and are resolved with simple strategies that use code present in the merged versions. Thus the opportunity arises of supporting the resolution of merge conflicts automatically by learning the way in which developers fix them. In this paper we propose a framework for automating the resolution of merge conflicts which learns from the resolutions made by developers and encodes such knowledge into conflict resolution rules applicable to conflicts not seen before. The proposed approach is text-based, does not depend on the programming languages of the merged files and exploits a well-known and general language (search and replacement regular expressions) to encode the conflict resolution rules. Evaluation results on 14,872 conflicts from 25 projects show that the system can synthesize a resolution for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 49% of the conflicts occurred during the merge process ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 89% if one considers conflicts that have at least one similar conflict in the data set) and can reproduce exactly the same solution that human developers have applied in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 55% of the cases ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 62% for single line conflicts).