Merging Procedure Research Articles

Study on Arrival Aircraft Sequencing Based on Optimization of Point Merge Procedure

With the air transport industry developing dramatically, the problem of flight congestion in the terminal area (TMA) has been aggravated. Particularly for arrival aircraft, the extraholding and delay not only increase the workload of air traffic controllers and aircrew but also reduce the operational efficiency. To struggle with this issue, it is crucial to study how to improve the operational efficiency of arrival aircraft. Under the background of the rapid promotion of Point Merge (PM) procedure program in the busy TMAs in the world, this paper firstly combines the optimization of PM procedure and arrival aircraft sequencing, aiming to maximize the operational benefits of arrival aircraft in TMA. Firstly, the framework of the PM procedure is set up to replace the traditional approach route known as Standard Instrument Arrival (STAR). After that, the PM procedure optimization model is established to provide a better approach route for each arrival aircraft. Then for a flow of arrival traffic, an aircraft sequencing model is established to readjust the landing sequence of arrival aircraft, thus further improving the operational benefits of arrival aircraft. Finally, taking the TMA of Shanghai Hongqiao International Airport as the case, we compare the impact of different flight procedures and different landing sequences on the operational benefits of arrival aircraft. The experimental results show that the application of the PM procedure optimization model and sequencing model can effectively improve the operational benefits on the premise of ensuring safety, thus realizing the safe and orderly approach in TMA.

A new Delaunay triangulation-based approach to characterize the pore network in granular materials

The ability of granular materials to retain fine particles transported within their void space by seepage flow depends strongly on the geometric characteristics of their pore network (pore sizes and constriction sizes). Hence, characterizing the pore network of a granular assembly obtained by means of a micro-tomography scanning or a numerical discrete simulation is of great importance in assessment of its filtration efficiency. Here, we determine characteristics of the pore network of virtual samples composed of spherical particles simulated by using the DEM. A new criterion is proposed to merge neighboring tetrahedra issued from the weighted Delaunay triangulation. To do so, we extend the concept of inscribed void sphere, initially defined for each tetrahedron, to each polyhedral sub-domain constituted of merged tetrahedra. This inscribed void sphere fits the best the void space within the sub-domain. Flat tetrahedra are first eliminated by a primary merging procedure taking into account two basic geometric conditions required for each pore. Adjacent sub-domains are then merged depending on the level of overlap between their inscribed void spheres. The pore size distributions and constriction size distributions (CSD) of granular samples with different grain size distributions obtained with the new merging criterion are compared to those given by two other criteria often used in the literature. The new criterion allows us to reduce greatly the inherent subjectivity in characterizing the granular pore network and to remediate the drawbacks of the two considered criteria in the literature. Moreover, CSDs given by these different criteria tend to converge for gap-graded and widely graded materials. The CSDs obtained with the new merging criterion are used to estimate the controlling constriction sizes $$D_c^*$$ of the considered samples, and the estimated values of $$D_c^*$$ are compared to Kenney and Lau’s empirical rule.

Development of a Large Colonoscopy-Based Longitudinal Cohort for Integrated Research of Colorectal Cancer: Partners Colonoscopy Cohort.

Conventional adenomas (CAs) and serrated polyps (SPs) are precursors to colorectal cancer (CRC). Understanding metachronous cancer risk is poor due to lack of accurate large-volume datasets. We outline the use of natural language processing (NLP) in forming the Partners Colonoscopy Cohort, an integrated longitudinal cohort of patients undergoing colonoscopies. We identified endoscopy quality data from endoscopy reports for colonoscopies performed from 2007 to 2018 in a large integrated healthcare system, Mass General Brigham). Through modification of an established NLP pipeline, we extracted histopathological data (polyp location, histology and dysplasia) from corresponding pathology reports. Pathology and endoscopy data were merged by polyp location using a four-stage algorithm. NLP and merging procedures were validated by manual review of 500 pathology reports. 305,656 colonoscopies in 213,924 patients were identified. After merging, 76,137 patients had matched polyp data for 334,750 polyps. CAs and SPs were present in 86,707 (28.5%) and 55,373 (18.2%) colonoscopies. Among patients with polyps at index screening colonoscopy, 14,931 (33.4%) had follow-up colonoscopy (median 46.4, interquartile range 33.8-62.4months); 91 (0.2%) and 1127 (2.5%) patients developed metachronous CRC and high-risk polyps (polyps ≥ 10mm or CAs having high-grade dysplasia/villous/tublovillous histology or SPs with dysplasia). Genetic data were available for 23,787 (31.7%) patients with polyps from the Partners Biobank. The validation study showed a positive predictive value of 100% for polyp histology and locations. We created the Partners Colonoscopy Cohort providing essential infrastructure for future studies to better understand the natural history of CRC and improve screening and post-polypectomy strategies.

