Search Results (13)

Accurate correction of radial lens distortion is a fundamental requirement in computer vision and photogrammetry, as geometric inaccuracies directly affect 3D reconstruction, mapping, and geospatial measurements, particularly in high-precision imaging systems. In this study, we propose a fully analytical, non-iterative method for truncated inverse modeling of radial lens distortion, applicable to general radial distortion polynomials that contain constant terms. Unlike classical truncated Lagrange series reversion, which relies on recursive expansion and combinatorial series construction, the proposed formulation determines inverse distortion coefficients directly through a system of constrained algebraic inverse polynomials. This enables deterministic computation of inverse parameters without iterative refinement, numerical root finding, or combinatorial complexity. The method was evaluated using ultra-wide-angle smartphone camera imagery exhibiting severe barrel distortion modeled by an eighth-degree depressed radial distortion polynomial. Its performance was compared with a commonly used iterative inverse modeling approach. The analytical formulation demonstrated improved numerical stability and substantially reduced reprojection errors when correcting highly nonlinear distortion profiles, achieving sub-pixel accuracy in image rectification. In contrast, the iterative approach exhibited instability and significantly larger reprojection errors under identical conditions. These results demonstrate that the proposed framework provides a general, robust, and repeatable solution for inverse radial distortion modeling, particularly for high-order polynomial models. The method offers clear practical advantages for camera calibration pipelines in photogrammetry, remote sensing, robotics, and other applications requiring high-fidelity imaging. Full article

Motor starter calibration requires long-distance rotation of a setting cam to locate current graduation points, generating substantial non-value-added mechanical travel time on production lines. This paper proposes a cam pre-adjustment angle prediction method that integrates a phenomenological gray-box bimetallic model with a hierarchical combinatorial algorithm framework. A generalized lumped-parameter model incorporating heat dissipation correction and mechanical gap compensation is constructed to describe the electrothermal–mechanical coupling of the bimetallic strip. An improved fuzzy C-means (IFCM) algorithm addresses the cold-start problem for new material batches, and an adaptive particle swarm optimization (APSO) algorithm performs online parameter identification. To handle the process asymmetry arising from the unidirectional cam rotation mechanism, an optimized gray wolf optimizer with one-sided error control (GWO-OSE) based on an asymmetric loss function is employed to inversely determine the optimal pre-adjustment angle while actively suppressing over-prediction. Validation on 1200 production line samples across three material batches demonstrates an over-prediction rate of only 2.8%, a mean absolute angle prediction error of 23.9°, a reduction in single-product calibration time of approximately 12 s, and an improvement in overall production line efficiency of 24.5%. This efficiency gain results from the process-level redesign facilitated by the pre-adjustment strategy rather than from minimizing absolute prediction error, and the proposed method provides an engineering-applicable optimization strategy for reducing non-value-added calibration time in motor starter production lines. Full article

Metal–organic frameworks (MOFs) are among the most structurally diverse classes of crystalline nanomaterials, offering exceptional tunability, porosity, and chemical modularity. These characteristics have positioned MOFs as promising platforms for nanomedicine, bioimaging, and integrated nanotheranostic applications. However, the rational design of MOFs that satisfy stringent biomedical requirements, including high drug loading capacity, controlled and stimuli responsive release, selective targeting, physiological stability, biodegradability, and multimodal imaging capability, remains challenging due to the vast combinatorial design space and the complex interplay between physicochemical properties and biological responses. The objective of this review is to critically examine recent advances in artificial intelligence approaches based on Transformer architectures for the design and optimization of MOFs aimed at next-generation nanotheranostics. In contrast to prior reviews that broadly survey machine learning methods for MOF research, this article focuses specifically on Transformer-based models and their ability to capture long-range, hierarchical, and multiscale relationships governing MOF structure, chemistry, and functional behavior. We review state-of-the-art models, including MOFormer, MOFNet, MOFTransformer, and Uni MOF, and discuss graph-based and sequence-based representations used to encode MOF topology and composition. This review highlights how Transformer-based models enable predictive assessment of properties directly relevant to nanotheranostic performance, such as adsorption energetics, framework stability, diffusion pathways, pore accessibility, and surface functionality. By explicitly linking these predictive capabilities to drug delivery efficiency, imaging performance, targeted therapeutic action, and combined diagnostic and therapeutic applications, this work delineates the specific contribution of Transformer-based artificial intelligence to biomedical translation. Finally, we discuss emerging opportunities and remaining challenges, including generative Transformer models for inverse MOF design, self-supervised learning on hybrid experimental and computational datasets, and integration with autonomous synthesis and screening workflows. By defining the scope, novelty, and contribution of Transformer-based design strategies, this review provides a focused roadmap for accelerating the development of MOF-based platforms for next-generation nanotheranostics. Full article

