Automatic Result Verification Research Articles

Perbandingan Turnaround Time Antara Verifikasi Manual dan Verifikasi Otomatis terhadap Hasil Pemeriksaan Profil Lipid di Laboratorium Klinik Pramita Cabang Pajajaran Kota Bandung

Turnaround Time (TAT) for laboratory services is an indicator of the quality of laboratory services to meet customer satisfaction and as a benchmark for laboratory performance. Laboratory results are very important to be delivered in the shortest possible time for the rapid diagnosis and management of patients. One of the factors that causes the length of TAT in the reporting process at the analytical stage is the verification process of inspection results which is still carried out manually by ATLM personnel. Implementing automatic verification of examination results is one way to overcome the long waiting time (TAT) for laboratory services. The aim of this research is to determinate the TAT of manual verification and automatic verification of lipid profile examination results, and testing the difference in TAT between manual verification and automatic verification of lipid profile examination results at the analytical stage at the Pramita clinical laboratory, Pajajaran branch in Bandung city. The method used in this research is to use descriptive comparative method with a quantitative approach. Based on the research that has been done, the average ± standard deviation TAT for manual verification of the results of lipid profile examination was 112.44 ± 46.02 minutes, while the automatic verification TAT was 68.03 ± 28.07 minutes. The results of the independent sample t-test between TAT manual verification and automatic verification of the lipid profile examination results with a significance value of Sig.(2-tailed) 0.000 <0.05. The conclusion of this research is that TAT verification of lipid profile examination results at the analytical stage decreased after automatic verification was implemented. There is a significant difference in Turnaround Time between manual verification and automatic verification of the results of lipid profile examinations at the analytical stage at the Pramita Clinical Laboratory, Pajajaran branch in Bandung city.
 

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction.

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification.

ПРИМЕНЕНИЕ КОМПЛЕКСА ЭЛЕКТРОННЫХ ПРОБЛЕМНО ОРИЕНТИРОВАННЫХ ОБУЧАЮЩИХ СИСТЕМ ПО ТЕОРЕТИЧЕСКОЙ МЕХАНИКЕ

The subject of the article is a complex of electronic problem-oriented training systems on theoretical mechanics, developed at the Department of Mechanics and Engineering Graphics of the Astrakhan State Technical University. Components of the complex are: an electronic textbook (including sections "Theoretical material", "Tests and tasks", "Workshop"); bank of multivariate tasks for independent work of students and control of their knowledge; generator of training tasks of a given complexity with automatic verification of results; complex of mws-programs (Maple) for computer simulation and automation of calculation of mechanical systems with visualization of results; a training website where the listed components are located. The article describes the software that is being developed aiming step-by-step solution of model problems of theoretical mechanics. A technique for solving static problems is presented, as an example is taken a model task on the subject "Determination of the reactions of solid support" using the training website. The complex is used for the full-time-, part-time students and distance learning students in the autonomous and networked modes. The advantages of the complex have been described. Today a special relevance of the complex is stipulated by a significant decrease of the number of classroom tutorials and an increase of the number of hours for students’ independent work. The effectiveness of the training methodology using the electronic problem-oriented training systems has been confirmed by the implementation certificates approved by three Russian universities.

Bivariate Product Cubature Using Peano Kernels for Local Error Estimates

The error estimates of automatic integration by pure floating-point arithmetic are intrinsically embedded with uncertainty. This in critical cases can make the computation problematic. To avoid the problem, we use product rules to implement a self-validating subroutine for bivariate cubature over rectangular regions. Different from previous self-validating integrators for multiple variables (Storck in Scientific Computing with Automatic Result Verification, pp. 187---224, Academic Press, San Diego, [1993]; Wolfe in Appl. Math. Comput. 96:145---159, [1998]), which use derivatives of specific higher orders for the error estimates, we extend the ideas for univariate quadrature investigated in (Chen in Computing 78(1):81---99, [2006]) to our bivariate cubature to enable locally adaptive error estimates by full utilization of Peano kernels theorem. The mechanism for active recognition of unreachable error bounds is also set up. We demonstrate the effectiveness of our approach by comparing it with a conventional integrator.

AUTOMATIC RESULT VERIFICATION BY COMPLETE RUN-TIME CHECKING OF COMPUTATIONS

Two-Stage Programming (2SP) is an experimental programming language, the first implementation of the Specification-Consistent Coordination Model (SCCM). The SCCM proposes a new, mixed-paradigm (functional/imperative) approach to developing reliable programs based on complete run-time checking of computations with respect to a given specification. A 2SP program consists of a functional specification and an imperative coordination tightly connected to the specification. The coordination maps the specification to an imperative and possibly parallel/distributed program. Normal termination of a 2SP program execution implies the correctness of the computed results with respect to the specification, for that execution. We present the basic feautures of the SCCM/2SP, a new message-spassing system of 2SP with integrated run-time checking, and a larger case study. We show that 2SP provides: functional specifications, specification-consistent imperative coordinations, automatic run-time result verification and error detection, enhanced debugging support, and good efficiency.