Enhanced computerised diagnosis of Alzheimer’s disease from brain MRI images using a classifier merger strategy

This paper targets a novel classifier merging methodology for automated and precise judgement of Alzheimer's disease. The six diverse joining rules (mean, median, product, maximum, minimum, and voting) are presented with their significance in the consolidating of classifiers with that of the individual classifiers. The approval of the proposed combination procedure is performed on benchmark ADNI dataset. The underlying emphasis uncovered the four individual classifiers out of thirteen classifiers, to be specific BayesNet, linear discriminant classifier (ldc), quadratic Bayes normal classifier (udc), and Kernel Support vector machine (KSVM) from various machine learning groups, gained the best performance values of 74.77%, 71.62%, 77.76, and 76.13% separately. The classifier merging model decorated from these four best algorithms displayed a much healthier performance with a shared mean error rate of 0.2123 in contrast to the mean error rate of 0.2493 before Ensemble. Our examinations effectively show that a classifier merging procedure produces better outcomes and orders subjects more precisely than base-level classifiers.

Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

The joint interpretation of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data has developed rapidly in recent years due to continuously evolving image processing technology. Nowadays, most feature extraction methods are carried out by convolving the raw data with fixed-size filters, whereas the structural and texture information of objects in multiple scales cannot be sufficiently exploited. In this article, a shearlet-based structure-aware filtering approach, abbreviated as ShearSAF, is proposed for HSI and LiDAR feature extraction and classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is firstly adopted on raw HSIs to reduce the dimensions. Then, the KPCA-reduced HSI and LiDAR data are converted to the shearlet domain for texture and area feature extraction. In contrast, superpixel segmentation algorithm utilizes the raw HSI data to obtain the initial oversegmentation map. Subsequently, by utilizing a well-designed minimum merging cost that fully considers spectral (HSI and LiDAR data), texture, and area features, a region merging procedure is gradually conducted to produce a final merging map. Further, a scale map that locally indicates the filter size is achieved by calculating the edge distance. Finally, the KPCA-reduced HSI and LiDAR data are convolved with the locally adaptive filters for feature extraction, and a random forest (RF) classifier is thus adopted for classification. The effectiveness of our ShearSAF approach is verified on three real-world datasets, and the results show that the performance of ShearSAF can achieve an accuracy higher than that of comparison methods when exploiting small-size training sample problems. The codes of this work will be available at http://jiasen.tech/papers/ for the sake of reproducibility.

E-democracy Applications in Greek Social Enterprises

Electronic democracy and its features are slowly starting to be adopted in various countries and organisations across the world, as its practical advantages are gradually being recognised and concerns about security are being dealt with. Meanwhile, social enterprises are rising as a strong alternative against the purely private economy, and for this type of enterprises, democracy is in the core of their governance process. However, the links between these two topics remain unexplored. The current research investigated the use of e-democracy applications by Greek social enterprises, as well as the opinions of their members regarding the value of e-democracy. The results show relatively small adoption rate, but wide interest in merging e-democracy procedures in their daily activities.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3ppb, smaller than the RMSE of the two base products independently (LUR: 14.87ppb, WDSN: 10.45ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Multiobjective Optimization and Local Merge for Clustering Attributed Graphs.

Methods for detecting the community structure in complex networks have mainly focused on network topology, neglecting the rich content information often associated with nodes. In the last few years, the compositional dimension contained in many real-world networks has been recognized fundamental to find network divisions which better reflect group organization. In this paper, we propose a multiobjective genetic framework which integrates the topological and compositional dimensions to uncover community structure in attributed networks. The approach allows to experiment different structural measures to search for densely connected communities, and similarity measures between attributes to obtain high intracommunity feature homogeneity. An efficient and efficacious post-processing local merge procedure enables the generation of high quality solutions, as confirmed by the experimental results on both synthetic and real-world networks, and the comparison with several state-of-the-art methods.

