Deconfinement and degrees of freedom in pppp and A−AA-A collisions at LHC energies

We present the extraction of the temperature by analyzing the charged particle transverse momentum spectra in lead-lead (Pb-Pb) and proton-proton (${\bf pp}$) collisions at LHC energies from the ALICE Collaboration using the Color String Percolation Model (CSPM). From the measured energy density ${\bm \varepsilon}$ and the temperature T the dimensionless quantity ${\bm \varepsilon/}T^{4}$ is obtained to get the degrees of freedom (DOF), ${\bm \varepsilon}/T^{4}$ = DOF ${ \pi^{2}}$/30. We observe for the first time a two-step behavior in the increase of DOF, characteristic of deconfinement, above the hadronization temperature at temperature $\sim$ 210 MeV for both Pb-Pb and ${\bf pp}$ collisions and a sudden increase to the ideal gas value of $\sim$ 47 corresponding to three quark flavors in the case of Pb-Pb collisions.Comment: 7 pages and 4 figure

Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)

The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.</p

Implementation of machine learning techniques to predict impact parameter and transverse spherocity in heavy-ion collisions at the LHC

Machine learning techniques have been quite popular recently in the high-energy physics community and have led to numerous developments in this field. In heavy-ion collisions, one of the crucial observables, the impact parameter, plays an important role in the final-state particle production. This being extremely small (i.e. of the order of a few fermi), it is almost impossible to measure impact parameter in experiments. In this work, we implement the ML-based regression technique via Gradient Boosting Decision Trees (GBDT) to obtain a prediction of impact parameter in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV using A Multi-Phase Transport (AMPT) model. After its successful implementation in small collision systems, transverse spherocity, an event shape observable, holds an opportunity to reveal more about the particle production in heavy-ion collisions as well. In the absence of any experimental exploration in this direction at the LHC yet, we suggest an ML-based regression method to estimate centrality-wise transverse spherocity distributions in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV by training the model with minimum bias collision data. Throughout this work, we have used a few final state observables as the input to the ML-model, which could be easily made available from collision data. Our method seems to work quite well as we see a good agreement between the simulated true values and the predicted values from the ML-model.Comment: 5 pages, 2 figures, Conference: 9th Annual Large Hadron Collider Physics (LHCP 2021), June 7-12, 202

A trial sequential meta-analysis of TNF-α –308G\u3eA (rs800629) gene polymorphism and susceptibility to colorectal cancer

© 2019 The Author(s). Purpose: Tumor necrosis factor-α (TNF-α), secreted by the activated macrophages, may participate in the onset and progression of colorectal cancer (CRC). The association of TNF-α –308 G\u3eA (rs1800629) single-nucleotide polymorphism (SNP) with CRC risk has been investigated by many studies but the results are inconclusive. A trial sequential meta-analysis was performed for precise estimation of the relationship between TNF-α –308 G\u3eA gene polymorphism with CRC risk. Methods: Medline (PubMed), EMBASE (Excerpta-Medica) and Google Scholar were mined for relevant articles. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the significance of association. Results: The pooled analysis indicated no risk associated with TNF-α –308 G\u3eA SNP and overall CRC risk in five genetic comparison models, i.e. allelic (A vs. G: P = 0.524; OR = 1.074, 95% CI = 0.863–1.335), homozygous (AA vs. GG: P = 0.489; OR = 1.227, 95% CI = 0.688–2.188), heterozygous (AG vs. GG: P = 0.811; OR = 1.024, 95% CI = 0.843–1.244), dominant (AA+AG vs. GG: P = 0.630; OR = 1.055, 95% CI = 0.849–1.311) and recessive (AA vs. AG+GG: P = 0.549; OR = 1.181, 95% CI = 0.686–2.033). Subgroup analysis revealed that TNF-α –308 G\u3eA SNP is associated with reduced risk of CRC in Asian ethnicity. The study showed no publication bias. Conclusions: No association of TNF-α –308 G\u3eA SNP with overall CRC risk was found. This SNP is likely to be protective against CRC in Asian population when compared with Caucasian population. Larger prospective-epidemiological studies are warranted to elucidate the roles of TNF-α –308 G\u3eA SNP in the etiology of CRC and to endorse the present findings