An Adaptive Numerical Integration Algorithm with Automatic Result Verification for Definite Integrals

We present a verified numerical integration algorithm with an adaptive strategy for smooth integrands. Verified representations of the remainder term are derived and a new adaptive strategy is given which delivers a desired integral enclosure with an error usually bounded by a specified error bound. Here, we discuss the distribution of the specified error bound onto the subintervals used in the algorithm more closely and present numerical results depending on the distribution of the error.

Scientific Computing with Automatic Result Verification.

Automatic Result Verification.- I. Numerical Methods with Result Verification.- A Method for Producing Verified Results for Two-point Boundary Value Problems.- A Kind of Difference Method for Enclosing Solutions of Ordinary Linear Boundary Value Problems.- A Self-validating Method for Solving Linear Programming Problems with Interval Input Data.- Enclosing the Solutions of Linear Equations by Interval Iterative Processes.- Errorbounds for Quadratic Systems of Nonlinear Equations Using the Precise Scalar Product.- Inclusion of Eigenvalues of General Eigenvalue Problems of Matrices.- Verified Inclusion for Eigenvalues of Certain Difference and Differential Equations.- II. Applications in the Technical Sciences.- VIB - Verified Inclusions of Critical Bending Vibrations.- Stability Test for Periodic Differential Equations on Digital Computers with Applications.- The Periodic Solutions of the Oregonator and Verification of Results.- On Arithmetical Problems of Geometric Algorithms in the Plane.- III. Improving the Tools.- Precise Evaluation of Polynomials in Several Variables.- Evaluation of Arithmetic Expressions with Guaranteed High Accuracy.- Standard Functions for Real and Complex Point and Interval Arguments with Dynamic Accuracy.- Inverse Standard Functions for Real and Complex Point and Interval Arguments with Dynamic Accuracy.- Inclusion Algorithms with Functions as Data.- FORTRAN-SC. A Study of a FORTRAN Extension for Engineering/Scientific Computation with Access to ACRITH.

A test environment using symbolic program trace

In this work aspects of testing numeric programs for automatic result verification will be discussed. An outline of a test environment for analysing numeric programs based on symbolic program trace will be introduced, which supports soure, code inspection, test data generation and input data set classification. Applying the test tool during the whole software life cycle a data base of input/output relations will be created, which allows to guarantee functionality of a product related to the tested data. In this way, the effort invested for testing and analysing of numeric software can be saved and its reliability can be improved.

Numerics with automatic result verification

Floating-point arithmetic is the fast way to perform scientific and engineering calculations. Today, individual floating-point operations are maximally accurate as a rule. However, after only two or just a few operations, the result can be completely wrong. Computers now carry out up to 10 11 floating-point operations in a second. Thus, particular attention must be paid to the reliability of the computed results. In recent years, techniques have been developed in numerical analysis which make it possible for the computer itself to verify the correctness of computed results for numerous problems and applications. Moreover, the computer frequently establishes the existence and uniqueness of the solution in this way. For example, a verified solution of a system of ordinary differential equations is just as valid as a solution obtained by a computer algebra system, which as a rule still requires a valid formula evaluation. Furthermore, the numerical routine remains applicable even if the problem does not have a closed-form solution. This paper first illustrates some problems with ordinary floating-point arithmetic by means of some simple examples. Then, necessary extensions of computer arithmetic are entered into. Accumulation must be performed in fixed-point arithmetic. The continuum will be brought into the computer in the form of intervals. In order to have simple access to the new techniques, programming languages themselves have to be extended. ACRITH-XSC, Pascal-XSC and C-XSC are examples of such language extensions, which have been implemented with varying amounts of compiler effort. Result verification is carried out by means of mathematical fixed-point theorems such as Brouwer's fixed-point theorem and its generalizations. Numerical computation with result verification is of fundamental significance for many applications, for example, for simulation or mathematical modeling. Models which are frequently developed by means of heuristic methods can only be refined systematically if computational errors can be essentially excluded. This paper gives an introduction to the whole field of numerical computation with automatic result verification, written with nonspecialists in mind. It extends from the design of hardware for arithmetic operations in VLSI to compilers, algorithms, numerical analysis, and deep into applications. A symbiosis of these new techniques with a computer system for symbolic computation would be highly desirable.