Unknown Point Research Articles

Crystal Structure, Topological and Hirshfeld Surface Analysis of a Zn(II) Zwitterionic Schiff Base Complex Exhibiting Nonlinear Optical (NLO) Properties Using Z-Scan Technique

A mononuclear Zn(II) complex of (Zn(H2L) (CH3OH) Cl2) (1) has been synthesized by using a nonlinear optically active Zwitterionic Schiff base which is 4-((2-hydroxy-3-methoxybenzylidene) amino) benzoic acid (H2L). Complex 1 has been structurally analyzed by FTIR and UV spectroscopy, TGA, Powder-XRD and single crystal X-ray diffraction. X-Ray crystallographic studies revealed Zn(II) complex crystallizes in a P21/c space group and exists in a distorted trigonal bipyramidal geometry (τ = 0.68). The topological analysis of complex 1 showed that the underlying net is characterized by an unknown topological type and point symbol {342.468.510}, and multilevel analysis of complex packing as dimer gives a 18-c uninodal net with unknown topological type and point symbol {348.499.56}. The calculation results of a Hirshfeld surface analysis have been investigated to explore the H-bonding within the crystal. Third-order non-linear properties were also studied, which revealed that the lower input power (5.0 mM) for the material shows full transparency; however, it becomes opaque for higher input power. Such limiting behavior of complex 1 suggests its potential for instrumental protective devices against high laser illuminations.

WITHDRAWN: 3D recognition & automated access control reconstruction from a single image

A novel algorithm has also been proposed for handling occlusion in 3D object recognition using monochrome images. Binocular Stereo vision system has been used to capture the left and right images of an image scene to infer 3D information. Surface model of the model object has been created using Computer Aided Design (CAD) software, as complex surfaces can easily be identified. A feature detector has been proposed to detect the local features of the object model and virtual camera has been used to create the comparator models for the computed 3D poses. The stereo images have been pre-processed using affine transformation to remove geometric distortions or deformations due to non-ideal camera angles. Bilinear interpolation technique has been used to fill the unknown points in the projected image during the image rectification process. During the process of recognition, the objects are recognized by matching the comparator models with the rectified image regions. A modified geometric mapping technique has been proposed for 3D reconstruction of the rectified mapped stereo images. The 3D reconstructed image has been compared with the object model for validation.

Integrated 3D geological modeling of Sonda-Jherruck coal field, Pakistan

Despite being one of the largest coal fields in Pakistan, the Sonda-Jherruck coal resource is yet to be exploited. Exploration studies were done between 1981 and 1989 by the Geological Survey of Pakistan (GSP) and the United States Geological Survey (USGS). While much exploratory data is available, computer-aided geological modeling has not been carried out. The resource estimation was carried out using the traditional circular 891 method. The specified distance between observational points remains constant, disregarding continuity in seam thickness that leads to higher uncertainty. Fault modeling and geologic mapping were not performed in preliminary reports. Fault lines based on aerial maps are indicated. This study generated 3D solid seam models and a fault model of the deposit. Coal seams were modeled to produce spatial distribution maps for seam thickness. The overall in-place coal resources of the deposit are estimated to be 4.66 billion tons, however mineable resources are estimated to be 1.59 billion tons. The method of fault detection is based on drill hole data. Dipping was calculated using regular grid data and the unknown points were estimated using the inverse distance weighting squared method. The assessed fault zones were compared with the USGS fault lines and an apparent similarity was observed.

A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling: A case study of Cretaceous McMurray reservoirs in a block of Canada

An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies; the 2D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3D multi-point pattern probabilities at the unknown points, to realize the reconstruction of a 3D model from 2D cross-section. To solve the problems of reducing pattern variability in the 2D training image and increasing sampling uncertainty, an adaptive spatial sampling method is introduced, and an iterative simulation strategy is adopted, in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability. The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics, and accurately reflect the morphology and distribution pattern of the lateral accretion layer. Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape, spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect. The test by sparse wells shows that the simulation accuracy is above 85%, and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%.

