arXiv subjects

Muhammad Waqas

Publications and source records attributed to Muhammad Waqas.

At least 19 recordsLinked to original sources

arXiv2026

LAAF: A Layered Accountability Architecture Framework for LLM Applications

Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authoritative even when ungrounded or incorrect. When such an output contributes to harm, who is answerable, and through what mechanisms can responsibility be traced, explained, and acted upon? Following PRISMA guidance, five databases were searched from January 2022 to March 2026 against four review questions; of 4,512 records identified, 122 primary studies were included, together with 12 regulatory and standards documents analysed as primary sources. The review consolidates a sociotechnical account of accountability as an actor-forum relation resolved into five dimensions, and synthesises mechanisms across four families: technical controls, human oversight, organisational governance, and documentation and traceability, each with a maturity assessment. The corpus is read through a four-layer classification device spanning provenance, application logic, human oversight, and governance and redress, cross-cut by traceability, role clarity, and continuous monitoring. Both are mapped onto the EU AI Act, whose high-risk obligations have applied since 2 August 2026, the NIST AI RMF with its Generative AI Profile, ISO/IEC 42001, and sectoral guidance in healthcare, consumer finance, education, and the public sector. Four persistent gaps emerge: under-specification of human oversight, absence of shared accountability metrics, disciplinary disconnection, and limited empirical evaluation, alongside five structural tensions that no surveyed instrument resolves. The review closes by consolidating the classification device into an integrated accountability architecture, LAAF, with cybersecurity aligned to the OWASP LLM Top 10 (2025); it is a synthesis of the surveyed evidence rather than a validated artefact.

cs.AI↗

arXiv2026

Lattice-data-driven specific heat and isentropic bulk modulus of SU(3) gluon matter at finite temperature

We investigate the specific heat and isentropic bulk modulus of finite-temperature pure SU(3) gauge matter within a lattice-data-driven phenomenological framework. The equation of state is formulated in terms of a temperature-dependent effective gluon mass constrained { by lattice QCD pressure data as input, allowing the pressure}, trace anomaly, gluon number density, energy per thermally active gluonic mode, and derivative-sensitive response functions to be derived in a thermodynamically consistent manner. The resulting pressure and trace anomaly reproduce the characteristic lattice behavior across the deconfinement region, while the effective gluonic degrees of freedom increase rapidly above $T_c$. The normalized specific heat $C_V/T^3$ develops a pronounced enhancement in the vicinity of $T_c$, reflecting the rapid temperature variation of the energy density across the deconfinement region. The isentropic bulk modulus $K_S/T^4$ also rises sharply across the transition region, indicating a substantial stiffening of the equation of state. At high temperatures, both response functions gradually approach values close to their massless conformal Stefan--Boltzmann reference values, with $\left(C_V/T^3\right)_{\rm SB}=32\pi^2/15\simeq 21.06$ and $\left(K_S/T^4\right)_{\rm SB}=32\pi^2/135\simeq 2.34$. These findings indicate that the specific heat and isentropic bulk modulus provide complementary constraints on the temperature evolution of nonconformal dynamics in pure SU(3) gauge matter.

hep-ph↗

arXiv2026

Photon-calibrated event-activity bias and subcollision geometry in d+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV with PYTHIA 8 Angantyr

Event-activity selections in small nuclear collision systems couple collision geometry to the soft response accompanying a hard scattering. We examine this coupling in d+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV with PYTHIA 8.316 Angantyr, using direct photons, terminal pre-decay neutral pions, and anti-kT jets. A hard-bias factor compares the probability for a hard event to enter an activity class with an $N^{ND}_{coll}$ -weighted minimum-bias reference. The model predicts a depleted most-active class and an enhanced peripheral class for both photons and pions. Photon-tagged events also sample a smaller mean impact parameter and more nondiffractive subcollisions than pion-tagged or inclusive HardQCD events. Comparing fully correlated Angantyr events with an Ncoll-reweighted minimum-bias reference and a factorized diagnostic separates the explicit geometric contribution from residual hard-soft correlations. STAR-like and PHENIX-like particle-level activity proxies preserve the class ordering but give different class probabilities. The calculation is generator-level and does not identify a unique microscopic origin for the residual correlations.

