This paper presents the observation of four-top-quark ($$t\bar{t}t\bar{t}$$ t t ¯ t t ¯ ) production in proton-proton collisions at the LHC. The analysis is performed using an integrated luminosity of 140 $$\hbox {fb}^{-1}$$ fb - 1 at a centre-of-mass energy of 13 TeV collected using the ATLAS detector. Events containing two leptons with the same electric charge or at least three leptons (electrons or muons) are selected. Event kinematics are used to separate signal from background through a multivariate discriminant, and dedicated control regions are used to constrain the dominant backgrounds. The observed (expected) significance of the measured $$t\bar{t}t\bar{t}$$ t t ¯ t t ¯ signal with respect to the standard model (SM) background-only hypothesis is 6.1 (4.3) standard deviations. The $$t\bar{t}t\bar{t}$$ t t ¯ t t ¯ production cross section is measured to be $$22.5^{+6.6}_{-5.5}$$ 22 . 5 - 5.5 + 6.6  fb, consistent with the SM prediction of $$12.0 \pm 2.4$$ 12.0 ± 2.4 fb within 1.8 standard deviations. Data are also used to set limits on the three-top-quark production cross section, being an irreducible background not measured previously, and to constrain the top-Higgs Yukawa coupling and effective field theory operator coefficients that affect $$t\bar{t}t\bar{t}$$ t t ¯ t t ¯ production.

PurposeThe increase in automobile usage across the world has fortified the opportunities of maintenance service garages. However, there are significant numbers of challenges in front of maintenance service providers at all stages of the business. This paper identifies, analyzes and prioritizes various challenges associated with the establishment and survival of garages specific to Indian context.Design/methodology/approachIn this paper, challenges for automotive service garage are identified through expert opinion, garage survey and literature. A structural hierarchical framework of the identified challenges is established through structural models, including interpretive structural modeling and analytic hierarchy process.FindingsThis paper has identified nine challenges, namely proliferation of new models and variants; technological advancements in automobile systems; demand of better service quality; space and ambience requirements; labor requirements; requirement of modern support equipments, tools and spares; safety requirements and prevention of occupational hazards; environmental norms and concerns; proper documentation requirements. The drivers and dependent variables have been identified. A hierarchical framework of challenges has been established.Practical implicationsThis paper provides a comprehensive list of challenges and their priority in establishing an automobile maintenance garage business in Indian context. This will help the budding entrepreneurs and existing maintenance organizations to focus on the challenges that necessitate immediate attention and corrective actions.Originality/valueThis paper provides a significant contribution in the literature of garage maintenance services, which is established on the viewpoint of different collaborators associated with this business. This study will be a foundation to investigate further in this domain.

A search for forward proton scattering in association with light-by-light scattering mediated by an axion-like particle is presented, using the ATLAS Forward Proton spectrometer to detect scattered protons and the central ATLAS detector to detect pairs of outgoing photons. Proton-proton collision data recorded in 2017 at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV were analysed, corresponding to an integrated luminosity of 14.6 fb−1. A total of 441 candidate events were selected. A search was made for a narrow resonance in the diphoton mass distribution, corresponding to an axion-like particle (ALP) with mass in the range 150–1600 GeV. No excess is observed above a smooth background. Upper limits on the production cross section of a narrow resonance are set as a function of the mass, and are interpreted as upper limits on the ALP production coupling constant, assuming 100% decay branching ratio into a photon pair. The inferred upper limit on the coupling constant is in the range 0.04–0.09 TeV−1 at 95% confidence level.

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Emerging 5G and next generation 6G wireless are likely to involve myriads of connectivity, consisting of a huge number of relatively smaller cells providing ultra-dense coverage. Guaranteeing seamless connectivity and service level agreements in such a dense wireless system demands efficient network management and fast service recovery. However, restoration of a wireless network, in terms of maximizing service recovery, typically requires evaluating the service impact of every network element. Unfortunately, unavailability of real-time KPI information, during an outage, enforces most of the existing approaches to rely significantly on context-based manual evaluation. As a consequence, configuring a real-time recovery of the network nodes is almost impossible, thereby resulting in a prolonged outage duration. In this article, we explore deep learning to introduce an intelligent, proactive network recovery management scheme in anticipation of an eminent network outage. Our proposed method introduces a novel utilization-based ranking scheme of different wireless nodes to minimize the service downtime and enable a fast recovery. Efficient prediction of network KPI (Key Performance Index), based on actual wireless data demonstrates up to ∼ 54% improvement in service outage.