Merging Approach Research Articles

Study of material homogeneity in the long fiber thermoset injection molding process by image texture analysis

To quantify the homogeneity of fiber dispersion in short fiber-reinforced polymer composites, a method for image texture analysis of 3-dimensional X-ray micro computed tomography (µCT) images is presented in this work. The adaption of the method to the specific requirements of the composite material is accomplished using a statistical region merging approach. Subsequently, the method is applied for evaluating the homogeneity of specimens from an intermediate step of the long fiber thermoset injection molding process as well as molded parts. This new injection molding process enables the manufacturing of parts with a flexible combination of short and long glass fibers. By using a newly developed screw element based on the Maddock mixing element design, the material homogeneity of parts molded in the long fiber injection molding process is improved.

Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

Global Registration of Multiview Unordered Forest Point Clouds Guided by Common Subgraphs

To register multiview, unordered point clouds from forest scenes, we must establish how scans are associated. The proposed method, called RegisMUF, can register arbitrary forest point clouds from aiming scenarios without knowledge of initial position and orientation, without requiring artificial targets, and without recording the order of the scanning sequents. One of the novel contributions of the proposed method is the optimization of a scanning network. We exploit common subgraphs to connect data between spatial subsets and subsequently predict the overlapping areas between adjacent scenarios. In parallel, we propose a rapid coarse strategy and an accurate tree-oriented refinement strategy. Finally, all the scenarios converge to an anchor by combining the minimum loop expansion approach and a parallel merging approach. We experimentally evaluate three challenging data sets: one data set from the FGI benchmark Evo, Finland with five scans, and two other forest data sets from Jiangxi province, China, with 15 and 23 scans. For each data set, the network-building module of RegisMUF produced 100% correct connections to associate all the scans. Together, 108 pairwise cases are exploited to evaluate the matching module in RegisMUF, and the results reveal that the proposed method is superior to or on par with state-of-the-art practices in terms of registration accuracy and successful-registration rate. In end-to-end tests, RegisMUF performs impressively both in terms of registration accuracy and computational cost.

A Morphology-Based Adaptively Spatio-Temporal Merging Algorithm for Optimally Combining Multisource Gridded Precipitation Products With Various Resolutions

Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products.

Timetable merging for the Periodic Event Scheduling Problem

We propose a new mixed integer programming based heuristic for computing new benchmark primal solutions for instances of the PESPlib. The PESPlib is a collection of instances for the Periodic Event Scheduling Problem (PESP), comprising periodic timetabling problems inspired by real-world railway timetabling settings, and attracting several international research teams during the last years. We describe two strategies to merge a set of good periodic timetables. These make use of the instance structure and minimum weight cycle bases, finally leading to restricted mixed integer programming formulations with tighter variable bounds. Implementing this timetable merging approach in a concurrent solver, we improve the objective values of the best known solutions for the smallest and largest PESPlib instances by 1.7 and 4.3 percent, respectively.

Comparison of untargeted and targeted perfluoroalkyl acids measured in adolescent girls

Quantitative biomonitoring (e.g., targeted analysis) has served as the gold standard for environmental exposure biomonitoring for several decades. Recent advancements to broaden exposomic research brought new semi-quantitative untargeted assays that capture a wide range of endogenous metabolites and exogenous exposures in a single assay for discovery, though usually at the expense of absolute quantitation. The high-resolution mass spectrometers (HRMS) typically used in untargeted workflows are sensitive and robust, but there do not yet exist comprehensive comparisons between environmental chemicals at population exposure levels measured using targeted and untargeted assays. Using liquid chromatography (LC)-HRMS, we measured per- and polyfluoroalkyl substances (PFAS) including perfluorohexane sulfonate (PFHxS), n-perfluorooctanoic acid (PFOA), n-perfluorooctanesulfonic acid (PFOS), and perfluorononanoic acid (PFNA) in plasma of 180 girls from New York City, and compared them to previously obtained targeted measures using correlation and rank order methods. We showed high agreement between the methods with Spearman Rhos ranging from 0.69 to 0.92 and weighted Kappa's from 0.62 to 0.82 for tertiles among the PFAS. This finding demonstrates that semi-quantitative data from untargeted assays designed for exposomics can be reliably used to estimate environmental exposures occurring in the general population, providing an economic alternative to targeted assays. We also describe an approach that can be used to compare relative quantitation measurements from an untargeted assay to traditional targeted measures to establish fit-for-purpose usability and validation. These results suggest that environmental exposure measures from untargeted assays can serve as reliable inputs into statistical analysis for discovery and for determining their resultant biological impacts. Future efforts to develop new statistical approaches for standardization and merging with targeted measures—toward harmonization—will further enhance the utility of untargeted assays in environmental epidemiology.

Complexity in Health: Can Design Help Support Interdisciplinary Solutions?

