Double differential cross section (DDCS) of First-Born approximation is calcu-lated for the ionization of metastable 3d-state hydrogen atoms by electron impact energy at 150 eV and 250 eV. A multiple scattering theory is applied in the present study. The present results are compared with the other related the-oretical results for the ionization of hydrogen atoms from different metastable states and ground-state experimental results. The findings demonstrate a strong qualitative agreement with the existing results. The obtained results have an extensive scope for further study of such an ionization process.
Boron neutron capture therapy(BNCT)is recognized as a precise binary targeted radiotherapy technique that effectively eliminates tumors through the^(10)B(n,α)^(7)Li nuclear reaction.Among various neutron sources,accelerator-based sources have emerged as particularly promising for BNCT applications.The^(7)Li(p,n)^(7)Be reaction is highly regarded as a potential neutron source for BNCT,owing to its low threshold energy for the reaction,significant neutron yield,appropriate average neutron energy,and additional benefits.This study utilized Monte Carlo simulations to model the physical interactions within a lithium target subjected to proton bombardment,including neutron moderation by an MgF_(2)moderator and subsequent BNCT dose analysis using a Snyder head phantom.The study focused on calculating the yields of epithermal neutrons for various incident proton energies,finding an optimal energy at 2.7 MeV.Furthermore,the Snyder head phantom was employed in dose simulations to validate the effectiveness of this specific incident energy when utilizing a^(7)Li(p,n)^(7)Be neutron source for BNCT purposes.
Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant challenge, as the need for robust security measures becomes increasingly imperative. This paper presented an innovative method based on differential analyses to detect abrupt changes in network traffic characteristics. The core concept revolves around identifying abrupt alterations in certain characteristics such as input/output volume, the number of TCP connections, or DNS queries—within the analyzed traffic. Initially, the traffic is segmented into distinct sequences of slices, followed by quantifying specific characteristics for each slice. Subsequently, the distance between successive values of these measured characteristics is computed and clustered to detect sudden changes. To accomplish its objectives, the approach combined several techniques, including propositional logic, distance metrics (e.g., Kullback-Leibler Divergence), and clustering algorithms (e.g., K-means). When applied to two distinct datasets, the proposed approach demonstrates exceptional performance, achieving detection rates of up to 100%.