The Random Domino Automaton—a stochastic cellular automaton forest-fire model—is formulated for the Bethe lattice geometry. The equations describing the stationary state of the system are derived using combinatorial analysis. The special choice of parameters that define the dynamics of the system leads to a solvable reduction in the set of equations. Analysis of the equations shows that by changing the parameter responsible for cluster removal, the size distribution of clusters smoothly transitions from (near) exponential to inverse power, beyond which the system is unstable. The analysis shows the crucial role of combining more than two clusters in elongating the tail of the size distribution generated by the system and, thus, in increasing the range of validity of the inverse power law. We also point out an interesting connection of the proposed model with Catalan-like integer sequences. Full article

This paper introduces a novel inverse design framework that combines pixelated microstrip antenna modeling, convolutional neural network (CNN), and binary particle swarm optimization (BPSO) to automate the process of generating antenna structures from specified performance targets. The framework operates through a streamlined workflow: the radiating patch is discretized into a 10 × 10 binary matrix to enable combinatorial design space exploration; a CNN is trained on 150,000 simulated datasets to predict S-parameters as a surrogate for time-consuming electromagnetic simulations; and BPSO optimizes pixel states guided by a fitness function that minimizes reflection coefficients at target frequencies. By representing the patch as binary pixels, the approach exponentially expands the design space from traditional parametric limits to about 10³⁰ combinatorial possibilities, overcoming the inefficiencies of manual trial-and-error design. Comparative studies with genetic algorithms (GAs) and simulated annealing (SA) demonstrate that the BPSO-CNN framework achieves faster convergence and lower S11 error at target frequencies. This work not only advances the state of the art in intelligent antenna design but also provides a scalable paradigm for automated electromagnetic device optimization. Full article

Selection bias can cause recommendation systems to over-rely on users’ historical behavior and ignore potential interests, thus reducing the diversity and accuracy of recommendations. Our research on selection bias reveals that the existing literature often overlooks the impact of sentiment factors on selection bias. In recommendation tasks, sentiment bias—stemming from users’ sentiment reactions—can lead to the suggestion of low-quality products to important users and unfair recommendations of niche items (targeted at specific markets or purposes). Addressing sentiment bias and enhancing recommendations for key users could help balance research on selection bias. Sentiment bias is embedded in user ratings and reviews. To mitigate this bias, it is essential to analyze user ratings and comments to uncover genuine sentiments. To this end, we have developed a sentiment analysis module aimed at eliminating discrepancies between reviews and ratings, providing accurate sentiment scores, extracting users’ true opinions, and reducing sentiment bias. Additionally, we have designed a combinatorial function that adapts to three distinct scenarios for bias correction. Moreover, we introduce the concept of dynamic debiasing, where the modeling time is not fixed but varies over time. On this basis, we propose a dynamic selection debiased recommendation method based on sentiment analysis. This paper demonstrates how the three approaches—sentiment analysis for data sparsity, combinatorial functions for dataset optimization, and time-dynamic modeling with inverse propensity weighting—can effectively mitigate selection bias. Our experiments with multiple real-world datasets show that our model can significantly enhance recommendation performance. Full article

