Students’ misconceptions about random variables

This article describes some misconceptions about random variables and related counter-examples, and makes suggestions about teaching initial topics on random variables in general form instead of doing it separately for discrete and continuous cases. The focus is on post-calculus probability courses.     

Antimicrobial, antileishmanial and cytotoxic compounds from Piper chaba

The petroleum ether and chloroform extracts of the root of Piper chaba showed antimicrobial, antileishmanial and cytotoxic activities. Further bioactivity-guided fractionation led to the isolation of Bornyl piperate (1), piperlonguminine (2) and piperine (3). This is the first report of isolation of compounds (1) and (2) from P. chaba. It was observed that the isolated compounds (1 and 2) showed potent antifungal activity when compared with standard drug Nystatin, and significant cytotoxic activity with the IC?? values of 0.76 and 0.83?μg?mL?1, respectively. These compounds were also found to have weak antibacterial and antileishmanial activities. This is the first report about the antileishmanial activity of Piper isolates.     

Extraction and analysis of different Cannabis samples by headspace solid-phase microextraction combined with gas chromatography-mass spectrometry

A headspace solid-phase microextraction combined with GC-MS method was developed for the extraction and analysis of cannabinoids from Cannabis samples. Different commercially available fibres were evaluated; polydimethylsiloxane 100 microm was selected as the most efficient one. In order to enhance sensitivity and reduce analysis time, the sampling temperature was studied and it showed that extraction should be performed at a high temperature (150 degrees C). In relation with the high lipophilicity of cannabinoids, a relatively long desorption time (3 min) was necessary to ensure a total transfer from the fibre into the injection port of the gas chromatograph. The method was finally applied to the extraction of Swiss marijuana samples from different regions. Data treatment by principal component analysis and hierarchical cluster analysis allowed a discrimination of the different batches.     

A comparison of three random disciplines

Consider a general system (or subsystem) with a single queue. In this context, we compare three different disciplines:ROS (Random Order of Service),RI (Random Insertion), andRAP (Randomly Assigned Priorities). The first of these (ROS) is a classical discipline; the other two are introduced in this paper. It turns out that the waiting time distributions for two of these disciplines are exactly the same, while the third discipline has strikingly different characteristics.Work done by this author while he was with IBM Zurich Research Laboratory.     

Linear and nonlinear coupling properties of a novel multicore circular dielectric waveguide

In this work we investigate the linear and nonlinear coupling properties of a novel multicore circular dielectric waveguide. The proposed device consists of a circular central core and many circular sectoral cores at the periphery, while the whole structure can be considered as a nonlinear multicore composite optical coupler. Hybrid guided modes in a circular sectoral dielectric waveguide are derived using circular harmonic expansion for the electromagnetic fields and the Point Matching Method (PMM) for the application of boundary conditions. Several cases are investigated varying some of the parameters of the geometry and the optical frequency in order to produce dispersion diagrams. In advance, the electric and magnetic field distributions for the fundamental guided modes are produced, while linear and nonlinear coupling coefficients as well as the sectoral waveguide mode effective area are derived.     

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.