The development of a zygote into a functional organism requires that this single progenitor cell gives rise to numerous distinct cell types. Attempts to exhaustively tabulate the interactions within developmental signaling networks that coordinate these hierarchical cell fate transitions are difficult to interpret or fit to data. An alternative approach models the cellular decision-making process as a flow in an abstract landscape whose signal-dependent topography defines the possible developmental outcomes and the transitions between them. Prior applications of this formalism have built landscapes in low-dimensional spaces without explicit maps to gene expression. Here, we present a computational geometry framework for fitting dynamical landscapes directly to high-dimensional single-cell data. Our method models the time evolution of probability distributions in gene expression space, enabling landscape construction with minimal free parameters and precise characterization of dynamical features, including fixed points, unstable manifolds, and basins of attraction. We demonstrate the applicability of this framework to multicolor flow-cytometry and RNA-seq data. Applied to a stem cell system that models ventral neural tube patterning, we recover a family of morphogen-dependent landscapes whose valleys align with canonical neural progenitor types. Remarkably, simple linear interpolation between landscapes captures signaling dependence, and chaining landscapes together reveals irreversible behavior following transient morphogen exposure. Our method combines the interpretability of landscape models with a direct connection to data, providing a general framework for understanding and controlling developmental dynamics.

GenSC: a novel and general local search framework for set covering problem

Abstract In this work we present an a priori error analysis for solving the unsteady advection equation on cut cell meshes along a straight ramp in two dimensions. The space discretization uses a lowest order upwind-type discontinuous Galerkin scheme involving a Domain of Dependence (DoD) stabilization to correct the update in the neighborhood of small cut cells. Thereby, it is possible to employ explicit time stepping schemes with a time step length that is independent of the size of the very small cut cells. Our error analysis is based on a general framework for error estimates for first-order linear partial differential equations that relies on consistency, boundedness, and discrete dissipation of the discrete bilinear form. We prove these properties for the space discretization involving DoD stabilization. This allows us to prove, for the fully discrete scheme, a quasi-optimal error estimate of order one half in a norm that combines the $$L^\infty $$ L ∞ -in-time $$L^2$$ L 2 -in-space norm and a seminorm that contains velocity weighted jumps. We also provide corresponding numerical results.

MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.

Malnutrition, sarcopenia and nutrition therapy for patients with diabetes - A general framework and focus on hospital care.

Geoff: The generic optimization framework & frontend for particle accelerator controls

Imaging through scattering is challenging, as even a thin layer can randomly perturb light propagation and obscure hidden objects. Accurate closed-form modeling of forward scattering remains difficult, particularly for dynamically varying or thick layers. Here, we introduce a plug-and-play inverse solver based on video diffusion models with a physically grounded forward model tailored to dynamic scattering layers. Our method extends Diffusion Posterior Sampling (DPS) to the spatio-temporal domain, thereby capturing statistical correlations between video frames and scattered signals more effectively. Leveraging these temporal correlations, our approach recovers high-resolution spatial details that spatial-only methods typically fail to reconstruct. We also propose an inference-time optimization with a lightweight mapping network, enabling joint estimation of low-dimensional forward-model parameters without additional training. This joint optimization significantly enhances adaptability to unknown, time-varying degradations, making our method suitable for blind inverse scattering problems. We validate across diverse conditions, including different scene types, layer thicknesses, and scene-layer distances. And real-world experiments using multiple datasets confirm the robustness and effectiveness of our approach, even under real noise and forward-model approximation mismatches. Finally, we validate our method as a general video-restoration framework across dehazing, deblurring, inpainting, and blind restoration under complex optical aberrations.

GMO: A general multimodal optimization framework applicable to various global metaheuristic algorithms

HAD-Gen: Human-like and diverse driving behavior modeling for controllable scenario generation.

In recent years, constellations have received widespread attention due to their potential to provide services including telecommunication, Earth observation, and so on. One promising method to increase the operational responsiveness of the constellation is orbital maneuvering. To this end, this paper establishes a general framework for low-thrust maneuvering and constellation reconfiguration strategy achieving emergency multitarget continuous coverage. By integrating analytical methods with the Broyden–Fletcher–Goldfarb–Shanno algorithm, a low-thrust four-stage maneuvering strategy is proposed, achieving coordinated sequential convergence of all orbital elements for circular orbits. Furthermore, a multitarget coverage orbit determination method based on sequential quadratic programming is proposed to facilitate constellation reconfiguration for continuous coverage. The proposed methods are validated through extensive Monte Carlo simulations involving a large number of randomly generated test scenarios, demonstrating significantly improved range of application and precision. A 360-satellite Walker–Delta constellation case demonstrates that, when combined with low-thrust maneuvers, the proposed methods enable continuous access to 10 emergency targets while reducing the maximum revisit interval up to 87.0%, significantly improving operational flexibility and system performance. In conclusion, the proposed framework not only exhibits computational efficiency but also demonstrates significantly application range improvement compared to previous studies, laying a foundation for the operational management of future large-scale space systems, including megaconstellations.