Sand screenout is a critical challenge in hydraulic fracturing, affecting both the construction process and operational safety. This paper proposes a sand screenout warning model that integrates a combinatorial neural network and physical approaches to enhance both the speed and accuracy of sand screenout warnings. Firstly, the combined neural network uses a Transformer to capture key features during fracturing construction from historical data, and the extracted features are input to the Gated Recurrent Unit (GRU) for temporal prediction and the Crested Porcupine Optimizer (CPO) to further optimise the GRU-Transformer hyperparameters of the model. Additionally, the physical model improves the conventional inverse slope method by incorporating a threshold and sliding module, which enhances slope calculation and warning accuracy. The results showed that for fracturing pressure prediction, the proposed CPO-GRU-Transformer model obtained an RMSE value of 0.842 MPa, MAE of 0.613 Mpa, and $R^2$ of 0.971, a smaller RMSE and MAE and a larger $R^2$ than the three pressure prediction models, namely LSTM, GRU, and CPO-GRU. The proposed sand screenout warning model has been applied in the field construction of the U shale gas area in the Sichuan Basin. The warning points of the model proposed in this study were advanced by 73.5 s on average compared with the manual warning points in the three validated fracturing segments, with a successful warning rate of 85.71%, which greatly avoids the possibility of sand screenout and provides a method of fast calculation speed and high prediction accuracy, providing an early warning of sand screenout. Full article

We review combinatorial quantum gravity, an approach that combines Einstein’s idea of dynamical geometry with Wheeler’s “it from bit” hypothesis in a model of dynamical graphs governed by the coarse Ollivier–Ricci curvature. This drives a continuous phase transition from a random to a geometric phase due to a condensation of loops on the graph. In the 2D case, the geometric phase describes negative-curvature surfaces with two inversely related scales: an ultraviolet (UV) Planck length and an infrared (IR) radius of curvature. Below the Planck scale, the random bit character survives; chunks of random bits of the Planck size describe matter particles of excitation energy given by their excess curvature. Between the Planck length and the curvature radius, the surface is smooth, with spectral and Hausdorff dimension 2. At scales larger than the curvature radius, particles see the surface as an effective Lorentzian de Sitter surface, the spectral dimension becomes 3, and the effective slow dynamics of particles, as seen by co-moving observers, emerges as quantum mechanics in Euclidean 3D space. Since the 3D distances are inherited from the underlying 2D de Sitter surface, we obtain curved trajectories around massive particles also in 3D, representing the large-scale gravity interactions. We thus propose that this 2D model describes a generic holographic screen relevant for real quantum gravity. Full article

Herein, we propose a new method based on Fourier-transform near-infrared spectroscopy (FT-NIR) for detecting impurities in seed cotton. Based on the spectral data of 152 seed cotton samples, we screened the characteristic wavelengths in full-band spectral data with regard to potential correlation with the trash content of seed cotton. Then, we applied joint synergy interval partial least squares (siPLS) and combinatory algorithms with the competitive adaptive reweighted sampling method (CARS) and the successive projection algorithm (SPA). In addition, we used the sparrow search algorithm (SSA), gray wolf algorithm (GWO), and eagle algorithm (BES) to optimize parameters for support vector machine (SVM) analysis. Finally, the feature wavelengths optimized via the six feature wavelength extraction algorithms were modeled and analyzed via partial least squares (PLS), SSA-SVM, GWO-SVM, and BES-SVM, respectively. The correlation coefficients, R_c and R_p, of the calibration and prediction sets were subsequently used as model evaluation indices; comparative analysis highlighted that the preferred option was the inverse estimation model as this could accurately predict the trash content of seed cotton. Subsequently, we found that the accuracy of predicting the content of impurities in seed cotton when applying the optimized SVM model of SSA combined with the feature wavelengths screened via siPLS-SPA was optimal. Thus, the optimal modeling method for inverse impurity content was siPLS-SPA-SSA-SVM, with an R_c value of 0.9841 and an R_p value of 0.9765. The rapid application development (RPD) value was 6.7224; this is >3, indicating excellent predictive ability. The spectral inversion model for determining the impurity rate of mechanized harvested seed cotton samples established herein can, therefore, determine the impurity rate in a highly accurate manner, thus providing a reference for the subsequent construction of a portable spectral detector of impurity rate. This will help objectively and quantitatively characterize the impurity rate of mechanized harvested seed cotton and provide a new tool for rapidly detecting impurities in mechanized harvested wheat. Our findings are limited by the small sample size and the fact that the model developed for estimating the impurity content of seed cotton was specific to a local experimental field and certain varieties of cotton. Full article

