Climate change significantly impacts public health, driving the emergence and spread of epidemics. Climate health models are essential for assessing and predicting disease outbreaks influenced by climatic variables like temperature and precipitation. For instance, dengue and malaria correlate with temperature changes, while cholera is linked to precipitation anomalies. Advances in AI-enabled weather prediction (AI-NWP) have improved forecasting, but integrating climate models with health systems is hindered by the lack of comprehensive, granular health datasets. This study introduces EpiClim: India's Epidemic-Climate Dataset, the first weekly district-wise dataset for major epidemics in India from 2009 to the present, sourced from the Integrated Disease Surveillance Programme (IDSP). The dataset, covering diseases like dengue, malaria, and acute-diarrheal disease, bridges the gap between climate and health data, enabling the integration of climate forecasts with epidemic prediction models. This work lays the foundation for coupling predictive climate health models with weather and climate models, advancing efforts to mitigate climate-induced public health crises.

We study the opinion dynamics in a population by considering a variant of Kuramoto model where the phase of an oscillator represents the opinion of an individual on a single topic. Two extreme phases separated by $\pi$ represent opposing views. Any other phase is considered as an intermediate opinion between the two extremes. The interaction (or attitude) between two individuals depends on the difference between their opinions and can be positive (attractive) or negative (repulsive) based on the defined thresholds. We investigate the opinion dynamics when these thresholds are varied. We observe explosive transition from a bipolarized state to a consensus state with the existence of scattered and tri-polarized states at low values of threshold parameter. The system exhibits multistability between various states in a sizeable parameter region. These transitions and multistability are studied in populations with different degrees of diversity represented by the width of conviction distribution. We found that a more homogeneous population has greater tendency to exhibit bipolarized, tri-polarized and clustered states while a diverse population helps mitigate polarization among individuals by reaching to a consensus sooner. Ott-Antonsen analysis is used to analyse the system's long term macroscopic behaviour and verify the numerical results. We also study the effects of neutral individuals that do not interact with others or do not change their attitude on opinion formation, nature of transitions and multistability. Furthermore, we apply our model to language data to study the assimilation of diverse languages in India and compare the results with those obtained from model equations.

This Letter reports measurements of muon-neutrino disappearance and electron-neutrino appearance and the corresponding antineutrino processes between the two NOvA detectors in the NuMI neutrino beam. These measurements use a dataset with double the neutrino mode beam exposure that was previously analyzed, along with improved simulation and analysis techniques. A joint fit to these samples in the three-flavor paradigm results in the most precise single-experiment constraint on the atmospheric neutrino mass-splitting, $\Delta m^2_{32}= 2.431^{+0.036}_{-0.034} (-2.479^{+0.036}_{-0.036}) \times 10^{-3}$~eV$^2$ if the mass ordering is Normal (Inverted). In both orderings, a region close to maximal mixing with $\sin^2\theta_{23}=0.55^{+0.06}_{-0.02}$ is preferred. The NOvA data show a mild preference for the Normal mass ordering with a Bayes factor of 2.4 (corresponding to 70\% of the posterior probability), indicating that the Normal ordering is 2.4 times more probable than the Inverted ordering. When incorporating a 2D $\Delta m^2_{32}\textrm{--}\sin^2 2\theta_{13}$ constraint based on Daya Bay data, this preference strengthens to a Bayes factor of 6.6 (87\%).

We present limits on spin-independent inelastic WIMP-nucleus scattering using the 737.1 kg $\cdot$ day dataset from the CDEX-1B experiment. Expected nuclear recoil spectra for various inelastic WIMP masses $m_\chi$ and mass splittings $\delta$ are calculated under the standard halo model. An accurate background model of CDEX-1B is constructed by simulating all major background sources. The model parameters are then determined through maximum likelihood estimation and Markov Chain Monte Carlo fitting. The resulting 90\% confidence level upper limits on the WIMP-nucleon cross section $\sigma_{\mathrm{n}}$ exclude certain DAMA/LIBRA allowed regions: the \chi^2 < 4 regions for \delta < 30 keV at $m_\chi = 250$ GeV and the \chi^2 < 9 region for \delta < 50 keV at $m_\chi = 500$ GeV. The method is applicable to other inelastic dark matter scenarios, and the upcoming CDEX-50 experiment is expected to improve sensitivity by four orders of magnitude.

