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Ribbed channel flows are encountered in numerous engineering applications to promote turbulence and enhance heat transfer in various cooling passages, e.g., in turbine blades and at combustor walls. Numerical simulations have become an increasingly useful tool for studying the behavior of fluids in rib-roughened channels to provide insights into the complex turbulence structures and flow separatio

Acousmatic Concerts at IAC PROGRAMME 22–26 APRIL 2026 IAC.LU.SE Scan the QR code for detailed programme and artist bios! Wednesday, 22 April 17:00 & 19:00 ELIDE SULSENTI: Tundo, Sdraulig (Black Room) Performance with cello and headphones. Limited to 15 listeners per concert! Matteo Tundo – Cuoio (for cello and electronics) Charlie Sdraulig – Arc (for cello and electronics) Total duration: ca. 30 m

https://www.iac.lu.se/sites/iac.lu.se/files/2026-05/Intonal_2026_broschyr_A5_FINAL.pdf - 2026-05-30

This paper presents a numerical investigation of the ignition and combustion process of a primary reference fuel in a partially premixed light-duty internal combustion (PPC) engine. Partially pre-mixed combustion is achieved by employing a multiple injection strategy with three short injection events of fuel pulses. The timing of the first two fuel pulses, 48 and 22 crank angle degrees before top

The goal of this study is to investigate the applicability of asymmetrical flow field-flow fractionation (AsFlFFF)–multi angle laser light scattering (MALLS), and to develop a method for analysis of cationic potato amylopectin (CPAP) having ultrahigh molecular mass (UHMr). Use of the aqueous carrier having low salt content (3 mM NaN3) resulted in a distortion in AsFlFFF fractograms of CPAP with a

Biomolecular recognition is often characterised by low affinity where many weak interactions work either alone or in concert, resulting in an inherent dynamic situation. For example the well-studied weak binding of cell-cell interactions is predominantly based on a range of carbohydrates that interact with numerous (protein) ligands. Finding appropriate binders to these carbohydrate structures may

MASM22/FMSN3/FMSN30F0: Linear and Logistic Regression, 7.5hp FMSN40: … with Data Gathering, 9hp - Lecture 11, spring 2026 Multinomial and Ordinal logistic regression Quantile regression Categorical Y Multinomial Ordinal Distr.free MASM22/FMSN3/FMSN30F0: Linear and Logistic Regression, 7.5 hp FMSN40: . . . with Data Gathering, 9 hp Lecture 11, spring 2026 Multinomial and Ordinal logistic regression

https://www.maths.lu.se/fileadmin/maths/matematisk_statistik/FMSN30_FMSN40_MASM22/lecture_11_vt26.pdf - 2026-05-09

MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5hp FMSN40: … with Data Gathering, 9hp - Lecture 10, spring 2026 Generalized linear models GLM Poisson Negative binomial MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5 hp FMSN40: . . . with Data Gathering, 9 hp Lecture 10, spring 2026 Generalized linear models Mathematical Statistics / Centre for Mathematical Sciences Lund Univer

https://www.maths.lu.se/fileadmin/maths/matematisk_statistik/FMSN30_FMSN40_MASM22/lecture_10_vt26.pdf - 2026-05-09

MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5hp FMSN40: … with Data Gathering, 9hp - Lecture 8, spring 2026 Multiple logistic regression, Likelihood ratio test, influence, residuals, variable selection Multiple logistic regression loglike Validation Comparison MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5 hp FMSN40: . . . with Data Gathering, 9 hp Lecture 8, spring 2026 M

https://www.maths.lu.se/fileadmin/maths/matematisk_statistik/FMSN30_FMSN40_MASM22/lecture_08_vt26.pdf - 2026-05-09

MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5hp FMSN40: … with Data Gathering, 9hp - Lecture 7, spring 2026 Logistic regression: Probabilities, odds and odds ratios, Maximum-likelihood estimates, Wald test Introduction Logistic Maximum likelihood Properties MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5 hp FMSN40: . . . with Data Gathering, 9 hp Lecture 7, spring 2026 Logi

https://www.maths.lu.se/fileadmin/maths/matematisk_statistik/FMSN30_FMSN40_MASM22/lecture_07_vt26.pdf - 2026-05-09

MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5hp FMSN40: … with Data Gathering, 9hp - Lecture 9, spring 2026 Goodness-of-fit Classification ROC-curve MASM22/FMSN30/FMSN30F: Linear and Logistic Regression, 7.5 hp FMSN40: . . . with Data Gathering, 9 hp Lecture 9, spring 2026 Goodness-of-fit Mathematical Statistics / Centre for Mathematical Sciences Lund University 4/5-26 Anna Lindgren -

https://www.maths.lu.se/fileadmin/maths/matematisk_statistik/FMSN30_FMSN40_MASM22/lecture_09_vt26.pdf - 2026-05-09