C-reactive protein predicts complications in community-associated S. aureus bacteraemia: a cohort study

BackgroundStaphylococcus aureus (S. aureus) bacteraemia is increasingly acquired from community settings and is associated with a mortality rate of up to 40% following complications. Identifying risk factors for complicated S. aureus bacteraemia would aid clinicians in targeting patients that benefit from expedited investigations and escalated care.MethodsIn this prospective observational cohort study, we aimed to identify risk factors associated with a complicated infection in community-onset S. aureus bacteraemia. Potential risk factors were collected from electronic medical records and included: - patient demographics, symptomology, portal of entry, and laboratory results.ResultsWe identified several potential risk factors using univariate analysis. In a multiple logistic regression model, age, haemodialysis, and entry point from a diabetic foot ulcer were all significantly protective against complications. Conversely, an unknown entry point of infection, an entry point from an indwelling medical device, and a C-reactive protein concentration of over 161 mg/L on the day of admission were all significantly associated with complications.ConclusionsWe conclude that several factors are associated with complications including already conducted laboratory investigations and portal of entry of infection. These factors could aid the triage of at-risk patients for complications of S. aureus bacteraemia.

Revisiting Visual-Inertial Structure-From-Motion for Odometry and SLAM Initialization

In this letter, an efficient closed-form solution for the state initialization in visual-inertial odometry (VIO) and simultaneous localization and mapping (SLAM) is presented. Unlike the state-of-the-art, we do not derive linear equations from triangulating pairs of point observations. Instead, we build on a direct triangulation of the unknown 3D point paired with each of its observations. We show and validate the high impact of such a simple difference. The resulting linear system has a simpler structure and the solution through analytic elimination only requires solving a 6 × 6 linear system (or 9 × 9 when accelerometer bias is included). In addition, all the observations of every scene point are jointly related, thereby leading to a less biased and more accurate solution. The proposed formulation attains up to 50 percent decreased velocity and point reconstruction error compared to the standard closed-form solver, while it is 4× faster for a 7-frame set. Apart from the inherent efficiency, fewer iterations are needed by any further non-linear refinement thanks to better parameter initialization. In this context, we also present a non-linear optimizer that optionally refines the initial parameters. The superior performance of the proposed solver is established by quantitative comparisons with the state-of-the-art solver.

Stock Market Inclusion and İts Connection with Economic Activity in Turkey

This study explores the relationship between stock market inclusion and economic activity (liveliness) in Turkey by taking advantage of the recent contributions in causality theory. Stock market inclusion is represented by the seasonally adjusted real stock market trade volume per capita (TV) and economic activity by the seasonally adjusted real gross domestic product per capita (GDP). We use quarterly series covering the period 2003:1-2020:2 and employ asymmetric bootstrap and asymmetric Fourier bootstrap causality testing procedures to obtain robust parameter estimates. Both procedures adopt a nonlinear methodology but the latter is distinguished from the first in the sense that it follows a Fourier series approximation which allows for structural breaks of unknown number, form, and point. Empirical findings suggest that the Fourier-type asymmetric bootstrap causality procedure, thanks to its trigonometric components, captures two unidirectional (one-way) causalities; one running from the positive components of TV to those of GDP and the other running from the negative components of TV to those of GDP, but not vice versa. These findings verified a strong influence on GDP of the alterations i.e. positive and negative shocks in stock market conditions.

Empirical Likelihood Ratio Test for the Epidemic Change Model

Change point problem has been studied extensively since 1950s due to its broad applications in many elds such as nance, biology and so on. As a special case of the multiple change point problem, the epidemic change point problem has received a lot of attention especially in medical studies. In this paper, a nonparametric method based on the empirical likelihood is proposed to detect the epidemic changes of the mean after unknown change points. Under some mild conditions, the asymptotic null distribution of the empirical likelihood ratio test statistic is proved to be the extreme distribution. The consistency of the test is also proved. Simulations indicate that the test behaves comparable to the other available tests while it enjoys less constraint on the data distribution. The method is applied to the Standford heart transplant data and detects the change points successfully.

