arXiv subjects

Pallavi Gupta

Publications and source records attributed to Pallavi Gupta.

17 recordsLinked to original sources

arXiv2026

Magnetic moments of strange hidden-bottom pentaquarks and the role of spin flavor correlations

We investigate the magnetic moments of strange hidden-bottom pentaquark states within the constituent quark model, considering both molecular and compact configurations. The system with quark content $qqqb\bar{b}$ ($q=u,d,s$) is analyzed in three configurations: a baryon-meson molecular form $(\bar b q_1)(b q_2 q_3)$, a diquark-diquark-antiquark structure $(b q_1)(q_2 q_3)\bar b$, and a diquark-triquark configuration $(b q_1)(\bar b q_2 q_3)$. Negative-parity states with $J^P = 1/2^-$, $3/2^-$, and $5/2^-$ are studied for strangeness $\mathcal{S}=-1,-2,-3$. For the dominant spin couplings, the two compact configurations yield identical or numerically very close magnetic moments. This indicates that the magnetic properties are governed primarily by the global spin-flavor structure and heavy-quark suppression effects rather than by the specific clustering of quarks. A systematic suppression with increasing strangeness and a clear spin hierarchy are observed across all configurations. Due to the large bottom-quark mass, heavy-quark contributions are strongly suppressed, and the magnetic moments are dominated by light-strange spin correlations. These results provide useful theoretical benchmarks for future experimental and lattice studies of exotic multiquark states.

hep-ph↗

arXiv2026

Probing the spin parity structure of hidden charm pentaquarks from spectroscopy and magnetic moments

We investigate the spin parity JP assignments of experimentally observed hidden charm pentaquark states within a baryon meson molecular framework. The pentaquark mass spectrum is obtained using the Gursey Radicati mass formula, with parameters fixed through a global fit to 41 experimentally established hadron masses. The resulting spectrum is then used to assign JP quantum numbers to the observed pentaquark candidates. Within this framework, the nonstrange states Pc 4312, Pc4440, and Pc 4457 are identified with the JP = 1/2, 3/2, and 5/2 configurations, respectively. The recently reported Belle state Pcs4459, which carries strangeness, is interpreted as the strange member of the SU3 flavor octet with JP = 3/2. Magnetic moments are subsequently evaluated using explicitly constructed wave functions. Their systematic behavior across SU3 flavor multiplets and different spin parity assignments satisfies the expected sum-rule relations and indicates that magnetic moments can serve as a useful observable for refining the quantum-number identification of hidden charm pentaquark states in future studies.

hep-ph↗

arXiv2025

Interpretable Hybrid Deep Q-Learning Framework for IoT-Based Food Spoilage Prediction with Synthetic Data Generation and Hardware Validation

The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains, where perishable goods are highly susceptible to environmental conditions. Existing methods often lack adaptability to dynamic conditions and fail to optimize decision making in real time. To address these challenges, we propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction. This hybrid architecture captures temporal dependencies within sensor data, enabling robust and adaptive decision making. In alignment with interpretable artificial intelligence principles, a rule-based classifier environment is employed to provide transparent ground truth labeling of spoilage levels based on domain-specific thresholds. This structured design allows the agent to operate within clearly defined semantic boundaries, supporting traceable and interpretable decisions. Model behavior is monitored using interpretability-driven metrics, including spoilage accuracy, reward-to-step ratio, loss reduction rate, and exploration decay. These metrics provide both quantitative performance evaluation and insights into learning dynamics. A class-wise spoilage distribution visualization is used to analyze the agents decision profile and policy behavior. Extensive evaluations on simulated and real-time hardware data demonstrate that the LSTM and RNN based agent outperforms alternative reinforcement learning approaches in prediction accuracy and decision efficiency while maintaining interpretability. The results highlight the potential of hybrid deep reinforcement learning with integrated interpretability for scalable IoT-based food monitoring systems.