Traffic synchronization in terminal airspace to enable continuous descent operations in trombone sequencing and merging procedures: An implementation study for Frankfurt airport

This paper proposes to enhance the current tromboning paradigm with a four dimensional trajectory negotiation and synchronization process with the aim to maximise the number of neutral Continuous descent operations (CDOs, descents with idle thrust and no speed-brakes usage) achieved by the arriving traffic in terminal maneuvering areas (TMAs). An optimal control problem has been formulated and solved in order to generate a set of candidate CDO trajectories per aircraft, while a mixed-integer-linear programming model has been built in order to optimally assign routes of the arrival procedure and required times of arrival (RTAs) to the arriving traffic when still in cruise. The assessment has been performed for Frankfurt am Main airport (Germany), by using arrival traffic gathered from historical data. Results show that, after assigning an RTA and a route to every arriving aircraft, it is possible to maximize the number of aircraft performing CDOs while ensuring a safe time separation throughout the arrival procedure. For low traffic scenarios, the totality of traffic can be successfully scheduled, while for high traffic scenarios this is not the case and not all aircraft can be scheduled if neutral CDOs are flown. However, by assuming different arbitrarily defined arrival times to the TMA or by considering more additional shortcuts in the trombone procedure it is possible to increase the number of aircraft scheduled. Besides improving current operations in the short-mid term, the methodology presented in this paper could become a technical enabler towards a fully deployed trajectory based operations (TBO) environment.

Frequency domain data merging in operational modal analysis based on least squares approach

Assembling of multi-setup measurements emerges as a challenging problem in the structural health monitoring applications and may cause some important issues in the estimation of global modal parameters such as frequency, damping ratio and modal shape vector. To overcome this problem, a novel frequency domain pre-identification data merging method is proposed in this study. In the proposed methodology, to obtain a single measurement set, a least squares approach is employed resulting in a global response that is scaled from the multi-setup data. For the verification of the proposed merging procedure, one numerical, two experimental studies and one real data application have been conducted. The results obtained from the numerical, experimental and real data analysis indicate that the presented methodology provides rather high-quality estimations for multi-setup measurement problems.

Developmental perspectives of numerical thinking for the interpretation of physical quantities

This research reveals the perspectives in the teaching of numerical thinking through a documentary review. The representation of physical elements such as functions, vectors and operators, and their subsequent interpretation through numerical thinking gives meaning to the physical quantities that such elements represent. A documentary sample integrated for 40 sources on numerical thinking such as articles published in indexed journals, postgraduate dissertations, and books is considered. A qualitative content analysis method is used. First, an encoding procedure is applied for tagging the extracted information from the source documents. Then, a split and merge procedure is considered in order to establish from the tags the dimensions and categories that allow determining the conceptual relationships that support the developmental perspectives of numerical thinking. The method reveals that the numerical thinking can be developed in the global context from four perspectives, namely, historical, theoretical, curricular, and social perspectives. From these results, an incorporation of the such perspectives can be institutionalized for promoting curricular, didactic and evaluative new proposals for numerical thinking teaching.

Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian

We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.

KAJIAN YURIDIS TENTANG REGULASI PEMEKARAN DAERAH BERDASARKAN UNDANG-UNDANG NOMOR 32 TAHUN 2004 TENTANG PEMERINTAHAN DAERAH BESERTA PERATURAN PELAKSANA (Studi Pembentukan Provinsi Kapuas Raya di Kalimantan Barat)

In the context of regional expansion, Article 4 (paragraph 3) of Act. Number 32 of 2004 about Regional Government give assurance that the possibility of the formation of a merger of several local or regional sections of biting or expansion of an area into two regions or more. By referring to Act. No. 32 of 2004 and the procedure refers to Government Regulation Number 129 of 2000 about Formation Conditional and Criteria for Expansion, Elimination and Regional Merger, the administration proposed splitting of the Province of West Kalimantan Kapuas Raya with the formation of the Province are complete and are qualified conditions of administrative, technical and physical territoriality. By the Governor of West Kalimantan, give information to the Commission II Advisory Council of the Republic of Indonesia. As suggested in the current domain and the Central Government Advisory Council of the Republic of Indonesia came to pass repeal of Government Regulation Number 129 of 2000 which has been used as a guideline proposed for establishing the Province of Kapuas Raya and replaced by Government Regulation Number 78 of 2007 on Procedures for Formation, Elimination and Region Merging. Cancellation of Government Regulation Number 129 of 2000, has implications for certainty of law (legality) the completeness of the proposed administrative division of West Kalimantan Province. With the Government Regulation Number 129 of 2000 jo. Government Regulation Number 78 of 2007 then, needs to be done in a normative study of the Act. 32 of 2004 by Government Regulation Number 129 of 2000 and Government Regulation Number 78 of 2007. Based on the result of analysis the problems examined, it is known that the proposed of the Province of West Kalimantan Kapuas Raya based on existing provisions in the Government Regulation Number 129 of 2000. While the enactment of Government Regulation Number 78 of 2007 automatically revoke the previous government regulation and no longer valid, because the Argumentum contrario Government Regulation Number 78 of 2007 Concerning Formation, Elimination and Regional Merger may not be used against the events referred to regulations occurred prior to the otherwise applicable government regulation. Based on the results the Government recommended, given the division of the Province of West Kalimantan province with the establishment of Kapuas Raya domain was already in the Central Government to get the draft Law Kapuas Raya Province Formation of Advisory Council of the Republic of Indonesia, the final step until now realized yet, in expect for the entire Local Government and Regional Representatives Council of the District Sintang, Sanggau, Sekadau, Melawi and Kapuas Hulu regency or provincial government more intensive approach the House of Representatives Commission II of the Republic of Indonesia and the Minister of Foreign Affair.

