Eutrophication and harmful algal blooms, driven by complex interactions among nutrients and climate, threaten freshwater ecosystems globally, particularly in densely populated Asian regions where rapid urbanization and agricultural intensification exacerbate nutrient pollution. Understanding the non-linear interactions among water temperature, nutrient levels, and chlorophyll-a (Chl-a) dynamics is crucial for addressing eutrophication in freshwater ecosystems. Many existing studies, however, tend to oversimplify these relationships and lack validation with long-term field data. Here, we conducted multi-year field monitoring (2020-2024) of key environmental factors, including total nitrogen (TN), total phosphorus (TP), water temperature, and Chl-a, across three reservoirs in Guangdong Province, China: Tiantangshan (S1), Baisha River (S2), and Meizhou (S3). Strong positive correlations were found between Chl-a and TN, TP, and temperature. Numerical analysis of the long-term data revealed TN as a more influential driver than TP for Chl-a proliferation in these systems, with Chl-a increasing by an average of 4.2 ug/L per unit increase in TN, compared to 2.8 ug/L per unit increase in TP. Based on the collected data, we developed and calibrated a dynamic multi-factor hydro-ecological model. The model accurately reproduced the observed Chl-a patterns, identifying synergistic effects between temperature and nutrients, particularly a 15% enhancement in Chl-a growth rate when temperature exceeded 25 concurrent with high TN.

Recommender systems shape music listening worldwide due to their widespread adoption on online platforms. Growing concerns about representational harms that these systems may cause are increasingly part of the scientific and public debate, wherein music listener perspectives are oftentimes reported and discussed, but rarely contextualised through a psychosocial and cultural lens. We address this gap by interviewing a group of Italian music listeners and analysing their narratives through Emotional Textual Analysis. Our findings reveal that listeners often engage with platforms in routinized ways, yet lack a critical understanding of how recommender systems operate and experience a sense of detachment from algorithmic processes. Moreover, while listeners perceive cultural and linguistic distinctions in music, their awareness of gender-related representational issues remains relatively limited. Overall, results underscore the importance of integrating psychosocial insights with technical approaches in the design of trustworthy and culturally sensitive music recommender systems.

In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses. When observable environmental covariates are used, this is also known as factorial regression. Early work along these lines can be traced back to Stringfield & Salter (1934) and Yates & Cochran (1938), who proposed a method nowadays best known as Finlay-Wilkinson regression. This method, in turn, has close ties with regression on latent environmental covariates and factor-analytic variance-covariance structures for genotype-environment interaction. Extensions of these approaches - reduced rank regression, kernel- or kinship-based approaches, random coefficient regression, and extended Finlay-Wilkinson regression - will be the focus of this paper. Our objective is to demonstrate how seemingly disparate methods are very closely linked and fall within a common model-based prediction framework. The framework considers environments as random throughout, with genotypes also modelled as random in most cases. We will discuss options for assessing uncertainty of predictions, including cross validation and model-based estimates of uncertainty, the latter one being estimated using our new suggested approach. The methods are illustrated using a long-term rice variety trial dataset from Bangladesh.

We extend the important generalizations by Yannelis [25] and Cornet et al [7] of the classical result of Gale, Nikaido, Kuhn and Debreu (the "GNKD theorem") regarding existence of market equilibrium, by broadening the applicability of their results, which apply only to economies with commodity space that can be modeled by a locally convex Hausdorff space, to the wider class of economies with commodity spaces describable by any Hausdorff topological vector space with nontrivial continuous dual.

Python type annotations enable static type checking, but most code remains untyped because manual annotation is time-consuming and tedious. Past approaches to automatic type inference fall short: static methods struggle with dynamic features and infer overly broad types; AI-based methods are unsound and miss rare types; and dynamic methods impose extreme overheads (up to 270x), lack important language support such as inferring variable types, or produce annotations that cause runtime errors. This paper presents RightTyper, a novel hybrid approach for Python that produces accurate and precise type annotations grounded in actual program behavior. RightTyper grounds inference in types observed during actual program execution and combines these observations with static analysis and name resolution to produce substantially higher-quality type annotations than prior approaches. Through principled, statistically guided adaptive sampling, RightTyper balances runtime overhead with the need to observe sufficient execution behavior to infer high-quality type annotations. We evaluate RightTyper against static, dynamic, and AI-based systems on both synthetic benchmarks and real-world code, and find that it consistently achieves higher semantic similarity to ground-truth and developer-written annotations, respectively, while incurring only approximately 27% runtime overhead.