cs.LG↗

arXiv2025

Hearing Health in Home Healthcare: Leveraging LLMs for Illness Scoring and ALMs for Vocal Biomarker Extraction

The growing demand for home healthcare calls for tools that can support care delivery. In this study, we explore automatic health assessment from voice using real-world home care visit data, leveraging the diverse patient information it contains. First, we utilize Large Language Models (LLMs) to integrate Subjective, Objective, Assessment, and Plan (SOAP) notes derived from unstructured audio transcripts and structured vital signs into a holistic illness score that reflects a patient's overall health. This compact representation facilitates cross-visit health status comparisons and downstream analysis. Next, we design a multi-stage preprocessing pipeline to extract short speech segments from target speakers in home care recordings for acoustic analysis. We then employ an Audio Language Model (ALM) to produce plain-language descriptions of vocal biomarkers and examine their association with individuals' health status. Our experimental results benchmark both commercial and open-source LLMs in estimating illness scores, demonstrating their alignment with actual clinical outcomes, and revealing that SOAP notes are substantially more informative than vital signs. Building on the illness scores, we provide the first evidence that ALMs can identify health-related acoustic patterns from home care recordings and present them in a human-readable form. Together, these findings highlight the potential of LLMs and ALMs to harness heterogeneous in-home visit data for better patient monitoring and care.

eess.AS↗

arXiv2025

A Systematic Review on Women's Participation in Agricultural Work and Nutritional Outcomes

While agriculture is recognised as vital for improving nutrition, the evidence linking women's participation to sustained nutritional gains remains inconclusive. This review synthesizes studies published between 2000 and 2024 to reflect current agricultural practices and nutritional challenges. We examine how agricultural practices and time use affect nutritional outcomes among rural women through pathways such as income generation food preparation and intra-household labour allocation. A structured methodology with clear inclusion and exclusion criteria was used to assess gender-sensitive and nutrition-sensitive interventions. Using narrative synthesis the review categorizes findings around key themes and contextual factors including socio-economic status seasonality and labour intensity. The results show that while increased involvement in agriculture can boost household dietary diversity and income it also raises time burdens that affect food preparation childcare and self-care. Positive outcomes occur when interventions enhance women's decision-making power income access and use of time-saving technologies whereas negative outcomes emerge when excessive workloads compromise energy balance and limit rest. A conceptual framework is presented to map the dual pathways linking agriculture time use and nutrition capturing the roles of labour distribution social norms and resource access. The framework underscores the need to integrate gender equity time efficiency and nutritional objectives into agricultural policies. In conclusion agricultural interventions have potential for nutritional improvement if they are carefully designed to avoid unintended negative impacts on women.

econ.GN↗

arXiv2023

Placing of the recently observed bottom strange state $B_{sJ}(6063)$ and $B_{sJ}(6114)$ in bottom spectra

We have employed HQET to give the spin-parity quantum numbers for recently observed bottom strange states $B_{sJ}(6063)$ and $B_{sJ}(6114)$ by LHCb collaborations. By exploring flavour independent parameters $ \Delta_{F}^{(c)} =\Delta_{F}^{(b)}$ and $ \lambda_{F}^{(c)} = \lambda_{F}^{(b)}$, we calculated masses of experimentally missing bottom strange meson states $2S, 1P, 1D$. We have also analyzed these bottom strange masses by taking ${1/m_Q}$ corrections which lead modifications of parameter terms as $ \Delta_{F}^{(b)} =\Delta_{F}^{(c)} + \delta\Delta_F$ and $ \lambda_{F}^{(b)} = \lambda_{F}^{(c)}\delta\lambda_F$. Further, we have analyzed their two-body decays, couplings, and branching ratios via the emission of light pseudoscalar mesons. Based on predicted masses and decay widths, we tentatively identified the states $B_{sJ}(6063)$ as $2^3S_1$ and $B_{sJ}(6114)$ as $1^3D_1$. Our predictions provide crucial information for future experimental studies.