Sector showers for hadron collisions

In conventional parton showers (including ones based on dipoles/antennae), a given (Born + m)-parton configuration can typically be reached via O(m!) different “shower histories”. In the context of matrix-element-correction and merging procedures, accounting for these histories mandates fairly complex and resource-intensive algorithms. A so far little-explored alternative in the shower context is to divide the branching phase spaces into distinct “sectors”, each of which only receives contributions from a single branching kernel. This has a number of consequences including making the shower operator bijective; i.e., each parton configuration now has a single unique “inverse”. As a first step towards developing a full-fledged matrix-element-correction and merging procedure based on such showers, we here extend the sector approach for antenna showers to hadron-hadron collisions, including mass and helicity dependence.

Simulating hard photon production with Whizard

One of the important goals of the proposed future hbox {e}^{+}hbox {e}^{-} collider experiments is the search for dark matter particles using different experimental approaches. The most general search approach is based on the mono-photon signature, which is expected when production of the invisible final state is accompanied by a hard photon from initial state radiation. Analysis of the energy spectrum and angular distributions of those photons can shed light on the nature of dark matter and its interactions. Therefore, it is crucial to be able to simulate the signal and background samples in a uniform framework, to avoid possible systematic biases. The Whizard program is a flexible tool, which is widely used by hbox {e}^{+}hbox {e}^{-} collaborations for simulation of many different “new physics” scenarios. We propose the procedure of merging the matrix element calculations with the lepton ISR structure function implemented in Whizard. It allows us to reliably simulate the mono-photon events, including the two main Standard Model background processes: radiative neutrino pair production and radiative Bhabha scattering. We demonstrate that cross sections and kinematic distributions of mono-photon in neutrino pair-production events agree with corresponding predictions of the mathcal{KK} MC, a Monte Carlo generator providing perturbative predictions for SM and QED processes, which has been widely used in the analysis of LEP data.

Study on the Optimization Method of Point Merge Procedure Based on Benefit in the Terminal Area

With the rapid development of the air transport industry, the problem of airspace congestion and flight delay in the terminal area (TMA) becomes more and more serious. In order to improve the efficiency of flight operations in TMA, point merge procedure had been devised. This paper takes the approach routes in TMA as the research object, taking into account such conditions as obstacle clearance, flight interval, and procedure area. Based on the flight time, fuel consumption, pollutant emission, and noise impact, an optimization model of point merge procedure is constructed. Genetic algorithm is used to optimize the structure of procedure. The Shanghai Hongqiao International Airport is selected for simulation verification, and the actual flow distribution of the airport is analyzed as an example. The results show that the average flight time was reduced by 0.26 min, the average fuel consumption was reduced by 1,240.64 kg, the average NOx emissions were reduced by 1.09 kg, and the noise impact range was contracted by 55 km2 after optimization. The point merge procedure optimization method can be expected to reduce the flight time, fuel consumption, and environmental impact of flights in TMA, so as to optimize the aircraft approach trajectory.

