Signature of the γ\gamma+jet and dijet production mediated by an excited quark with QCD next-to-leading order accuracy at the LHC

We present a detailed study of the production and decay of the excited quark at the QCD next-to-leading order (NLO) level at the Large Hadron Collider, using the narrow width approximation and helicity amplitudes method. We find that the QCD NLO corrections can tighten the constraints on the model parameters and reduce the scale dependencies of the total cross sections. We discuss the signals of the excited quark production with decay mode $q^{\ast}\rightarrow q\gamma$ and $q^{\ast}\rightarrow qg$, and present several important kinematic distributions. Moreover, we give the upper limits of the excited quark excluded mass range and the allowed parameter space for the coupling constants and the excited quark mass.Comment: 20 pages, 13 figures; version published in PR

Threshold resummation for the production of a color sextet (antitriplet) scalar at the LHC

We investigate threshold resummation effects in the production of a color sextet (antitriplet) scalar at next-to-next-to-leading logarithmic (NNLL) order at the LHC in the frame of soft-collinear effective theory. We show the total cross section and the rapidity distribution with NLO+NNLL accuracy, and we compare them with the NLO results. Besides, we use recent dijet data at the LHC to give the constraints on the couplings between the colored scalars and quarks.Comment: 21 pages,9 figures,3 tables; Version published in EPJ

Inclusiveness Matters: A Large-Scale Analysis of User Feedback

In an era of rapidly expanding software usage, catering to the diverse needs of users from various backgrounds has become a critical challenge. Inclusiveness, representing a core human value, is frequently overlooked during software development, leading to user dissatisfaction. Users often engage in discourse on online platforms where they indicate their concerns. In this study, we leverage user feedback from three popular online sources, Reddit, Google Play Store, and Twitter, for 50 of the most popular apps in the world to reveal the inclusiveness-related concerns from end users. Using a Socio-Technical Grounded Theory approach, we analyzed 23,107 posts across the three sources and identified 1,211 inclusiveness related posts. We organize our empirical results in a taxonomy for inclusiveness comprising 6 major categories: Fairness, Technology, Privacy, Demography, Usability, and Other Human Values. To explore automated support to identifying inclusiveness-related posts, we experimented with five state-of-the-art pre-trained large language models (LLMs) and found that these models' effectiveness is high and yet varied depending on the data source. GPT-2 performed best on Reddit, BERT on the Google Play Store, and BART on Twitter. Our study provides an in-depth view of inclusiveness-related user feedback from most popular apps and online sources. We provide implications and recommendations that can be used to bridge the gap between user expectations and software so that software developers can resonate with the varied and evolving needs of the wide spectrum of users

Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network

While a lack of diversity is a longstanding problem in computer science and engineering, universities and organizations continue to look for solutions to this issue. Among the first of its kind, we launched INSPIRE: STEM for Social Impact, a program at the University of Victoria, Canada, aimed to motivate and empower students from underrepresented groups in computer science and engineering to develop digital solutions for society impactful projects by engaging in experiential learning projects with identified community-partners. The twenty-four students in the program came from diverse backgrounds in terms of academic areas of study, genders, ethnicities, and levels of technical and educational experience. Working with six community partners, these students spent four months learning and developing solutions for a societal and/or environmental problem with potential for local and global impacts. Our experiences indicate that working in a diverse team with real clients on solving pressing issues produces a sense of competence, relatedness, and autonomy which are the basis of self-determination theory. Due to the unique structure of this program, the three principles of self-determination theory emerged through different experiences, ultimately motivating the students to build a network of like-minded people. The importance of such a network is profound in empowering students to succeed and, in retrospect, remain in software engineering fields. We address the diversity problem by providing diverse, underrepresented students with a safe and like-minded environment where they can learn and realize their full potential. Hence, in this paper, we describe the program design, experiences, and lessons learned from this approach. We also provide recommendations for universities and organizations that may want to adapt our approach