hep-ph↗

arXiv2021

Interpreting the Caste-based Earning Gaps in the Indian Labour Market: Theil and Oaxaca Decomposition Analysis

The UN states that inequalities are determined along with income by other factors - gender, age, origin, ethnicity, disability, sexual orientation, class, and religion. India, since the ancient period, has socio-political stratification that induced socio-economic inequality and continued till now. There have been attempts to reduce socio-economic inequality through policy interventions since the first plan, still there are evidences of social and economic discrimination. This paper examines earning gaps between the forward castes and the traditionally disadvantaged caste workers in the Indian labour market using two distinct estimation methods. First, we interpret the inequality indicator of the Theil index and decompose Theil to show within and between-group inequalities. Second, a Threefold Oaxaca Decomposition is employed to break the earnings differentials into components of endowment, coefficient and interaction. Earnings gaps are examined separately in urban and rural divisions. Within-group, inequalities are found larger than between groups across variables; with a higher overall inequality for forward castes. A high endowment is observed which implies pre-market discrimination in human capital investment such as nutrition and education. Policymakers should first invest in basic quality education and simultaneously expand post-graduate diploma opportunities, subsequently increasing the participation in the labour force for the traditionally disadvantaged in disciplines and occupations where the forward castes have long dominated.

econ.GN↗

arXiv2021

Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort

Many countries are now experiencing the third wave of the COVID-19 pandemic straining the healthcare resources with an acute shortage of hospital beds and ventilators for the critically ill patients. This situation is especially worse in India with the second largest load of COVID-19 cases and a relatively resource-scarce medical infrastructure. Therefore, it becomes essential to triage the patients based on the severity of their disease and devote resources towards critically ill patients. Yan et al. 1 have published a very pertinent research that uses Machine learning (ML) methods to predict the outcome of COVID-19 patients based on their clinical parameters at the day of admission. They used the XGBoost algorithm, a type of ensemble model, to build the mortality prediction model. The final classifier is built through the sequential addition of multiple weak classifiers. The clinically operable decision rule was obtained from a 'single-tree XGBoost' and used lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP) values. This decision tree achieved a 100% survival prediction and 81% mortality prediction. However, these models have several technical challenges and do not provide an out of the box solution that can be deployed for other populations as has been reported in the "Matters Arising" section of Yan et al. Here, we show the limitations of this model by deploying it on one of the largest datasets of COVID-19 patients containing detailed clinical parameters collected from India.

cs.LG↗

arXiv2019

Placing the newly observed state $B_{J}(5840)$ in bottom spectra along with states $B_{1}(5721)$, $B_{2}^{}(5747)$, $B_{s1}(5830)$, $B_{2s}^{}(5840)$ and $B_{J}(5970)$

In this article, we study the two body strong decays with the emission of light pseudo-scalar mesons $(π, η, K)$ for higher excited bottom states with in the framework of HQET. Inspired from the recent observation of bottom meson $B_{J}(5840)$ by LHCb collaboration \cite{9}, we classify the six possible $J^{P}$'s for this state on the basis of the theoretically available masses. By analyzing the strong decay widths and the branching ratios for all these six cases of $B_{J}(5840)$, we justify one of them to be the most favorable assignment for it. We also examined the recently observed bottom state $B_{J}(5970)$ as 2S$1^{-}$ and states $B_{J}(5721)$ and $B_{2}^{*}(5747)$ with their strange partners $B_{s1}(5830)$ and $B_{2s}^{*}(5840)$ for their $J^{P}$'s as $1P_{3/2}1^{+}$ and $1P_{3/2}2^{+}$ respectively. The predicted coupling constants $g_{XH}$, $\widetilde{g}_{HH}$ and $g_{TH}$ helps in redeeming the strong decay width of experimentally missing bottom states $B(2 ^{1}S_{0})$, $B_{s}(2 ^{3}S_{1})$, $B_{s}(2 ^{1}S_{0})$, $B(1 ^{1}D_{2})$, $B_{s}(1 ^{3}D_{1})$ and $B_{s}(1 ^{1}D_{2})$. These predictions provide a crucial information for upcoming experimental studies.

