Merging Mechanism Research Articles

Adaptive micro partition and hierarchical merging for accurate mixed data clustering

Heterogeneous attribute data (also called mixed data), characterized by attributes with numerical and categorical values, occur frequently across various scenarios. Since the annotation cost is high, clustering has emerged as a favorable technique for analyzing unlabeled mixed data. To address the complex real-world clustering task, this paper proposes a new clustering method called Adaptive Micro Partition and Hierarchical Merging (AMPHM) based on neighborhood rough set theory and a novel hierarchical merging mechanism. Specifically, we present a distance metric unified on numerical and categorical attributes to leverage neighborhood rough sets in partitioning data objects into fine-grained compact clusters. Then, we gradually merge the current most similar clusters to avoid incorporating dissimilar objects into a similar cluster. It turns out that the proposed approach breaks through the clustering performance bottleneck brought by the pre-set number of sought clusters k and cluster distribution bias, and is thus capable of clustering datasets comprising various combinations of numerical and categorical attributes. Extensive experimental evaluations comparing the proposed AMPHM with state-of-the-art counterparts on various datasets demonstrate its superiority.

Generating complex networks through a vertex merging mechanism: Empirical and analytical analysis

One of the most well-known mechanisms contributing to the emergence of networks with a power-law degree distribution is preferential attachment. In this study, we examined a family of network evolution models based on the merging of two arbitrary vertices, which is shown to also lead to the creation of power-law distributed networks. These models simultaneously apply rules for both node addition and merging, which reflects that many real systems exhibit the processes of growth and shrink. At each iteration, when two vertices merge, the neighbors of one of the vertices become neighbors of the other, and the vertex itself is removed from the network. In addition, at each iteration, a new vertex appears that is attached to randomly selected nodes. As an enhancement, we incorporate a triadic closure mechanism into the evolution to increase the clustering coefficient, a key characteristic of real social networks. We show that in the process of evolution any initial network converges to a stationary state with a power law degree distribution, while the number of edges, the average degree, and the average clustering coefficient saturate to a certain limit values depending on the model parameters.

Characteristics of Merging Plasma Plumes for Materials Process Using Two Atmospheric Pressure Plasma Jets

Atmospheric pressure plasma jets (APPJs) have attracted significant attention due to their ability to generate plasma without vacuum systems, facilitating their use in small areas of plasma processing applications across various fields, including medicine, surface treatment, and agriculture. In this study, we investigate the interaction between two helium plasma jets, focusing on the effects of varying flow rate, voltage, and directional angle. By examining both in-phase and out-of-phase configurations, this research aims to elucidate the fundamental mechanisms of plasma plume merging, which has critical implications for optimizing plasma-based material processing systems. We demonstrate that while increasing voltage and flow rate for the in-phase condition leads to an extended plasma plume length, the plumes do not merge, maintaining a minimal gap. Conversely, plasma plume merging is observed for the out-of-phase condition, facilitated by forming a channel between the jets. This study further explores the impact of these merging phenomena on plasma chemistry through optical emission spectroscopy, revealing substantial differences in the emission intensities of OH, the second positive system of N2, and the first negative system of N2+. These findings offer valuable insights into controlling plasma jet interactions for enhanced efficiency in plasma-assisted processes, particularly where plume merging can be leveraged to improve the treatment area and intensity.

Two-Stage Satellite Combined-Task Scheduling Based on Task Merging Mechanism

Satellites adopt a single-task observation mode in traditional patterns. Although this mode boasts high imaging accuracy, it is accompanied by a limited number of observed tasks and a low utilization rate of satellite resources. This limitation becomes particularly pronounced when dealing with extensive and densely populated observation task sets because the inherent mobility of satellites often leads to conflicts among numerous tasks. To address this issue, this paper introduces a novel multi-task merging mechanism aimed at enhancing the observation rate of satellites by resolving task conflicts. Initially, this paper presents a task merging method based on the proposed multitask merging mechanism, referred to as the constrained graph (CG) task merging approach. This method can merge tasks while adhering to the minimal requirements specified by users. Subsequently, a multi-satellite merging scheduling model is established based on the combined task set. Considering the satellite combined-task scheduling problem (SCTSP), an enhanced fireworks algorithm (EFWA) is proposed that incorporates the CG task synthesis mechanism. This algorithm incorporates local search strategies and a population disturbance mechanism to enhance both the solution quality and convergence speed. Finally, the efficacy of the CG algorithm was validated through a multitude of simulation experiments. Moreover, the effectiveness of the EFWA is confirmed via extensive comparisons with other algorithms, including the basic ant colony optimization (ACO) algorithm, enhanced ant colony optimization (EACO) algorithm, fireworks algorithm (FWA), and EFWA. When the number of tasks in the observation area are dense, such as in the case where the number of tasks is 700, the CG + EFWA (CG is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage) method improves observation benefits by 70.35% (compared to CG + EACO, CG is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage), 78.93% (compared to MS + EFWA, MS is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage), and 39.03% (compared to MS + EACO, MS is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage).

Vehicle Ego-Trajectory Segmentation Using Guidance Cues

Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle’s path and describing it with a segmentation map. This can play an important role in both autonomous driving and advanced driver assistance systems, as it enhances the accuracy of perceiving and forecasting the vehicle’s movements across different driving scenarios. In this work, we propose a deep learning approach for ego-trajectory segmentation that leverages a state-of-the-art segmentation network augmented with guidance cues provided through various merging mechanisms. These mechanisms are designed to direct the vehicle’s path as intended, utilizing training data obtained with a self-supervised approach. Our results demonstrate the feasibility of using self-supervised labels for ego-trajectory segmentation and embedding directional intentions within the network’s decisions through image and guidance input concatenation, feature concatenation, or cross-attention between pixel features and various types of guidance cues. We also analyze the effectiveness of our approach in constraining the segmentation outputs and prove that our proposed improvements bring major boosts in the segmentation metrics, increasing IoU by more than 12% and 5% compared with our two baseline models. This work paves the way for further exploration into ego-trajectory segmentation methods aimed at better predicting the behavior of autonomous vehicles.

Convective Phenomenes in the Context of Meso-β-scale Convective Structures

This review article presents the results of radar studies of convective phenomena in Moldavia and the North Caucasus using Eulerian (ECS) and Lagrangian (LCS) coordinate systems. Application of the Lagrangian approach allowed us to exclude the influence of the tropospheric displacement and to obtain integral grid patterns of thunderstorm-hail processes. These structures are especially well manifested at small wind shears (up to 1 m/sec/km). At large shears, the mesh structures are transformed predominantly into linear structures. The methodology for obtaining integral pictures of radio echoes of thunderstorm processes is described. Intersections of linear elements, which we call faces, occur at nodes. The latter plays a particularly important role in the dynamics and kinematics of convective storms. The latter play a particularly important role in the dynamics and kinematics of convective storms. The development of storms occurs along the facets and at the nodes of meso-β-scale convective structures (MMCS), which explains the mechanisms of splitting and merging of storms: in the first case the facets diverge, in the second case they converge. The relations of motion vectors for different types of storms are obtained. It is shown that the direction of the radio echo canopy coincides with the storm motion trajectory; the evolution vector (propagation) for the most powerful storms deviates from the storm displacement direction by 80°–135°. The structure of the updated band within which the Flanking Line is formed for supercells and multicells is studied. Mnemonic rules have been derived that allow one to infer from instantaneous patterns of anvil orientation and the mutual location of storms whether they are converging or diverging, and to identify left- or right-moving storms. A hypothesis on the internal structure of the cold front of the 2nd kind is stated. The main conclusion of the work is that the evolution of storms is determined by the configuration of meso-β-scale convective structures. This explains various convective phenomena from unified positions. The results are applicable in works on modification of convective cloudiness, for ultra-short-term forecasts of dangerous phenomena, storm warnings of the population, rescue services, etc.

X Meta : SSD-HDD-hybrid Optimization for Metadata Maintenance of Cloud-scale Object Storage

Object storage has been widely used in the cloud. Traditionally, the size of object metadata is much smaller than that of object data, and thus existing object storage systems (such as Ceph and Oasis) can place object data and metadata, respectively, on hard disk drives (HDDs) and solid-state drives (SSDs) to achieve high I/O performance at a low monetary cost. Currently, however, a wide range of cloud applications organize their data as large numbers of small objects of which the data size is close to (or even smaller than) the metadata size, thus greatly increasing the cost if placing all metadata on expensive SSDs. This article presents x Meta , an SSD-HDD-hybrid optimization for metadata maintenance of cloud-scale object storage. We observed that a substantial portion of the metadata of small objects is rarely accessed and thus can be stored on HDDs with little performance penalty. Therefore, x Meta first classifies the hot and cold metadata based on the frequency of metadata accesses of upper-layer applications and then adaptively stores the hot metadata on SSDs and the cold metadata on HDDs. We also propose a merging mechanism for hot metadata to further improve the efficiency of SSD storage and optimize range key query and insertion for hot metadata by designing composite keys. We have integrated the x Meta metadata service with Ceph to realize a high-performance, low-cost object store (called xCeph). The extensive evaluation shows that xCeph outperforms the original Ceph by an order of magnitude in the space requirement of SSD storage, while improving the throughput by up to 2.7×.

Masked autoencoders with generalizable self-distillation for skin lesion segmentation.

In the field of skin lesion image segmentation, accurate identification and partitioning of diseased regions is of vital importance for in-depth analysis of skin cancer. Self-supervised learning, i.e., MAE, has emerged as a potent force in the medical imaging domain, which autonomously learns and extracts latent features from unlabeled data, thereby yielding pre-trained models that greatly assist downstream tasks. To encourage pre-trained models to more comprehensively learn the global structural and local detail information inherent in dermoscopy images, we introduce a Teacher-Student architecture, named TEDMAE, by incorporating a self-distillation mechanism, it learns holistic image feature information to improve the generalizable global knowledge learning of the student MAE model. To make the image features learned by the model suitable for unknown test images, two optimization strategies are, Exterior Conversion Augmentation (EC) utilizes random convolutional kernels and linear interpolation to effectively transform the input image into one with the same shape but altered intensities and textures, while Dynamic Feature Generation (DF) employs a nonlinear attention mechanism for feature merging, enhancing the expressive power of the features, are proposed to enhance the generalizability of global features learned by the teacher model, thereby improving the overall generalization capability of the pre-trained models. Experimental results from the three public skin disease datasets, ISIC2019, ISIC2017, and PH indicate that our proposed TEDMAE method outperforms several similar approaches. Specifically, TEDMAE demonstrated optimal segmentation and generalization performance on the ISIC2017 and PH datasets, with Dice scores reaching 82.1% and 91.2%, respectively. The best Jaccard values were 72.6% and 84.5%, while the optimal HD95% values were 13.0% and 8.9%, respectively.

Impact of long-term thermal exposure on the microstructure and creep resistance of (TiB+TiC+Y2O3)/α-Ti composite

In this study, the influences of thermal exposure on the microstructure and creep resistance of matrix alloy and (TiB+TiC+Y2O3)/α-Ti composites were systematically studied. The results indicate that long-term thermal exposure leads to β-Ti dissolution, as well as the precipitation of a large number of S1 type silicides and α2 particles. The high temperature and internal stress by thermal exposure promote β-Ti→α-Ti phase transition. In order to reduce the total energy, nanoscale {101̅1} transformation twins were formed at the α/β interfaces. The introduction of precipitates and nano twins leads to a significant improvement in the creep resistance after thermal exposure. The creep life can be increased by up to 108 %, while the steady-state creep rate can be reduced by an order of magnitude. After creep, there was no significant increase in the size of precipitated silicides and α2 particles. The rod-shaped S1 type silicide has a better pinning strengthening effect on dislocations than spherical S2 type silicide. Nano twins improve the stability of the α/β interfaces during creep. Some twins were coarsened after creep by merging mechanism. Twins and reinforcements provide nucleation sites for sub-grains during the creep process.

RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Brains are the control center of the nervous system in human bodies, and brain tumor is one of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most effective way to brain tumors early detection in clinical diagnoses due to its superior imaging quality for soft tissues. Manual analysis of brain MRI is error-prone which depends on empirical experience and the fatigue state of the radiologists to a large extent. Computer-aided diagnosis (CAD) systems are becoming more and more impactful because they can provide accurate prediction results based on medical images with advanced techniques from computer vision. Therefore, a novel CAD method for brain tumor classification named RanMerFormer is presented in this paper. A pre-trained vision transformer is used as the backbone model. Then, a merging mechanism is proposed to remove the redundant tokens in the vision transformer, which improves computing efficiency substantially. Finally, a randomized vector functional-link serves as the head in the proposed RanMerFormer, which can be trained swiftly. All the simulation results are obtained from two public benchmark datasets, which reveal that the proposed RanMerFormer can achieve state-of-the-art performance for brain tumor classification. The trained RanMerFormer can be applied in real-world scenarios to assist in brain tumor diagnosis.

Bio-inspired multi-hop clustering algorithm for FANET

Clustering has received extensive attention in recent years as an important approach to address the poor scalability in flying ad hoc networks (FANETs). However, due to the highly mobile nature of FANETs, frequent changes in cluster topology result in increased control overhead and reduced communication efficiency. To tackle these challenges, a novel heuristic clustering algorithm called the physarum-inspired clustering algorithm (PICA) is proposed in this paper. It leverages the intelligence and adaptability of Physarum polycephalum. The PICA adopts a distributed multi-hop clustering approach. During the cluster formation phase, and inspired by the foraging behavior of Physarum polycephalum, the algorithm incorporates link stability, residual energy, and link communication quality as evaluation metrics, thereby ensuring cluster coverage and stability. By selecting the most stable node within an n-hop range as the cluster head, the cluster stability is significantly enhanced. In the cluster maintenance phase, the PICA improves the backup cluster head selection mechanism and introduces damage detection and cluster merging mechanisms to further enhance the robustness and stability of clusters. Experimental results demonstrate that compared to existing EMASS and OSCA algorithms, the proposed PICA reduces the average end-to-end delay by 31.6 % and 25.27 %, improves the average packet delivery ratio by 27.7 % and 19.12 %, and decreases the average inter-cluster switching times by 14.71 % and 14.38 %, respectively. In conclusion, the PICA effectively addresses the issue of poor scalability in FANETs by leveraging the intelligence of multi-headed myxobacteria and adopting a multi-hop clustering approach. As indicated by experimental results, the proposed algorithm demonstrates significant performance improvements in terms of network metrics.

Quasi-full bandgap generating mechanism by coupling negative stiffness and inertial amplification

Current research on mechanical metastructures cannot achieve extremely wide and low bandgaps because of engineering constraints, such as size, stability, and weight. To isolate vibration in both ultralow and middle-high frequency ranges, we propose a new bandgap merging mechanism that coordinates the Bragg and locally resonant bandgaps to form a quasi-full bandgap, starting from zero frequency. In the proposed mechanism, negative stiffness and inertial amplification are introduced to overcome the mass and stiffness limitations, allowing us to tune the lower boundary of the locally resonant bandgap and the cut-off frequency of the Bragg bandgap. The quasi-full bandgap can be achieved analytically by determining certain parameter conditions. Here, the dispersion curves become two horizontal lines independent of the wave vectors with frequencies always zero and another constant. A damping mechanism is introduced to lower the resonance peak below 0 dB for full-band vibration attenuation. To prove the bandgap tunability at large wave amplitude, the nonlinear dispersion relation of the proposed metastructure is solved with the Harmonic Balance Method. Numerical simulations and experiments are also conducted to confirm the results. In general, the proposed bandgap merging mechanism may provide a route for controlling time-varying, ultralow, and wide-frequency vibrations.

The Legal Risks that Local Chinese Companies face when being Acquired by Foreign Multinational Companies and the Measure

With the comprehensive and in-depth development of reform and opening up in the last century, as well as since China's accession to the WTO in 2001, foreign multinational companies have aimed at China, accelerated the pace of entering our market. A large number of mergers and acquisitions of Chinese enterprises have penetrated into many industrial fields in China. International mergers and acquisitions have become one of the important ways for multinational companies to enter our market, and many enterprises in China have encountered or will encounter mergers and acquisitions by multinational companies. Due to the lack of specific and detailed laws and regulations in China, the lack of a systematic regulatory mechanism for foreign companies merging with Chinese companies, and the significant differences and gamesmanship between Chinese and foreign merger and acquisition laws, local Chinese companies are exposed to huge risks in the process of being merged by foreign companies. This paper uses legal analysis to explore the legal risks faced by Chinese enterprises in international M&A. It proposes ways to deal with the risks by improving M&A laws and anti-monopoly laws, creating a national brand protection system and actively implementing anti-malicious takeover measures.

Analysis of Mesoscale Eddy Merging in the Subtropical Northwest Pacific Using Satellite Remote Sensing Data

Mesoscale eddies are ubiquitous in the ocean, yet our understanding of their evolutions, particularly eddy merging processes, remains enigmatic. In this study, the merging processes of two cyclonic–cyclonic and two anticyclonic–anticyclonic eddies are analyzed in the Subtropical Northwest Pacific using satellite remote sensing altimetry data. The results reveal that, as eddies approach each other, their contours become connected, leading to the formation of multi-core eddies. Simultaneously, the merging process prompts substantial exchanges of energy and vorticity, resulting in the dissipation of one eddy and the emergence of a more extensive merged eddy. Throughout the merging process, the eddy contours elongate upwards along the centerline (the line connecting eddy centers) and there are distinct changes in both the horizontal and vertical morphology of the eddies. Notably, after the merging, the eddies distinctly exhibit intensified signals of sea surface temperature and vertical temperature anomaly, an outcome of their transformative fusion. The findings of this study significantly enhance our understanding of mesoscale eddy dynamics, particularly in the intricate eddy merging process. However, it is important to note that, due to limitations in vertical observational data, this study does not provide a quantitative portrayal of the vertical mechanisms of eddy merging, which also underscores a pivotal avenue for future research in the field.

A novel open-set clustering algorithm

DOS (Delta Open Set) is an interesting clustering algorithm that transforms cluster identification into set identification. It identifies the objects whose neighborhoods coincide as an open-set, and an open-set corresponds to a cluster. However, once the dataset is complex, DOS tends to identify overlapping clusters as one category. We believe the main reason is that DOS unifies the neighborhood radius by a specific function, resulting in the inability to cope with various object distributions. To improve DOS, we propose DOS-IN (Irregular Neighborhoods). Specifically, DOS-IN generates irregular neighborhoods based on the similarity between objects to self-adapt to diverse object distributions. As a result, DOS-IN not only can accurately distinguish overlapping clusters but also has fewer input parameters. In addition, DOS-IN introduces the small-cluster merging mechanism to address the shortcoming of DOS in recognizing Gaussian clusters. The experimental results show that DOS-IN is completely superior to DOS. Compared with baseline methods, DOS-IN outperforms them on 7 out of 10 datasets, with at least 13.8% (NMI) and 2.4% (RI) improvement in accuracy. The code of DOS-IN is available at https://github.com/Youth-49/2023-DOS-IN.

Microstructure, Mechanical Properties, In Vitro Biodegradability, and Biocompatibility of Mg-Zn/HA Composites for Biomedical Implant Applications.

Recently, Mg-Zn/hydroxyapatite (HA) composites have attracted much attention as potential candidates for use in bone implants. In this paper, the MgZn/HA composites were prepared using powder metallurgy (PM) and the merging mechanism of MgZn and HA particles was investigated by adjusting the weight ratio of the HA powder. The evolution of the HA distribution in the matrix was examined using SEM and micro-CT images. Afterward, the mechanical properties and biocompatibility of the composites were discussed in detail. The results revealed that the mechanical properties and biocompatibility of the Mg-Zn/HA composites were significantly affected by the HA content. Composites with a low HA content showed increased porosity, improved mechanical strength, and enhanced corrosion resistance after ball milling and cold pressing. These results underscore the importance of optimizing the HA content in Mg-Zn/HA composites for bone implants. Based on our findings, PM Mg-Zn/HA composites with a moderate HA content demonstrate the most promising characteristics as bone implants. The insights gained from this work contribute to the advancement of bone implant materials and hold great potential for enhancing orthopedic surgery outcomes.

Fabrication of n-shaped micro copper structures through localized electrochemical deposition

Localized electrochemical deposition is a cost-effective technique for fabricating 3D microstructures. In this study, to fabricate a complete n-shaped micro copper structure, multi-layer deposition was adopted, and the v-groove was removed by an asymmetric trajectory. Thereafter, the merging mechanism of the n-shaped structure was analyzed using COMSOL simulations. The results demonstrated that multilayer deposition was suitable for connecting the copper column in different directions; the optimized layer was 15 layers with a height of 15 μm. The simulation showed that the v-groove was caused by the speed difference between the column and bumps, and an asymmetric trajectory with a height difference of 55 μm could suppress vertical deposition and remove the v-groove.

Vortex evolution and merging mechanism across 180° sharp bend for laminar inflow

The present study deals with the fundamentals of vortex evolution and merging across a 180° sharp bend for laminar inflow (Re = 800). A three-dimensional flow and pressure distribution across the bend are analyzed by using particle image velocimetry measurements. The results show that the sudden flow redirection and adverse pressure gradient create a recirculation region in the vicinity of the divider wall. The vorticity and turbulent kinetic energy distribution highlight the presence of a shear layer between the recirculation and mainstream turning flow. A Rayleigh instability analysis shows the existence of centrifugal instabilities outside the shear layer in the mainstream turning flow. The imbalance of centrifugal and pressure forces act in unison to produce a net force that creates Dean instability across the mainstream turning flow. Consequently, two pairs of counter-rotating Dean vortices develop after the first turn, possibly merging into a single pair after the subsequent 90° turn. Finally, the evolution and merging of Dean vortices are explained with the help of a conceptual diagram illustrating the force interaction and the formation of counter-rotating currents.

An innovative multifunctional biomimetic adaptive building envelope based on a novel integrated methodology of merging biological mechanisms

Architectural envelopes have a key role in reducing a building's energy consumption and achieving thermal comfort. Many adaptive building envelopes are developed through synthesis with biomimetics for increased performance, but most adapt to a single environmental factor, while living organisms evolved several adaptation solutions that are high-response and multi-functional. However, existing methodologies to develop biomimetic adaptive building envelopes focus on monofunctional systems, which are limited in addressing multifunctional. This paper proposes a novel integrated methodology to develop the design of a multi-functional biomimetic adaptive building envelope by transforming the biological adaptation systems in nature into bio-inspired adaptation mechanisms and merging them into one technical system hosting multiple functions. The merging process is conducted based on the various static and dynamic biological mechanisms' sizes, forms, processes, and functions. The methodology is demonstrated through the MBio-ABE case study, which results from the merging process of different biological mechanisms for Mimosa pudica, Cactus (Echinocactus grusonii), and Stone Plant (Lithops salicola). Thereafter, building performance simulation using EnergyPlus is conducted and the results are evaluated through an optimization process using an algorithm based on Python software to measure the thermal comfort level. The results showed a considerable improvement in the thermal performance of the multi-functional biomimetic adaptive building envelope, which decreased the temperature inside by 3 °C through only natural ventilation. Additionally, the average thermal comfort conditions over the year increased by 11.3%. Obviously, the merging process plays a significant role in achieving multifunctionality in the biomimetic adaptive building envelope to improve the performance of traditional building envelopes.

Probing the delay time of supermassive black hole binary mergers with gravitational waves

ABSTRACT Merging supermassive black hole binaries are expected as a consequence of galaxy mergers, yet the detailed evolution path and underlying merging mechanisms of these binaries are still subject to large theoretical uncertainties. In this work, we propose to combine the (future) gravitational wave measurements of supermassive black hole binary merger events with the galaxy merger rate distributions from galaxy surveys/cosmological simulations, to infer the delay time of binary mergers, as a function of binary mass. The delay time encodes key information about binary evolution, which can be used to test the predictions of various evolution models. With a Mock data set of supermassive black hole binary merger events, we discuss how to infer the distribution of delay time with hierarchical Bayesian inference and test evolution models with the Bayesian model selection method. The astrophysical model uncertainties are also considered in the hierarchical Bayesian inference and Bayesian model selection.