Probability Of Separation Research Articles

A Geometry-based Ambiguity Validation (GBAV) Method for GNSS carrier phase observation

Integer ambiguity validation is an indispensable and critical step in GNSS carrier phase positioning for precise and reliable positioning applications. The crucial problems associated with any ambiguity validation methods are as follows. 1) The fixed ambiguity vector can be separated from all other ambiguity candidates under certain tests (separability). 2) The probability of fixing to wrong ambiguity combinations (mis-fixing) can be controlled to an acceptable level based on different application requirements. Traditional ambiguity validation methods, such as the R-ratio and the difference tests which use one statistical test to control both separability and mis-fixing rate, are widely used due to easier computation. The performances of these methods are generally acceptable. However, experiments show that these tests with a fixed threshold can cause either a small percentage of mis-fixing or overly conservative with long observation time. In this paper, we propose a new Geometry Based Ambiguity Validation (GBAV) method which uses two statistical tests to control geometry separability and mis-fixing probability separately. The thresholds for both tests can be strictly determined based on user requirements to control the quality of ambiguity resolution. Three 24-hour GNSS (GPS, BDS) datasets (two short baselines and one middle-range baseline) are processed using the proposed GBAV method, and compared with the popular R-ratio method. The results show that by giving proper control on the mis-fixing probability (0.01%), there is no mis-fixing case in all three datasets.

Relative dispersion with finite inertial ranges

Relative dispersion experiments are often analysed using theoretical predictions from two- and three-dimensional turbulence. These apply to infinite inertial ranges, assuming the same dispersive behaviour over all scales. With finite inertial ranges, the metrics are less conclusive. We examine this using pair separation probability density functions (PDFs), obtained by integrating a Fokker–Planck equation with different diffusivity profiles. We consider time-based metrics, such as the relative dispersion, and separation-based metrics, such as the finite scale Lyapunov exponent (FSLE). As the latter cannot be calculated from a PDF, we introduce a new measure, the cumulative inverse separation time (CIST), which can. This behaves like the FSLE, but advantageously has analytical solutions in the inertial ranges. This allows the establishment of consistency between the time- and space-based metrics, something which has been lacking previously. We focus on three dispersion regimes: non-local spreading (as in a two-dimensional enstrophy inertial range), Richardson dispersion (as in an energy inertial range) and diffusion (for uncorrelated pair motion). The time-based metrics are more successful with non-local dispersion, as the corresponding PDF applies from the initial time. Richardson dispersion is barely observed, because the self-similar PDF applies only asymptotically in time. In contrast, the separation-based CIST correctly captures the dependencies, even with a short (one decade) inertial range, and is superior to the traditional FSLE at large scales. Nevertheless, it is advantageous to use all measures together, to seek consistent indications of the dispersion.

Compression and Three-Point Bending Analyzes of Aerospace Sandwich Composites produced with Polymeric Core Materials with ANSYS

Honeycomb materials are widely used in aircraft structures. However, these structures have disadvantages in longitudinal compression forces. While aluminum is generally used as the face material in sandwich composites used in aircraft structures, Nomex honeycomb or aluminum honeycomb structures are used as the core material, depending on the design. The reduced interface area between the core and the surface layer also increases the probability of separation of the surface and core under an applied load. In this study, attention is drawn to the combination of polymeric foam - honeycomb structure as a potential material to eliminate or minimize these disadvantages of honeycomb core structures. In this study, polyurethane foam filled PLA (polylactic acid) honeycomb core sandwich composite with carbon fiber faces was subjected to compression and three-point bending tests in ANSYS. The results obtained show that this new material can be used in aerospace materials.