<h3>Key Messages</h3> The collaborative nature of design practices is well-suited to creating the conditions for productively integrating varied disciplines to solve challenging problems in global health. The tensions that can arise from merging different disciplines and approaches within public health and beyond, rather than hinder progress, can be surfaced for stronger solutions to emerge. We explore 3 productive tensions that result from integrating global health and design: 1.Integrating explicit and implicit knowledge 2.Challenging linearity with iteration 3.Enabling collective ownership of processes and solutions For these tensions to be productive, design and global health practitioners must do more to be open and adapt to the heritage of other disciplines; both those that have always played a role in global health and those that are just beginning to garner attention in the field. Designers can do more to establish early that they are not there to replace but rather to “accompany” other disciplines through collaboration.

اثر استخدام مدخل نموذج 6TS و المدونة الالکترونیة فی تنمیة مهارات القراءة النقدیة والکتابة فی اللغة الإنجلیزیة لدی طلاب المرحلة الجامعیة

Abstract The challenges that the whole world have to deal with since Covid-19 pandemic headed all systems in most countries to find solutions. Most countries continued the learning process through online platforms. As a result, there is a need to examine online applications in teaching EFL. Moreover, considering the impact of reflecting students’ lives inside their schoolrooms so they can feel the relevance between real life and what is taught inside classrooms, teachers should echo the use of recent technology within the schoolrooms. Providing opportunity for EFL students to mirror their real communication, share experience and communicate through blogging is considered a significant footstep for developing their skills. The purpose of the current study was to examine the impact of merging 6TS approach and blogging in developing university students critical reading and writing skills. It was predicted that university students receiving training program based on merging 6TS approach and blogging would develop their critical reading and writing skills. Questions articulated to realize the aim of the study focused on: determining critical reading and writing skills suitable for university students and Finding out differences between experimental and control group regarding pre- and post- test for critical reading and writing skills. A critical reading checklist, writing skills checklist, pre-post critical reading test, and pre-post writing skills test were used for data collection. The statistical analysis of the results verified that merging 6TS approach and blogging confidently impacts students critical reading and writing skills.

Retrial Queues with Unreliable Servers and Delayed Feedback

In this paper, models of unreliable multi-server retrial queues with delayed feedback are examined. The Bernoulli retrial is allowed upon the arrival of both primary (from outside) and feedback customers (from orbit), as well as the Bernoulli feedback that may occur after each service in this system. Servers can break down both during the service of customers and when they are idle. If a server breaks down during the service of a customer, then the interrupted customer, in accordance with the Bernoulli scheme, decides either to leave the system or join a common orbit of retrial and feedback customers. An approximate method, based on the space merging approach of three-dimensional Markov chains, is proposed for the calculation of the steady-state probabilities, as well as performance measures of the system. The results of the numerical experiments are demonstrated.

Comparison of merging strategies for building machine learning models on multiple independent gene expression data sets

AbstractHigh‐dimensional gene expression data are regularly studied for their ability to separate different groups of samples by means of machine learning (ML) models. Meanwhile, a large number of such data are publicly available. Several approaches for meta‐analysis on independent sets of gene expression data have been proposed, mainly focusing on the step of feature selection, a typical step in fitting a ML model. Here, we compare different strategies of merging the information of such independent data sets to train a classifier model. Specifically, we compare the strategy of merging data sets directly (strategy A), and the strategy of merging the classification results (strategy B). We use simulations with pure artificial data as well as evaluations based on independent gene expression data from lung fibrosis studies to compare the two merging approaches. In the simulations, the number of studies, the strength of batch effects, and the separability are varied. The comparison incorporates five standard ML techniques typically used for high‐dimensional data, namely discriminant analysis, support vector machines, least absolute shrinkage and selection operator, random forest, and artificial neural networks. Using cross‐study validations, we found that direct data merging yields higher accuracies when having training data of three or four studies, and merging of classification results performed better when having only two training studies. In the evaluation with the lung fibrosis data, both strategies showed a similar performance.

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

Clustering methods in data mining are widely used to detect hotspots in many domains. They play an increasingly important role in the era of big data. As an advanced algorithm, the density peak clustering (DPC) algorithm is able to deal with arbitrary datasets, although it does not perform well when the dataset includes multiple densities. The parameter selection of cut-off distance dc is normally determined by users’ experience and could affect clustering result. In this study, a density-peak-based clustering method is proposed to detect clusters from datasets with multiple densities and shapes. Two improvements are made regarding the limitations of existing clustering methods. First, DPC finds it difficult to detect clusters in a dataset with multiple densities. Each cluster has a unique shape and the interior includes different densities. This method adopts a step by step merging approach to solve the problem. Second, high densities of points can automatically be selected without manual participation, which is more efficient than the existing methods, which require user-specified parameters. According to experimental results, the clustering method can be applied to various datasets and performs better than traditional methods and DPC.