hep-ph↗

arXiv2026

A Multimodal Domain-Adversarial Network for Fragmentation Background Suppression in AMS Heavy Nuclei Measurements

The Alpha Magnetic Spectrometer (AMS) aboard the International Space Station provides high-precision measurements of cosmic-ray nuclei fluxes from charge Z=1 to Z=28 and beyond. With negligible charge confusion from non-interacting nuclei, the precision of nuclei flux measurements is primarily limited by fragmentation backgrounds originating from heavier cosmic rays interacting within detector materials, particularly between tracker Layers 1 and 2 (L1-L2). As AMS extends its measurements to heavier and rarer nuclei, these fragmentation backgrounds become increasingly dominant, necessitating advanced background suppression methods. To address this challenge, we introduce a Multimodal Domain-Adversarial (MDA) neural network designed to effectively suppress these interaction backgrounds. The MDA model fuses heterogeneous data from the silicon tracker and time-of-flight detectors using specialized sub-networks combined via multi-head attention. Crucially, a domain-adversarial training strategy is employed to learn invariant representations, enabling the model, which is trained on Monte Carlo simulations, to be reliably applied to flight data. Using phosphorus (P) as a benchmark, we demonstrate its background suppression capabilities. This approach provides a robust, generalizable framework applicable to the measurement of other rare cosmic-ray nuclei with AMS.

hep-ex↗

arXiv2026

Projection Guided Personalized Federated Learning for Low Dose CT Denoising

Low-dose CT (LDCT) reduces radiation exposure but introduces protocol-dependent noise and artifacts that vary across institutions. While federated learning enables collaborative training without centralizing patient data, existing methods personalize in image space, making it difficult to separate scanner noise from patient anatomy. We propose ProFed (Projection Guided Personalized Federated Learning), a framework that complements the image space approach by performing dual-level personalization in the projection space, where noise originates during CT measurements before reconstruction combines protocol and anatomy effects. ProFed introduces: (i) anatomy-aware and protocol-aware networks that personalize CT reconstruction to patient and scanner-specific features, (ii) multi-constraint projection losses that enforce consistency with CT measurements, and (iii) uncertainty-guided selective aggregation that weights clients by prediction confidence. Extensive experiments on the Mayo Clinic 2016 dataset demonstrate that ProFed achieves 42.56 dB PSNR with CNN backbones and 44.83 dB with Transformers, outperforming 11 federated learning baselines, including the physics-informed SCAN-PhysFed by +1.42 dB.

eess.IV↗

arXiv2026

Collectivity Signatures in High-Multiplicity pp Collisions from Hybrid Hydro+Tsallis Modeling of Pion Spectra

The transverse momentum (pT) distributions up to pT = 20 GeV/c for pions produced in the ten different multiplicity classes (MCs) of symmetric pp collisions at sqrt(s) = 7 TeV have been investigated. Two distinct models, the Tsallis-Pareto type function (model) and the combined BGBW model and Tsallis-Pareto type model have been employed to fit the pT distributions via the minimum chi-square method. The combined Hydro+Tsallis model is more reliably describing the pT spectra than the Tsallis-Pareto model. The Tsallis temperature (T), non-extensivity parameter (q), normalization constant (N0), Kinetic freeze-out temperature (T0), transverse flow velocity (betaT), and (mean pT) have been extracted through the fitting procedure via the employed models. The Tsallis-Pareto model gives T, q, N0 and mean pT while Hydro+Tsallis model gives T0, betaT, T, q, N0 and mean pT. Incorporating the values of the extracted T and q the thermodynamic quantities and response functions, including energy density (epsilon), particle density (n), entropy density (s), pressure (P), specific heat at constant volume (CV), squared speed of sound (cs2), mean free path (lambda), Knudsen number (Kn), isothermal compressibility (kappaT), and expansion coefficient (alpha) have been calculated at the freeze-out stage. It has been observed that T, betaT, mean pT, N0, epsilon, n, s, P, CV, cs2, and alpha increase with increasing(decreasing) the charged particles multiplicity density dNch/deta(MCs). While T0, q, lambda, Kn, and kappaT decrease with increasing(decreasing) dNch/deta(MCs). These systematic variations in the trends of parameters might suggest the gradual transition towards collectivity and thermal equilibration in the high multiplicity pp events, possibly signalling enhanced collective dynamics and partial thermalization in small collision systems.