Sorbent extraction behavior of cesium and strontium from nitric acid solutions using a new high thermal stability material

In this study a new low-cost carbonaceous material was prepared from husks of opuntia-ficus-indica as a starting material (precursor) which was accomplished by chemical activation route using H3PO4 impregnation. The material has been identified by different analytical tools. The sorption performance of Cs(I) and Sr(II) from HNO3 solutions was examined through batch system. Variations of the distribution coefficients (Kd) as a function of HNO3 concentration in the range 0.001–5.0 M were presented. Some of separation probabilities were suggested. The results attained signals that the Sr(II) selectivity is higher than that of Cs(I) at high molarities. The retention capacity (qe) of Cs(I) and Sr(II) ions increased with growing temperature. The capacities at 0.001 M HNO3 are 34 and 108 mg/g for Cs(I) and Sr(II), respectively. Whereas, at 2.0 M HNO3 capacities were about 4 and 37 mg/g for each of Cs(I) and Sr (II), respectively. This studies demonstrates that the prepared carbonaceous sorbent is an economically effective sorbent for retention of Cs(I) and Sr(II) species from HNO3 solutions. Cs(I) and Sr(II) removal potential was tested from simulated low- and intermediate-level radioactive waste samples.

Fracture Incidence of Kedo-S Square Pediatric Rotary Files: A Prospective Clinical Study.

Objective The primary focus of this clinical study was to analyze the probability of occurrence of instrument fracture after root canal preparation of primary molars with the help of Kedo-S Square pediatric rotary file. Materials and Methods Three experienced specialists treated 100 primary maxillary and mandibular molars (335 root canals) using a standardized protocol over 2 months. Biomechanical preparations were carried out using Kedo-S Square file, as per the suggestions given by the manufacturer. Every instrument in Group A helped handle three clinical cases, while for groups B, C and D, they helped in handling 5, 9, and 12 cases, respectively. Making use of an operational microscope, the rotary files, after being pulled out from the canal, were observed. The values were tabulated, and descriptive statistics were performed. Results There were two fractures (2%), of which 1 occurred in group C in the apical 1/3rd of distobuccal canal of maxillary molar, and the other occurred in group D in the apical 1/3rd of mesiobuccal canal of maxillary molar. Conclusions The fracture rate of Kedo-S Square rotary file is quite low. It is primarily in the buccal canals of the maxillary molars and the apical third of the root canal that the instrument has a greater probability of separation.

A stochastic model, simulation, and application to aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir–Blodgett matrix

Abstract A stochastic model of nanocrystals clusters formation is developed and applied to simulate an aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir–Blodgett matrix. Simulations are compared with our experimental results. The stochastic model suggested governs mobilities both of individual nanocrystals and its clusters (arrays). We give a comprehensive analysis of the patterns simulated by the model, and study an influence of the surrounding medium (solvent) on the aggregation processes. In our model, monomers have a finite probability of separation from the cluster which depends on the temperature and binding energy between nanocrystals, and can also be redistributed in the composition of the cluster, leading to its compaction. The simulation results obtained in this work are compared with the experimental data on the aggregation of CdS nanocrystals upon evaporation of the Langmuir–Blodgett matrix. This system is a typical example from real life and is noteworthy in that the morphology of nanocrystals after evaporation of the matrix cannot be described exactly by a model based only on the motion of individual nanocrystals or by a cluster-cluster aggregation model.

A versatile combinatorial approach of studying products of long cycles in symmetric groups

Let SX denote the symmetric group of permutations on the set X. Studies on triples of permutations satisfying certain conditions have a long history. Particularly interesting cases are when one of the involved permutations is a long cycle, and another involved one is either an involution or also a long cycle. Suppose {B1,B2,…,Bk} is a family of disjoint sets. Here we solve the problem of enumerating the pairs of long cycles on ⋃i=1kBi whose product is a member of the direct product SB1×SB2×⋯×SBk. For those pairs of long cycles the projection of whose product onto SBi contains di cycles, we also obtain the first explicit counting formula.As a consequence, in a unified way, we recover a number of results, including results of Boccara, Walkup, Zagier, Stanley, Féray and Vassilieva, as well as Hultman. We obtain a number of new results as well. In particular, we obtain new explicit formulas concerning factorizations of a permutation into long cycles, and we obtain the first nontrivial explicit formula for computing strong separation probabilities. The latter gives an answer to an open problem of Stanley.

