Optimal Scaling Approaches for Perfusion MRI with Distorted Arterial Input Function (AIF) in Patients with Ischemic Stroke

Background: Diagnosis and timely treatment of ischemic stroke depends on the fast and accurate quantification of perfusion parameters. Arterial input function (AIF) describes contrast agent concentration over time as it enters the brain through the brain feeding artery. AIF is the central quantity required to estimate perfusion parameters. Inaccurate and distorted AIF, due to partial volume effects (PVE), would lead to inaccurate quantification of perfusion parameters. Methods: Fifteen patients suffering from stroke underwent perfusion MRI imaging at the Tri-Service General Hospital, Taipei. Various degrees of the PVE were induced on the AIF and subsequently corrected using rescaling methods. Results: Rescaled AIFs match the exact reference AIF curve either at peak height or at tail. Inaccurate estimation of CBF values estimated from non-rescaled AIFs increase with increasing PVE. Rescaling of the AIF using all three approaches resulted in reduced deviation of CBF values from the reference CBF values. In most cases, CBF map generated by rescaled AIF approaches show increased CBF and Tmax values on the slices in the left and right hemispheres. Conclusion: Rescaling AIF by VOF approach seems to be a robust and adaptable approach for correction of the PVE-affected multivoxel AIF. Utilizing an AIF scaling approach leads to more reasonable absolute perfusion parameter values, represented by the increased mean CBF/Tmax values and CBF/Tmax images.

Estimation Approaches of Machine Learning in Scrum Projects: A Review

It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon forstrengthening the collaboration betweendeveloper and customer.Estimation has always been challenging in Agile as requirements are volatile. This encourages researchersto work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paperreviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation. Keywords: Machine Learning, Scrum, Scrum Projects, Effort Estimation, Agile Software Development

A Critical Review On Mixing Parameters For High Content Reclaimed Asphalt Mixtures

High content reclaimed asphalt (RA) mixtures have been identified as one of the options to reduce the environmental and economic impacts of pavements construction. However, the process of designing and producing high content RA mixtures is challenging and the asphalt industry have serious concerns towards quality and long-term performance these mixtures. In laboratory, several parameters affect mixture characteristic, and if not controlled, may results into inaccurate estimation of performance. This state- of-the-art study aims to identify critical parameters for high content RA mixture production and highlight the effects of these parameters on mixture performance. The mixing parameters adopted in several laboratory studies have been highlighted and compared. The best practices to mix recycled asphalt in laboratory are reviewed in order to optimize the laboratory mixing. Based on review, important considerations for evaluating laboratory performance have been discussed.

An experimental approach to the influence of partial rebar corrosion and concrete degradation on behavior of FRP-strengthened RC beams

Human factors are one of the main reasons for structural damage as they decrease the bearing capacity and also lead to an inaccurate estimation of the structure. Previous studies show that the use of CFRP in the damaged structures can significantly increase their bending and shear capacity. This study examines the capacity and cracks distribution in eight RC (reinforced concrete) beams (210 × 250 × 250 cm), each of which was rehabilitated with seven CFRP (carbon fiber-reinforced polymer) strips using the strip method. Each beam, except for the control specimen, experiences different types of concrete and rebar damages, which are finally compared with those of the control specimen. The results indicated that rebar damage in all the beams was significant and the effects of concrete damage were minimized by CFRP strips. Moreover, the force–displacement diagrams indicate the greatest force for the control specimen. Other specimens reached up to 80% of the force experienced by the control specimen. Finally, the parametric study showed that the influence of the crack width on decreasing the bearing capacity was more significant compared with the other parameters.

Direct Measure Matching for Crowd Counting

Traditional crowd counting approaches usually use Gaussian assumption to generate pseudo density ground truth, which suffers from problems like inaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a new measure-based counting approach to regress the predicted density maps to the scattered point-annotated ground truth directly. First, crowd counting is formulated as a measure matching problem. Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching. Third, we propose a self-supervised mechanism by devising a Sinkhorn scale consistency loss to resist scale changes. Finally, an efficient optimization method is provided to minimize the overall loss function. Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech, UCF-QNRF, JHU++ and NWPU have validated the proposed method.

