This study aimed to evaluate whether voxel spacing-aware radiomic feature extraction improves reproducibility, variability, and discriminative performance compared to conventional preprocessing methods in anisotropic CT data. A curated cohort of 685 pulmonary nodules from the LIDC-IDRI dataset was analyzed. Three preprocessing strategies-no resampling, isotropic resampling, and voxel spacing-aware extraction-and one postprocessing approach, voxel spacing weighting, were systematically compared. A modified version of PyRadiomics was developed to compute texture and shape features directly from native images while incorporating physical voxel dimensions without interpolation. Among the 94 extracted features, spacingaware preprocessing improved reproducibility in 58 features, reduced variability in 48, and enhanced univariate discrimination in 37. An ensemble feature selection combining six statistical and machine learning methods identified between 18 and 20 robust features per method. Logistic Regression models trained with spacing-aware features achieved the highest composite performance score (1.498), balancing discrimination and generalizability. SHAP interpretability analysis confirmed the clinical relevance of selected geometric and texture features. Overall, voxel spacing-aware preprocessing preserved native spatial structure and mitigated the effects of voxel geometry and interpolation artifacts, yielding stable and clinically interpretable radiomic features. These findings support the adoption of spacing-aware pipelines in heterogeneous and multi-center CT radiomics studies to enhance feature robustness and reproducibility.

A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation or optimization. Static cost analysis derives the precise cost—or upper and lower bounds for it—of executing programs, as functions in terms of the program's input data size. We present a generalization of automated cost analysis that can handle abstract programs and, hence, can analyze the impact on the cost effect of program transformations . This kind of relational property requires provably precise cost bounds which are not always produced by cost analysis. Therefore, we certify by deductive verification that the inferred abstract cost bounds are correct and sufficiently precise. It is the first approach solving this problem. Both, abstract cost analysis and certification, are based on quantitative abstract execution (QAE) which in turn is a variation of abstract execution, a recently developed symbolic execution technique for abstract programs. To realize QAE the new concept of a cost invariant is introduced. QAE is implemented and runs fully automatically on a benchmark set consisting of representative optimization rules.

This study explores the application of Artificial Intelligence (AI) in spine surgery, with a focus on enhancing precision and accuracy in outcome prediction. Leveraging machine learning (ML) models – including GaussianNB, ComplementNB, KNN, and Decision Trees – we analyze a rich dataset derived from 244 spine surgery patients. This dataset comprises 24 diverse variables, capturing elements such as pre-surgical conditions, socioeconomic status, psychometric evaluations, and various analytical metrics. Notably, one critical variable is the surgery’s success, serving as the primary outcome for prediction.The data was meticulously categorized into seven distinct groups, reflecting various aspects of the surgical process and patient backgrounds. This structured approach enabled a targeted and nuanced analysis, deepening our understanding of the key factors instrumental in predicting surgical outcomes. We employed a stratified split methodology for our dataset, dedicating 80% to training and 20% to testing. This was supplemented by 5-fold cross-validation and an extensive grid search optimization for refining the KNN and Decision Trees models.Our results underscore the profound capability of AI in predicting the outcomes of spine surgeries. The KNN model showed remarkable proficiency, particularly in analyzing groups defined by pre-surgical and analytical variables, demonstrating its prowess in handling complex medical datasets. This study not only evidences the effectiveness of specific ML models in medical prognostics but also highlights AI’s transformative potential in healthcare. It underlines the critical role of AI in advancing medical diagnostics and decision-making for surgeries that entail multifaceted data analysis. These insights pave the way for future research into the broader application of AI in medicine, promising more personalized and effective treatment strategies and effective treatment approaches.

Abstract This paper presents a new framework to synthesize lower-bounds on the worst-case cost for non-deterministic integer loops. As in previous approaches, the analysis searches for a metering function that under-approximates the number of loop iterations. The key novelty of our framework is the specialization of loops, which is achieved by restricting their enabled transitions to a subset of the inputs combined with the narrowing of their transition scopes. Specialization allows us to find metering functions for complex loops that could not be handled before or be more precise than previous approaches. Technically, it is performed (1) by using quasi-invariants while searching for the metering function, (2) by strengthening the loop guards, and (3) by narrowing the space of non-deterministic choices. We also propose a Max-SMT encoding that takes advantage of the use of soft constraints to force the solver look for more accurate solutions. We show our accuracy gains on benchmarks extracted from the 2020 Termination and Complexity Competition by comparing our results to those obtained by the "Image missing" system.

Abstract A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation, optimization, or parallelization. We present a generalization of automated cost analysis that can handle abstract programs and, hence, can analyze the impact on the cost of program transformations. This kind of relational property requires provably precise cost bounds which are not always produced by cost analysis. Therefore, we certify by deductive verification that the inferred abstract cost bounds are correct and sufficiently precise. It is the first approach solving this problem. Both, abstract cost analysis and certification, are based on quantitative abstract execution (QAE) which in turn is a variation of abstract execution, a recently developed symbolic execution technique for abstract programs. To realize QAE the new concept of a cost invariant is introduced. QAE is implemented and runs fully automatically on a benchmark set consisting of representative optimization rules.