Oxygen Vacancy and Adsorbed Superoxides Dependent Photocatalytic Activity of TiO2 Quantum Dot Thin Films for Degradation of Methylene Blue with Variation of Precursor Concentration

Growth of TiO2 quantum dot thin films on ITO glass substrate by hydrothermal method at 180 °C with variations of titanium precursor concentration (0.3, 0.5, 0.7, and 1 ml) is reported. UV–vis absorption spectrum shows a strong absorbance peak in the UV region. The bandgap of samples ranged from 3.2 to 3.6 eV. FESEM analysis shows a layered sieve-like porous morphological structure. The particle size is less than 10 nm. The FTIR analysis reveals the presence of isolated adsorbed Ti–OH and –OH groups which take part in photocatalysis reactions to oxidize dye molecules. The superoxides and hydroxyl radicals were yielded by the protonation process. The oxygen vacancies, confirmed from XPS analysis, play an important role in the formation of impurity level states below the conduction band with the accumulation of a large number of electrons. These electrons further take part in the hydrogen production process. This investigation demonstrates that thin film with 0.5 ml precursor volume shows enhanced MB dye degradation activity under UV light. The PL emission is quenched in the UV region for a sample having 0.5 ml precursor volume, enhancing the probability of charge carrier separation. Thus significant results in photocatalytic MB dye degradation activity are obtained.

Cross-Project Defect Prediction via Landmark Selection-Based Kernelized Discriminant Subspace Alignment

Cross-project defect prediction (CPDP) refers to identifying defect-prone software modules in one project (target) using historical data collected from other projects (source), which can help developers find bugs and prioritize their testing efforts. Recently, CPDP has attracted great research interest. However, the source and target data usually exist redundancy and nonlinearity characteristics. Besides, most CPDP methods do not exploit source label information to uncover the underlying knowledge for label propagation. These factors usually lead to unsatisfactory CPDP performance. To address the above limitations, we propose a landmark selection-based kernelized discriminant subspace alignment (LSKDSA) approach for CPDP. LSKDSA not only reduces the discrepancy of the data distributions between the source and target projects, but also characterizes the complex data structures and increases the probability of linear separability of the data. Moreover, LSKDSA encodes label information of the source data into domain adaptation learning process and makes itself with good discriminant ability. Extensive experiments on 13 public projects from three benchmark datasets demonstrate that LSKDSA performs better than a range of competing CPDP methods. The improvement is 3.44%-11.23% in g-measure, 5.75%-11.76% in AUC, and 9.34%-33.63% in MCC, respectively.

General stochastic separation theorems with optimal bounds

Phenomenon of stochastic separability was revealed and used in machine learning to correct errors of Artificial Intelligence (AI) systems and analyze AI instabilities. In high-dimensional datasets under broad assumptions each point can be separated from the rest of the set by simple and robust Fisher’s discriminant (is Fisher separable). Errors or clusters of errors can be separated from the rest of the data. The ability to correct an AI system also opens up the possibility of an attack on it, and the high dimensionality induces vulnerabilities caused by the same stochastic separability that holds the keys to understanding the fundamentals of robustness and adaptivity in high-dimensional data-driven AI. To manage errors and analyze vulnerabilities, the stochastic separation theorems should evaluate the probability that the dataset will be Fisher separable in given dimensionality and for a given class of distributions. Explicit and optimal estimates of these separation probabilities are required, and this problem is solved in the present work. The general stochastic separation theorems with optimal probability estimates are obtained for important classes of distributions: log-concave distribution, their convex combinations and product distributions. The standard i.i.d. assumption was significantly relaxed. These theorems and estimates can be used both for correction of high-dimensional data driven AI systems and for analysis of their vulnerabilities. The third area of application is the emergence of memories in ensembles of neurons, the phenomena of grandmother’s cells and sparse coding in the brain, and explanation of unexpected effectiveness of small neural ensembles in high-dimensional brain.

Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization

With the increasing complexity of optimization problems in the real world, more and more intelligent algorithms are used to solve these problems. Elephant herding optimization (EHO), a recently proposed metaheuristic algorithm, is based on the nomadic habits of elephants on the grassland. The herd is divided into multiple clans, each individual drawing closer to the patriarchs (clan updating operator), and the adult males are separated during puberty (separating operator). Biogeography-based optimization (BBO) is inspired by the principles of biogeography, and finally achieves an equilibrium state by species migration and drifting between geographical regions. To solve the numerical optimization problems, this paper proposes an improved elephant herding optimization using dynamic topology and biogeography-based optimization based on learning, named biogeography-based learning elephant herding optimization (BLEHO). In BLEHO, we change the topological structure of the population by dynamically changing the number of clans of the elephants. For the updating of each individual, we use the update of the operator based on biogeography-based learning or the operator based on EHO. In the separating phase, we set the separation probability according to the number of clans, and adopt a new separation operator to carry out the separation operation. Finally, through elitism strategy, a certain number of individuals are preserved directly to the next generation without being processed, thus ensuring a better evolutionary process for the population. To verify the performance of BLEHO, we used the benchmarks provided by IEEE CEC 2014 for the test. The experimental results were compared with some classical algorithms (ABC, ACO, BBO, DE, EHO, GA, and PSO) and the most advanced algorithms (BBKH, BHCS, CCS, HHO, PPSO, SCA, and VNBA) and analyzed by Friedman rank test. Finally, we also applied BLEHO to the simple traveling salesman problem (TSP). The results show that BLEHO has better performance than other methods.

Do Workers Bargain Over Wages? A Test Using Dual Jobholders

This paper examines the behavior of dual jobholders to test a simple model of wage bargaining versus wage posting in which workers facing hours constraints in their primary job take a second, flexible-hours job for additional income. When a secondary job offers a sufficiently high wage, a worker either bargains with the primary employer for a wage increase or separates. The bargaining model provides a number of predictions that we test using matched employer-employee administrative data from Washington State. The estimates match the model’s predictions quite well. First, separation probabilities in the primary job are sensitive to wages in the secondary job, but hours are not. Second, hours and separations in the secondary job are sensitive to wages in the primary job due to income effects. Third, wage bargaining takes place mainly among workers in the highest wage quartile; for these workers, wage increases in the secondary job lead to wage increases in the primary job. In contrast, for workers in the lowest wage quartile, wage increases in the secondary job lead to higher separation rates but no significant wage increase in the primary job, consistent with wage posting. These patterns suggest that high-wage workers receive a larger share of the surplus generated by the employment relationship.

Firm Relocations, Commuting and Relationship Stability

In this paper, we study the impact of firm relocations on commuting distance and the probability of married couples and cohabiting couples with children separating. We use Swedish register data for 2010-2016 and select employees of relocating firms with one workplace and more than 10 employees. Focusing on this sample allows us to use plausibly exogenous variation in the commuting distance arising from the relocation. We extend the literature on the effect of commuting on relationship stability by reducing the possibility for unobserved time-variant factors to bias our estimates. While previous literature has focused on the difference between short- and long-distance commuting, we focus on changes in the commuting distance that are externally induced by firm management. We find a small but statistically significant negative effect of increased firm relocation distance on family stability. A 10 km change in commuting distance leads to a 0.09 percentage point higher probability of separation if the commuter remains with the firm for the next 5 years.

The Global Financial Crisis and Employee Mental Health

We find that the Global Financial Crisis (2007-2009) had an adverse effect on employee mental health. To identify the causal effects of the credit shock, we exploit the plausibly exogenous variation in firms’ need to refinance their long-term debt in 2008, a period when refinancing became more difficult due to tightening bank lending standards. Using administrative data from the Netherlands, we demonstrate that antidepressant use grew significantly more in the 2008-2012 period among employees of firms in need of high debt refinancing. A higher probability of job separation in these firms suggests lower job security as a transmission channel.

Derivation of Pauli Equation and its Bipartite Form based on Budiyono-Rohrlich Ontic Extension and Epistemic Restriction of Statistical Model of Quantum Mechanics

In this report, we discuss a possible way to derive the Pauli equation of a spin−1/2 particle based on Budiyono-Rohrlich ontic extension and epistemic restriction axioms of a statistical model of quantum mechanics. Applying the conservation of energy and probability continuity equation, we show that for a pure quantum mechanical particle spin, the corresponding equation can be reconstructed by assuming the existence of separable probability distribution functions of spin. The associated particle spin is still considered as a pure quantum phenomenon which has no classical counterpart. We also discuss the possibility of constructing a bipartite Pauli equation of nonlocal interacting two spin−1/2 particles. We examine the corresponding necessary conditions for the separability of the Bell-like-states and Werner-like-states through the Peres–Horodecki positive partial transpose criterion.