A MONTE CARLO SIMULATION STUDY ON THE DOME EFFECT

A dome-shape deformation has been found to affect the photogrammetric surface reconstruction in several real and simulated experiments. Its origin has been recognised in inaccurate estimation of the camera parameters and many papers already concentrated on conditions to avoid its development, especially as far as block design is concerned. This paper presents a Monte Carlo simulation to investigate surface reconstruction elevation errors in UAV (Unmanned Aerial Vehicle) photogrammetric blocks. The simulation tests are designed to find out the effect of block shape, camera axis inclination, side-lap, cross strips addition and block control by GCP or GNSS-assisted on the extent of the deformations. The main findings are: i) that GNSS-assisted blocks are generally more robust compared to GCP-controlled ones; ii) that, in GNSS-assisted blocks, unless a mix of nadiral and inclined strips is present, at least one fixed GCP must be provided; iii) that cross strip can conveniently be slimmed to save flight time and processing time; iv) that the effectiveness of GNSS deteriorate as the block shape slims out.

Estimating Time Spent at the Waste Collection Point by a Garbage Truck with a Multiple Regression Model

The planning of the garbage trucks’ routes is an essential process in waste collection companies. The main issues in garbage truck routing are determining the optimal routes, minimizing time, decreasing the costs, and reducing the pollution’s emission. In the literature, the time spent at a waste collection point (WCP) is considered as the average time, or it is not included at all. Time spent at a WCP is determined by the processes of picking up, emptying, and putting down the waste containers and the factors specific for different WCPs. Those factors impact the time spent at WCP significantly. Excluding time spent at a WCP or taking the average of that in the planning approach may lead to the inaccurate estimation of total collection time. The aim of this article is to present the multiple regression model for estimating time spent at a WCP. We analyzed the impact of the WCP factors (i.e., building type and number of containers) on the time that a garbage truck spends at it. We initially considered seven chosen factors, five categorical and two numerical. Based on this, we developed the multiple regression model based on linear regression use. Later, the proposed model was validated based on data obtained from the municipal company operating in Wroclaw city, Poland. The study confirmed that the defined factors significantly affect garbage truck’s time spent at a WCP and should be taken into account during waste collection planning processes’ performance.

Kinetostatic Modeling of Dual-Drive H-type Gantry with Exchangeable Flexure Joints

The flexure joints are proposed to replace the rigid assembly between the cross-arm and the moving carriages of dual-drive H-type gantry (DHG), for higher reliability and fine rotational alignments. In prior literature, the flexure joint of the DHG is modeled as an ideal linear torsional spring, resulting in inaccurate estimation of the cross-arm's angle. In this work, a generalized analytical kinetostatic model of flexure-linked DHG is built by considering the geometric nonlinearities. The expressions of beam coefficients in the model are obtained from either beam constraint model (BCM) or Timoshenko BCM (TBCM), according to the given criterion of length-to-thickness ratio. The model is capable to accurately estimate any two variables among the rotation angle of the cross-arm, the misalignment of two carriages, and the net driving force, as long as the other is known. Simulations and experiments on the testbed validate the accuracy and show practical appeals of the proposed model.

A Study of Load Imbalance Influence on Power Quality Assessment for Distribution Networks

Power quality studies for distribution networks are very important for future network expansions realized by utility companies, so the accuracy of such studies is critical. Load data, including information on load imbalance, could have in many situations a significant influence on the correct estimation of many power quality indicators. This paper investigates the impact of load imbalance on several phase imbalance indicators and voltage quality indicators by comparing the values of these indicators, as calculated in a power quality study using, sequentially, different sets of load data characterized by different load imbalances. The results of this study confirm the original hypothesis, showing that the use of inaccurate consumption profiles for loads leads to an inaccurate estimation of some power quality indicators. In addition, the results highlight the difficulty of approximating the actual consumption profiles of electrical loads so that this approximation does not affect the correctness of the estimation of phase imbalance and voltage quality indicators.

Calibration for Parameter Estimation of Signals with Complex Noise via Nonstationarity Measure

The signals in numerous complex systems of engineering can be regarded as nonlinear parameter trend with noise which is identically distributed random signals or deterministic stationary chaotic signals. The commonly used methods for parameter estimation of nonlinear trend in signals are mainly based on least squares. It can cause inaccurate estimation results when the noise is complex (such as non-Gaussian noise, strong noise, and chaotic noise). This paper proposes a calibration method for this issue in the case of single parameter via nonstationarity measure from the perspective of the stationarity of residual sequence. Some numerical studies are conducted for validation. Results of numerical studies show that the proposed calibration method performs well for various models with different noise strengths and types (including random noise and chaotic noise) and can significantly improve the accuracy of initial estimates obtained by least squares method. This is the first time that the nonstationarity measure is applied to the parameter calibration. All these results will be a guide for future studies of other parameter calibrations.