Variable Equation of State for Generalized Dark Energy Model

We present a model for the present accelerating Universe and focus on the different important physical variables involved in the model under the phenomenological assumption $\Lambda \propto H^2$ with a prescription for equation of state parameter in the form $\omega(t)=\omega_0+\frac{\omega_1\tau}{t^2}$, where $\omega_0$ and $\omega_1$ are two constants and $\tau$ is a parameter having dimension of time $t^2$. General expressions for the density parameter $\Omega$ and deceleration parameter $q$ are obtained which under specific bound reproduce some of the previous results. We explore physical features of these parameters which (i) provide the scenario of complete evolution of the cosmos with $\omega(t)$ and (ii) agree mostly with the observational status of the present phase of the accelerating Universe.Comment: 6 Latex pages, 3 figure

Teaching of Asian mother tongues in Scottish schools : an analysis of their roles in cognitive, social and personal development

Abstract available: p. i-ii

Scenario of Accelerating Universe from the Phenomenological \Lambda- Models

Dark matter, the major component of the matter content of the Universe, played a significant role at early stages during structure formation. But at present the Universe is dark energy dominated as well as accelerating. Here, the presence of dark energy has been established by including a time-dependent $\Lambda$ term in the Einstein's field equations. This model is compatible with the idea of an accelerating Universe so far as the value of the deceleration parameter is concerned. Possibility of a change in sign of the deceleration parameter is also discussed. The impact of considering the speed of light as variable in the field equations has also been investigated by using a well known time-dependent $\Lambda$ model.Comment: Latex, 9 pages, Major change

Prediction model for diabetes mellitus using machine learning algorithms for enhanced diagnosis and prognosis in healthcare

Diabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed and human lives can be saved with early diagnosis. Smart prediction techniques like Machine Learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, and five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC), and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring

A New Class of Cluster–Matrix Nanocomposite Made of Fully Miscible Components

Nanocomposite materials, consisting of two or more phases, at least one of which has a nanoscale dimension, play a distinctive role in materials science because of the multiple possibilities for tailoring their structural properties and, consequently, their functionalities. In addition to the challenges of controlling the size, size distribution, and volume fraction of nanometer phases, thermodynamic stability conditions limit the choice of constituent materials. This study goes beyond this limitation by showing the possibility of achieving nanocomposites from a bimetallic system, which exhibits complete miscibility under equilibrium conditions. A series of nanocomposite samples with different compositions are synthesized by the co-deposition of 2000-atom Ni-clusters and a flux of Cu-atoms using a novel cluster ion beam deposition system. The retention of the metastable nanostructure is ascertained from atom probe tomography (APT), magnetometry, and magnetotransport studies. APT confirms the presence of nanoscale regions with ≈100 at% Ni. Magnetometry and magnetotransport studies reveal superparamagnetic behavior and magnetoresistance stemming from the single-domain ferromagnetic Ni-clusters embedded in the Cu-matrix. Essentially, the magnetic properties of the nanocomposites can be tailored by the precise control of the Ni concentration. The initial results offer a promising direction for future research on nanocomposites consisting of fully miscible elements

Microbiome dysbiosis in gallbladder cancer: A systemic review

Gallbladder cancer (GBC) starts in the epithelial tissue (lining of the bile duct and gallbladder). It is a type of aggressive cancer called adenocarcinoma that can spread to other tissues. Among all cases of biliary tract cancer, 50% is from GBC. It is a deadly cancer with a survival rate of 17.6% between 2007 and 2013. GBC is rarely found in the Western world, but it is commonly found in South Asia. In Southeast Asian countries, GBC plays a significant role in cancer-related morbidity and mortality. GBC incidence exhibits marked regional variability, a rare condition in the western population but having a higher frequency in India, especially the Indo-Gangetic belt and some northeast districts excluding Nagaland. This might be attributed to the differences in environmental factors and genetic predisposition modulating carcinogenesis. In GBC, only 10% of cases are identified in the early stages. The low rate of early detection is due to the lack of screening techniques and the aggressive characteristics of the tumor. Various risk factors are associated with GBC, for example, chronic cholecystitis with or without gallstones, obesity, exposure to heavy metals such as lead and arsenic, bacterial infection, congenital biliary cysts, and abnormal pancreaticobiliary duct junction. The risk factors can cause chronic gallbladder mucosa irritation, leading to dysplasia and neoplasia. GBC can form metaplasia to dysplasia in a time span of 5–15 years, then to carcinoma in situ, and finally to invasive cancer. Dysbiosis is responsible for various diseases, including cancer. Multiple triggers can cause dysbiosis, for example, environmental changes, inflammation, infection, medications, dietary changes, or genetic predisposition. Various researches show that Helicobacter pylori, human papillomavirus, Hepatitis B virus, and Hepatitis C virus microbial species can cause cancer. They are the major species responsible for 90% of infection-associated cancers. Various studies demonstrate that the strains of Salmonella and Helicobacter colonize are linked to developing GBC. While the mechanisms linking gut microbiota to GBC are not fully understood, several studies have suggested a potential association. According to a study, certain gut microbiomes, such as Fusobacterium nucleatum, found glut in GBC tissues, compared to adjacent normal tissues. By evaluating gut microbiome dysbiosis, we can see the potential link between gut microbiome dysbiosis and GBC; it may provide valuable insights into the development and progression of GBC. It could lead to the identification of new diagnostic markers and the development of novel therapeutic strategies. In GBC, the evaluation of gut microbiome dysbiosis (involving evaluating the composition, diversity, and functional capacity of the gut microbiome in patients) has emerged as a promising method for understanding the molecular mechanisms and identifying biomarkers for early prevention and detection of GBC and also investigating the possibility of any link between the gut microbiome and host immune response. In conclusion, evaluating gut microbiome dysbiosis in GBC is a promising direction for identifying potential early detection and prevention biomarkers. Additional investigation is therefore needed to determine the role of gut microbiome dysbiosis in the development and progression of GBC and to identify reliable biomarkers for clinical use