The crystalline basement aquifer of the Pra Basin in Ghana is essential to the water supply systems of the region. This region is experiencing the ongoing pollution of major river networks from illegal mining activities. Water management is difficult due to the limited knowledge of hydrochemical controls on the groundwater. This study investigates its evolution based on analyses from a previous groundwater sampling campaign and mineralogical investigation of outcrops. The dominant reactions driving the average groundwater composition were identified by means of a combinatorial inverse modelling approach under the hypothesis of local thermodynamical equilibrium. The weathering of silicate minerals, including albite, anorthite, plagioclase, K-feldspar, and chalcedony, explains the observed median groundwater composition in the transition and discharge zones. Additional site-specific hypotheses were needed to match the observed composition of the main recharge area, including equilibration with carbon dioxide, kaolinite, and hematite in the soil and unsaturated zones, respectively, and the degradation of organic matter controlling the sulfate/sulfide content, thus pointing towards kinetic effects during water–rock interactions in this zone. Even though an averaged water composition was used, the inverse models can “bridge” the knowledge gap on the large basin scale to come up with quite distinct “best” mineral assemblages that explain observed field conditions. This study provides a conceptual framework of the hydrogeochemical evolution for managing groundwater resources in the Pra Basin and presents modelling techniques that can be applied to similar regions with comparable levels of heterogeneity in water chemistry and limited knowledge of aquifer mineralogy. The combinatorial inverse model approach offers enhanced flexibility by systematically generating all plausible combinations of mineral assemblages from a given pool of mineral phases, thereby allowing for a comprehensive exploration of the reactions driving the chemical evolution of the groundwater. Full article

Polyamines are ubiquitous, amine-rich molecules with diverse processes in biology. Recent work has highlighted that polyamines exert profound roles on the mammalian immune system, particularly inflammation and cancer. The mechanisms by which they control immunity are still being described. In the context of inflammation and autoimmunity, polyamine levels inversely correlate to autoimmune phenotypes, with lower polyamine levels associated with higher inflammatory responses. Conversely, in the context of cancer, polyamines and polyamine biosynthetic genes positively correlate with the severity of malignancy. Blockade of polyamine metabolism in cancer results in reduced tumor growth, and the effects appear to be mediated by an increase in T-cell infiltration and a pro-inflammatory phenotype of macrophages. These studies suggest that polyamine depletion leads to inflammation and that polyamine enrichment potentiates myeloid cell immune suppression. Indeed, combinatorial treatment with polyamine blockade and immunotherapy has shown efficacy in pre-clinical models of cancer. Considering the efficacy of immunotherapies is linked to autoimmune sequelae in humans, termed immune-adverse related events (iAREs), this suggests that polyamine levels may govern the inflammatory response to immunotherapies. This review proposes that polyamine metabolism acts to balance autoimmune inflammation and anti-tumor immunity and that polyamine levels can be used to monitor immune responses and responsiveness to immunotherapy. Full article

The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such a Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only. Full article

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties. Full article