hep-ph↗

arXiv2026

Agentic Fog: A Policy-driven Framework for Distributed Intelligence in Fog Computing

Fog and edge computing require adaptive control schemes that can handle partial observability, severe latency requirements, and dynamically changing workloads. Recent research on Agentic AI (AAI) increasingly integrates reasoning systems powered by Large Language Models; however, these tools are not applicable to infrastructure-level systems due to their high computational cost, stochastic nature, and poor formal analyzability. In this paper, a generic model, Agentic Fog (AF), is presented, in which fog nodes are represented as policy-driven autonomous agents that communicate via p2p interactions based on shared memory and localized coordination. The suggested architecture decomposes a system's goals into abstract policy guidance and formalizes decentralized fog coordination as an exact potential game. The framework is guaranteed to converge and remain stable under asynchronous updates, bounded-rational best-response dynamics, and node failures. Simulations demonstrate that the AF system achieves lower average latency and adapts more efficiently to varying demand than greedy heuristics and integer linear programming under dynamic conditions. The sensitivity analysis also demonstrates the capability to perform optimally under different memory and coordination conditions.

cs.DC↗

arXiv2025

Foundation Models in Biomedical Imaging: Turning Hype into Reality

Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine's trajectory toward more granular sub-specialization. This tension, coupled with data scarcity, domain heterogeneity, and limited interpretability, creates a gap between benchmark success and real-world clinical value. We argue that the immediate role of FMs lies in augmenting, not replacing, clinical expertise. To separate hype from reality, we introduce REAL-FM (Real-world Evaluation and Assessment of Foundation Models), a multi-dimensional framework for assessing data, technical readiness, clinical value, workflow integration, and responsible AI. Using REAL-FM, we find that while FMs excel in pattern recognition, they fall short in causal reasoning, domain robustness, and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond oversimplified benchmark settings, and a lack of prospective outcome-based validation. We further examine FM reasoning paradigms, including sequential logic, spatial understanding, and symbolic domain knowledge. We envision that the path forward lies not in a monolithic medical oracle, but in coordinated subspecialist AI systems that are transparent, safe, and clinically grounded.

q-bio.QM↗

arXiv2025

MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification

Domain generalization in computational histopathology is hindered by heterogeneity in whole slide images (WSIs), caused by variations in tissue preparation, staining, and imaging conditions across institutions. Unlike machine learning systems, pathologists rely on domain-invariant morphological cues such as nuclear atypia (enlargement, irregular contours, hyperchromasia, chromatin texture, spatial disorganization), structural atypia (abnormal architecture and gland formation), and overall morphological atypia that remain diagnostic across diverse settings. Motivated by this, we hypothesize that explicitly modeling biologically robust nuclear morphology and spatial organization will enable the learning of cancer representations that are resilient to domain shifts. We propose MorphGen (Morphology-Guided Generalization), a method that integrates histopathology images, augmentations, and nuclear segmentation masks within a supervised contrastive learning framework. By aligning latent representations of images and nuclear masks, MorphGen prioritizes diagnostic features such as nuclear and morphological atypia and spatial organization over staining artifacts and domain-specific features. To further enhance out-of-distribution robustness, we incorporate stochastic weight averaging (SWA), steering optimization toward flatter minima. Attention map analyses revealed that MorphGen primarily relies on nuclear morphology, cellular composition, and spatial cell organization within tumors or normal regions for final classification. Finally, we demonstrate resilience of the learned representations to image corruptions (such as staining artifacts) and adversarial attacks, showcasing not only OOD generalization but also addressing critical vulnerabilities in current deep learning systems for digital pathology. Code, datasets, and trained models are available at: https://github.com/hikmatkhan/MorphGen

cs.CV↗

arXiv2025

The Next Layer: Augmenting Foundation Models with Structure-Preserving and Attention-Guided Learning for Local Patches to Global Context Awareness in Computational Pathology