hep-ph↗

arXiv2018

Masses and Strong Decay properties of Radially Excited Bottom states B(2S)and B(2P) with their Strange Partners Bs(2S) and Bs(2P)

In this paper, we analyzed the experimentally available radially excited charm mesons to predict the similar spectra for the n=2 bottom mesons. In the heavy quark effective theory, we explore the flavor independent parameters to calculate the masses for the experimentally unknown n=2 bottom mesons B(2S), B(2P), Bs(2S) and Bs(2P). We have also analyzed these bottom masses by applying the QCD and 1/mQ corrections to the lagrangian leading to the modification of flavor symmetry parameters as. Further strong decay widths are determined using these calculated masses to check the sensitivity of these corrections for these radially excited mesons. The calculated decay widths are in the form of strong coupling constant geHH, egSH and egTH. We concluded that these corrections are less sensitive for n=2 masses as compared to n=1 masses. Branching ratios and branching fractions of these states are calculated to have a deeper understanding of these states. These predicted values can be confronted with the future experimental data.

hep-ph↗

arXiv2018

Analysis of strong decays of charmed mesons $D^_2(2460)$, $D_0(2560)$, $D_2(2740)$, $D_1(3000)$, $D^_2(3000)$ and their spin partners $D^_1(2680)$, $D^_3(2760)$ and $D^*_0(3000)$

Using the effective Lagrangian approach, we examine the recently observed charm states $D^{*}_{J}(2460)$, $D_{J}(2560)$, $D_{J}(2740)$, $D_{J}(3000)$ and their spin partners $D^{*}_{J}(2680)$, $D^{*}_{J}(2760)$ and $D^{*}_{J}(3000)$ with $J^{P}$ states $1P_{\frac{3}{2}}2^{+}$, $2S_{\frac{1}{2}}0^{-}$, $1D_{\frac{5}{2}}2^{-}$, $2P_{\frac{1}{2}}1^{+}$ and $2S_{\frac{1}{2}}1^{-}$, $1D_{\frac{5}{2}}3^{-}$, $2P_{\frac{1}{2}}0^{+}$ respectively. We study their two body strong decays, coupling constants and branching ratios with the emission of light pseudo-scalar mesons $(π, η, K)$. We also analyze the newly observed charm state $D^{*}_{2}(3000)$ and suggest it to be either $1F(2^{+})$ or $2P(2^{+})$ state and justify one of them to be the most favorable assignment for $D^{*}_{2}(3000)$. We study the partial and the total decay width of unobserved states $D(1^{1}F_{3})$, $D_{s}(1 ^{1}F_{3})$ and $D_{s}(1 ^{1}F_{2})$ as the spin and the strange partners of the $D^{*}_{2}(3000)$ charmed meson. The branching ratios and the coupling constants $g_{TH}$, $\widetilde{g}_{HH}$, $g_{YH}$, $\widetilde{g}_{SH}$ and $g_{ZH}$ calculated in this work can be confronted with the future experimental data.

hep-ph↗

arXiv2016

Properties of JP = 1/2+ baryon octets at low energy

The statistical model in combination with detailed balance principle is able to phenomenological calculate and analyze spin and flavor dependent properties like magnetic moments (with effective masses, effective charge, with both effective mass and effective charge), quark spin polarization and distribution, strangeness suppression factor. The magnetic moments of the octet baryons are analyzed within the statistical model, by putting emphasis on the SU(3) symmetry breaking effects generated by the mass difference between the strange and non strange quarks. The work presented here assume hadrons with a sea having admixture of quark-gluon Fock states. The results obtained have been compared with theoretical models and experimental data.