For the model problem of the heat equation discretized by an implicit Euler method in time and a conforming finite element method in space, we prove the efficiency of a posteriori error estimators with respect to the energy norm of the error, when considering the numerical solution as the average between the usual continuous piecewise affine-in-time and piecewise constant-in-time reconstructions. This illustrates how the efficiency of the estimators is not only possibly dependent on the choice of norm, but also on the choice of notion of numerical solution.

The classical Kramers-Henneberger transformation connects, via a series of unitary transformations, the dynamics of a quantum particle of mass $m$ located in a trap at position $α(t)$, with the dynamics of a charge $e$ moving in an electric field $e{\cal{E}}(t)=-m\ddotα(t)$ within the dipole approximation. In this paper, we extend the classical Kramers-Henneberger transformation to the quantum electrodynamic and quantum optical realm, by explicitly treating the trap location quantum mechanically, thus taking into account the quantum fluctuations of the time-dependent displacement force. Compared to the classical case, we show that quantum electrodynamic corrections appear, and we propose an optomechanical realization for the quantized position of the trap to show that such corrections can manifest in state-of-the-art experiments. These results open the path to novel quantum simulation of quantum electrodynamics and quantum optics of attoscience and ultrafast physics by using ultracold trapped atoms and ions.

The process of programmed cell death, namely apoptosis, is a natural mechanism that regulates healthy tissue, multicellular structures, and homeostasis. An improved understanding of apoptosis can significantly enhance our knowledge of biological processes and systems. For instance, pathogens can manipulate the apoptotic process to either evade immune detection or to facilitate their spread. Furthermore, of particular clinical interest is the ability of cancer cells to evade apoptosis, hence allowing them to survive and proliferate uncontrollably. Thus, in this work, we propose a phase-field framework for simulating intrinsic or extrinsic apoptosis induced by an activation field, including deriving the configurational mechanics underlying such phenomena. Along with exploring varying conditions needed to initiate or reduce apoptosis, this can serve as a starting point for computational therapeutic testing. To showcase model capabilities, we present simulations exhibiting different types of cellular dynamics produced when varying the mechanisms underlying apoptosis. The model is subsequently applied to probe different morphological transitions, such as cell shrinkage, membrane blebbing, cavity formation and fragmentation. Lastly, we compare the characteristics observed in our simulations to electron microscopy images, providing additional support for the model.

Shot-to-shot fluctuations in electron beams from laser wakefield accelerators present a significant challenge for applications. Here, we show that instead of using such fluctuating beams directly, employing them to drive a plasma photocathode-based wakefield refinement stage can produce secondary electron beams with greater stability, higher quality, and improved reliability. Our simulation-based analysis reveals that drive beam jitters are compensated by both the insensitivity of beam-driven plasma wakefield acceleration, and the decoupled physics of plasma photocathode injection. While beam-driven, dephasing-free plasma wakefield acceleration mitigates energy and energy spread fluctuations, intrinsically synchronized plasma photocathode injection compensates charge and current jitters of incoming electron beams, and provides a simultaneous quality boost. Our findings suggest plasma photocathodes are ideal injectors for hybrid laser-plasma wakefield accelerators, and nurture prospects for demanding applications such as free-electron lasers.

