Taiz University

This study investigates the role of the BNNC tip in determining hydrogen storage performance and presents novel insights into how tip geometry influences adsorption and spectroscopic signatures. A total of 96 BNNC configurations were evaluated using density functional theory at the wb97xd level with counterpoise corrections, Gibbs free energy, and Zero-point energy to obtain corrected hydrogen adsorption energetics. Notably, we demonstrate for the first time that IR spectra can distinguish between edge motifs, revealing how tip structure governs edge chemistry. In parallel, Raman spectra provide direct access to both hydrogenation sites and disclination angles, enabling simultaneous characterization of adsorption behavior and tip-induced topologies. The observed hydrogen adsorption preferences, together with the complementary IR and Raman fingerprints, underscore the potential of BNNCs as tunable hydrogen storage media for future energy applications.

Introduction: Injury to the inferior alveolar nerve is a potential complication during the removal of mandibular third molars, with a substantially increased risk when a direct anatomical relationship exists between the tooth roots and the mandibular canal. In such cases, coronectomy offers a surgical alternative that reduces the risk of nerve injury; however, the clinical relevance of postoperative root migration remains incompletely understood. Objective: This study analyzed the timing and extent of root migration after coronectomy, examined influencing factors, assessed radiological indications, and compared findings with existing literature. Method: This retrospective analysis was based on data from 73 coronectomies performed in 57 patients. During the study, panoramic X-ray images were examined with particular attention to radiological signs indicating an increased risk of inferior alveolar nerve injury, as well as combinations of these signs. The extent of root migration was determined using digital panoramic radiographs at follow-up examinations conducted at 1, 3, 6, 12, and 24 months. Additionally, the effects of age, anatomical factors related to impaction, tooth angulation, and the operator's level of professional experience were evaluated. Results: Root migration was detected in 90.4% of cases. Mean migration was 1.5 mm at 1 month, 2.4 mm at 3 months, 2.8 mm at 6 months, 3.2 mm at 12 months, and 3.5 mm at 24 months. The most pronounced migration occurred during the first 6 months postoperatively. A significant negative correlation was observed between patient age and the extent of migration. Horizontal, i.e., ramus space availability influenced migration, whereas tooth angulation and operator experience showed no significant effect. Conclusion: Coronectomy is an effective surgical option for patients at high risk of inferior alveolar nerve injury; nevertheless, the probability of subsequent intervention should be considered. With appropriate indication and follow-up, favorable long-term outcomes can be achieved. Orv Hetil. 2026; 167(12): 476-484.

In this paper, we introduce PASTA (Perceptual Assessment System for explanaTion of Artificial intelligence), a novel framework for a human-centric evaluation of XAI techniques in computer vision. Our first key contribution is a human evaluation of XAI explanations on four diverse datasets—COCO, Pascal Parts, Cats Dogs Cars, and MonumAI—which constitutes the first large-scale benchmark dataset for XAI, with annotations at both the image and concept levels. This dataset allows for robust evaluation and comparison across various XAI methods. Our second major contribution is a data-based metric for assessing the interpretability of explanations. It mimics human preferences, based on a database of human evaluations of explanations in the PASTA-dataset. With its dataset and metric, the PASTA framework provides consistent and reliable comparisons between XAI techniques, in a way that is scalable but still aligned with human evaluations. Additionally, our benchmark allows for comparisons between explanations across different modalities, an aspect previously unaddressed. Our findings indicate that humans tend to prefer saliency maps over other explanation types. Moreover, we provide evidence that human assessments show a low correlation with existing XAI metrics that are numerically simulated by probing the model.

LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many resources to invest. While the usefulness and feasibility of Bayesian approaches remain unclear for LLM inference, this position paper argues that the control layer of an agentic AI system (that orchestrates LLMs and tools) is a clear case where Bayesian principles should shine. Bayesian decision theory provides a framework for agentic systems that can help to maintain beliefs over task-relevant latent quantities, to update these beliefs from observed agentic and human-AI interactions, and to choose actions. Making LLMs themselves explicitly Bayesian belief-updating engines remains computationally intensive and conceptually nontrivial as a general modeling target. In contrast, this paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system, not necessarily the LLM agent parameters. This paper articulates practical properties for Bayesian control that fit modern agentic AI systems and human-AI collaboration, and provides concrete examples and design patterns to illustrate how calibrated beliefs and utility-aware policies can improve agentic AI orchestration.

This study evaluated the insecticidal efficacy of various thieno[2,3-b]pyridine compounds (1, 3, 4a-4c, and 5a-5c) against adults of Aphis gossypii on eggplant. The insecticidal activity was assessed using the leaf-dip technique after 24 h. Several compounds exhibited remarkable potency at very low concentrations, as indicated by their low LC50 values. Notably, compound 4c demonstrated the highest efficacy with a very low LC50 value of 0.005 ppm (0.003-0.008). Regression analysis showed good fits, with slopes ranging from approximately 0.46 to 0.86 and confidence intervals supporting the reliability of the results. Using density functional theory, we calculated the highest occupied molecular orbital and lowest unoccupied molecular orbital electronic distributions for all of the compounds. To further pinpoint reactive regions, we generated electrostatic potential maps, which highlighted the most electrophilic and nucleophilic areas in every compound. Molecular docking was additionally employed to predict the binding interactions and affinities between the synthesized compounds and acetylcholinesterase. Molecular docking showed the strength and type of interactions of these compounds with acetylcholinesterase. All compounds illustrated excellent binding affinities with a high correlation between their docking scores and insecticidal efficacy. These findings suggest that the synthesized thieno[2,3-b]pyridine derivatives are promising candidates for developing effective insecticidal agents.