Let S be a set of n points in the plane. We present several different algorithms for finding a pair of points in S such that any disk that contains that pair must contain at least cn points of S, for some constant c > 0. The first is a randomized algorithm that finds a pair in O(n log n) expected time for points in general position, and c = 1/2 - root(1 + 2 alpha)/12, for any 0 < alpha < 1. The second algorithm, also for points in general position, takes quadratic time, but the constant c is improved to 1/2 - 1/ 12 approximate to 1/4.7. The second algorithm can also be used as a subroutine to find the pair that maximizes the number of points inside any disk that contains the pair, in O(n2 log n) time. We also consider variants of the problem. When the set S is in convex position, we present an algorithm that finds in linear time a pair of points such that any disk through them contains at least n/3 points of S. For the variant where we are only interested in finding a pair such that the diametral disk of that pair contains many points, we also have a linear-time algorithm that finds a disk with at least n/3 points of S. Finally, we present a generalization of the first two algorithms to the case where the set S of points is coloured using two colours. We also consider adapting these algorithms to solve the same problems when S is a set of points inside of a simple polygon P, with the notion of a disk replaced by that of a geodesic disk.
Rheumatoid arthritis (RA) features chronic synovial inflammation and systemic immune disorders. Current treatments mainly rely on systemic immunosuppression, raising risks of infection and long-term immune injury. Local therapies reduce such risks but struggle to regulate systemic immunity. Emerging studies show the synovial immunometabolic microenvironment links local inflammation to systemic responses. Here, we developed a strategy based on chemically attenuated photosynthetic bacteria that couples lactate metabolism with photo-driven hydrogen production to achieve local metabolic reprogramming and systemic immune re-education. We screened a photosynthetic bacterial strain with high lactate metabolic capacity, which also exhibited enhanced hydrogen production. To ensure safety, bacteria were PEGylated to mask immunogenic moieties while preserving metabolic activity. Intra-articular injection disrupted pathological crosstalk and restored the Treg/Th17 balance locally. Circulating IL-6 levels decreased, restoring the systemic regulatory role of Tregs, thereby alleviating distant joint inflammation. These results demonstrate that local synovial metabolic reprogramming by engineered bacteria can induce systemic immune re-education, offering a conceptually new therapeutic strategy for treating autoimmune arthritis.
Machine-learning (ML) models are increasingly used to accelerate the discovery of oxide materials for solar thermochemical hydrogen production (STCH), where oxygen vacancy formation enthalpy (Delta ho) is a key descriptor of redox behaviour. However, the predictive reliability of Delta ho depends on the choice of regression model and on how the underlying dataset defines the learning problem, including its compositional diversity, oxygen non-stoichiometry, and thermodynamic definition of the target. This aspect has commonly been overlooked in previous studies. We develop an ML workflow that explicitly evaluates dataset effects in the prediction of Delta ho. Rather than treating available data as a single source, three independent density functional theory (DFT) datasets were analysed as distinct learning problems, as they differ in compositional space, oxygen nonstoichiometry, and computational treatment of reduction, including phase transitions. This work shows how feature selection (FS) is strongly dependent on the dataset, although recurrent B-site-related descriptors emerge across all cases, supporting a consistent physicochemical interpretation. The highest predictive accuracy is obtained for the largest and most stoichiometrically homogeneous dataset, whereas the lowest extrapolation risk is found in the dataset that more evenly spans the Delta ho, the compositional, and stoichiometric space. External validation further showed that the model trained on the dataset with greater physical and chemical representativeness achieved the closest agreement with reference values. Thus, this work shows that high accuracy and low mean absolute error do not necessarily imply reliable prediction across a broad discovery space; other factors such as extrapolation risk and external validation are essential.
This study examines how firms operating in emerging-economy contexts build organizational resilience, emphasizing the entrepreneurial mechanisms through which strategic resources are transformed into adaptive capacity. Drawing on perspectives from international entrepreneurship and dynamic capabilities, the study argues that entrepreneurial ambidexterity is a core factor enabling firms to navigate uncertainty and technological turbulence. Using survey data from 588 firms across multiple industries in Middle Eastern countries, the study employs structural equation modeling to test mediation and moderation effects. The findings show that organizational capabilities and resource alignment enhance organizational resilience primarily through entrepreneurial ambidexterity, which facilitates opportunity exploration and exploitation under volatile conditions. Technological turbulence further conditions these relationships, shaping the effectiveness of entrepreneurial responses to environmental change. By theorizing resilience as an entrepreneurial outcome rather than a static organizational attribute, this study advances international entrepreneurship research by explaining how firms in emerging markets enact resilience through ambidextrous entrepreneurial action in turbulent international environments. The findings extend dynamic capabilities theory by demonstrating how international and technological turbulence jointly influence entrepreneurial adaptation beyond developed-economy settings.
Heat stress is a growing concern for public human health systems, underscoring the need for reliable thermal stress forecasting tools. This study introduces a novel approach to estimating the Universal Thermal Climate Index (UTCI) for southern South America using a reduced subset of variables from data-driven weather forecast models. These data-driven models offer rapid, accurate, and publicly accessible forecasts of meteorological variables. However, they do not provide all the variables required to calculate the UTCI. To address this limitation, this study provides a method to estimate heat stress in southern South America using advanced feature selection techniques and regression/classification models. We apply a wrapper evolutionary approach based on the Probabilistic Coral-Reef Optimization with Substrate Layers algorithm (PCRO-SL), previously tested in complex optimization problems, to identify key meteorological variables at both individual grid points and within homogeneous UTCI regions defined through K-means clustering. Various regression and classification models are then constructed and evaluated against reanalysis ground-truth UTCI data. The combination of PCRO-SL and Light Gradient Boosting Machine emerges as the most effective approach. The method is successfully implemented to estimate UTCI during three heat waves using forecasts from data-driven models. The results demonstrate a good predictive skill for forecasts up to three days in advance, outperforming a traditional numerical weather prediction model. This research represents a significant advance towards the development of a thermal stress early warning system for the region, potentially enhancing public health interventions through timely preventive measures against extreme thermal conditions. In this study, we present an alternative approach to estimate the Universal Climate Thermal Index (UTCI), a key indicator of heat stress, using a refined subset of meteorological variables available across multiple datasets, including emerging data-driven weather models. Using southern South America as a pilot region, our methodology expands the applicability of UTCI estimation and establishes a foundation for an early-warning system for heat stress. The method integrates advanced feature selection techniques to identify an optimal set of inputs for non-linear regression models. We further assess the ability of state-of-the-art data-driven forecasting systems to estimate both meteorological inputs and the UTCI during heat wave events. These models, powered by machine learning and data science, remain underexplored in the South American context. Our findings show that their performance is competitive with traditional physics-based forecasting systems in predicting meteorological variables and thermal stress. Beyond its scientific contribution, this research addresses an urgent societal challenge: the escalating risk of heat stress driven by climate change, with direct implications for human health and well-being. The work is inherently interdisciplinary, bridging meteorology, climate science, environmental epidemiology, and machine learning to advance innovative forecasting tools. Nonlinear methods outperform linear ones in estimating heat stress by capturing more complex relationships between factors Data-driven models surpass traditional physical models in forecasting weather variables and heat stress during three heat wave events. Feature selection by regions proves to enhance interpretability and aligns with the physical processes governing each climate zone.