We investigate reversible and irreversible thermodynamic processes in cosmology and their impact on the Hubble tension. Gravitationally induced adiabatic matter creation/annihilation is treated as irreversible, while energy exchange between the cosmic bulk and horizon is modeled as reversible. Two scenarios are proposed: Model I features matter creation/annihilation across all species with energy transfer to effective entropic dark energy; Model II considers dark matter creation/annihilation with energy flow from baryonic matter and radiation. The creation rate is parameterized as $Γ(t)=Γ_0 H$, with energy transfer controlled by $γ$. We constrain both models using Pantheon$+$ supernovae, CMB distance priors, baryon acoustic oscillations, gamma-ray bursts, and cosmic chronometers, with and without SH$_0$ES. When SH$_0$ES is included, matter annihilation ($Γ_0<0$) is statistically preferred, yielding $H_0 = 71.75 \pm 0.79$ km s$^{-1}$ Mpc$^{-1}$ (Model I) and $H_0 = 71.06 \pm 0.81$ km s$^{-1}$ Mpc$^{-1}$ (Model II), corresponding to $1.2σ$ and $1.8σ$ consistency with the SH$_0$ES value $73.17 \pm 0.86$ km s$^{-1}$ Mpc$^{-1}$. Matter creation ($Γ_0>0$) or pure energy flow ($Γ=0$) do not improve the tension. Without SH$_0$ES, information criteria show no preference over $Λ$CDM. For the matter annihilation/creation with energy flow, the effective entropic dark energy evolves dynamically, mimicking radiation and matter before recombination and approaching a cosmological constant at late times. These results demonstrate that thermodynamically motivated interactions can alleviate the Hubble tension when calibrated with local measurements, while remaining consistent with cosmological data.

The population of bright galaxies at $z\gtrsim10$ discovered by JWST, including the so-called "blue monsters", has been difficult to reconcile with standard galaxy evolution models. To shed light on this extraordinary population, we study the $z\sim8$ galaxies discovered by the BoRG-$JWST$ survey. These slightly-lower redshift analogs are comparable in UV luminosity to the blue monsters, and their lower redshift makes it much easier to access key rest frame optical diagnostics with NIRspec. We find that BoRG-$JWST$ galaxies are consistent with being dust-poor based on their blue UV slopes and Balmer decrement ratios. We find no strong evidence for dominant active galactic nuclei contribution to the UV brightness, based on line-ratio diagnostics, though some contribution cannot be excluded. We further infer the stellar mass, star formation and UV-brightness history of the BoRG-$JWST$ galaxies by fitting their rest-frame UV-optical spectra. We see evidence for stochastic episodes of star formation for all the BoRG-$JWST$ galaxies, providing a temporal boosting of UV luminosity in short timescales. The UV-bright blue monsters at $z\gtrsim10$ can be explained by the presence of stars with ages below 100 Myr.

This study investigates Shiksha copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India. The system combined LLMs and human expertise through a structured process in which English and Kannada lesson plans were co-created by curators and AI; teachers then further customized these curated plans for their classrooms using their own expertise alongside AI support. Drawing on a large-scale mixed-methods study involving 1,043 teachers and 23 curators, we examine how educators collaborate with AI to generate context-sensitive lesson plans, assess the quality of AI-generated content, and analyze shifts in teaching practices within multilingual, low-resource environments. Our findings show that teachers used Shiksha copilot both to meet administrative documentation needs and to support their teaching. The tool eased bureaucratic workload, reduced lesson planning time, and lowered teaching-related stress, while promoting a shift toward activity-based pedagogy. However, systemic challenges such as staffing shortages and administrative demands constrained broader pedagogical change. We frame these findings through the lenses of teacher-AI collaboration and communities of practice to examine the effective integration of AI tools in teaching. Finally, we propose design directions for future teacher-centered EdTech, particularly in multilingual and Global South contexts.

Symmetry-enriched topological (SET) phases combine intrinsic topological order with global symmetries, giving rise to novel symmetry phenomena. While SET phases with Abelian anyons are relatively well understood, those involving nonabelian anyons remain elusive. This obscurity stems from the multidimensional internal gauge spaces intrinsic to nonabelian anyons -- a feature first made explicit in [1,2] and further explored and formalized in our recent works [3-8]. These internal spaces can transform in highly nontrivial ways under global symmetries. In this work, we employ an exactly solvable model -- the multifusion Hu-Geer-Wu string-net model introduced in a companion paper [9] -- to reveal how the internal gauge spaces of nonabelian anyons transform under symmetries. We uncover a universal mechanism, global symmetry fragmentation (GSF), whereby symmetry-invariant anyons exhibit internal Hilbert space decompositions into eigensubspaces labeled by generally fractional symmetry charges. Meanwhile, symmetry-permuted anyons hybridize and fragment their internal spaces in accordance with their symmetry behavior. These fragmented structures realize genuinely nonlinear symmetry representations -- to be termed coherent representations -- that transcend conventional linear and projective classifications, reflecting the categorical nature of symmetries in topological phases. Our results identify nonlinear fragmentation as a hallmark of nonabelian SETs and suggest new routes for symmetry-enabled control in topological quantum computation.

