Merger Model Research Articles

Analytical study of the merging rate of low-mass intermediate-mass black holes in preparation for the future Einstein Telescope

The detection of gravitational wave (GW) signals by Advanced LIGO, Virgo, and KAGRA interferometers opened a new chapter in our understanding of the formation of compact objects. In particular, the detection of GW190521 is observational confirmation of the existence of intermediate-mass black holes (IMBHs); yet more direct observations are needed to better understand the mechanisms behind their formation. In this study, we explore the potential of the next-generation ground-based detector, the Einstein Telescope (ET), to advance our understanding of astrophysics through the detection of GWs emitted by IMBHs. To achieve this, the ET is designed to have improved sensitivity in the low-frequency range of approximately 2-10 Hz, enabling the detection of GWs originating from binary systems containing IMBHs with masses in the range of approximately 10$^2$-10$^5$ $M_ odot We consider black holes (BHs) in the pair-instability form via the hierarchical merger model in galaxies, and approximate the number of events that could be observed by the ET. Our findings indicate that ET could detect a binary black hole (BBH) merger rate of around $2 yr^ $ for BH masses ranging from 10 to 200 M$ odot $, with around 100 $Gpc^ yr^ $ of this rate specifically attributed to BHs in the 100–200 M$ odot $ mass range, which we classify as low-mass IMBHs in this study. This suggests that ET could detect several dozen events similar to GW190521. The exact locations of these BBH mergers are not specified and we count our BH mergers across the entire universe up to a redshift of $z$ approx 2. Observations made with the ET are expected to significantly enhance our comprehension of galactic BH growth, and the existence and characteristics of low-mass IMBHs.

Using Four-Component Instructional Design to Create an Interactive Point-of-Care Ultrasound Curriculum for Physician Associate Students.

Many physician assistant/associate (PA) programs lack point-of-care ultrasound (POCUS) education in the PA curricula and a standardized approach to ultrasound training. The four-component instructional design (4C/ID) model merges 4 concepts for developing effective instructional design content for complex content delivery, such as ultrasound. This research study created an interactive, 2-part ultrasound curriculum with an instructional designer for PA students using the 4C/ID model. Two interactive ultrasound curriculum models were created from the 4C/ID model using a knowledge-based precurriculum quiz, learning tasks with online modules and recordings, part-task practice with ultrasound experts, skills checkoffs, and a postcurriculum quiz including questions on students' perspectives of ultrasound. Two-tailed paired t-test analysis assessed the effectiveness of training. Evaluation of prelearning and postlearning quizzes demonstrated multiple statistically significant assessments supporting the use of instructional design technology for a PA program's ultrasound curriculum. This small study demonstrated that the 4C/ID model may be beneficial for PA POCUS training. The 2-part interactive curriculum improved PA student competency of ultrasound principles and applications and increased confidence for future clinical use of ultrasound.

Predicting pathological complete response following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer using merged model integrating MRI-based radiomics and deep learning data

BackgroundTo construct and compare merged models integrating clinical factors, MRI-based radiomics features and deep learning (DL) models for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).MethodsTotally 197 patients with LARC administered surgical resection after nCRT were assigned to cohort 1 (training and test sets); meanwhile, 52 cases were assigned to cohort 2 as a validation set. Radscore and DL models were established for predicting pCR applying pre- and post-nCRT MRI data, respectively. Different merged models integrating clinical factors, Radscore and DL model were constituted. Their predictive performances were validated and compared by receiver operating characteristic (ROC) and decision curve analyses (DCA).ResultsMerged models were established integrating selected clinical factors, Radscore and DL model for pCR prediction. The areas under the ROC curves (AUCs) of the pre-nCRT merged model were 0.834 (95% CI: 0.737–0.931) and 0.742 (95% CI: 0.650–0.834) in test and validation sets, respectively. The AUCs of the post-nCRT merged model were 0.746 (95% CI: 0.636–0.856) and 0.737 (95% CI: 0.646–0.828) in test and validation sets, respectively. DCA showed that the pretreatment algorithm could yield enhanced clinically benefit than the post-nCRT approach.ConclusionsThe pre-nCRT merged model including clinical factors, Radscore and DL model constitutes an effective non-invasive tool for pCR prediction in LARC.