In this study, we tackle the challenging task of predicting secondary structures from protein primary sequences, a pivotal initial stride towards predicting tertiary structures, while yielding crucial insights into protein activity, relationships, and functions. Existing methods often utilize extensive sets of unlabeled amino acid sequences. However, these approaches neither explicitly capture nor harness the accessible protein 3D structural data, which is recognized as a decisive factor in dictating protein functions. To address this, we utilize protein residue graphs and introduce various forms of sequential or structural connections to capture enhanced spatial information. We adeptly combine Graph Neural Networks (GNNs) and Language Models (LMs), specifically utilizing a pre-trained transformer-based protein language model to encode amino acid sequences and employing message-passing mechanisms like GCN and R-GCN to capture geometric characteristics of protein structures. Employing convolution within a specific node's nearby region, including relations, we stack multiple convolutional layers to efficiently learn combined insights from the protein's spatial graph, revealing intricate interconnections and dependencies in its structural arrangement. To assess our model's performance, we employed the training dataset provided by NetSurfP-2.0, which outlines secondary structure in 3-and 8-states. Extensive experiments show that our proposed model, SSRGNet surpasses the baseline on f1-scores.

Extreme events such as earthquakes, floods, and power blackouts often display burst phenomena where multiple extreme events occur in quick succession or in bunches. This study examines bunching of extreme events on a complex network using a random walk transport model. We find that in a modular network, a small cluster sparsely connected with the rest, shows bunching and correlations among extreme events. The bunching and correlations emerge naturally in our system. We use several characterization techniques, namely the recurrence time distribution, autocorrelation function, bursty trains, burstiness parameter and memory coefficient to quantify the bunching and correlations of extreme events. Our study shows that the network structure plays a significant role in the bunching of extreme events.

The PHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) has measured electrons from heavy flavor (charm and bottom) decays for 0.3 < p_T < 9 GeV/c at midrapidity (|y| < 0.35) in Au+Au collisions at sqrt(s_NN) = 200 GeV. The nuclear modification factor R_AA relative to p+p collisions shows a strong suppression in central Au+Au collisions, indicating substantial energy loss of heavy quarks in the medium produced at RHIC. A large azimuthal anisotropy, v_2, with respect to the reaction plane is observed for 0.5 < p_T < 5 GeV/c indicating non-zero heavy flavor elliptic flow. Both R_AA and v_2 show a p_T dependence different from those of neutral pions. A comparison to transport models which simultaneously describe R_AA(p_T) and v_2(p_T) suggests that the viscosity to entropy density ratio is close to the conjectured quantum lower bound, i.e., near a perfect fluid.

The emergence of COVID-19 has severely compromised the arsenal of antiviral and antibiotic drugs. Drug discovery is a multistep process with a high failure rate, high cost and it takes approximately 10-12 years for the development of new molecules into the clinical candidate. On the other side, drug repurposing also called old drugs for new uses, is an attractive alternative approach for a new application of marketed FDA approved or investigational drugs. In the current pandemic situation raised due to COVID-19, repurposing of existing FDA approved drugs are emerging as the first line of the treatment. The causative viral agent of this highly contagious disease and acute respiratory syndrome coronavirus (SARS-CoV) shares high nucleotide similarity. Therefore, many existing viral targets are structurally expected to be similar to SARS-CoV and likely to be inhibited by the same compounds. Here, we selected three viral key proteins based on their vital role in viral life cycle: ACE2 (helps in entry into the human host), viral nonstructural proteins RNA-dependent RNA polymerase (RdRp) NSP12, and NSP16 which helps in replication, and viral latency (invasion from immunity). The FDA approved drugs chloroquine (CQ), hydroxychloroquine (HCQ), remdesivir (RDV) and arbidol (ABD) are emerging as promising agents to combat COVID-19. Our hypothesis behind the docking studies is to determine the binding affinities of these drugs and identify the key amino acid residues playing a key role in their mechanism of action. The docking studies were carried out through Autodock and online COVID-19 docking server. Further studies on a broad range of FDA approved drugs including few more protein targets, molecular dynamics studies, in-vitro and in-vivo biological evaluation are required to identify the combination therapy targeting various stages of the viral life cycle.