Quantifying measurement precision in quantum systems is vital for advancing quantum technologies such as sensing, communication, and computation. The quantum Fisher information (QFI) sets the ultimate precision bound in Hermitian systems; however, extending this concept to non-Hermitian systems, even those with real spectra, poses conceptual challenges due to their non-unitary dynamics. We compare three probability-conserving approaches for evaluating QFI in such systems: (i) simple normalization, (ii) metric formalism, and (iii) master-equation framework. Although all three ensure probability conservation, they differ in physical interpretation and in how they quantify estimation precision. Our study is particularly motivated by previous studies that have shown that the simple normalization method for non-Hermitian Hamiltonian generated dynamics may lead to misleading or even unphysical conclusions for certain quantum information theoretic tasks. We emphasize, in this article, that the metric formalism naturally enables the use of standard Hermitian metrology tools in cases where it provides a coherent and physically consistent framework for non-Hermitian systems.

Topological entropy serves as a viable candidate for quantifying mixing and complexity of a highly chaotic system. Particularly in turbulence, this is determined as the exponential stretching rate of a fluid material line that typically necessitates a Lagrangian description. We extend our recent work [A. Manoharan, S. Subramanian, and A. Joy, Phys. Rev. E 112, 015106] to three dimensions, and present an exact Eulerian framework to compute the topological entropy of stationary turbulent flows. The only prerequisite is a distribution of eigenvalues of the local strain-rate tensor and their decorrelation times. This can be easily obtained from a single wire probe at a fixed location, thereby eliminating the need for Lagrangian particle tracking which is formidable due to the chaotic nature of the flow. We believe that our results lend great utility in experiments targeting transport and mixing in many industrial and natural flows.

We report the experimental results of controlling the pedestal-top electron density by applying resonant magnetic perturbation with the in-vessel control coils and the main gas puff in the 2024-2025 KSTAR experimental campaign. The density is reconstructed using a parametrized psi_N grid and the five channels of the line-averaged density measured by a two-colored interferometer. The reconstruction procedure is accelerated by deploying a multi-layer perceptron to run in about 120 microseconds and is fast enough for real-time control. A proportional-integration controller is adopted, with the controller gains being estimated from the system identification processes. The experimental results show that the developed controller can follow a dynamic target while exclusively using both actuators. The absolute percentage errors between the electron density at psi_N=0.89 and the target are approximately 1.5% median and a 2.5% average value. The developed controller can even lower the density by using the pump-out mechanism under RMP, and it can follow a more dynamic target than a single actuator controller. The developed controller will enable experimental scenario exploration within a shot by dynamically setting the density target or maintaining a constant electron density within a discharge.

Plasma diagnostics often employ computerized tomography to estimate emissivity profiles from a finite, and often limited, number of line-integrated measurements. Decades of algorithmic refinement have brought considerable improvements, and led to a variety of employed solutions. These often feature an underlying, common structure that is rarely acknowledged or investigated. In this paper, we present a unifying perspective on sparse-view tomographic reconstructions for plasma imaging, highlighting how many inversion approaches reported in the literature can be naturally understood within a Bayesian framework. In this setting, statistical modelling of acquired data leads to a likelihood term, while the assumed properties of the profile to be reconstructed are encoded within a prior term. Together, these terms yield the posterior distribution, which models all the available information on the profile to be reconstructed. We show how credible reconstructions, uncertainty quantification and further statistical quantities of interest can be efficiently obtained from noisy tomographic data by means of a stochastic gradient flow algorithm targeting the posterior. This is demonstrated by application to soft x-ray imaging at the TCV tokamak. We validate the proposed imaging pipeline on a large dataset of generated model phantoms, showing how posterior-based inference can be leveraged to perform principled statistical analysis of quantities of interest. Finally, we address some of the inherent, and thus remaining, limitations of sparse-view tomography. All the computational routines used in this work are made available as open access code.

