Higgs boson decays into {\gamma}{\gamma} and Z{\gamma} in the MSSM and BLSSM

We calculate Higgs decay rates into {\gamma}{\gamma} and Z{\gamma} in the Minimal Supersymmetric Standard Model (MSSM) and (B-L) Supersymmetric Standard Model (BLSSM) by allowing for contributions from light staus and charginos. We show that sizable departures are possible from the SM predictions for the 125 GeV state and that they are testable during run 2 at the Large Hadron Collider. Furthermore, we illustrate how a second light scalar Higgs signal in either or both these decay modes can be accessed at the CERN machine rather promptly within the BLSSM, a possibility instead precluded to the MSSM owing to the much larger mass of its heavy scalar state.Comment: Plots slightly modified, no significant chang

The Compliance Chronic Renal Failure Patient on Restrictions Liquids in Hemodialysis Therapy

Introduction: Nonadherence is a rampant problem among patients undergoing dialysis and can impact multiple aspects of patient care, including medications, and treatment regimens as well as dietary and fluid restriction. The purpose of this descriptive correlative research, on hemodyalysa patient with chronic renal failure was to know the influencing factors of compliance patient to fluid restriction. Method: This study used descriptive correlative design, Data was analysed by using distibution frequency and chi square for analysys relation between variable. Result: The result revealed there were nor significant statistic difference at p > 0.05 between age, gender, education level, frequency of hemodyalysa and health education from nurse to compliance patient to fluid restriction (p = 0.647; p = 0.717; p = 0.345; p = 0.774; p = 0.273). Discussion: Level of patient adherence to therapy not influenced by demographi factor but by the quality of interaction health workers and other factors. This study recommended for further analysis of the factors that influence the level of compliance of the patient as psychological factors (belieft , motivation), socio-economic, and social support

Search for Mono-Higgs Signals in bbˉb\bar b Final States Using Deep Neural Networks

We study mono-Higgs signatures emerging in an illustrative new physics scenario involving Standard Model Higgs boson decays to bottom quark pairs using Hybrid Deep Neural Networks. We use a Multi-Layer Perceptron to analyze the kinematic observables and optimize the signal-to-background discrimination. The global color flow structure of hard jets emerging from the decay of heavy particles with different color charges is crucial to single out the mono-Higgs signature. Upon embedding the different color flow structures for signal and backgrounds into constructed images, we use a Convolution Neural Network to analyze the latter. Specifically, the approach takes initially a mono-type data as input, frittering away invaluable multi-source and multi-scale information. We then discuss a general architecture of Hybrid Deep Neural Networks that supports instead mixed input data. In comparison with single input Deep Neural Networks, like MultiLayers Perceptron or Convolution Neural Network, the Hybrid Deep Neural Networks provide higher capacity in feature extraction and thus in signal vs background classification performance. We provide reference results for the case of the High-Luminosity Large Hadron Collider.Comment: published versio

Sharpening the A→Z(∗)hA\to Z^{(*)}h Signature of the Type-II 2HDM at the LHC through Advanced Machine Learning

The $A\to Z^{(*)}h$ decay signature has been highlighted as possibly being the first testable probe of the Standard Model (SM) Higgs boson discovered in 2012 ($h$) interacting with Higgs companion states, such as those existing in a 2-Higgs Doublet Model (2HDM), chiefly, a CP-odd one ($A$). The production mechanism of the latter at the Large Hadron Collider (LHC) takes place via $b\bar b$-annihilation and/or $gg$-fusion, depending on the 2HDM parameters, in turn dictated by the Yukawa structure of this Beyond the SM (BSM) scenario. Among the possible incarnations of the 2HDM, we test here the so-called Type-II, for a twofold reason. On the one hand, it intriguingly offers two very distinct parameter regions compliant with the SM-like Higgs measurements, i.e., where the so-called `SM limit' of the 2HDM can be achieved. On the other hand, in both configurations, the $AZh$ coupling is generally small, hence the signal is strongly polluted by backgrounds, so that the exploitation of Machine Learning (ML) techniques becomes extremely useful. In this paper, we show that the application of advanced ML implementations can be decisive in establishing such a signal. This is true for all distinctive kinematical configurations involving the $A\to Z^{(*)}h$ decay, i.e., below threshold ($m_A<m_Z+m_h$), at its maximum ($m_Z+m_h<m_A<2m_t$) and near the onset of $t\bar t$ pair production ($m_A \approx 2m_t$), for which we propose Benchmark Points (BPs) for future phenomenological analyses.Comment: JHEP accepted version., 33 pages, 15 figures, 2 table