Learning cooperative multi-agent policies directly from high-dimensional, multimodal sensory inputs like pixels and audio (from pixels) is notoriously sample-inefficient. Model-free Multi-Agent Reinforcement Learning (MARL) algorithms struggle with the joint challenge of representation learning, partial observability, and credit assignment. To address this, we propose a novel framework based on a shared, generative Multimodal World Model (MWM). Our MWM is trained to learn a compressed latent representation of the environment's dynamics by fusing distributed, multimodal observations from all agents using a scalable attention-based mechanism. Subsequently, we leverage this learned MWM as a fast, "imagined" simulator to train cooperative MARL policies (e.g., MAPPO) entirely within its latent space, decoupling representation learning from policy learning. We introduce a new set of challenging multimodal, multi-agent benchmarks built on a 3D physics simulator. Our experiments demonstrate that our MWM-MARL framework achieves orders-of-magnitude greater sample efficiency compared to state-of-the-art model-free MARL baselines. We further show that our proposed multimodal fusion is essential for task success in environments with sensory asymmetry and that our architecture provides superior robustness to sensor-dropout, a critical feature for real-world deployment.

In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms. Experiments on three widely used datasets demonstrate the superior performance of our method in sentiment classification.

Hodge theorem and harmonic spinors are studied in a physics-oriented approach in the present paper. New mathematical results on the harmonic spinors are as follows. Harmonic spinors defined by partial differential operators could be of two types: trivial without topological defects, and having nontrivial topological structures, for example, phase singularities or phase vortices. There could exist a nontrivial harmonic vector field associated with nontrivial harmonic spinor, for example, ${\bf v}_{vortex}$ associated with Weyl 2-spinor. The $Z_2$-vortex is re-visited in the perspective of harmonic spinors leading to a remarkable result that the gauge potential is exactly the same as the nontrivial harmonic vector field associated with the 2-spinor. It is proposed that a discrete symmetry group $SL(2, Z)$ has a role in connection with the continuous group $SU(2)$ similar to the discrete group $Z_2$ in $U(1)$.

An efficient technique of computing on encrypted data allows a client with limited capability to perform complex operations on a remote fault-tolerant server without leaking anything about the input or output. Quantum computing provides information-theoretic security to solve such a problem, and many such techniques have been proposed under the premises of half-blind quantum computation. However, they are dependent on a fixed non-parametric resource set that comprises some universal combination of $H,S,T,CX, CZ$ or $CCX$ gates. In this study, we show that recursive decryption of the parametric gate, $R_z(\theta)$, is possible exactly when $\theta=\pm\pi/2^m$ for $m\in \mathbb{Z^{+}}$, and approximately with arbitrary precision $\epsilon$ for given $\theta$. We also show that a blind algorithm based on such a technique needs at most $O(\log_2^2(\pi/\epsilon))$ computation steps and communication rounds, while the techniques based on a non-parametric resource set require $O(\ln^{3.97}(1/\epsilon))$ rounds. We use these results to propose a universal scheme of half-blind quantum computation for computing on encrypted data using arbitrary rotation gates. This substantial reduction in the depth of blind circuit is an affirmative step towards the practical application of such techniques in secure NISQ-era computing.

NOvA is a long-baseline neutrino oscillation experiment that measures oscillations in charged-current $\nu_{\mu} \rightarrow \nu_{\mu}$ (disappearance) and $\nu_{\mu} \rightarrow \nu_{e}$ (appearance) channels, and their antineutrino counterparts, using neutrinos of energies around 2 GeV over a distance of 810 km. In this work we reanalyze the dataset first examined in our previous paper [Phys. Rev. D 106, 032004 (2022)] using an alternative statistical approach based on Bayesian Markov Chain Monte Carlo. We measure oscillation parameters consistent with the previous results. We also extend our inferences to include the first NOvA measurements of the reactor mixing angle $\theta_{13}$ and the Jarlskog invariant. We use these results to quantify the strength of our inferences about CP violation, as well as to examine the effects of constraints from short-baseline measurements of $\theta_{13}$ using antineutrinos from nuclear reactors when making NOvA measurements of $\theta_{23}$. Our long-baseline measurement of $\theta_{13}$ is also shown to be consistent with the reactor measurements, supporting the general applicability and robustness of the PMNS framework for neutrino oscillations.

In this work, we consider a two level $P\sigma_{z}$ pseudo-Hermitian system in contact with a thermal bath to study various thermodynamic properties. The system is realized in terms of infinitely many invariant subspaces. We find explicit solution in each subspace analytically. The quantum system undergoes a $P\sigma_{z}$ phase transition in each invariant subspace, when the non-Hermitian coupling exceeds a certain critical value ($\mu_c$). We calculate various thermodynamic quantities and observe that these quantities show divergences exactly at the exceptional points (EPs) of the theory.