Dictionary-based inverse filtering methods for blind image deconvolution

In this paper, we study a novel inverse filtering method by using a dictionary approach. The main idea is to combine a learned dictionary for the representation of the deconvoluted image and an inverse filter based on nonnegativity and support constraints, to deconvolute the observed image with an unknown point spread function. The advantage of this approach is that the target image can be represented with more details by learned basis in the dictionary. We also employ the alternating direction method of multipliers to solve the resulting optimization problem. Experimental results are presented to show that the performance of the proposed methods are better than other testing methods for several testing images.

An inexact PAM method for computing Wasserstein barycenter with unknown supports

Wasserstein barycenter is the centroid of a collection of discrete probability distributions which minimizes the average of the $$\ell _2$$ -Wasserstein distance. This paper focuses on the computation of Wasserstein barycenters under the case where the support points are free, which is known to be a severe bottleneck in the D2-clustering due to the large scale and nonconvexity. We develop an inexact proximal alternating minimization (iPAM) method for computing an approximate Wasserstein barycenter, and provide its global convergence analysis. This method can achieve a good accuracy with a reduced computational cost when the unknown support points of the barycenter have low cardinality. Numerical comparisons with the 3-block B-ADMM in Ye et al. (IEEE Trans Signal Process 65:2317–2332, 2017) and an alternating minimization method involving the LP subproblems on synthetic and real data show that the proposed iPAM can yield comparable even a little better objective values in less CPU time, and hence, the computed barycenter will render a better role in the D2-clustering.

Understanding how team process-sequences emerge over time and their relationship to team performance

PurposeThe purpose of this study was to explore the team process-sequences executed within and across performance episodes and their relation to team performance. In doing so, this effort responds to the call for examining the temporal and dynamic aspects of teams.Design/methodology/approachData (i.e. observations and audio recordings) was collected from the stand-up meetings of three high-performing Scrum teams across six points in time during two consecutive performance episodes (i.e. beginning, midpoint, end). After content coding the data, lag sequential analyses was used to examine patterns of executed team processes to determine whether particular process-sequences occurred significantly different from others.FindingsTeams shifted between transition and action phase processes during performance episodes. During and across performance episodes, process-sequences primarily consisted of transition processes. When teams executed process-sequences consisting solely of action phase processes, their focus was on monitoring processes.Research limitations/implicationsThis study hopes that the findings here will serve to spur researchers to more fully investigate the relationship between process-sequences and team performance across various team types. However, limitations (e.g. small sample size, unknown point of teams’ life cycle and focus on explicit team processes) should be taken into account when building on the present findings.Originality/valueThis study contributes to a better understanding of the temporal and dynamic nature of team processes by analyzing how the team process and process-sequences occur across time. In addition, this study moves beyond most studies that assess team processes as static retrospective perceptions and consider their natural ordering.

Consistency of an Estimator for Change Point in Volatility of Financial Returns

A non parametric Auto-Regressive Conditional Heteroscedastic model for financial returns series is considered in which the conditional mean and volatility functions are estimated non-parametrically using Nadaraya Watson kernel. A test statistic for unknown abrupt change point in volatility which takes into consideration conditional heteroskedasticity, dependence, heterogeneity and the fourth moment of financial returns, since kurtosis is a function of the fourth moment is considered. The test is based on L2norm of the conditional variance functions of the squared residuals. A non-parametric change point estimator in volatility of financial returns is further obtained. The consistency of the estimator is shown theoretically and through simulation. An application of the estimator in change point estimation in volatility of United States Dollar/Kenya Shilling exchange rate returns data set is made. Through binary segmentation procedure, three change points in volatility of the exchange rate returns are estimated and further accounted for.

