Experimental Results in DIS, SIDIS and DES from Jefferson Lab

Jefferson Lab’s electron accelerator in its present incarnation, with a maximum beam energy slightly above 6 GeV, has already enabled a large number of experiments expanding our knowledge of nucleon and nuclear structure (especially in Deep Inelastic Scattering—DIS—at moderately high x, and in the resonance region). Several pioneering experiments have yielded first results on Deeply Virtual Compton Scattering (DVCS) and other Deep Exclusive Processes (DES), and the exploration of the rich landscape of transverse momentum‐dependent (TMD) structure functions using Semi‐Inclusive electron scattering (SIDIS) has begun. With the upgrade of CEBAF to 12 GeV now underway, a significantly larger kinematic space will become available. The 12 GeV program taking shape will complete a detailed mapping of inclusive, TMD and generalized distribution functions for quarks,antiquarks and gluons in the valence region and beyond

Implementation of a new bi-directional solar modelling method for complex facades within the ESP-r building simulation program

This paper provides an overview of a new method for modelling the total solar energy transmittance. It is implemented in the ESP-r building simulation program to model complex façades such as double glazed façades with external, internal or integrated shading devices. This new model has been validated and tested for several cases. The new model required changes to the solar control simulation algorithm and the user interface, so a new “Advanced optics menu” was also introduced into ESP-r. The paper presents the interface development and application of the new technique to different simulation configurations (especially different complex façades with shading devices) in a standard office building

Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets

This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared. The design and setting aim to measure and evaluate: a) annotation speed; b) annotation quality; and c) applicability to the use case of discourse network generation. While the results indicate only slight increases in terms of annotation speed, the authors find a moderate boost in annotation quality. Additionally, with the help of manual annotation of the actors and filtering out of the false positives, the machine learning based annotation suggestions allow the authors to fully recover the core network of the discourse as extracted from the articles annotated during the experiment. This is due to the redundancy which is naturally present in the annotated texts. Thus, assuming a research focus not on the complete network but the network core, an AI-based annotation can provide reliable information about discourse networks with much less human intervention than compared to the traditional manual approach