In this paper, we describe some interesting properties of a spin-oscillator system with non-Hermitian coupling. As shown earlier, the Hilbert space of this problem can be described by infinitely-many closed two-dimensional invariant subspaces together with the global ground state. We expose the appearance of exceptional points (EP) on such two-dimensional subspaces together with quantum phase transitions marking the transit from real to complex eigenvalues. We analytically compute some information-theoretic measures for this intriguing system, namely, the thermal entropy as well as the von Neumann and Rényi entropies using the framework of the so-called $G$-inner product. Such entropic measures are verified to be non-analytic at the points which mark the quantum phase transitions on the space of parameters -- a naive comparison with Ehrenfest's classification of phase transitions then suggests that these transitions are of the first order as the first derivatives of the entropies are discontinuous across such transitions.

The inclusive electron neutrino charged-current cross section is measured in the NOvA near detector using $8.02\times10^{20}$ protons-on-target (POT) in the NuMI beam. The sample of GeV electron neutrino interactions is the largest analyzed to date and is limited by $\simeq$ 17\% systematic rather than the $\simeq$ 7.4\% statistical uncertainties. The double-differential cross section in final-state electron energy and angle is presented for the first time, together with the single-differential dependence on $Q^{2}$ (squared four-momentum transfer) and energy, in the range 1 GeV \leq E_{\nu} &lt; 6 GeV. Detailed comparisons are made to the predictions of the GENIE, GiBUU, NEUT, and NuWro neutrino event generators. The data do not strongly favor a model over the others consistently across all three cross sections measured, though some models have especially good or poor agreement in the single differential cross section vs. $Q^{2}$.

A novel mathematical model for the hypothalamic pituitary adrenal (HPA) axis is proposed to comprehend the oscillations observed in hormone concentration and potential dysfunction within the HPA axis in stressful situation. This model integrates impact of hippocampal receptors on the secretion of corticotropin-releasing hormone (CRH), an additional signaling pathway involving Arginine Vasopressin (AVP) for the production and secretion of adrenocorticotropic hormone (ACTH), the inclusion of a daylight-related function for modeling circadian rhythms, and a short negative feedback loop from the pituitary to the hypothalamus in a minimal mechanistic model of the HPA axis. This expansion allows us to estimate model parameters that led to a significant reduction in the mean absolute percent error, thereby enhancing the model's predictive accuracy with a demonstration of a strong fit to the validation dataset. Through sensitivity and correlation analyses, our study shows the parameters that exert the most significant influence on the dynamics of cortisol within the system. This study reveals intricate interdependencies within the model and among the various components of the HPA axis. Despite current advancements in the model to comprehend the dynamics of the HPA Axis, these models are still lacking in addressing major factors such as the impact of genetic and epigenetics, the role of the amygdala in processing and transmitting stress signals, and other minor impacting factors.

We present a study on the charge current quasi elastic scattering of $\nu_\mu$ from nucleon and nuclei which gives a charged muon in the final state. To describe nuclei, the Fermi Gas model has been used with proposed Pauli suppression factor. The diffuseness parameter of the Fermi distribution has been obtained using experimental data. We also investigate different parametrizations for electric and magnetic Sach's form factors of nucleons. Calculations have been made for CCQES total and differential cross sections for the cases of $\nu_{\mu}-N$, $\nu_{\mu}-^{12}C$ and $\nu_{\mu}-^{56}Fe$ scatterings and are compared with the data for different values of the axial mass. The present model gives excellent description of measured differential cross section for all the systems.

The flux of cosmic ray muons at the Earth's surface exhibits seasonal variations due to changes in the temperature of the atmosphere affecting the production and decay of mesons in the upper atmosphere. Using data collected by the NOvA Near Detector during 2018--2022, we studied the seasonal pattern in the multiple-muon event rate. The data confirm an anticorrelation between the multiple-muon event rate and effective atmospheric temperature, consistent across all the years of data. Previous analyses from MINOS and NOvA saw a similar anticorrelation but did not include an explanation. We find that this anticorrelation is driven by altitude--geometry effects as the average muon production height changes with the season. This has been checked with a CORSIKA cosmic ray simulation package by varying atmospheric parameters, and provides an explanation to a longstanding discrepancy between the seasonal phases of single and multiple-muon events.