Strategies for targeting undruggable targets

ABSTRACT Introduction Undruggable targets refer to clinically meaningful therapeutic targets that are ‘difficult to drug’ or ‘yet to be drugged’ via traditional approaches. Featuring characteristics of lacking defined ligand-binding pockets, non-catalytic protein–protein interaction functional modes and less-investigated 3D structures, these undruggable targets have been targeted with novel therapeutic entities developed with the progress of unconventional drug discovery approaches, such as targeted degradation molecules and display technologies. Area covered This review first presents the concept of ‘undruggable’ exemplified by RAS and other targets. Next, detailed strategies are illustrated in two aspects: innovation of therapeutic entities and development of unconventional drug discovery technologies. Finally, case studies covering typical undruggable targets (Bcl-2, p53, and RAS) are depicted to further demonstrate the feasibility of the strategies and entities above. Expert opinion Targeting the undruggable expands the scope of therapeutically reachable targets. Consequently, it represents the drug discovery frontier. Biomedical studies are capable of dissecting disease mechanisms, thus broadening the list of undruggable targets. Encouraged by the recent approval of the KRAS inhibitor Sotorasib, we believe that merging multiple discovery approaches and exploiting various novel therapeutic entities would pave the way for dealing with more ‘undruggable’ targets in the future.

Merging Scientific Silos: Integrating Specialized Approaches for Thinking about and Using Spatial Data That Can Provide New Directions for Persistent Fisheries Problems

By merging our specialization silos, fisheries professionals can expand the options that are available to them to address difficult fisheries and aquatic conservation problems, which require an understanding of spatial patterns in geographically large systems. Our purpose is to start a profession‐wide conversation about additional ways to think about and use spatial data. We use case studies to illustrate how identifying and merging multiple specialized approaches (e.g., fish tracking, fish surveys, geomorphology, social science, jurisdictional viewpoints) can create an ensemble that has advantages over the use of any single approach alone. Thus, our perspective is not about solving a specific technical problem with a new tool, but about the benefits of identifying gaps in data from one specialized approach, and filling those gaps with data from other specialized approaches. If multiple approaches are coordinated through a larger, problem‐specific planning strategy, the result can be better outcomes for difficult problems through creative integration. We encourage others to add constructive ideas to the views initiated here.

A call to rethink the Global North university: Mobilising disabled students’ experiences through the encounter of Critical Disability Studies and Epistemologies of the South

In the 1970s, disabled people and other marginalised social groups battled an exclusionary Global North university. Disability Studies emerged from those struggles as epistemologies shaped around a Westernised understanding of disability and inequalities, based on dialectic visions of progress and subjective liberation. Today, the advance of neoliberalism in universities, and its connection with colonial legacies, are embedded in different historical contingencies, and disabled students face new forms of discrimination. By merging analytical approaches from post-structural Critical Disability Studies and Epistemologies of the South, this article draws upon interviews with disabled students conducted in an Italian university to explore how neoliberal and capitalistic practices exclude certain knowledges and modalities of being university students. Through disabled students’ experiences, the article advances epistemologies that encompass processes of decolonisation and de-ableism of the university and argues for the Global North university to be an institution that can democratically reconcile polyhedral subjective possibilities of being.

Estimating the Additive Heritability of Historiometric Eminence in a Super-Pedigree Comprised of Four Prominent Families

By merging analytical approaches from the fields of historiometrics and behavior genetics, a social pedigree-based estimate of the heritability of eminence is generated. Eminent individuals are identified using the Pantheon dataset. A single super-pedigree, comprised of four prominent and interrelated families (including the Wedgwood-Darwin, Arnold-Huxley, Keynes-Baha'u'lláh, and Benn-Rutherford pedigrees) is assembled, containing 30 eminent individuals out of 301 in total. Each eminent individual in the super-pedigree is assigned a relative measure of historical eminence (scaled from 1 to 100) with noneminent individuals assigned a score of 0. Utilizing a Bayesian pedigree-based heritability estimation procedure employing an informed prior, an additive heritability of eminence of .507 (95% CI [.434, .578]) was found. The finding that eminence is additively heritable is consistent with expectations from behavior-genetic studies of factors that are thought to underlie extraordinary accomplishment, which indicate that they are substantially additively heritable. Owing to the limited types of intermarriage present in the data, it was not possible to estimate the impact of nonadditive genetic contributions to heritability. Gene-by-environment interactions could not be estimated in the present analysis either; therefore, the finding that eminence is simply a function of additive genetic and nonshared environmental variance should be interpreted cautiously.