Gravitational waves from spin-precessing binaries exhibit equatorial asymmetries absent in non-precessing systems, leading to net linear momentum emission and contributing to the remnant's recoil. This effect, recently incorporated into only a few waveform models, is crucial for accurate recoil predictions and improved parameter estimation. We present an upgrade to the SEOBNRv5PHM model -- SEOBNRv5PHM_w/asym -- which includes equatorial asymmetric contributions to the l=m<=4 waveform modes in the co-precessing frame. The model combines post-Newtonian inputs with calibrated amplitude and phase corrections and a phenomenological merger-ringdown description, tuned against 1523 quasi-circular spin-precessing numerical relativity waveforms and single-spin precessing test-body plunging-geodesic waveforms. We find that SEOBNRv5PHM_w/asym improves the agreement with NR waveforms across inclinations, with median unfaithfulness reduced by up to 50% compared to SEOBNRv5PHM, and achieves 30-60% lower unfaithfulness than IMRPhenomXPNR and 76-80% lower than TEOBResumS_Dali. The model significantly improves the prediction of the recoil velocity, reducing the median relative error with numerical relativity from 70% to 1%. Bayesian inference on synthetic injections demonstrates improved recovery of spin orientations and mass parameters, and a reanalysis of GW200129 shows a threefold increase in the spin-precessing Bayes factor, highlighting the importance of these effects for interpreting spin-precessing events.

The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200,000 DESI spectra comprising galaxies, quasars and stars. We demonstrate that the VAE can compress the dimensionality of a spectrum by a factor of 100, while still retaining enough information to accurately reconstruct spectral features. We then detect anomalous spectra as those with high reconstruction error and those which are isolated in the VAE latent representation. The anomalies identified fall into two categories: spectra with artefacts and spectra with unique physical features. Awareness of the former can help to improve the DESI spectroscopic pipeline; whilst the latter can lead to the identification of new and unusual objects. To further curate the list of outliers, we use the Astronomaly package which employs Active Learning to provide personalised outlier recommendations for visual inspection. In this work we also explore the VAE latent space, finding that different object classes and subclasses are separated despite being unlabelled. We demonstrate the interpretability of this latent space by identifying tracks within it that correspond to various spectral characteristics. For example, we find tracks that correspond to increasing star formation and increase in broad emission lines along the Balmer series. In upcoming work we hope to apply the methods presented here to search for both systematics and astrophysically interesting objects in much larger datasets of DESI spectra.

This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate characterization of chemical interactions and product formation in surface and subsurface environments is essential for advancing critical mineral extraction and related geoscience applications.

Community Notes (CNs) of X enables users to collaboratively moderate misleading content. To resolve conflicting moderation, CNs infers a latent ideological dimension and selects notes garnering cross-partisan support. As this system is now deployed worldwide, we evaluate its operation across diverse polarization contexts. We analyze all 1.9 million moderation notes receiving 135 million ratings by March 2025, cross-referencing ideological scaling data on 13 countries. Our results show that the CNs algorithm effectively captures the main polarizing dimensions across countries, surfacing notes that garner cross-partisan support. This also means that, by design, CNs systematically under-moderate polarizing content. We analyze notes relating to four recent elections in the US (2024), the UK (2024), France (2024) and Germany (2025) and demonstrate that they are systematically under-moderated when compared to other notes, posing potential risks to civic discourse and electoral processes.

The ESA/NASA Rosalind Franklin rover, planned for launch in 2028, will carry the first laser desorption ionization mass spectrometer (LDI-MS) to Mars as part of the Mars Organic Molecule Analyzer (MOMA) instrument. MOMA will contribute to the astrobiology goals of the mission through the analysis of potential organic biosignatures. Due to minimal availability of comparable equipment, laboratory analyses using similar techniques and instrumentation have been limited. Until now, the Thermo LTQ-XL platform has been used as the main analog instrument by the MOMA team despite significant differences between the instruments. In this study, we present a series of modifications that bring this commercial benchtop LDI-MS closer to MOMA operating parameters, enabling rapid testing of samples for MOMA validation experiments. We demonstrate that our instrument can detect organic standards in mineral matrices, with MS/MS enabling structural identification even in complex mixtures. Performance was additionally validated against an existing LDI-MS prototype through the comparison of spectra derived from natural samples from a Mars analog site in the Atacama Desert. Lastly, analysis of Mars analog synthetic mineral mixes highlights the capacity of the instrument to characterize both the mineralogical and organic signals in mission-relevant samples. This modified benchtop instrument will serve as a platform for collaborative research to prepare for MOMA operations, test LDI parameters, and generate pre-flight reference data in support of the mission science and astrobiology specific goals.