hep-ph↗

arXiv2016

Masses and decay widths of radially excited Bottom mesons

Inspired from the experimental information coming from LHC [2,3] and Babar [4] for radially higher excited charmed mesons, we predict the masses and decays of the n=2 S-wave and P- wave bottom mesons using the effective lagrangian approach. Using heavy quark effective theory approach, non-perturbative parameters (?, ?1 and ?2) are fitted using the available experimental and theoretical informations on charm masses. Using heavy quark symmetry and the values of these fitted parameters, the masses of radially excited even and odd parity bottom mesons with and without strangness are predicted. These predicted masses led in constraining the decay widths of these 12 states, and also shed light on the unknown values of the higher hadronic coupling constants eeg 2 SH and eeg 2 TH. Studying the properties like masses, decays of 2S and 2P states and some hadronic couplings would help forthcoming experiments to look into these states in future.

hep-ph↗

arXiv2016

Heavy-light charm mesons spectroscopy and decay widths

We present the mass formula for heavy-light charm meson for one loop, using heavy quark effective theory. Formulating an effective Lagrangian, the masses of the ground state heavy mesons have been studied in the heavy quark limit including leading corrections from finite heavy quark masses and nonzero light quark masses using a constrained fit for the eight equation having eleven parameters including three coupling constants g, h and g'. Masses determined from this approach is fitted to the experimentally known decay widths to estimate the strong coupling constants, showing a better match with available theoretical and experimental data

hep-ph↗

arXiv2015

Study of 1D stranged-charm meson family using HQET

Recently LHCb predicted spin 1 and spin 3 states D* s1(2860) and D* s3(2860) which are studied through their strong decays, and are assigned to fit the 13D1and 13D3 states in the charm spectroscopy. In this paper,using the heavy quark effective theory, we state that assigning D*s1(2860) as the mixing of 13D1 - 23S1 states, is rather a better justification to its observed experimental values than a pure state. We study its decay modes variation with hadronic coupling constant gxh and the mixing angle . We appoint spin 3 state D* s3(2860) as the missing 1D 3- JP state, and also study its decay channel behavior with coupling constant gyh. To appreciate the above results, we check the variation of decay modes for their spin partners states i.e. 1D2 and 1D'2 with their masses and strong coupling constant i.e. gxh and gyh. Our calculation using HQET approach give mixing angle between the 13D1 - 23S1 state for D* s1(2860) to lie in the range (-1.6 radians < theta < -1.2 radians). Our calculation for coupling constant values gives gxh to lie between value 0:17 < gxh < 0:20 and gyh to be 0.40. We expect from experiments to observe this mixing angle to verify our results.

hep-ph↗

arXiv2011

Effect of spatial constraints on fragment production

Multifragmentation is the most extensively studied phenomena in this energy domain. In the past people have tried to develop various methods of clusterization[1]. Among these methods minimum spanning tree[1] is one of the fastest method. In the present study our aim is to understand the role of spatial constraints on fragmentation.

nucl-th↗

arXiv2007

Response Prediction of Structural System Subject to Earthquake Motions using Artificial Neural Network

This paper uses Artificial Neural Network (ANN) models to compute response of structural system subject to Indian earthquakes at Chamoli and Uttarkashi ground motion data. The system is first trained for a single real earthquake data. The trained ANN architecture is then used to simulate earthquakes with various intensities and it was found that the predicted responses given by ANN model are accurate for practical purposes. When the ANN is trained by a part of the ground motion data, it can also identify the responses of the structural system well. In this way the safeness of the structural systems may be predicted in case of future earthquakes without waiting for the earthquake to occur for the lessons. Time period and the corresponding maximum response of the building for an earthquake has been evaluated, which is again trained to predict the maximum response of the building at different time periods. The trained time period versus maximum response ANN model is also tested for real earthquake data of other place, which was not used in the training and was found to be in good agreement.

cs.AI↗