Foundation models have recently emerged as powerful feature extractors in computational pathology, yet they typically omit mechanisms for leveraging the global spatial structure of tissues and the local contextual relationships among diagnostically relevant regions - key elements for understanding the tumor microenvironment. Multiple instance learning (MIL) remains an essential next step following foundation model, designing a framework to aggregate patch-level features into slide-level predictions. We present EAGLE-Net, a structure-preserving, attention-guided MIL architecture designed to augment prediction and interpretability. EAGLE-Net integrates multi-scale absolute spatial encoding to capture global tissue architecture, a top-K neighborhood-aware loss to focus attention on local microenvironments, and background suppression loss to minimize false positives. We benchmarked EAGLE-Net on large pan-cancer datasets, including three cancer types for classification (10,260 slides) and seven cancer types for survival prediction (4,172 slides), using three distinct histology foundation backbones (REMEDIES, Uni-V1, Uni2-h). Across tasks, EAGLE-Net achieved up to 3% higher classification accuracy and the top concordance indices in 6 of 7 cancer types, producing smooth, biologically coherent attention maps that aligned with expert annotations and highlighted invasive fronts, necrosis, and immune infiltration. These results position EAGLE-Net as a generalizable, interpretable framework that complements foundation models, enabling improved biomarker discovery, prognostic modeling, and clinical decision support

q-bio.QM↗

arXiv2025

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research

Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting actionable insights from these vast and heterogeneous datasets remains a key challenge. The rise of foundation models (FMs) -- large deep-learning models pretrained on extensive amounts of data serving as a backbone for a wide range of downstream tasks -- offers new avenues for discovering biomarkers, improving diagnosis, and personalizing treatment. This paper presents a comprehensive review of widely adopted integration strategies of multimodal data to assist advance the computational approaches for data-driven discoveries in oncology. We examine emerging trends in machine learning (ML) and deep learning (DL), including methodological frameworks, validation protocols, and open-source resources targeting cancer subtype classification, biomarker discovery, treatment guidance, and outcome prediction. This study also comprehensively covers the shift from traditional ML to FMs for multimodal integration. We present a holistic view of recent FMs advancements and challenges faced during the integration of multi-omics with advanced imaging data. We identify the state-of-the-art FMs, publicly available multi-modal repositories, and advanced tools and methods for data integration. We argue that current state-of-the-art integrative methods provide the essential groundwork for developing the next generation of large-scale, pre-trained models poised to further revolutionize oncology. To the best of our knowledge, this is the first review to systematically map the transition from conventional ML to advanced FM for multimodal data integration in oncology, while also framing these developments as foundational for the forthcoming era of large-scale AI models in cancer research.

q-bio.QM↗

arXiv2025

Hope Speech Detection in code-mixed Roman Urdu tweets: A Positive Turn in Natural Language Processing

Hope is a positive emotional state involving the expectation of favorable future outcomes, while hope speech refers to communication that promotes optimism, resilience, and support, particularly in adverse contexts. Although hope speech detection has gained attention in Natural Language Processing (NLP), existing research mainly focuses on high-resource languages and standardized scripts, often overlooking informal and underrepresented forms such as Roman Urdu. To the best of our knowledge, this is the first study to address hope speech detection in code-mixed Roman Urdu by introducing a carefully annotated dataset, thereby filling a critical gap in inclusive NLP research for low-resource, informal language varieties. This study makes four key contributions: (1) it introduces the first multi-class annotated dataset for Roman Urdu hope speech, comprising Generalized Hope, Realistic Hope, Unrealistic Hope, and Not Hope categories; (2) it explores the psychological foundations of hope and analyzes its linguistic patterns in code-mixed Roman Urdu to inform dataset development; (3) it proposes a custom attention-based transformer model optimized for the syntactic and semantic variability of Roman Urdu, evaluated using 5-fold cross-validation; and (4) it verifies the statistical significance of performance gains using a t-test. The proposed model, XLM-R, achieves the best performance with a cross-validation score of 0.78, outperforming the baseline SVM (0.75) and BiLSTM (0.76), with gains of 4% and 2.63% respectively.

cs.CL↗

arXiv2025

Fabrication of Soft and Comfortable Pressure-Sensing Shoe Sole for Intuitive Monitoring of Human Quality Gaits