The set C of complex-valued continuous functions on [0,\infty) is a ring by the addition and the convolution. It has the quotient field Q(C), by which J. Mikusinski developed his operational calculus. In this paper, we revisit a derivation and a transforming operator for Q(C) written in his textbook, and define another transforming operator related to the q-shift operator, which gives structures of a q-difference field and a difference field of Mahler type to Q(C). Appropriate derivatives are also considered.

We study the possibility of discriminating between metric theories within the Parametrized Post-Newtonian formalism. In this approach, the two-dimensional quantum state of a massive quantum clock becomes, after propagating at low speed and in a weak gravitational field, a function of the post-Newtonian parameters and thus a signature of a metric theory. To discriminate among metric theories, we resort to quantum-state discrimination strategies such as minimum-error and unambiguous discrimination. In particular, we show that it is possible to refute the hypothesis that a particular metric theory describes spacetime with a single detection event and that it is possible to discriminate with certainty between two different metrics, also with a single detection event. In general, the success probability of the discrimination strategy is a harmonic function of the product of the difference of the proper time corresponding to each quantum clock state, the energy difference between the energy eigenstates of the quantum clock, the propagation length, and speed. It is thus possible to find suitable length and speed scales such that the success probability is close to one by selecting a quantum system with the highest energy difference and the longest natural lifetime. According to this, atomic nuclei such as thorium are considered the most suitable quantum clocks. We also show that the use of a ensemble of quantum clocks leads to a significant increase in the probability of success in discriminating between post-Newtonian parameters that differ by $10^{-5}$. This facilitates achieving a probability of success approaching unity with distances on the scale of several kilometers and velocities approximating one-thousandth of the speed of light for a ensemble of only 10 quantum clocks.

The formation of the first stars and galaxies marked the onset of chemical enrichment, yet direct observations of such primordial systems remain elusive. Here we present James Webb Space Telescope spectroscopic observations of LAP1-B, an ultra-faint galaxy at redshift z_{spec}=6.625 +/-0.001, corresponding to a cosmic age of 800 million years after the Big Bang, strongly magnified by gravitational lensing. LAP1-B exhibits a gas-phase oxygen abundance of (4.2 +/- 1.8) x 10^{-3} times the solar value, making it the most chemically primitive star-forming galaxy discovered to date. The galaxy displays an exceptionally hard ionizing radiation field, which is inconsistent with chemically enriched stellar populations or accreting black holes but matches theoretical predictions for an exceptionally metal-deficient stellar population. It also shows an elevated carbon-to-oxygen abundance ratio for its metallicity in the interstellar medium, consistent with nucleosynthetic yields from a stellar population formed in the absence of initial metals. The lack of detectable stellar continuum constrains the stellar mass to <3,300 Msun, while the dynamical mass, derived from emission-line kinematics, exceeds the combined stellar and gas mass and indicates a dominant dark matter halo. Our findings establish LAP1-B as a "fossil in the making", a direct high-redshift progenitor of the ancient ultra-faint dwarf galaxies observed in the local Universe, offering a rare window into the earliest stages of galaxy formation.

Causal inference relies on the untestable assumption of no unmeasured confounding. Sensitivity analysis can be used to quantify the impact of unmeasured confounding on causal estimates. Among sensitivity analysis methods proposed in the literature for unmeasured confounding, the latent confounder approach is favoured for its intuitive interpretation via the use of bias parameters to specify the relationship between the observed and unobserved variables and the sensitivity function approach directly characterizes the net causal effect of the unmeasured confounding without explicitly introducing latent variables to the causal models. In this paper, we developed and extended two sensitivity analysis approaches, namely the Bayesian sensitivity analysis with latent confounding variables and the Bayesian sensitivity function approach for the estimation of time-varying treatment effects with longitudinal observational data subjected to time-varying unmeasured confounding. We investigated the performance of these methods in a series of simulation studies and applied them to a multi-center pediatric disease registry data to provide practical guidance on their implementation.