Methodological overview and data‐merging approaches in the study of plant–frugivore interactions

Recording species interactions is one of the main challenges in ecological studies. Frugivory has received much attention for decades as a model for mutualisms among free‐living species, and a variety of methods have been designed and developed for sampling and monitoring plant–frugivore interactions. The diversity of techniques poses an important challenge when comparing, combining or replicating results from different sources with different methodologies. With the emergence of modern techniques, such as molecular analysis or multimedia remote recorders, issues when combining data from different sources have become especially relevant. We provide an overview of all the techniques used for monitoring endozoochorous primary seed dispersal, focusing on a critical appraisal of the advantages and limitations, as well as the context‐dependency nature, of the different methods. We propose five data merging approaches potentially useful to combine frugivory interactions data from different methodologies. Additionally, we provide two case studies where we combine empirical data from plant–animal interactions in Mediterranean shrublands using different methodologies. Data merging resulted in a net increase in the number of distinct pairwise interactions recorded and compensated biases inherent to different methods, resulting in a more robust estimation of network topological descriptors. These case studies clarify the context‐dependent character of the merging approaches, highlighting the value of collecting detailed information on the sampling effort in terms of reliable results and reproducibility. Finally, we discuss the trends with different methodological approaches used in the last decades and future perspectives in this field.

A New Direction to The Maximal Flow Problem: A Route Merging Approach

A New Direction to The Maximal Flow Problem: A Route Merging Approach

A triple collocation-based 2D soil moisture merging methodology considering spatial and temporal non-stationary errors

Random error in remotely sensed (RS) and modeled soil moisture (SM) products is typically assumed to be statistically stationary for the purpose of SM merging applications. In reality, such error is often non-stationary, which may undermine applications based on a stationary assumption. Here, we introduce a dual time-space (2D) SM merging approach that considers a class of inferred error that is non-stationary and undetectable in time (or space) but stationary and detectable in space (or time). Such 2D merging is realized in a least-squares framework where spatial and temporal error variances for each product are estimated via a triple collocation (TC) analysis. As a test case, a 2D-merged SM product is obtained by combining three independent SM products – including two RS SM products and one modeled SM product. Results show that the 2D merging method can effectively handle non-stationary errors and, as a result, produces a superior merged SM product than classical 1D merging methods. The spatial and temporal correlations with in-situ observations are 0.62 and 0.63 [−] on average for 2D merging methods, and 0.58 and 0.59 [−] on average for classical 1D merging methods. By providing a more effective way to detect and remove non-stationary errors during the process of merging multi-source SM products, this approach will improve future global multi-source merged SM products.

Robust multi-dataset identification with frequency domain decomposition

Right after its invention in the late nineties, the Frequency Domain Decomposition (FDD) identification technique became very popular in the operational modal analysis community due to its simplicity and robustness. The underlying idea of this technique consists of computing the singular value decomposition of the Power Spectral Densities (PSDs) estimated from the measured vibration responses with the periodogram (also known as “Welch’s”) approach to identify the natural frequencies and mode shape vectors of the tested structural system. When dealing with multi-dataset output-only modal analysis, the classic approach for extracting the global mode shape vectors from all the measured datasets consists of estimating the mode shape parts corresponding to each dataset, and then scaling the different parts with the aid of the reference sensors. In this paper, two new merging approaches are proposed to scale the different mode shape parts. The first consists of (i) re-scaling the different PSDs prior to the identification; (ii) forming a global matrix containing all the re-scaled PSDs; and (iii) applying the FDD approach to the global PSD matrix to estimate the global mode shape vectors. The second merging strategy consists of (i) forming a global matrix containing all the PSDs without any prior re-scaling; (ii) applying the FDD approach to the PSD matrix to estimate the global mode shape vectors; and (iii) re-scaling the different mode shape parts by making use of the reference singular vectors. In order to illustrate the benefits of the two proposed merging approaches with regards to their classic counterpart from a practical perspective, a real-live application example is presented as the last part of the paper.

Risk spillover from energy market uncertainties to the Chinese carbon market

A thorough understanding of the risk spillover from energy market uncertainties to the Chinese carbon market is significant for risk management and the construction of a unified national carbon market in China. Based on the generalized autoregressive score-driving model, this paper measures China's domestic energy market uncertainty using the conditional volatility of Daqing crude oil returns. Through merging copula approach and CoVaR methods, this paper reveals asymmetric risk spillovers from the international and China's domestic energy market uncertainties to Hubei and Shenzhen carbon pilots, which are the most representative carbon markets in China. Although both the international and China's domestic energy market uncertainties exert significant risk spillover effects on Chinese carbon pilots, the magnitudes of their effects are different. Comparing two carbon pilots, the Shenzhen market is more affected by energy market uncertainties than the Hubei market, thus revealing differences between carbon pilots. The findings in this paper provide meaningful information on investment portfolios and policies for constructing a unified national carbon market.