HANDLING OF CORRELATIONS OF SPECTRAL QUANTITIES IN TRACEABILITY CHAIN – BASICS FOR A PYTHON-BASED ANALYSIS FRAMEWORKE

In this contribution a framework is presented that aims to help for handling correlations within measurement uncertainty calculations for spectral quantities. Taking correlations for spectral quantities into account is necessary as they directly influence the measurement uncertainties especially for integral quantities. Therefore, determination of correlations within traceability chains at national metrology institutes (NMIs) and disseminations of correlated data to test laboratory level is encouraged and a major goal of the EMPIR project 19NRM02 “Revision and extension of standards for test methods for LED lamps, luminaires and modules” (RevStdLED). The presented python-based analysis framework is used in photometry and spectroradiometry at PTB to calculate the results and associated measurement uncertainty for spectral irradiance, spectral irradiance responsivity and luminous responsivity based on spectral calibrations.

Interpretation strategy for the MMPI-2 protocol – a case study of a patient with bipolar disorder during remission of hypomanic symptoms

The paper presents an exemplary strategy for interpreting MMPI-2 data. Due to the multitude of diagnostic tools, the complex nature of the questionnaire and the time-consuming analysis of the data obtained, all examples of interpretation may be helpful for a psychologist. Polish reports supporting the broad interpretation of the MMPI-2 are sparse. The presented case study illustrates an interpretative path replicable in any clinical psychological practice. The article is based on the official guidelines of the Psychological Test Laboratory of the Polish Psychological Association. They were developed by Marek Matkowski and Tomasz Kucharski, specialists dealing with the discussed tool, and represent individual diagnostic recommendations created under the supervision of experienced clinicians and a one-time consultation with MSc Andrzej Janiak. Respecting the original copyrights belonging to the Psychological Test Laboratory of the Polish Psychological Association in Poland, I recommend purchasing only official and authorised materials in the above-mentioned Laboratory and familiarising with its official position on the diagnostic path in MMPI-2.

Local search approaches for the test laboratory scheduling problem with variable task grouping

The Test Laboratory Scheduling Problem (TLSP) is a real-world scheduling problem that extends the well-known Resource-Constrained Project Scheduling Problem (RCPSP) by several new constraints. Most importantly, the jobs have to be assembled out of several smaller tasks by the solver, before they can be scheduled. In this paper, we introduce different metaheuristic solution approaches for this problem. We propose four new neighborhoods that modify the grouping of tasks. In combination with neighborhoods for scheduling, they are used by our metaheuristics to produce high-quality solutions for both randomly generated and real-world instances. In particular, Simulated Annealing managed to find solutions that are competitive with the best known results and improve upon the state-of-the-art for larger instances. The algorithm is currently used for the daily planning of a large real-world laboratory.

PROCEDURE MODEL FOR STRUCTURED RELATIONAL MODELING OF REQUIREMENTS TO SUPPORT REQUIREMENTS-ORIENTED DECISION MAKING

The development of complex technical systems is characterized by a large number of system elements as well as their interactions. With regard to requirements management, many requirements have to be considered, which can have different relations to each other. If these requirements are used as basis for criteria in the decision making process, these relations must also be considered in the multi-criteria evaluation of product alternatives. Therefore, a computer-aided approach is presented in this paper, which allows the systematic modeling of requirement interactions focusing on multi-criteria decision making. For this purpose, basic relation types are identified, which are used to model submatrices in order to derive the Requirement Relation Matrix (RRM). Matrix-based as well as graph-based visualization methods are used for the RRM in order to improve the alternatives with the knowledge about the relational linkage. In addition, the effects of changes in requirements can be transferred to the decision making process. The approach is exemplarily applied to the extension of a test laboratory by a test bench.