Angiotensin-Converting Enzyme Gene I/D Polymorphism Is Associated With Systemic Lupus Erythematosus Susceptibility: An Updated Meta-Analysis and Trial Sequential Analysis

Angiotensin-converting enzyme (ACE) gene is indispensable for endothelial control and vascular tone regulatory systems, usually affected in Systemic Lupus Erythematosus (SLE). ACE insertion/deletion (I/D) polymorphism may influence the progress of SLE. Earlier studies have investigated this association without any consistency in results. We performed this meta-analysis to evaluate the precise association between ACE I/D polymorphism and SLE susceptibility. The relevant studies were searched until December, 2017 using Medline (PubMed), Google-Scholar and EMBASE search engines. Twenty-five published studies involving 3,308 cases and 4,235 controls were included in this meta-analysis. Statistically significant increased risk was found for allelic (D vs. I: p = 0.007; OR = 1.202, 95% CI = 1.052–1.374), homozygous (DD vs. II: p = 0.025; OR = 1.347, 95% CI = 1.038–1.748), dominant (DD+ID vs. II: p = 0.002; OR = 1.195, 95% CI = 1.070–1.334), and recessive (DD vs. ID+II: p = 0.023; OR = 1.338, 95% CI = 1.042–1.718) genetic models. Subgroup analysis stratified by Asian ethnicity revealed significant risk of SLE in allelic (D vs. I: p = 0.045; OR = 1.238, 95% CI = 1.005–1.525) and marginal risk in dominant (DD+ID vs. II: p = 0.056; OR = 1.192, 95% CI = 0.995–1.428) models; whereas, no association was observed for Caucasian and African population. Publication bias was absent. In conclusion, ACE I/D polymorphism has significant role in overall SLE risk and it can be exploited as a prognostic marker for early SLE predisposition

Individual differences in susceptibility to online influence: A theoretical review

© 2017 The Authors Scams and other malicious attempts to influence people are continuing to proliferate across the globe, aided by the availability of technology that makes it increasingly easy to create communications that appear to come from legitimate sources. The rise in integrated technologies and the connected nature of social communications means that online scams represent a growing issue across society, with scammers successfully persuading people to click on malicious links, make fraudulent payments, or download malicious attachments. However, current understanding of what makes people particularly susceptible to scams in online contexts, and therefore how we can effectively reduce potential vulnerabilities, is relatively poor. So why are online scams so effective? And what makes people particularly susceptible to them? This paper presents a theoretical review of literature relating to individual differences and contextual factors that may impact susceptibility to such forms of malicious influence in online contexts. A holistic approach is then proposed that provides a theoretical foundation for research in this area, focusing on the interaction between the individual, their current context, and the influence message itself, when considering likely response behaviour

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Asynchrony-Aware Static Analysis of Android Applications

Software applications developed for the Android platform are very popular. Due to this, static analysis of these applications has received a lot of attention recently. An Android application is essentially an asynchronous, event-driven program. The Android framework manages the state of the application by invoking callbacks, called lifecycle callbacks, in pre-defined orders. Unfortunately, the existing static analysis techniques treat the callbacks synchronously. Additionally, they do not model all possible orderings of lifecycle callbacks. These may result in unsound analysis results. In this work, we present a precise representation of control flow of Android applications called Android inter-component control flow graph (AICCFG). In this representation, the asynchronous nature of the callbacks is modeled accurately. Further, all interleavings of callbacks of different components of an Android application are modeled in AICCFG. We use this representation to design a typestate analysis of Android applications. Android applications use a rich set of resources such as camera and media player whose safe usage is governed by some state machines. Using the typestate analysis, we can verify whether an application uses a resource safely or not. We have implemented the construction of AICCFG and the typestate analysis in the Soot framework. We have also implemented a variant of typestate analysis which uses the unsound control flow model used commonly in the literature. To compare our AICCFG based analysis with this, we present a benchmark of Android applications called AsyncBench. It comprises applications that use various resources in both safe and unsafe manner. The experiments over this benchmark demonstrate the benefits of our more precise control flow model and the typestate analysis