Optimal Sequential Detection of Signals With Unknown Appearance and Disappearance Points in Time

The paper addresses a sequential changepoint detection problem, assuming that the duration of change may be finite and unknown. This problem is of importance for many applications, e.g., for signal and image processing where signals appear and disappear at unknown points in time or space. In contrast to the conventional optimality criterion in quickest change detection that requires minimization of the expected delay to detection for a given average run length to a false alarm, we focus on a reliable maximin change detection criterion of maximizing the minimal probability of detection in a given time (or space) window for a given local maximal probability of false alarm in the prescribed window. We show that the optimal detection procedure is a modified CUSUM procedure. We then compare operating characteristics of this optimal procedure with popular in engineering the Finite Moving Average (FMA) detection algorithm and the ordinary CUSUM procedure using Monte Carlo simulations, which show that typically the later algorithms have almost the same performance as the optimal one. At the same time, the FMA procedure has a substantial advantage -- independence to the intensity of the signal, which is usually unknown. Finally, the FMA algorithm is applied to detecting faint streaks of satellites in optical images.

Exact penalty functions in the problem of choosing the optimal wholesale order in the face of rapid fluctuations in demand

The current article discusses a different situation in the market, when there is a rush demand for a new product followed by a sharp drop in demand. The trading company uses the following scheme for the wholesale order of goods. The ordered product is divided into two parts, and the first batch of goods arrives immediately, it is sold over a certain period of time (0, Z,]. The second batch of goods is delivered at the time 7’, but at the time interval [0, 7]. This batch is sold at a discount and is completely sold out. Time T' corresponds to the end of the sale of the entire product. The time points 7), T' are selected by the trading firm from the condition of maximizing income. The need to consider such a wholesale order scheme is related to the fact that, firstly, the warehouses of the trading firms have limited capacity and cannot accommodate all the ordered goods, and secondly, a manufacturer may not offer the entire ordered batch of goods, since not all goods can be produced at the initial (zero) point of time immediately after receiving the order. At the time 71, the trading company completely sells the first batch of goods and receives financial resources, part of which is paid to the manufacturer. At the moment of time Т', Ме сотр/ефе sale of all purchased goods is completed. The choice of time points 7; and T' allow to determine the volume of the first batch of ordered goods and the total volume of product ordered from the manufacturer. In the article, a mathematical model is proposed that makes it possible to choose the optimal ordering strategy for a trading company in the conditions of excessive growth of demand for the new product in time 71,72 at some unknown point in time and т. Е [73,74], and the subsequent sharp drop in demand in the period of time [73, 74] due to the saturation of the market with a new product. Four possible variants of optimization problems are considered. A method of exact penalty function is suggested, which allows one to find their solutions.

About Resolvability of the Problem of Lightning Coordinates Finding by the Time of Lightning Discharge Arrival

On the unit sphere, the geometric problem of calculating the position of a point relative to three given points is considered. We know the length of three spherical segments that go out from the given points in the direction of the unknown point. The requirement must be fulfilled: the distance from each point to an unknown point must be equal to the sum of the length of the segment outgoing from this point, and some increment, the same for all three segments. In the article, the conditions for the solvability of a geometric problem are established by the methods of spherical trigonometry and vector algebra. It is proved that when they are fulfilled, the problem is always solvable. The number of solutions is two, except in rare cases where there is only one solution. A solution method is presented. One of the practical applications is the problem of determining the time and location of a cloud-to-ground lightning discharge, which is directly reduced to this problem.

Mosum: A Package for Moving Sums in Change-Point Analysis

Time series data, i.e., temporally ordered data, is routinely collected and analysed in in many fields of natural science, economy, technology and medicine, where it is of importance to verify the assumption of stochastic stationarity prior to modeling the data. Nonstationarities in the data are often attributed to structural changes with segments between adjacent change-points being approximately stationary. A particularly important, and thus widely studied, problem in statistics and signal processing is to detect changes in the mean at unknown time points. In this paper, we present the R package mosum, which implements elegant and mathematically well-justified procedures for the multiple mean change problem using the moving sum statistics.