The study discusses the design and fabrication of flexible pressure sensors using Ecoflex/Graphene composites. The fabricated sensor is used for the application of intuitive monitoring of human quality gaits and implementation of the soft and comfortable shoe sole for rehabilitation of the patients with foot disorder is also taken into consideration. The sensor is fabricated using molding and casting technique by sandwiching the thin film Ecoflex/Graphene composites between the copper (Cu) electrodes with the dimension of 15 x 15 mm2 with high sensitivity. There are five pressure sensors integrated in the shoe sole, a sensor at the forefoot, three sensors at the midfoot and one sensor at the lower foot (heel). The behavior of the sensor is negative piezoresistive in which the resistance decreases as the pressure increases. The sensors are embedded in a soft and comfortable shoe sole and then integrated with a laptop or mobile application to monitor and analyze human gait in real-time. Furthermore, a dedicated Graphical User Interface (GUI) is designed to read the data. The pressure sensors are integrated with ESP32 microcontroller which wirelessly transmit data to the GUI and smart phones which could be further used in the intuitive monitoring, rehabilitation of the patients with foot disorder or neuromotor diseases.

eess.SY↗

arXiv2024

Evolution of Effective Temperature, Kinetic Freeze-out Temperature and transverse flow velocity in pp Collision

This article focuses on the study of strange hadrons at 0.2 TeV centre of mass energy, recorded by STAR at RHIC, and at 0.9 TeV, 5.02 TeV and 7 TeV, recorded by CMS at LHC, in pp collision in the rapidity range from 0 to 2. The transverse momentum distributions of these strange particles have been processed using two statistical models, the Tsallis and the modified Hagedorn model. Both models fit the experimental data well. We extracted different freezeout parameters from the fit procedure using the abovementioned functions. We found that with increasing the collision energy, the effective temperature (T), in the case of the Tsallis model, and kinetic/thermal freeze-out temperature (T0) and transverse flow velocity, in the case of the modified Hagedorn model, increase because of greater energy transfer among the participants at higher colliding energies. Both T and T0 are observed to increase with the increase in the rest masses of the outgoing particles revealing the multi-freeze-out scenario. Furthermore, the multiplicity parameter (N0) decreases with the increase in the particle mass, confirming the mass differential freeze-out scenario. An inverse relationship between the non-extensivity parameter (q) and the masses of the produced particles has been noticed. Similarly, an inverse correlation between q and T has been found. For lighter particles, smaller T and greater q mean that they decouple from the system later and attain equilibrium slowly compared to heavier ones. In addition, a positive correlation between transverse flow velocity and T0 is noticed, which agrees with the literature.

hep-ph↗

arXiv2024

Systematic analysis of the pp collisions at LHC energies with Tsallis function

This work focuses on the study of identified hadrons and strange hadrons, recorded by CMS, and light nuclei and their anti-nuclei, recorded by ALICE, at 0.9 TeV, 2.76 TeV, 7 TeV and 13 TeV centre of mass energies in pp collision at mid rapidities. The transverse momentum distributions of these particles are analyzed using the Tsallis model, which fits the experimental data very well. Several important parameters for studying the characteristics of the medium produced during such collisions are extracted. The effective temperature (T) increases monotonically with increasing particle mass and also with increasing collision energy. The non-extensivity parameter (q) decreases with the mass of the particle. For heavier particles, greater T and smaller q mean that they decouple early from the system and attain equilibrium quickly compared to lighter ones. Furthermore, with an increase in collision energy, the multiplicity parameter N0 increases.

hep-ph↗

arXiv2024

Investigating the Bulk Properties of Charged Particles in Different $\eta$ Bins Using a Modified Tsallis Model

This paper presents a comprehensive analysis of the double-differential $p_T$ distributions of charged particles in twelve distinct pseudorapidity regions of equal width in $pp$ collisions at center-of-mass energies of 0.9, 2.36, and 7 TeV. Utilizing the modified Tsallis function with mean transverse flow velocity, our study demonstrates a very good agreement between experimental data and the model employed. The fit quality is consistently high across all $p_T$ ranges, as assessed by Data/Fit panels accompanying each plot. Extracted parameters, including kinetic freeze-out temperature ($T_0$), transverse flow velocity ($\beta_T$), non-extensivity parameter ($q$) and mean transverse momentum $\langle p_T \rangle$ dependencies are shown on pseudorapidity ($\eta$) and collision energy ($\sqrt{s}$). $T_0$, $\beta_T$ and $\langle p_T \rangle$ exhibit a decreasing trend with increasing $\eta$ due to lower in energy transfer along high $\eta$ regions, while they show a heightened sensitivity to $\sqrt{s}$. $q$ increases with $\eta$, indicating a closer thermal equilibrium in mid-$\eta$ particles. The paper also explores correlations among these parameters, emphasizing relationships between $T_0$, $\beta_T$, $q$ and $\langle p_T \rangle$. Our study provides valuable insights into the thermal and dynamic characteristics of high-energy proton-proton collisions, contributing to the broader understanding of the bulk properties of nuclear matter produced in these interactions.