Development of a Non-Linear Battery Management System Model for Lithium-Ion Batteries Using MATLAB Simulation

Abstract: This research focuses on developing a comprehensive battery model tailored for Battery Management Systems (BMS) using lithium-ion batteries. Given the increasing reliance on lithium-ion batteries in electric vehicles and portable electronic devices, this study aims to construct a non-linear battery model that accurately reflects the charging and discharging characteristics under various load conditions. The research identifies suitable characteristics of lithium-ion batteries essential for effective battery model construction. A MATLAB-Simulink approach is employed to build the model, which integrates both charging and discharging processes into a single simulation framework. This merged model enhances the existing non-linear models by offering improved accuracy in simulating battery behavior under fixed parameters. The outcome is expected to provide a reference model for future studies and applications in power management systems. Additionally, this research underscores the critical role of BMS in extending battery life and ensuring safety, making it a significant contribution to the field of energy management. The study's findings highlight the potential of the proposed model to serve as a foundation for further research and development in battery technologies.

A 3D finite deformation constitutive model for anisotropic shape memory polymer composites integrating viscoelasticity and phase transition concept

The phase transition theory of shape memory polymers (SMPs) often involves a phenomenological assumption that the reference configuration of the newly transformed phase deviates from that of the initial phase. This distinction serves as a crucial mechanism in the manifestation of the shape memory effect. However, elucidating the precise definition of the reference configuration of the transformed phase poses a significant challenge in the formulation of the constitutive model. To tackle this challenge, a three-dimensional (3D) finite deformation constitutive model incorporating effective phase evolution for SMPs has been developed. This model merges insights from the classical viscoelastic framework with the phase transition theory. The anisotropic thermo-viscoelastic constitutive model is further developed by introducing hyperelastic fibers, which integrate the anisotropy of the fibers into a continuous thermodynamic framework through structure tensors. Implemented within the ABAQUS software via a user material (UMAT) subroutine, the proposed model has been meticulously validated against experimental data, showcasing its prowess in simulating stress-strain responses and shape memory characteristics of SMPs and their composites (SMPCs). This innovative model stands as an invaluable instrument for the design and of sophisticated SMP and SMPC structures.

CoMPers: A configurable conflict management framework for personalized collaborative modeling

Context:Modeling is an activity in the software development life cycle where experts and stakeholders collaborate as a team. In collaborative modeling, adhering to the optimistic versioning paradigm allows users to make concurrent changes to the same model, but conflicts may arise. To achieve an integrated and consistent merged model, conflicts must be resolved. Objective:The primary objective of this study was to provide a customizable and extensible framework for conflict management in personalized change propagation during collaborative modeling. Methods:We propose CoMPers, a customizable and extensible conflict management framework designed to address various conflicts encountered in collaborative modeling. We present the duel algorithm for automatically detecting and resolving conflicts according to user preferences. The framework utilizes personalized change propagation to customize collaboration and supports the conflict management process by executing the duel algorithm based on user preferences. As a proof-of-concept, we have implemented the CoMPers framework and extended the EMF.cloud modeling framework to demonstrate its applicability. Results:We have constructed a proof-of-concept implementation and conducted a real-world case study, a benchmark experiment, and a user experience evaluation. Our findings demonstrate that: (1) CoMPers enables collaborators to configure propagation strategies according to their habits; (2) CoMPers successfully identifies all anticipated conflicts and achieves a 100% accuracy in conflict handling; (3) The majority of participants agreed that CoMPers is user-friendly for collaborative modeling. Conclusion:This paper presents the CoMPers framework, which is based on personalized change propagation, and helps collaborators customize conflict management activities. The results confirm the feasibility and advantages of consistent and concurrent modeling within the collaborative CoMPers platform, with an acceptable functionality for approximately ten collaborators.

