In the present study, an attempt has been made to experimentally investigate the flexural performance of ten simply supported reinforced concrete gable roof beams, including solid control specimen (i.e., without openings) and nine beams with web openings of different dimensions and configurations. The nine beams with openings have identical reinforcement details. All beams were monotonically loaded to failure under mid-span loading. The main variables were the number of the created openings, the total area of the created openings, and the inclination angle of the posts between openings. Of interest is the load-carrying capacity, cracking resistance and propagation, deformability, failure mode, and strain development that represent the behav

This study evaluates the flexural behavior of ultra-thin (50 mm) one‑way reinforced‑concrete (RC) slabs retrofitted with near‑surface mounted (NSM) carbon‑fiber‑reinforced polymer (CFRP) rods under quasi‑static loading. T300‑grade CFRP rods (≈4 mm diameter) were bonded in pre‑cut 7 mm × 7 mm grooves using a two‑part epoxy. As a proof-of-concept experimental baseline, three simply‑supported specimens (1000 mm × 500 mm × 50 mm) were tested in a six‑point bending configuration (four applied loads + two reactions): two conventional controls and one strengthened slab. A load‑control rate of ~15 kN/min was applied; the controls were cycled twice and the strengthened slab four times. Relative to the average of

The particle-hole state densities have been calculated for 232Th in
the case of incident neutron with  ,  1 Z Z T T T T and   2 Z T T .
The finite well depth, surface effect, isospin and Pauli correction are
considered in the calculation of the state densities and then the
transition rates. The isospin correction function ( ) iso f has been
examined for different exciton configurations and at different
excitation energies up to 100 MeV. The present results are indicated
that the included corrections have more affected on transition rates
behavior for        , , and    above 30MeV excitation energy

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of