This paper proposes a deletion algorithm for the marginal problem in propositional logic. The algorithm is based on the general Davis and Putnam deletion algorithm DP, expressed as a bucket elimination algorithm, representing sets of clauses with the same set of variables employing a Boolean array. The main contribution is the development of alternative procedures when deleting a variable which allow more efficient computations. In particular, it takes advantage of the case in which the variable to delete is determined by a subset of the rest of the variables. It also provides a set of useful results and tools for reasoning with Boolean tables. The algorithms are implemented using Python and the NumPy library. Experiments show that this procedure is feasible for intermediate problems and for difficult problems from hard Bayesian networks cases

Automated surface inspection of balsa wood panels is challenging because defects may be small, elongated, weakly contrasted, or visually similar to natural grain patterns. This study proposes YOLOv11–BiFPN–DAAF, an enhanced object-detection architecture that combines bidirectional multi-scale feature fusion with adaptive dual-attention feature refinement. The task was formulated as single-class detection, with all anomalous surface regions labeled as Defect. The dataset comprised 508 manually annotated RGB images, including independent internal and external production test sets. A preliminary screening identified YOLOv11-m512 as the reference configuration, followed by a controlled $2\times 2$ factorial ablation comprising the baseline, BiFPN, DAAF, and their combined integration. Each configuration was trained using five independent random seeds under identical experimental conditions. On the validation set, the combined architecture achieved a precision of $0.893\pm 0.004$, recall of $0.848\pm 0.006$, mAP@0.5 of $0.897\pm 0.004$, and mAP@0.5:0.95 of $0.389\pm 0.004$. Relative to the baseline, the largest improvement was obtained for mAP@0.5:0.95, with a relative gain of 9.89%, indicating improved localization under stricter IoU thresholds. The improvement was retained on the independent internal test set, where the proposed architecture reached mAP@0.5 and mAP@0.5:0.95 values of $0.892\pm 0.005$ and $0.384\pm 0.006$, respectively. On the external production test set, the corresponding values were $0.865\pm 0.007$ and $0.358\pm 0.008$, representing absolute improvements of 0.034 and 0.042 over the original YOLOv11 baseline. Under the matched experimental protocol, YOLOv11–BiFPN–DAAF also achieved the highest principal detection metrics among the evaluated representative detectors. Although BiFPN and DAAF introduced a moderate computational overhead, the architecture maintained an inference time of $9.6\pm 0.3$ ms per image. These findings support the potential of the proposed architecture for automated balsa wood panel inspection, while broader multi-site and hardware-level validation remains necessary before large-scale industrial deployment.

Exact probabilistic inference in Bayesian Networks (BNs) becomes increasingly expensive as network size and structural complexity grow, limiting its applicability in time-sensitive decision-support systems. This study presents a hybrid inference framework that accelerates the deterministic component of Bayesian reasoning by integrating Boolean Satisfiability (SAT) techniques with Bayesian Networks. The proposed approach transforms deterministic conditional probability table (CPT) entries into conjunctive normal form (CNF), enabling SAT-based logical inference over deterministic constraints while preserving the original Bayesian model for probabilistic reasoning. The framework was evaluated on 25 benchmark Bayesian networks using five independent executions per dataset under identical experimental conditions. Performance was assessed through execution time, instrumented operation counts, and inference coverage, with results reported as mean values, standard deviations, and 95% confidence intervals. Experimental results demonstrate substantial reductions in deterministic inference time while maintaining high coverage of deterministic variable assignments across the evaluated benchmarks. Throughout this paper, the reported performance gains refer exclusively to empirical reductions in execution time and instrumented operation counts. They should not be interpreted as evidence of a reduction in the asymptotic computational complexity of exact Bayesian inference, which remains #P-complete in the general case. Rather, the proposed framework provides an efficient mechanism for accelerating deterministic logical inference within Bayesian Networks under the evaluated benchmark conditions.

Student dropout in higher education institutions represents a critical challenge with broad economic and social implications. This study applies the Apriori association rule mining algorithm within a CRISP-DM framework to a dataset of 2,376 undergraduate students from a Latin American public university (2012–2020) to discover actionable patterns for early warning systems. From an initial base of 1,070,050 rules, 73 rules meeting thresholds of support ≥ 0.10, confidence ≥ 0.85, and lift > 3.22 were retained, of which 17 have FinalGrade=Low as consequent. The central finding is that low academic performance in the first semester (FstSemesGrade=Low) is the earliest and most consistent predictor of final failure (confidence 86.1%, lift 3.22), with significant amplification when combined with temporary employment (TypEmploymnt=Temporary), high school graduate parent education level (DegreeLevel=HighSchoolGrad), or first enrollment status. A complementary cluster of seven rules reveals that moderate exam performance (FinalExam=Medium) associates positively with high final grades (FinalGrade=High, lift 2.6), confirming that the exam score functions as a pivotal indicator in both risk and success trajectories. Visualized through horizontal bar charts and chord diagrams using the circlize R package, the rule network reveals dense co-occurrence between academic, socioeconomic, and demographic factors. These findings provide an interpretable, institution-ready framework for designing targeted early interventions in at-risk student populations.

Student dropout is a key challenge for higher education due to its academic, social, and institutional impacts. This study analyzes this issue at the Quevedo State Technical University (UTEQ) through a systematic literature review aimed at identifying the main associated factors and examining the potential of artificial intelligence to strengthen academic monitoring. The methodology was developed in accordance with PRISMA guidelines, ensuring transparency in the search, selection, and evaluation of studies. Of the more than 500 publications initially identified, 20 were included based on their thematic and methodological relevance. The results showed that socioeconomic and academic factors were the most influential, each accounting for 30% of the total. Furthermore, Colombia accounted for the largest volume of scientific output, followed by Peru, Mexico, and Chile. The study proposes incorporating machine learning techniques to detect at-risk students early, personalize interventions, and improve retention, performance, and educational continuity at UTEQ.

Balsa wood defects can resemble natural grain, making successful detection insufficient to establish precise localization or robustness to acquisition changes. This study investigates these limitations using the previously published YOLOv11–BiFPN–DAAF framework as a reference for analyzing adaptive multiscale attention. On a 508-image industrial dataset, five training seeds support selected-IoU evaluation, geometric box-error analysis, stratification by defect size, contrast, and elongation, and controlled comparisons of within-scale attention and mixing operators. Robustness is assessed under 15 brightness, contrast, noise, blur, and compression conditions and an external production campaign. Relative to YOLOv11, the complete framework increases internal-test AP from 0.865 to 0.892 at IoU 0.50, from 0.385 to 0.442 at IoU 0.75, and from 0.082 to 0.125 at IoU 0.90, while reducing mean geometric errors among matched detections. It improves mAP@0.5:0.95 by 0.035 (paired-seed 95% CI: 0.03061–0.03939) and exhibits smaller mean degradation under every tested perturbation, while external mAP@0.5:0.95 decreases from the internal value of 0.384 to 0.358. In the within-scale ablation, learned gating exceeds fixed mixing by 0.003 in mean mAP@0.5:0.95 (0.384 versus 0.381), with additional trainable capacity. Composite spatial-attention maps yield a concentration ratio of $4.8\pm 0.7$, indicating higher mean weights inside annotation boxes without establishing a causal suppression mechanism. Paired statistical analyses distinguish training-seed variability from test-panel uncertainty. The findings identify localization gains, acquisition sensitivities, and the observed contribution of adaptive refinement within the established architecture. They support further evaluation of difficult defects and independent production domains rather than unrestricted generalization claims.