Improved exciton dissociation efficiency by a carbon-quantum-dot doped workfunction modifying layer in polymer solar cells

In addition to the phase-separated morphology of donor and acceptor, the internal field created by work-function difference between cathode and anode can also influence the exciton dissociation probability. In this study, we have demonstrated enhanced photovoltaic performance by increasing exciton dissociation efficiency. To improve both work-function modification effect and charge transport properties, we have incorporated novel carbon quantum dots (CQD) having NH2 ligands into the polyethyleneimine (PEI) work-function modifying layer as a dopant. A study of net photocurrent density as a function of effective voltage showed that devices with a CQD-doped PEI layer had a much higher charge separation probability compared to devices with a pristine PEI layer. A Kelvin-probe force microscopy study demonstrated that a CQD-doped PEI layer induced lower work-function of ITO than that of ITO with a pristine PEI, which induced a stronger internal field. This strengthened internal field induced better exciton dissociation efficiency, thereby improving solar cell performance.

Photovoltaic Effect in Phthalocyanine-Based Organic Solar Cells: 1. Thermal Ionization of Molecular Excitons

Based on the previously obtained data [1, 2], according to which the photoelectric quantum yield (Y) in crystals of the β-form of metal-free phthalocyanine and copper phthalocyanine is the same for electrons and holes and has an Arrhenius temperature dependence with an activation energy EY equal to the difference between the band gap and the molecular exciton (ME) energy, a three-dimensional stochastic model of thermal ionization of MEs is proposed, which is considered as a sequence of reversible transitions from the ME to the lowest charge-transfer (CT) state and then to a CT state with an increasing charge-transfer distance. A relationship has been found between EY and the radial density of CT states at which the probability of charge separation is greater than that of the decay of these states.

Thermally assisted charge transfer and charge separation in organic donor–acceptor solar cells

We propose a mechanism based on thermally assisted charge transfer (CT) to study the thermal effect on the formation of CT states and subsequent charge separation in an organic donor–acceptor solar cell. We reveal that the difference between phonons in the donor and acceptor caused by elastic energy acts as a thermally assisted driving force for charge transfer. It is found that the system exhibits a quite different CT process in the high and low temperature regions. Remarkably, combined with the entropy driving mechanics, the thermally assisted CT yields charge separation probability as high as 70% at room temperature. Our model and results provide a microscopic quantum understanding of the relevant recent experiments and open up a route to realize high-efficiency organic solar cells by effectively taking advantage of the thermal effect.

A compound optimality criterion for D‐efficient and separation‐robust designs for the logistic regression model

AbstractThe ‐criterion is proposed to generate optimal designs for the logistic regression model with reduced separation probabilities. This compound criterion has two components: (a) the ‐efficiency of the candidate design and (b) a penalty term that captures the average distance of the candidate design's support points from the region of maximum prediction variance (MPV). A ‐optimal design maximizes the ‐criterion. The aim is to obtain compromise experimental designs with high ‐efficiencies that are more robust to separation than a ‐optimal design of equal size. This paper presents the ‐criterion and demonstrates examples of its potential use as a means of mitigating separation in the design phase of a binary response experiment. For the examples presented, the local ‐optimal designs offer a 20‐30% reduction in separation probability over the local ‐optimal designs while maintaining ‐efficiencies over 93%. A robust design methodology is also demonstrated, where a robust ‐optimal design is compared to a Bayesian ‐optimal design and shown to have comparable ‐efficiencies across a range of randomly drawn parameter values while offering a mean reduction in separation probability of 23.9%.

RETRACTED ARTICLE: Two phase cluster validation approach towards measuring cluster quality in unstructured and structured numerical datasets

This paper presents an improved cluster validation scheme called two phase cluster validation (TPCV) and aims to estimate the inter closeness and inter separation among the clusters in the cluster set of unsupervised clustering schemes based on probability measure for validating the cluster quality without prior identification. First phase, the TPCV computes the representative cluster centroid of each individual cluster in the cluster set based on standard mean operation and then it estimates the probability of inter closeness of each cluster with other clusters in the cluster set based on cluster centroid. Next phase, it calculates the probability of separation among the clusters in the cluster set based on cluster centroid by distance measure. Experimental results show that the TPCV scheme is simple and effective to estimate the cluster quality by measuring the probability of closeness and separation between the clusters in the result of unsupervised clustering scheme.