Technical University, Sofia

Stark full widths at half intensity maximum (FWHM) and shifts, for spectral lines within 29 N V multiplets, have been calculated for collisions with alpha particles, B III, B IV, B V and B VI ions, emplopying the semiclassica perturbation method. The obtained data are of particular interest for proton-boron fusion experiments where boron nitride (BN) targets are used.

In this paper we introduce the CDF manifold algorithm, which operates on datasets where a single target dimension is strictly increasing given a minimum of two or more input dimensions, which is very common in telco data. The manifold can then be used to compute the closest upper and lower limits to a given new point, as well as its CDF. Training takes O(n.ln[n]) steps in the best case and O(n3/2) in the worst case. Lookup takes O(ln[n]) steps in the best case and O(n1/2ln(n)) in the worst case. The asymptotic computational cost is proven with a theorem. We compare our manifold method versus a standard dense neural network and show the asymptotic advantages both in terms of speed and accuracy. We also address potential speed gains through the use of reference points. In summary, the manifold is a non-parametric explanatory method to find the tightest data-driven upper and lower limits of the output dimension given a new unseen input. This makes it ideal for planning new site deployments where we need to find actual measurements as a baseline performance.

This study investigates the effectiveness of a narrative-structured instructional approach in supporting mathematical learning in a bilingual firstgrade classroom. The intervention systematically integrates narrative elements across the main phases of the mathematics lesson—concept introduction, guided practice, consolidation, and assessment—with the aim of facilitating conceptual understanding and the development of disciplinary mathematical language. The research was conducted in a Bulgarian primary school using a quasiexperimental single-group design with 25 bilingual first-grade students. Students’ mathematical achievement was examined through equivalent assessments administered before and after the instructional intervention. In addition to statistical analysis of test results, a vector-based cognitive learning acquisition index (CLI) was used to evaluate multiple indicators of mathematical competence. The findings indicate a statistically significant improvement in students’ mathematical achievement following the implementation of the narrativestructured instructional model. Additional analysis based on the CLI index shows positive dynamics across several indicators, including the acquisition of mathematical terminology, the correct application of algorithms in problem solving, and the comprehension of word problems. Qualitative reflections from pupils further suggest that the narrative context supported engagement and improved the perceived comprehensibility of mathematical tasks. Although the single-group design does not permit causal inference, the results provide preliminary empirical evidence that structured narrative-based instructional approaches may support both conceptual understanding and disciplinary language development in linguistically diverse early primary classrooms.

This paper presents a detailed review of recent advancements in 3D indoor scene segmentation driven by deep learning techniques. It provides an overview of existing segmentation models, examines various data representations, data collection methods, augmentation techniques, and available datasets. A comparative analysis of loss functions and overview of evaluation metrics is conducted to highlight their impact on segmentation performance. Unlike previous surveys, this work introduces a new classification of data augmentation techniques and proposes two novel classification approaches for 3D instance and semantic segmentation. Furthermore, it unifies 3D semantic instance segmentation and 3D panoptic segmentation within an existing framework. The paper also identifies key challenges and open research directions, providing insights into future advancements in the field.

To remain competitive, machining processes must be optimized to provide increased productivity and higher quality products. The aim of most efforts in these machining processes is to establish the optimal parameters to obtain the maximum material removal rate with minimum surface roughness which represents two of the main quality responses. This paper focuses on the optimization of process parameters in dry turning of Inconel 718, a nickel-based superalloy with PVD-coated carbide inserts based on single-objective optimization Taguchi technique, desirability function approach combined with response surface methodology (RSM), which is known as the multi-objective Desirability Optimization Methodology (DOM). Taguchi's orthogonal-array design L9 (33) and ANOVA analysis of variance are used to study the relationship between cutting parameters (cutting speed, feed rate and depth of cut) and the dependent output variables i.e., the arithmetic mean deviation of the profile's surface roughness (Ra) and material removal rate (MRR). A regression analysis was used to develop a mathematical model based on the first-order model to predict the Ra and MRR model. Using multiple regression analysis, first order linear prediction model was obtained to find the correlation between surface roughness and MRR with independent variables. In the range of parameters investigated, the obtained mathematical models accurately represent the response index, and the results of the experiments demonstrate that the feed rate and the depth of cut are the most important factors influencing Ra and MRR, respectively. Finally, confirmatory tests proved that Taguchi's method, desirability function approach combined with linear regression models was successful in optimizing turning parameters for minimum surface roughness and maximum MRR.