The nonlinear breakup dynamics of a strip of active chiral fluid is considered, and it is shown that the strip thickness goes to zero as a power law in finite time. Applying slender body theory to the hydrodynamic equations of active chiral fluids, we predict the exponents analytically, and our predictions are shown to be in excellent agreement with numerical simulations. Qualitative agreement between experiment and simulation is also found.

An extended polymer collapses to form a globule when subjected to a quench below the collapse transition temperature. The process begins with the formation of clusters of monomers or ``pearls''. The nascent clusters merge, resulting in growth of the average cluster size $C_s$, eventually leading to a single globule. The aggregation of the clusters are known to be analogous to droplet coalescence. This suggests a striking resemblance between such an aggregation and cluster-cluster aggregation found in many {particle systems}, like in colloidal self-assembly, typically characterized by a universal dynamic scaling behavior. Motivated by that, here, we verify the presence of such dynamic scaling during the collapse of a polymer with varying bending stiffness $κ$, using molecular dynamic simulations. We probe the dynamics via time evolution of the size distribution of clusters $N_s(t)$ and growth of $C_s(t)$. Irrespective of $κ$, we observe the power-law scalings $C_s(t)\sim t^z$ and $N_s(t)\sim t^{-w} s^{-τ}$, of which only the cluster growth is universal with {$z\approx 1.67$.} Importantly, our results indeed show that $N_s(t)$ exhibits a dynamic scaling of the form $N_s(t)\sim s^{-2}f(s/t^z)$, indicative of a scale-free cluster growth. Interestingly, for flexible and weakly stiff polymers the dynamic exponents obey the relation $w=2z$, as also found in diffusion-controlled cluster-cluster aggregation of particles. For $κ\ge 5$, the exponents show deviation from this relation, which grows continuously with $κ$. We identify the differences in local structures of the clusters formed, leading to variations in cluster-size dependence of the effective diffusion constant to be the origin of the above deviation. We also discuss potential experimental strategies to directly visualize the observed dynamic scaling in a collapsing polymer.

Background: Adaptive interventions provide a guide for using ongoing information about individuals to decide whether and how to modify the type, amount, delivery modality, or timing of treatment, to improve intervention effectiveness while reducing cost and burden. The variables that inform treatment modification decisions are called tailoring variables. Specifying a tailoring variable requires describing what should be measured, when to measure it, when the measure should be used to make decisions, and what cutoffs should be used in making decisions. These questions are causal and prescriptive (what to do, when), not merely predictive. They involve tradeoffs between specificity and sensitivity, and between waiting for sufficient information versus intervening quickly. Purpose: There is little specific guidance in the literature on how to empirically choose tailoring variables, including cutoffs, measurement times, and decision times. Methods: We review possible approaches for comparing potential tailoring variables and propose a framework for systematically developing tailoring variables. Results: Although secondary observational data can be used to select tailoring variables, additional assumptions are needed. A specifically designed experiment for optimization (an optimization randomized controlled trial), e.g., a multi-arm randomized trial, sequential multiple assignment randomized trial, factorial experiment, or hybrid design, may provide a more direct way to answer these questions. Conclusions: Using randomization directly to inform tailoring variables provides the most direct causal evidence but requires more effort and resources than secondary data analysis. More research is needed on how best to design tailoring variables for effective, scalable interventions.

This letter proposes a platform-aware framework to characterize wireless links by empirically modeling the `near-platform' scattering and reflections induced by the hardware mounting structures of both endpoints. We model the link characteristics as a novel mutual antenna pattern: a joint function of the angle of arrival (AoA) and angle of departure (AoD). We demonstrate that while individual platform-aware patterns are mathematically unidentifiable from power measurements, the coupled mutual pattern can be effectively estimated in a least-squares sense. Our framework is evaluated using noisy measurement data, revealing that as few as 10 measurements per joint-angular bin are sufficient. The proposed methodology is validated through cross-validation of experimental subsets, demonstrating that the learned mutual radiation pattern reduces path loss estimation errors by up to 10 dB compared to traditional models using isolated anechoic chamber antenna gains.