1,195 research outputs found
On-shell versus curvature mass parameter fixing schemes in the three flavor quark-meson model with vacuum fluctuations
The vacuum effective potential and phase diagram for the three (2+1) flavor
quark-meson model have been computed and compared in an extended mean-field
approximation (e-MFA) where the model parameters are fixed by using different
renormalization prescriptions after including quark one-loop vacuum
fluctuations. When the vacuum one-loop divergence is regularized in the minimal
subtraction scheme and the curvature masses of the scalar and pseudo-scalar
mesons are used for fixing the parameters, the setting of the quark-meson model
with the vacuum term (QMVT) turns out to be inconsistent as one notes that the
curvature masses are defined by the evaluation of self-energy at zero momentum.
This work constitutes the first application of the consistent on-shell
parameter fixing scheme to the three flavor quark-meson (QM) model. In this
setting of the renormalized quark-meson (RQM) model, the physical (pole) masses
of the and pseudo-scalar mesons and the scalar
meson,the pion decay constant and kaon decay constant are put into the
relation of the running mass parameter and couplings by using the on-shell and
the minimal subtraction renormalization schemes. The nonstrange direction
normalized vacuum effective potential plots for both the RQM model and QMVT
model, are exactly identical for the 658.8 MeV while the nonstrange
direction order parameter temperature variations and phase diagrams for both
the models RQM and PQMVT are identical when the value is smaller by
10 MeV i.e. 648 MeV. This happens because the normalized vacuum
effective potential variation in the nonstrange direction is somewhat
influenced by its variation in the strange direction.Comment: 29 Pages, 22 figures. arXiv admin note: text overlap with
arXiv:2202.1166
production in Large extra dimension model at next-to-leading order in QCD at the LHC
We present next-to-leading order QCD corrections to production of two
bosons in hadronic collisions in the extra dimension ADD model. Various
kinematical distributions are obtained to order in QCD by taking
into account all the parton level subprocesses. We estimate the impact of the
QCD corrections on various observables and find that they are significant. We
also show the reduction in factorization scale uncertainty when effects are included.Comment: Journal versio
Meson Masses and Mixing Angles in 2+1 Flavor Polyakov Quark Meson Sigma Model and Symmetry Restoration Effects
The meson masses and mixing angles have been calculated for the scalar and
pseudoscalar sector in the framework of the generalized 2+1 flavor Polyakov
loop augmented quark meson linear sigma model. We have given the results for
two different forms of the effective Polyakov loop potential. The comparison of
results with the existing calculations in the bare 2+1 quark meson linear sigma
model, shows that the restoration of chiral symmetry becomes sharper due to the
influence of the Polyakov loop potential. We find that inclusion of the
Polyakov loop in quark meson linear sigma model together with the presence of
axial anomaly, triggers an early and significant melting of the strange
condensate. We have examined how the inclusion of the Polyakov loop
qualitatively and quantitatively affects the convergence in the masses of the
chiral partners in pseudoscalar (, , , ) and scalar
(, , ,) meson nonets as the temperature is varied on
the reduced temperature scale. The role of anomaly in determining the
isoscalar masses and mixing angles for the pseudoscalar ( and )
and scalar ( and )meson complex, has also been investigated in the
Polyakov quark meson linear sigma model. The interplay of chiral symmetry
restoration effects and the setting up of restoration trend has been
discussed and analyzed in the framework of the presented model calculations.Comment: 15 pages, 8 figures, 4 table
VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Leveraging BERT and Stacked Embeddings
Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion
Classification from essays written in reaction to news articles. Emotion
detection from complex dialogues is challenging and often requires
context/domain understanding. Therefore in this research, we have focused on
developing deep learning (DL) models using the combination of word embedding
representations with tailored prepossessing strategies to capture the nuances
of emotions expressed. Our experiments used static and contextual embeddings
(individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and
Transformer based models. We occupied rank tenth in the emotion detection task
by scoring a Macro F1-Score of 0.2717, validating the efficacy of our
implemented approaches for small and imbalanced datasets with mixed categories
of target emotions
Performance of Functionally Graded Exponential Annular Fins of Constant Weight