Great Recession and club convergence in Europe: A cross‐country, cross‐region panel analysis (2000–2015)

AbstractThe paper aims at investigating the impact of the Great Recession on per capita GDP convergence process across European regions and countries. Using the time‐varying factor model developed by Phillips and Sul for the period 2000–2015 and two different merging procedures to identify clubs, we provide evidence of the diverging impact of the Great Recession “between” the higher and the lower convergence clubs at both regional and country levels as well as of the strengthening of the convergence process “within” most clubs. In addition, we add further evidence to the common belief of a “multi‐speed” Europe by contrasting Eastern European countries' and regions' behavior vis‐à‐vis original European members' one, and by identifying the factors that affect club membership and resilience to the recent economic downturn. We find that the membership in the higher clubs and resilience to the Great Recession are positively affected by the presence of several local‐specific factors and macroeconomic characteristics.

Approximating Node-Weighted k-MST on Planar Graphs

We study the problem of finding a minimum weight connected subgraph spanning at least k vertices on planar, node-weighted graphs. We give a (4 + ε)-approximation algorithm for this problem. We achieve this by utilizing the recent Lagrangian-multiplier preserving (LMP) primal-dual 3-approximation for the node-weighted prize-collecting Steiner tree problem by Byrka et al. (SWAT’16) and adopting an approach by Chudak et al. (Math. Prog. ’04) regarding Lagrangian relaxation for the edge-weighted variant. In particular, we improve the procedure of picking additional vertices (tree merging procedure) given by Sadeghian (2013) by taking a constant number of recursive steps and utilizing the limited guessing procedure of Arora and Karakostas (Math. Prog. ’06). More generally, our approach readily gives a (4/3 ⋅ r + ε)-approximation on any graph class where the algorithm of Byrka et al. for the prize-collecting version gives an r-approximation. We argue that this can be interpreted as a generalization of an analogous result by Könemann et al. (Algorithmica ’11) for partial cover problems. Together with a lower bound construction by Mestre (STACS’08) for partial cover this implies that our bound is essentially best possible among algorithms that utilize an LMP algorithm for the Lagrangian relaxation as a black box. In addition to that, we argue by a more involved lower bound construction that even using the LMP algorithm by Byrka et al. in a non-black-box fashion could not beat the factor 4/3 ⋅ r when the tree merging step relies only on the solutions output by the LMP algorithm.

Gaussian sum pseudolinear Kalman filter for bearings‐only tracking

The efficacy of a bearings-only tracking algorithm, to a great extent, depends on the target-sensor geometry and motion. Although the pseudolinear Kalman filter and its variants have demonstrated comparable performance with the elite non-linear filters, they still suffer from bias problems and the tracking performance is inevitably affected by the relative geometry and motion relationships. In this study, an observability metric based on classical control theory is first presented to characterise the relative relationships between the target and the sensor. Then an efficient bearings-only tracking algorithm called Gaussian sum pseudolinear Kalman filter is developed. It is based on the bias-compensated pseudolinear Kalman filter and is built within a Gaussian sum framework. In the novel algorithm, a splitting and merging procedure will be triggered when a low degree of observability is detected. Simulation results show the significant performance improvement of the proposed algorithm.

RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements

The accurate representation of spatio-temporal patterns of precipitation is an essential input for numerous environmental applications. However, the estimation of precipitation patterns derived solely from rain gauges is subject to large uncertainties. We present the Random Forest based MErging Procedure (RF-MEP), which combines information from ground-based measurements, state-of-the-art precipitation products, and topography-related features to improve the representation of the spatio-temporal distribution of precipitation, especially in data-scarce regions. RF-MEP is applied over Chile for 2000—2016, using daily measurements from 258 rain gauges for model training and 111 stations for validation. Two merged datasets were computed: RF-MEP3P (based on PERSIANN-CDR, ERA-Interim, and CHIRPSv2) and RF-MEP5P (which additionally includes CMORPHv1 and TRMM 3B42v7). The performances of the two merged products and those used in their computation were compared against MSWEPv2.2, which is a state-of-the-art global merged product. A validation using ground-based measurements was applied at different temporal scales using both continuous and categorical indices of performance. RF-MEP3P and RF-MEP5P outperformed all the precipitation datasets used in their computation, the products derived using other merging techniques, and generally outperformed MSWEPv2.2. The merged P products showed improvements in the linear correlation, bias, and variability of precipitation at different temporal scales, as well as in the probability of detection, the false alarm ratio, the frequency bias, and the critical success index for different precipitation intensities. RF-MEP performed well even when the training dataset was reduced to 10% of the available rain gauges. Our results suggest that RF-MEP could be successfully applied to any other region and to correct other climatological variables, assuming that ground-based data are available. An R package to implement RF-MEP is freely available online at https://github.com/hzambran/RFmerge.