Present state and future perspectives of using pluripotent stem cells in toxicology research

The use of novel drugs and chemicals requires reliable data on their potential toxic effects on humans. Current test systems are mainly based on animals or in vitro–cultured animal-derived cells and do not or not sufficiently mirror the situation in humans. Therefore, in vitro models based on human pluripotent stem cells (hPSCs) have become an attractive alternative. The article summarizes the characteristics of pluripotent stem cells, including embryonic carcinoma and embryonic germ cells, and discusses the potential of pluripotent stem cells for safety pharmacology and toxicology. Special attention is directed to the potential application of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) for the assessment of developmental toxicology as well as cardio- and hepatotoxicology. With respect to embryotoxicology, recent achievements of the embryonic stem cell test (EST) are described and current limitations as well as prospects of embryotoxicity studies using pluripotent stem cells are discussed. Furthermore, recent efforts to establish hPSC-based cell models for testing cardio- and hepatotoxicity are presented. In this context, methods for differentiation and selection of cardiac and hepatic cells from hPSCs are summarized, requirements and implications with respect to the use of these cells in safety pharmacology and toxicology are presented, and future challenges and perspectives of using hPSCs are discussed

Measurements of differential cross-sections in top-quark pair events with a high transverse momentum top quark and limits on beyond the Standard Model contributions to top-quark pair production with the ATLAS detector at √s = 13 TeV

Cross-section measurements of top-quark pair production where the hadronically decaying top quark has transverse momentum greater than 355 GeV and the other top quark decays into ℓνb are presented using 139 fb−1 of data collected by the ATLAS experiment during proton-proton collisions at the LHC. The fiducial cross-section at s = 13 TeV is measured to be σ = 1.267 ± 0.005 ± 0.053 pb, where the uncertainties reflect the limited number of data events and the systematic uncertainties, giving a total uncertainty of 4.2%. The cross-section is measured differentially as a function of variables characterising the tt¯ system and additional radiation in the events. The results are compared with various Monte Carlo generators, including comparisons where the generators are reweighted to match a parton-level calculation at next-to-next-to-leading order. The reweighting improves the agreement between data and theory. The measured distribution of the top-quark transverse momentum is used to search for new physics in the context of the effective field theory framework. No significant deviation from the Standard Model is observed and limits are set on the Wilson coefficients of the dimension-six operators OtG and Otq(8), where the limits on the latter are the most stringent to date. [Figure not available: see fulltext.]

Improving topological cluster reconstruction using calorimeter cell timing in ATLAS

Clusters of topologically connected calorimeter cells around cells with large absolute signal-to-noise ratio (topo-clusters) are the basis for calorimeter signal reconstruction in the ATLAS experiment. Topological cell clustering has proven performant in LHC Runs 1 and 2. It is, however, susceptible to out-of-time pile-up of signals from soft collisions outside the 25 ns proton-bunch-crossing window associated with the event’s hard collision. To reduce this effect, a calorimeter-cell timing criterion was added to the signal-to-noise ratio requirement in the clustering algorithm. Multiple versions of this criterion were tested by reconstructing hadronic signals in simulated events and Run 2 ATLAS data. The preferred version is found to reduce the out-of-time pile-up jet multiplicity by ∼50% for jet pT ∼ 20 GeV and by ∼80% for jet pT 50 GeV, while not disrupting the reconstruction of hadronic signals of interest, and improving the jet energy resolution by up to 5% for 20 < pT < 30 GeV. Pile-up is also suppressed for other physics objects based on topo-clusters (electrons, photons, τ -leptons), reducing the overall event size on disk by about 6% in early Run 3 pileup conditions. Offline reconstruction for Run 3 includes the timing requirement

Software Performance of the ATLAS Track Reconstruction for LHC Run 3

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pileup) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60 pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two