An individual generalization framework based on independent samples towards a more reasonable fault diagnosis benchmark

Exploring Behavior at Infinity of Predator-Prey model and Developing the General Framework of Normal Form of Planar Systems

Human languages balance communicative informativity with complexity, conveying as much as needed through the simplest means required to do so. Yet, these concepts-informativity and complexity-have been operationalized in various ways, and it remains unclear which definitions best capture empirical linguistic patterns. A particularly successful operationalization is that offered by the Information Bottleneck framework, which suggests a balance between complexity and informativity across domains like color, kinship, and number. However, we show that the notion of complexity employed by this framework has some counterintuitive consequences. Focusing on color terms, we then study to what extent this and other notions of complexity play a role in explaining cross-linguistic regularity. We propose a method to assess their explanatory contributions; and to probe whether they enter in a joint optimization or in a trade-off competition. This offers a more general framework to study language change and the forces that shape it, where instead of showing that a given model is compatible with existing data, the data is used to adjudicate between candidatemeasures.

Problem. Effective spatial development of territorial communities requires integration of ecological and landscape planning into urban planning documentation. However, current Ukrainian legislation provides limited regulation of landscape planning as part of comprehensive spatial development plans, which complicates environmental protection and sustainable land use. Goal. The goal of this study is to analyze the normative and legal framework governing the development of landscape plans within comprehensive spatial development plans for territorial communities in Ukraine, identifying gaps and inconsistencies. Methodology. The research is based on a comprehensive analysis of Ukrainian legislation, including laws, government resolutions, and state building norms related to spatial planning and landscape regulation. Comparative legal analysis and document content analysis methods were used. Results. It was found that key regulatory acts, such as the Law on Regulation of Urban Development (2011) and the 2020 amendments on land use planning, provide a general framework but insufficient detail on landscape planning. The Cabinet of Ministers Resolution No. 926 (2021) and State Building Norms DBN B.1.1-14:2021 define the structure and requirements for landscape planning more clearly. Some discrepancies remain between required content and actual objects reflected on landscape plans. Originality. This study highlights the insufficient integration of landscape planning in urban development legislation and suggests the need for more precise normative regulation to ensure environmental and landscape protection within territorial community planning. Practical value. The results can inform legislative improvements and help planners better integrate landscape considerations into spatial development plans, promoting sustainable and balanced land use in Ukrainian territorial communities.

Nonlinear optical metasurfaces enable subwavelength control of light-matter interactions, yet simultaneous tunability of harmonic signal intensity and spectral response remains a fundamental challenge. Here, a local-to-nonlocal second harmonic (SH) generation process is presented, enabled by an electrically tunable polaritonic metasurface, allowing independent control of the SH spectral peak wavelength and intensity. The metasurface combines a localized surface plasmon resonance at the fundamental frequency with a transverse magnetic guided-mode resonance at the SH frequency. By engineering modal overlap within a multiple quantum well layer, voltage-controlled modulation of SH intensity and angle-controlled spectral tuning is achieved, demonstrating two decoupled degrees of freedom associated with local and nonlocal modes. Angle-resolved nonlinear reflection measurements confirm the independent tunability of the metasurface, validating the separation of excitation and emission pathways. This hybrid approach provides a general framework for nonlinear metasurfaces with enhanced flexibility and functional control, paving the way for applications in nonlinear signal processing, angle-multiplexed photonics, and entangled photon-pair generation for quantum optics.

Sonification, the process of translating data into sound, has recently gained traction as a tool for both disseminating scientific findings and enabling visually impaired individuals to analyze data. Despite its potential, most current sonification methods remain limited to one-dimensional data, primarily due to the absence of practical, quantitative, and robust techniques for handling multi-dimensional datasets. We analyze structural magnetic resonance imaging (MRI) data of the human brain by integrating two- and three-point statistical measures in Fourier space: the power spectrum and bispectrum. These quantify the spatial correlations of three-dimensional voxel intensity distributions, yielding reduced bispectra that capture higher-order interactions. To illustrate the potential of the approach, we focus on one representative reduced-bispectrum configuration (Q019036) in the OASIS-3 dataset (864 imaging sessions) and provide audio renderings for five age groups (40–50, 50–60, 60–70, 70–80, 80–100 years). As context, prior work with this configuration reported an age-prediction MAE of approx4.2 years based on neural networks, trained on bispectrum-derived features–not through sonification itself. But here we emphasize sonification rather than predictive benchmarking. Finally, we treat these audio examples as exploratory illustrations rather than a perceptual evaluation. Our results demonstrate that the information loss (e.g., normalized mean squared error) during the reconstruction of the original bispectra, specifically in configurations sensitive to brain aging, from the sonified signal is minimal. Future studies should include systematic statistical inference and perceptual validation to assess the robustness and perceptual utility of the method. Nevertheless, the approach presented here already provides a general framework for encoding multi-dimensional data into time-series-like arrays suitable for sonification, thereby opening new avenues for scientific exploration and improving accessibility for a broader audience.