hep-ph↗

arXiv2024

Comparative Analysis of Jet and Underlying Event Properties Across Various Models as a Function of Charged Particle Multiplicity at 7 TeV

In this study, a comprehensive analysis of jets and underlying events as a function of charged particle multiplicity in proton-proton (pp) collisions at a center-of-mass energy of $\sqrt{s} = 7$ TeV is presented. Various Monte Carlo (MC) event generators, including Pythia8.308, EPOS1.99, EPOSLHC, EPOS4$_{Hydro}$, and EPOS4$_{noHydro}$, are employed to predict particle production. The predictions from these models are compared with experimental data from the CMS collaboration. The charged particles are categorized into those associated with underlying events and those linked to jets. The analysis is restricted to charged particles with $|\eta| < 2.4$ and $p_{T} > 0.25$ GeV/c. Upon comparing the MC predictions with CMS data, it is observed that EPOS$4_{Hydro}$, EPOSLHC, and Pythia8 consistently reproduce the experimental results for all charged particles, underlying events, intrajet, and leading charged particles. For charged jet rates with $p_{T}^{ch.jet} > 5$ GeV/c, EPOS4$_{Hydro}$ and Pythia8 perform exceptionally well. In the case of charged jet rates with $p_{T}^{ch.jet} > 30$ GeV/c, EPOSLHC reproduces satisfactorily good results, while EPOS4$_{Hydro}$ exhibits good agreement with the data at higher charged particle multiplicities as compared to the other models. This can be attributed to the conversion of energy into flow when "Hydro=on," leading to an increase in multiplicity. EPOSLHC model described the data well due to the new collective flow effects, correlated flow treatment, and parametrization as compared to the EPOS1.99. However, the examination of the jet $p_{T}$ spectrum and normalized charged $p_{T}$ density reveals that EPOS4$_{Hydro}$, EPOS4$_{noHydro}$, and EPOSLHC exhibit good agreement with the experimental results, while Pythia8 and EPOS1.99 do not perform as well due to the lack of correlated flow treatment.

hep-ph↗

arXiv2024

Analyzing the Correlation Between Thermal and Kinematic Parameters in Various Multiplicity Classes within 7 and 13 TeV pp Collisions

We investigate the transverse momentum spectra of identified particles at 7 TeV and 13 TeV in pp collisions in the framework of the blast wave model with Tsallis statistics (TBW). Based on experimental data by ALICE Collaboration, we observe that the model describes the $p_T$ spectra well with the common Tsallis temperature (T) and flow velocity (\beta_T) but separate non-extensive parameters (q) for baryons and mesons. The parameter dependence on multiplicity as well as on collision energy is investigated, and a strong dependence on the former while a weak dependence on the latter is reported. The extracted parameters in this work consist of the initial temperature (T_i), the average transverse momentum ( ), the T, \beta_T, and the q. These parameters are found to increase a little with increasing energy, however, they (except the parameter q) decrease significantly with decreasing multiplicity. We observe that $\beta_T$ drops to zero after the multiplicity class VII, while, $T$ and $q$ do not change their behavior. Furthermore, our analysis explore the correlations among different parameters, including associations with the charged particle multiplicity per unit pseudorapidity (dN_{ch}/d\eta). The correlation between T and beta_T, T and dN_{ch}/d\eta, \beta_T and dN_{ch}/d\eta, T_i and and T_i and dN_{ch}/d\eta demonstrates a positive relationship, while, the correlation between T and q-1, and q-1 and dN_{ch}/d\eta is negative. Finally, we implement an extra flow correction on the T parameter. Our findings reveal that the Doppler-corrected temperature parameter aligns closely with the T in scenarios with lower multiplicities. However, as the multiplicity increases, a noticeable divergence emerges between these parameters, indicating a widening separation between them.

hep-ph↗