Reflections on the potential (and limits) of action research as ethos, methodology and practice: A case study of a women's empowerment programme in the Middle East

This paper argues that an evidence-based approach to advocacy led by and targeting women could amplify women's positioning in the political and economic realms. Participatory Action Research is examined as a process for mobilisation, coalition-building and evidence-based advocacy and action, through a case study of a multi-country British Council supported programme that incorporated an action research approach.1 Drawing from the experiences and perceptions of its participants, it offers reflective insights into the theory and practice of action research and its empowerment potential. The findings confirm a widespread support for the use of Participatory Action Research as a starting point for stronger advocacy work, showing its positive transformative effects on individuals, groups and coalition. Participatory Action Research contributes to evidence-based advocacy that is more relevant and inclusive, and arguably empowering for women advocates.Practitioners learned by doing with coaching support from INTRAC both virtual and face-to-face, while the British Council coordinated and supported the country teams. This included country-based as well as regional training and mentoring sessions across all stages of the research and advocacy.Scopu

Multi-scale cross-attention transformer encoder for event classification

We deploy an advanced Machine Learning (ML) environment, leveraging a multi-scale cross-attention encoder for event classification, towards the identification of the $gg\to H\to hh\to b\bar b b\bar b$ process at the High Luminosity Large Hadron Collider (HL-LHC), where $h$ is the discovered Standard Model (SM)-like Higgs boson and $H$ a heavier version of it (with $m_H>2m_h$). In the ensuing boosted Higgs regime, the final state consists of two fat jets. Our multi-modal network can extract information from the jet substructure and the kinematics of the final state particles through self-attention transformer layers. The diverse learned information is subsequently integrated to improve classification performance using an additional transformer encoder with cross-attention heads. We ultimately prove that our approach surpasses in performance current alternative methods used to establish sensitivity to this process, whether solely based on kinematic analysis or else on a combination of this with mainstream ML approaches. Then, we employ various interpretive methods to evaluate the network results, including attention map analysis and visual representation of Gradient-weighted Class Activation Mapping (Grad-CAM). Finally, we note that the proposed network is generic and can be applied to analyse any process carrying information at different scales. Our code is publicly available for generic use.Comment: Typos correcte

Searching for Charged Higgs Bosons in the B−LB-L Supersymmetric Standard Model at the High Luminosity Large Hadron Collider

Upon assuming the $B-L$ Supersymmetric Standard Model (BLSSM) as theoretical framework accommodating a multi-Higgs sector, we assess the scope of the High Luminosity Large Hadron Collider (HL-LHC) in accessing charged Higgs bosons ($H^\pm$) produced in pairs from $Z'$ decays. We show that, by pursuing both di-jet and tau-neutrino decays, several signals can be established for $H^\pm$ masses ranging from about $M_{W}$ to above $m_t$ and $Z'$ masses between 2.5 TeV and 3.5 TeV. The discovery can be attained, even in a background free environment in some cases, owing to the fact that the very massive resonating $Z'$ ejects the charged Higgs bosons at very high transverse momentum, a kinematic region where any SM noise is hugely depleted.Comment: 5 pages, 7 figures, matches published versio

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants’ type, number of test participants, test participants’ programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected articles discussed tools that target IoT and education, while other fields such as data science, robotics are emerging. Further, most tools use abstractions to hide implementation details and use similar interaction styles. The predominant platforms for the tools are web and mobile, while desktop-based tools are on the decline. Only a few tools were evaluated with a formal experiment, whilst the remaining ones were evaluated with evaluation studies or informal feedback. Most tools were evaluated with students with little to no programming skills. There is a lack of emphasis on usability principles in the design stage of the tools. Additionally, only one of the tools was evaluated for expressiveness. Other areas for exploration include supporting end users throughout the life cycle of applications created with the tools, studying the impact of tutorials on improving learnability, and exploring the potential of machine learning to improve debugging solutions developed with visual programming