Heterobifunctional RNA degraders, such as ribonuclease-targeting chimeras (RiboTACs), eliminate structured RNAs by recruiting endogenous effector proteins. Here, we expand the effector toolbox by co-opting microtubule-associated protein 1 light chain 3B (MAP1LC3B, also known as LC3B), a core autophagy component recently implicated in mRNA decay. An RNA-binding fragment (F1) with a mapped transcriptome-wide binding profile was conjugated to the LC3B ligand ispinesib to generate the degrader F1-ispinesib. Integration of RNA-seq and Chemical Cross-Linking and Isolation by Pull-down combined with NGS Sequencing (Chem-CLIP-seq) data identified collagen type XV alpha 1 chain (COL15A1) mRNA as a target selectively degraded by F1-Ispinesib. The induced COL15A1 mRNA decay was LC3B- and autophagy-dependent, confirmed by genetic and pharmacological inhibition. Cellular engagement of LC3B was validated by NanoBRET and photocatalytic proximity labeling. In human aortic smooth muscle cells, F1-ispinesib phenocopied siRNA-mediated COL15A1 knockdown, reducing protein levels, suppressing proliferation, and enhancing migration. A synthetic LC3B recruitment model also recapitulated the on-target COL15A1 mRNA decay, further supporting the degradation mechanism. These findings establish LC3B as a recruitable effector protein for small-molecule-directed RNA decay and provide a general URID framework for discovering degraders that harness diverse RNA-regulatory proteins.

ABSTRACT Modeling knowledge systems by determining relationships among key variables have been and currently is a fundamental and nontrivial challenge in real‐world scenarios. Many approaches have been developed to reach this goal, but many of them are heuristic and require of alternative procedures to provide robust and tractable rules. With this significant aim, attribute implications were introduced in the mathematical framework of Formal Concept Analysis. In this paper, we will introduce a novel procedure to obtain relationships among variables from any dataset in which a Galois connection has been defined. In particular, we will be focused on the general multiadjoint framework, which is one of the most general approached in which a Galois connection have been considered, although the obtained results and methodology can also be used in other well‐known approaches, such as in the residuated or heterogeneous frameworks.

Free-space optical (FSO) communication systems offer fiber-like bandwidth, high security, and rapid deployment; however, their performance is highly susceptible to atmospheric impairments, such as dust storms, which can cause fading that degrades link reliability. In this study, we analyze the performance of FSO links under a dust-induced fading channel modeled as a Beta distribution channel. We derive an expression for the instantaneous signal-to-noise ratio (SNR) distribution. Using the SNR expression, we construct a general framework that yields closed-form formulas for fundamental performance measures such as outage probability, average bit-error rate (BER), and ergodic capacity. The analysis considers both intensity modulation/direct detection (IM/DD) and coherent detection techniques, encompassing typical modulation schemes including modulation formats such as on–off keying (OOK), M-ary phase-shift keying (M-PSK), and M-ary quadrature amplitude modulation (M-QAM). The results show that dust-induced fading penalizes all modulations, though coherent detection achieves better error performance than IM/DD at equivalent SNR. For example, a coherent receiver requires approximately 4.4 dB lower average SNR than an IM/DD system to achieve the same outage probability. Overall, the proposed unified framework shows that dust-induced fading can severely degrade the performance of FSO links, while also quantifying how network operators can trade off complexity and performance when choosing between coherent and IM/DD detection under realistic dust-storm conditions.

Tiglic aldehyde (TA), an α,β-unsaturated aldehyde bearing an α-methyl substituent, serves as a prototypical system for exploring conformer-specific ionization and electronic dynamics in conjugated carbonyl compounds. Using high-resolution vacuum-ultraviolet mass-analyzed threshold ionization (VUV-MATI) spectroscopy combined with infrared double-resonance techniques, Franck-Condon simulations, and two-dimensional potential energy surface (2D-PES) mapping, we characterized the conformational and ionization behaviour of TA. Under jet-cooled conditions, TA exists almost exclusively as the s-trans conformer, with an accurately determined adiabatic ionization energy of 77 183 ± 4 cm-1. The 2D-PES analysis revealed that the s-trans conformer dominates in the neutral ground state but becomes less stable than the s-cis form upon ionization, consistent with electron removal from the carbonyl π orbital. The observed MATI vibrational progression arises mainly from excitation of the formyl torsional mode, which is strongly activated upon ionization. At higher vibrational energies, partial dephasing due to intramolecular vibrational redistribution among torsional sublevels likely contributes to the attenuation of spectral intensity. Theoretical results show that the shallow torsional potential and the sensitivity of the cationic geometry to electron correlation and basis-set effects underlie this conformational reordering. These findings emphasize the need for correlated ab initio and advanced DFT approaches-such as optimally tuned range-separated hybrids, Koopmans-compliant functionals, and self-interaction-corrected methods-to accurately describe the orbital response accompanying ionization. This work provides the first conformer-resolved view of TA and establishes a general framework for investigating conformer-specific ionization dynamics in α,β-unsaturated aldehydes.