Evaluated data file for neutron irradiation of Ta-181 at energies up to 200 MeV

New evaluated data file for 181Ta irradiated with neutrons at energies up to 200 MeV has been prepared. The data evaluation has been done using the results of calculations, measured data, systematics predictions, and covariance information. Calculations have been performed using a special version of the TALYS code implementing the geometry dependent hybrid model and models for the non-equilibrium light cluster emission. The TEFAL code and the FOX code from the BEKED package have been used for the formatting of the data

Advanced breakup-nucleon enhancement of deuteron-induced reaction cross sections

Following the EUROfusion PPPT-programme action for an advanced modeling approach of deuteron-induced reaction cross sections, as well as specific data evaluations in addition of the TENDL files, an assessment of the details and corresponding outcome for the latter option of TALYS for the breakup model has been carried out. The breakup enhancement obtained in the meantime within computer code TALYS, by using the evaluated nucleon-induced reaction data of TENDL-2019, is particularly concerned. Discussion of the corresponding results, for deuteron-induced reactions on $^{58}$Ni, $^{96}$Zr, and $^{231}$Pa target nuclei up to 200 MeV incident energy, includes limitations still existing with reference to the direct-reaction account

Statistical learning and big data applications

The amount of data generated in the field of laboratory medicine has grown to an extent that conventional laboratory information systems (LISs) are struggling to manage and analyze this complex, entangled information (“Big Data”). Statistical learning, a generalized framework from machine learning (ML) and artificial intelligence (AI) is predestined for processing “Big Data” and holds the potential to revolutionize the field of laboratory medicine. Personalized medicine may in particular benefit from AI-based systems, especially when coupled with readily available wearables and smartphones which can collect health data from individual patients and offer new, cost-effective access routes to healthcare for patients worldwide. The amount of personal data collected, however, also raises concerns about patient-privacy and calls for clear ethical guidelines for “Big Data” research, including rigorous quality checks of data and algorithms to eliminate underlying bias and enable transparency. Likewise, novel federated privacy-preserving data processing approaches may reduce the need for centralized data storage. Generative AI-systems including large language models such as ChatGPT currently enter the stage to reshape clinical research, clinical decision-support systems, and healthcare delivery. In our opinion, AI-based systems have a tremendous potential to transform laboratory medicine, however, their opportunities should be weighed against the risks carefully. Despite all enthusiasm, we advocate for stringent added-value assessments, just as for any new drug or treatment. Human experts should carefully validate AI-based systems, including patient-privacy protection, to ensure quality, transparency, and public acceptance. In this opinion paper, data prerequisites, recent developments, chances, and limitations of statistical learning approaches are highlighted

An optically driven quantum dot quantum computer

We propose a quantum computer structure based on coupled asymmetric single-electron quantum dots. Adjacent dots are strongly coupled by means of electric dipole-dipole interactions enabling rapid computation rates. Further, the asymmetric structures can be tailored for a long coherence time. The result maximizes the number of computation cycles prior to loss of coherence.Comment: 4 figure