Mergers, firm size, and volatility in a granular economy

We study the firm dynamics associated with mergers and acquisitions (M&A) and their implications at the micro and macro levels. Our paper presents three main findings: (i) mergers generate a more fat-tailed firm-size distribution, thereby amplifying granular fluctuations and increasing aggregate volatility; (ii) the impact of mergers depends on strategic market power and endogenous markups; and (iii) under endogenous markups, we provide a novel characterization of the firm size-volatility relationship in which volatility declines disproportionately with size. We build a quantitative model of domestic horizontal mergers and find a sizeable impact of mergers on aggregate volatility using counterfactual analysis.

Merging logical models: An application in Acute Myeloid Leukemia modeling.

Gene regulatory network (GRNs) models provide mechanistic understanding of the gene regulations and interactions that control various aspects of cellular behaviors. While researchers have constructed GRNs to model specific sets of gene regulations or interactions, little work has been made to integrate or merge these models into larger, more comprehensive ones that could encompass more genes, and improve the accuracy of predicting biological processes. Here, we present a workflow for merging logical GRN models, which requires sequential steps including model standardization, reproducing, merging and evaluations, and demonstrate its application in acute myeloid leukemia (AML) study. We demonstrate the feasibility and benefits of model merging by integrating two pairs of published models. Our integrated models were able to retain similar accuracy of the original publications, while increasing the coverage and explainability of the biological system. This approach highlights the integration of logical models in advancing system biology and enhancing the understanding of complex diseases.

Hybrid Teaching Design for the "Software Project Management" Course

This paper investigates a hybrid instructional design to improve the educational outcomes of the "Software Project Management" course. Confronted with the challenges of globalization and rapid technological progress, traditional lecture-based teaching has proven inadequate to fulfill the goals of contemporary education. The hybrid teaching model merges online self-directed learning with in-person interactive sessions, facilitating the acquisition and application of knowledge and the enhancement of practical skills through pre-class video instruction, classroom discussions, and case analyses. The educational objectives encompass the transmission and internalization of knowledge, the cultivation of professional ethics, and the improvement of students' comprehensive qualities. The instructional process is meticulously designed to include pre-class self-study, in-class knowledge assessment, case study discussions, practical coursework, and final evaluations. Moreover, the study places a special focus on integrating ideological and political elements within the curriculum to elevate students' ideological and moral qualities.

Waveform systematics in gravitational-wave inference of signals from binary neutron star merger models incorporating higher-order modes information

Waveform systematics in gravitational-wave inference of signals from binary neutron star merger models incorporating higher-order modes information

Quasi-two-dimensionality of three-dimensional, magnetically dominated, decaying turbulence

Decaying magnetohydrodynamic (MHD) turbulence is important in various astrophysical contexts, including early universe magnetic fields, star formation, turbulence in galaxy clusters, magnetospheres and solar corona. Previously known in the nonhelical case of magnetically dominated decaying turbulence, we show that magnetic reconnection is important also in the fully helical case and is likely the agent responsible for the inverse transfer of energy. Again, in the fully helical case, we find that there is a similarity in power law decay exponents in both 2.5D and 3D simulations. To understand this intriguing similarity, we investigate the possible quasi-two-dimensionalization of the 3D system. We perform Minkowski functional analysis and find that the characteristic length scales of a typical magnetic structure in the system are widely different, suggesting the existence of local anisotropies. Finally, we provide a quasi-two-dimensional hierarchical merger model which recovers the relevant power law scalings. In the nonhelical case, we show that a helicity-based invariant cannot constrain the system, and the best candidate is still anastrophy or vector potential squared, which is consistent with the quasi-two-dimensionalization of the system.

Constraining Near-simultaneous Radio Emission from Short Gamma-Ray Bursts Using CHIME/FRB

We use the Canadian Hydrogen Intensity Mapping Experiment (CHIME) Fast Radio Burst (FRB) Project to search for FRBs that are temporally and spatially coincident with gamma-ray bursts (GRBs) occurring between 2018 July 7 and 2023 August 3. We do not find any temporal (within 1 week) and spatial (within overlapping 3σ localization regions) coincidences between any CHIME/FRB candidates and all GRBs with 1σ localization uncertainties <1°. As such, we use CHIME/FRB to constrain the possible FRB-like radio emission for 27 short gamma-ray bursts (SGRBs) that were within 17° of CHIME/FRB’s meridian at a point either 6 hr prior up to 12 hr after the high-energy emission. Two SGRBs, GRB 210909A and GRB 230208A, were above the horizon at CHIME at the time of their high-energy emission and we place some of the first constraints on simultaneous FRB-like radio emission from SGRBs. While neither of these two SGRBs have known redshifts, we construct a redshift range for each GRB based on their high-energy fluence and a derived SGRB energy distribution. For GRB 210909A, this redshift range corresponds to z = [0.009, 1.64] with a mean of z = 0.13. Thus, for GRB 210909A, we constrain the radio luminosity at the time of the high-energy emission to L < 2 × 1046 erg s−1, L < 5 × 1044 erg s−1, and L < 3 × 1042 erg s−1 assuming redshifts of z = 0.85, z = 0.16, and z = 0.013, respectively. We compare these constraints with the predicted simultaneous radio luminosities from different compact object merger models.

High-redshift Merger Model for Low-frequency Gravitational Wave Background

In 2023, the Pulsar Timing Array Collaborations announced the discovery of a gravitational wave background (GWB), predominantly attributed to supermassive black hole binary (SMBHB) mergers. However, the detected GWB is several times stronger than the default value expected from galactic observations at low and moderate redshifts. Recent findings by the James Webb Space Telescope have unveiled a substantial number of massive, high-redshift galaxies, suggesting more massive SMBHB mergers at these early epochs. Motivated by these findings, we propose an “early merger” model that complements the standard merger statistics by incorporating these early, massive galaxies. We compare the early and standard “late merger” models, which assume peak merger rates in the local Universe, and match both merger models to the currently detected GWB. Our analysis shows that the early merger model has a significantly lower detection probability for single binaries and predicts a ∼30% likelihood that the first detectable single source will be highly redshifted and remarkably massive with rapid frequency evolution. In contrast, the late merger model predicts a nearly monochromatic first source at low redshift. The future confirmation of an enhanced population of massive high-redshift galaxies and the detection of fast-evolving binaries would strongly support the early merger model, offering significant insights into the evolution of galaxies and SMBHs.

The unmanned vehicle on-ramp merging model based on AM-MAPPO algorithm

In response to the issues of low merging success rates and poor safety in the on-ramp merging scenario within autonomous driving, we propose an on-ramp merging model for unmanned vehicles based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. Firstly, we introduce an Action-Mask (AM) to prevent the sampling of invalid actions during merging, thus enhancing safety by ensuring only valid actions are considered. Secondly, we incorporate noise advantage values to encourage unmanned vehicles to thoroughly explore the environment and avoid being trapped in local optimal solutions. Experimental results demonstrate that the AM-MAPPO algorithm model improves both safety and traffic efficiency.

Diverse representation-guided graph learning for multi-view metric clustering

Multi-view graph clustering has garnered tremendous interest for its capability to effectively segregate data by harnessing information from multiple graphs representing distinct views. Despite the advances, conventional methods commonly construct similarity graphs straightway from raw features, leading to suboptimal outcomes due to noise or outliers. To address this, latent representation-based graph clustering has emerged. However, it often hypothesizes that multiple views share a fixed-dimensional coefficient matrix, potentially resulting in useful information loss and limited representation capabilities. Additionally, many methods exploit Euclidean distance as a similarity metric, which may inaccurately measure linear relationships between samples. To tackle these challenges, we develop a novel diverse representation-guided graph learning for multi-view metric clustering (DRGMMC). Concretely, raw sample matrix from each view is first projected into diverse latent spaces to capture comprehensive knowledge. Subsequently, a popular metric is leveraged to adaptively learn similarity graphs with linearity-aware based on attained coefficient matrices. Furthermore, a self-weighted fusion strategy and Laplacian rank constraint are introduced to output clustering results directly. Consequently, our model merges diverse representation learning, metric learning, consensus graph learning, and data clustering into a joint model, reinforcing each other for holistic optimization. Substantial experimental findings substantiate that DRGMMC outperforms most advanced graph clustering techniques.

Mergers and acquisitions with private equity intermediation

We develop a search model of mergers and acquisitions (M&A), intermediated by private equity (PE) funds which may face pressure to sell. The selling pressure leads to the development of a secondary buyout (SBO) market, enabling PE funds to bail each other out. Interestingly, an increase in the number of PE funds can improve each fund’s value, because the enhanced benefits of SBOs can prevail over the reduction in value from narrower buy-sell spreads due to more intense competition. We calibrate the model using data for the US middle market and find that PE funds could lose 64% of their valuation without SBOs. Moreover, the increase in the number of funds from 2000 to 2017 contributes to a 48% increase in fund valuation due to the complementarity among funds. Nevertheless, our model predicts that this mechanism might have peaked in 2021, and more PE funds could decrease their value.

Enhancing Sustainable Development Goal Integration in Chilean Citizenship Education: A Thematic Analysis of Textbook Content and Instructional Strategies

This study examines the integration level of the three dimensions of the Sustainable Development Goals (SDGs) (social, economic, and environmental) in Chilean citizenship education textbooks for third and fourth grades’ students in high schools, aiming to identify gaps and propose a comprehensive model for thematic instruction. Utilizing the citizenship education student textbook designed by the Faculty of Educational Sciences at the San Sebastián University in Chile, this research employs thematic analysis to assess the representation of the SDGs within the educational content. The results reveal a positive aspect: the textbooks showcase all three SDG dimensions, highlighting their potential to foster comprehensive SDG comprehension. However, the emphasis predominantly lies on social inclusion, with economic and environmental dimensions receiving less attention. This imbalance could impede the provision of holistic sustainability education, especially as critical ecological issues and certain goals (SDGs 2, 7, 12, 14, and 15) remain under-represented. The proposed thematic instruction model merges sustainability with citizenship education through concept mapping, structured lesson plans, and active collaboration, enhancing SDG literacy. This research contributes to Education for Sustainable Development (ESD) by providing a comprehensive framework that transforms sustainability education globally, cultivating knowledgeable and proactive individuals prepared to address and resolve pressing global challenges.

Effects of wind speed, spacing distance and heat release rate on the combustion and flame merging characteristics of two extra-thin line fires

This paper investigated the mutual interaction between two extra-thin line fires under different wind speeds of 0.4, 0.6, 0.8, 1.0 and 1.2 m/s by using a small-scale wind tunnel. The experiments involved the spacing distances of 5, 10, 15, 20, 25, 30, 35 and 40 cm, and the heat release rates of 19.4, 25.7 and 32.7 kW, respectively. The results show that the flame merging probability increase, as the wind speed or the heat release rate increases. Compared with the two rectangular or square fires, the flame height and flame length will be smaller at the same heat release rate for the extra-thin line fires. Meanwhile, the line fires will decrease the flame merging, making the downstream flame temperature smaller than that of the upstream at the relatively larger wind speed or heat release rate. The interesting finding is that, with the increase of the heat release rate, the flame inclination angle will increase in the fully merging stage, which is opposite to that of square fires. Correspondingly, the flame merging model is built on the influences of the buoyancy force and inertial force of the horizontal wind, which can give the good explanation on this behavior.

Dual generative adversarial networks for merging ocean transparency from satellite observations

ABSTRACT Satellite ocean transparency data have low spatial coverage due to cloud shading, sun glint, swath width, and temporal revisit. Merging multiple satellite ocean transparency data can improve spatial coverage and create a high-accuracy data set. This study proposed a new satellite ocean transparency merging model based on dual generative adversarial networks (ZSD-merging GAN), and the products of full-coverage and high-accuracy ocean transparency were produced. The ZSD-merging GAN comprises the guess GAN and the merging GAN. The guess GAN is used to generate the guess of the ocean transparency merged product, while the merging GAN combines the guess and satellite ocean transparency data to produce the merged product. The experiments show that the spatial coverage of the ZSD-merging GAN product is 100%. The root-mean-square error (RMSE) and average relative error (ARE) between the ZSD-merging GAN product and unmerged ocean transparency data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on JPSS1 are 4.31 m and 11%, respectively, which are better than 5.59 m and 17% for historical average, 5.55 m and 19% for guess product, 5.08 m and 17% for Poisson blending product, and 5.12 m and 21% for Kriging interpolation product.

Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma

Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760–0.763]), providing a new tool for treatment planning in NPC.