The present work aims at investigating the performance of exponential annular fins of constant weight made of functionally graded materials (FGM). The work involves computation of efficiency and effectiveness of such fins and compares the fin performances for different exponential profiles and grading parameters, keeping the weight of the fin constant. The functional grading of thermal conductivity is assumed to be a power function of radial co-ordinate which consists of parameters, namely grading parameters, varying which different grading combinations can be investigated. Fin material density is assumed to be constant and temperature gradient exists only along the radial direction. The convective coefficient between the fin surface and the environment is also assumed to be constant. A general second-order governing differential equation has been derived for all the profiles and material grading. The efficiency and effectiveness of the annular fin of different geometry and grading combinations have been calculated and plotted and the results reveal the dependence of thermal behavior on geometry and grading parameter. The effect of variation of grading parameters on fin efficiency and effectiveness is reported. The results are provided in the form of 2-D graphs, which can be used as design monograms for further use
Machine Learning Classification of Alzheimer's Disease Stages Using Cerebrospinal Fluid Biomarkers Alone
Early diagnosis of Alzheimer's disease is a challenge because the existing
methodologies do not identify the patients in their preclinical stage, which
can last up to a decade prior to the onset of clinical symptoms. Several
research studies demonstrate the potential of cerebrospinal fluid biomarkers,
amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease
stages. In this work, we used machine learning models to classify different
stages of Alzheimer's disease based on the cerebrospinal fluid biomarker levels
alone. An electronic health record of patients from the National Alzheimer's
Coordinating Centre database was analyzed and the patients were subdivided
based on mini-mental state scores and clinical dementia ratings. Statistical
and correlation analyses were performed to identify significant differences
between the Alzheimer's stages. Afterward, machine learning classifiers
including K-Nearest Neighbors, Ensemble Boosted Tree, Ensemble Bagged Tree,
Support Vector Machine, Logistic Regression, and Naive Bayes classifiers were
employed to classify the Alzheimer's disease stages. The results demonstrate
that Ensemble Boosted Tree (84.4%) and Logistic Regression (73.4%) provide the
highest accuracy for binary classification, while Ensemble Bagged Tree (75.4%)
demonstrates better accuracy for multiclassification. The findings from this
research are expected to help clinicians in making an informed decision
regarding the early diagnosis of Alzheimer's from the cerebrospinal fluid
biomarkers alone, monitoring of the disease progression, and implementation of
appropriate intervention measures
A Comparative Study of Environmental Impact Assessment Reports of Housing Projects of Lucknow City, Uttar Pradesh, India
One of the most pressing issues with regard to the environment is linked to human settlement in world’s growing cities and towns. Several agencies use procedures for environmental impact assessment (EIA) of housing projects which might result in significant environmental impacts. The EIA study is necessary to prepare a detailed account of environmental impact of the proposed activity so that appropriate interventions could be taken. An attempt has been made in this paper to compare different elements of EIA in four major housing projects of Lucknow city, Uttar Pradesh, namely Parsvnath City, LDA Gomti Nagar Extension scheme, DLF Garden City and Omaxe residency using checklist method. The study focuses on various parameters such as total area, parking area, rainwater harvesting system, basement area, sewage treatment plant, water quality, solid waste, source of water, depth of ground water, distance from the city centre, nearest sensitive zones and overall settlement density. The review of the EIA of housing projects reveal that some of the newly developed projects are characterized by severe shortage of basic services like potable water, well laid-out drainage system, sewerage network, sanitation facilities, electricity, roads and waste disposal. These in turn result in numerous environmental and health impacts that must be addressed. The green cover and water bodies have been destroyed to give way to the rapidly developing urban settlements at the outskirts. The paper argues that through early planning before the start of the project as well as through all phases of the project’s development, if environmental concerns are considered simultaneously with other technical and economic criteria, it may be possible to develop the housing projects with the protection of natural resources of that area
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