Calculation of eigen frequencies of systems with damping

This paper considers a problem of detecting all resonance frequencies during the design phase for complex technical objects made of sub-systems of various physical natures with the unknown point of application of a harmonic input. Consequently, it is required to choose a proper calculation method for eigen frequencies and eigen vectors for those systems. Results. Is to analyze mathematical methods for calculating eigen frequencies and eigen vectors of complex technical systems with sub-systems of different nature and give recommendations on selecting a proper method and its justification. Results. It is demonstrated that in order to calculate eigen frequencies of mechanical systems, a use of a system of Cauchy differential equations is appropriate because it allows avoiding limitations caused by a traditional mathematical model. It is shown that the eigen frequency and eigen vector analysis helps one to determine eigen frequencies in presence of dampening as well as the ratio of amplitude of oscillation on those frequencies. Practical significance. The proposed method for selecting calculation technique for eigen frequencies and eigen vectors helps one to determine eigen frequencies in presence of dampening as well as the ratio of amplitude of oscillation on those frequencies. This method is applicable to the nonlinear system analysis in arbitrary points of nonlinearity.

Unconventionally Acquired Brain Injury: Guidance and Instruction About an Emerging Challenge to Warfighter Brain Health.

Special Operations Forces have made brain health a medical priority in recent years, and new guidance identified a new challenge-unconventionally acquired brain injury (UBI). Although this emerging condition is described as a cluster of neurosensory and cognitive symptoms with unknown etiology/ origin, there remain critical questions about how this diagnosis differs from other brain injuries. More importantly, there are limited recommendations about how medical personnel should approach the problem. The current discussion will provide context and information about UBI based on higher guidance and will also review the scant literature to provide context. Foremost, UBI can be distinguished from traumatic brain injury (TBI) largely due to an unknown point of injury. The described symptoms otherwise appear to be largely the same as TBI. Likewise, the recommended course of treatment is to follow the Clinical Practice Guidelines for mild TBI/TBI even if the injury is an actual or suspected UBI. Personnel must be careful to avoid entering sensitive information into the medical record, which may be particularly challenging if identifying the cause involves classified information about an unconventional weapon. Finally, we briefly discuss the literature about several suspected incidents fitting UBI diagnostic criteria, and we conclude with five primary takeaways for medical personnel to follow.

High-precision detection method for large and complex steel structures based on global registration algorithm and automatic point cloud generation

For extremely large and complicated steel structure buildings, the traditional monitoring method is inefficient and labor-intensive. However, LiDAR and UAV close-range photogrammetry can directly collect data in various large and complex environments, register sites, and fit nodes to monitor and analyze their structures. Taking a long-span steel structure monitoring as an example, this paper introduces the deformation monitoring scheme of steel structure with lidar and unmanned aerial vehicle close-range photogrammetry technology. The method of feature-based global registration is adopted to register the site point cloud. The initial value of feature constraint is taken as the error equation of observation value column, and the overall adjustment is carried out. The spatial transformation parameters and unknown point adjustment values are solved through the bundle adjustment model. Through iterative global registration algorithm, the correction error of observation value is controlled within a certain threshold range through constant constraint weighting and reconciliation of observation value until registration is completed, and the entire grid structure is generated. The eccentricity calculation of the spherical node and column in the grid structure is carried out by using the spherical node multi-link center point algorithm. Then, the dense point cloud generated by the photos taken by UAV close-range photogrammetry and the grid structure generated by LiDAR point cloud are used as mutual references, so that the deformation monitoring of long-span steel structures becomes more detailed and comprehensive.

A study on electrochemical hydrogen storage properties of truncated octahedron cobalt cerium molybdate nanocrystals synthesized by solution combustion method

Hydrogen energy is a key role in novel renewable energy production/consumption technologies. Traditional hydrogen energy systems are suffered from low density, high production cost, low efficiency, and storage complications. With the advent of solid-state hydrogen storage technology, many of above shortcomings are fulfilled, however, there are several unknown points, particularly in mixed metal oxides, which need more attention. Herein, we report the production of an engineered truncated octahedron shaped nanocrystal containing cobalt (Co), cerium (Ce), and Molybdenum (Mo) ions, emphasizing the general formula CoCe2(MoO4)4 via a solution combustion method. Experimental observations reveal that the CoCe2(MoO4)4 nanocrystals are purely formed in maltose at 700 °C, with almost regular truncated octahedron morphology, having an octahedral edge-size of around 45 nm. This morphological arrangement exhibits significantly an enhanced electrochemical hydrogen storage performance, in an alkaline medium, with maximum discharge capacity of ~4750 mAh/g.