Lifetimes of excited states in ${}^{229}\mathrm{Th}$, populated in the β decay of ${}^{229}\mathrm{Ac}$, have been measured using the advanced time-delayed $\beta \gamma \gamma$(t) method. Half-lives of 14 states have been determined including 11 of them for the first time. Twenty-seven new γ lines have been introduced into the β-decay scheme of ${}^{229}\mathrm{Ac}$ based on results of $\gamma \gamma$ coincidence measurements. Reduced transition probabilities have been determined for more than 70 γ transitions in ${}^{229}\mathrm{Th}$. Average $\left|D_0\right|$ values of 0.029(1), 0.077(3), and 0.024(5) $e$ fm have been deduced for the lowest $K^{\pi }=1/2^{\pm },3/2^{\pm }$, and $5/2^{\pm }$ parity partner bands, respectively. Excited states in ${}^{229}\mathrm{Th}$ and experimental transition rates have been interpreted within the quasiparticle-plus-phonon model. The half-life of the 3.5-eV, $3/2^{+}$ isomeric state is predicted to be about 10 h. Potential energy surfaces on the (${\beta }_2,{\beta }_3$) plane for the lowest single quasiparticle configurations in ${}^{229}\mathrm{Th}$ have been calculated using the Strutinsky method.

This document describes the technical layout and the expected performance of the Straw Tube Tracker (STT), the main tracking detector of the [equation]ANDA target spectrometer. The STT encloses a...

This paper proposes a new framework for personalizing e-commerce, which integrates multivariate user interfaces (MultiUI) with AI-generated content (AIGC). By utilizing customer behavioral data, our approach customizes both the visual layout and product descriptions for specific customer segments. This addresses the current research gap that often overlooks the synergy between UI design and content personalization. We conducted an empirical study to demonstrate the effectiveness of this integrated approach, showing that personalized user interface variants significantly improve customer engagement and conversion rates. In addition, we explore the potential of AIGC by using behavioral clusters to generate customized product descriptions. This showcases how AI can improve the relevance and appeal of product information, contributing to a more engaging and effective e-commerce experience. Although our initial findings using a simplified approach with ChatGPT are promising, future research will focus on refining AIGC models by incorporating domain-specific knowledge and leveraging comprehensive customer behavior data to generate highly tailored product descriptions. This research advances information processing in e-commerce by demonstrating how AI can be used to extract valuable insights from customer data, adapt UI designs, and generate personalized content, ultimately leading to more profitable online shopping experiences. Experimental studies showed that only about 10% of the most popular words were repeated in the product descriptions generated for the three different clusters. At the same time, two-thirds of the most popular words were dominant in only one of the clusters, confirming the satisfactory degree of matching descriptions to the specifics of customer groups.

One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to e-commerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI.